• Deep Learning Research Proposal

The word deep learning is the study and analysis of deep features that are hidden in the data using some intelligent deep learning models . Recently, it turns out to be the most important research paradigm for advanced automated systems for decision-making . Deep learning is derived from machine learning technologies that learn based on hierarchical concepts . So, it is best for performing complex and long mathematical computations in deep learning .

This page describes to you the innovations of deep learning research proposals with major challenges, techniques, limitations, tools, etc.!!!

One most important thing about deep learning is the multi-layered approach . It enables the machine to construct and work the algorithms in different layers for deep analysis . Further, it also works on the principle of artificial neural networks which functions in the same human brain. Since it got inspiration from the human brain to make machines automatically understand the situation and make smart decisions accordingly.  Here, we have given you some of the important real-time applications of deep learning.

Deep Learning Project Ideas

  • Natural Language Processing
  • Pattern detection in Human Face
  • Image Recognition and Object Detection
  • Driverless UAV Control Systems
  • Prediction of Weather Condition Variation
  • Machine Translation for Autonomous Cars
  • Medical Disorder Diagnosis and Treatment
  • Traffic and Speed Control in Motorized Systems
  • Voice Assistance for Dense Areas Navigation
  • Altitude Control System for UAV and Satellites

Now, we can see the workflow of deep learning models . Here, we have given you the steps involved in the deep learning model. This assists you to know the general procedure of deep learning model execution . Similarly, we precisely guide you in every step of your proposed deep learning model . Further, the steps may vary based on the requirement of the handpicked deep learning project idea. Anyway, the deep learning model is intended to grab deep features of data by processing through neural networks . Then, the machine will learn and understand the sudden scenarios for controlling systems.

Top 10 Interesting Deep Learning Research Proposal

Process Flow of Deep Learning

  • Step 1 – Load the dataset as input
  • Step 2 – Extraction of features
  • Step 3 – Process add-on layers for more abstract features
  • Step 4 – Perform feature mapping
  • Step 5 –Display the output

Although deep learning is more efficient to automatically learn features than conventional methods, it has some technical constraints. Here, we have specified only a few constraints to make you aware of current research. Beyond these primary constraints, we also handpicked more number of other constraints. To know other exciting research limitations in deep learning , approach us. We will make you understand more from top research areas.

Deep Learning Limitations

  • Test Data Variation – When the test data is different from training data, then the employed deep learning technique may get failure. Further, it also does not efficiently work in a controlled environment.
  • Huge Dataset – Deep learning models efficiently work on large-scale datasets than limited data

Our research team is highly proficient to handle different deep learning technologies . To present you with up-to-date information, we constantly upgrade our research knowledge in all advanced developments. So, we are good not only at handpicking research challenges but also more skilled to develop novel solutions. For your information, here we have given you some most common data handling issues with appropriate solutions. 

What are the data handling techniques?

  • Variables signifies the linear combo of factors with errors
  • Depends on the presence of different unobserved variables (i.e., assumption)
  • Identify the correlations between existing observed variables
  • If the data in a column has fixed values, then it has “0” variance.
  • Further, these kinds of variables are not considered in target variables
  • If there is the issue of outliers, variables, and missing values, then effective feature selection will help you to get rid out of it. 
  • So, we can employ the random forest method
  • Remove the unwanted features from the model
  • Repeat the same process until attaining maximum  error rate
  • At last, define the minimum features
  • Remove one at a time and check the error rate
  • If there are dependent values among data columns, then may have redundant information due to similarities.
  • So, we can filter the largely correlated columns based on coefficients of correlation
  • Add one at a time for high performance
  • Enhance the entire model efficiency
  • Addresses the possibility where data points are associated with high-dimensional space
  • Select low-dimensional embedding to generate related distribution
  •   Identify the missing value columns and remove them by threshold
  • Present variable set is converted to a new variable set
  • Also, referred to as a linear combo of new variables
  • Determine the location of each point by pair-wise spaces among all points which are represented in a matrix
  • Further, use standard multi-dimensional scaling (MDS) for determining low-dimensional points locations

In addition, we have also given you the broadly utilized deep learning models in current research . Here, we have classified the models into two major classifications such as discriminant models and generative models . Further, we have also specified the deep learning process with suitable techniques. If there is a complex situation, then we design new algorithms based on the project’s needs . On the whole, we find apt solutions for any sort of problem through our smart approach to problems.

Deep Learning Models

  • CNN and NLP (Hybrid)
  • Domain-specific
  • Image conversion
  • Meta-Learning

Furthermore, our developers are like to share the globally suggested deep learning software and tools . In truth, we have thorough practice on all these developing technologies. So, we are ready to fine-tuned guidance on deep learning libraries, modules, packages, toolboxes , etc. to ease your development process. By the by, we will also suggest you best-fitting software/tool for your project . We ensure you that our suggested software/tool will make your implementation process of deep learning projects techniques more simple and reliable .

Deep Learning Software and Tools

  • Caffe & Caffe2
  • Deep Learning 4j
  • Microsoft Cognitive Toolkit

So far, we have discussed important research updates of deep learning . Now, we can see the importance of handpicking a good research topic for an impressive deep learning research proposal. In the research topic, we have to outline your research by mentioning the research problem and efficient solutions . Also, it is necessary to check the future scope of research for that particular topic.

The topic without future research direction is not meant to do research!!!

For more clarity, here we have given you a few significant tips to select a good deep learning research topic.

How to write a research paper on deep learning?

  • Check whether your selected research problem is inspiring to overcome but not take more complex to solve
  • Check whether your selected problem not only inspires you but also create interest among readers and followers
  • Check whether your proposed research create a contribution to social developments
  • Check whether your selected research problem is unique

From the above list, you can get an idea about what exactly a good research topic is. Now, we can see how a good research topic is identified.

  • To recognize the best research topic, first undergo in-depth research on recent deep learning studied by referring latest reputed journal papers.
  • Then, perform a review process over the collected papers to detect what are the current research limitations, which aspect not addressed yet, which is a problem is not solved effectively,   which solution is needed to improve, what the techniques are followed in recent research, etc.
  • This literature review process needs more time and effort to grasp knowledge on research demands among scholars.
  • If you are new to this field, then it is suggested to take the advice of field experts who recommend good and resourceful research papers.
  • Majorly, the drawbacks of the existing research are proposed as a problem to provide suitable research solutions.
  • Usually, it is good to work on resource-filled research areas than areas that have limited reference.
  • When you find the desired research idea, then immediately check the originality of the idea. Make sure that no one is already proved your research idea.
  • Since, it is better to find it in the initial stage itself to choose some other one.
  • For that, the search keyword is more important because someone may already conduct the same research in a different name. So, concentrate on choosing keywords for the literature study.

How to describe your research topic?

One common error faced by beginners in research topic selection is a misunderstanding. Some researchers think topic selection means is just the title of your project. But it is not like that, you have to give detailed information about your research work on a short and crisp topic . In other words, the research topic is needed to act as an outline for your research work.

For instance: “deep learning for disease detection” is not the topic with clear information. In this, you can mention the details like type of deep learning technique, type of image and its process, type of human parts, symptoms , etc.

The modified research topic for “deep learning for disease detection” is “COVID-19 detection using automated deep learning algorithm”

 For your awareness, here we have given you some key points that need to focus on while framing research topics. To clearly define your research topic, we recommend writing some text explaining:

  • Research title
  • Previous research constraints
  • Importance of the problem that overcomes in proposed research
  • Reason of challenges in the research problem
  • Outline of problem-solving possibility

To the end, now we can see different research perspectives of deep learning among the research community. In the following, we have presented you with the most demanded research topics in deep learning such as image denoising, moving object detection, and event recognition . In addition to this list, we also have a repository of recent deep learning research proposal topics, machine learning thesis topics . So, communicate with us to know the advanced research ideas of deep learning.

Research Topics in Deep Learning

  • Continuous Network Monitoring and Pipeline Representation in Temporal Segment Networks
  • Dynamic Image Networks and Semantic Image Networks
  • Advance Non-uniform denoising verification based on FFDNet and DnCNN
  • Efficient image denoising based on ResNets and CNNs
  • Accurate object recognition in deep architecture using ResNeXts, Inception Nets and  Squeeze and Excitation Networks
  • Improved object detection using Faster R-CNN, YOLO, Fast R-CNN, and Mask-RCNN

Novel Deep Learning Research Proposal Implementation

Overall, we are ready to support you in all significant and new research areas of deep learning . We guarantee you that we provide you novel deep learning research proposal in your interested area with writing support. Further, we also give you code development , paper writing, paper publication, and thesis writing services . So, create a bond with us to create a strong foundation for your research career in the deep learning field.

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phd research proposal on deep learning

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10 Compelling Machine Learning Ph.D. Dissertations for 2020

10 Compelling Machine Learning Ph.D. Dissertations for 2020

Machine Learning Modeling Research posted by Daniel Gutierrez, ODSC August 19, 2020 Daniel Gutierrez, ODSC

As a data scientist, an integral part of my work in the field revolves around keeping current with research coming out of academia. I frequently scour for late-breaking papers that show trends and reveal fertile areas of research. Other sources of valuable research developments are in the form of Ph.D. dissertations, the culmination of a doctoral candidate’s work to confer his/her degree. Ph.D. candidates are highly motivated to choose research topics that establish new and creative paths toward discovery in their field of study. Their dissertations are highly focused on a specific problem. If you can find a dissertation that aligns with your areas of interest, consuming the research is an excellent way to do a deep dive into the technology. After reviewing hundreds of recent theses from universities all over the country, I present 10 machine learning dissertations that I found compelling in terms of my own areas of interest.

[Related article: Introduction to Bayesian Deep Learning ]

I hope you’ll find several that match your own fields of inquiry. Each thesis may take a while to consume but will result in hours of satisfying summer reading. Enjoy!

1. Bayesian Modeling and Variable Selection for Complex Data

As we routinely encounter high-throughput data sets in complex biological and environmental research, developing novel models and methods for variable selection has received widespread attention. This dissertation addresses a few key challenges in Bayesian modeling and variable selection for high-dimensional data with complex spatial structures. 

2. Topics in Statistical Learning with a Focus on Large Scale Data

Big data vary in shape and call for different approaches. One type of big data is the tall data, i.e., a very large number of samples but not too many features. This dissertation describes a general communication-efficient algorithm for distributed statistical learning on this type of big data. The algorithm distributes the samples uniformly to multiple machines, and uses a common reference data to improve the performance of local estimates. The algorithm enables potentially much faster analysis, at a small cost to statistical performance.

Another type of big data is the wide data, i.e., too many features but a limited number of samples. It is also called high-dimensional data, to which many classical statistical methods are not applicable. 

This dissertation discusses a method of dimensionality reduction for high-dimensional classification. The method partitions features into independent communities and splits the original classification problem into separate smaller ones. It enables parallel computing and produces more interpretable results.

3. Sets as Measures: Optimization and Machine Learning

The purpose of this machine learning dissertation is to address the following simple question:

How do we design efficient algorithms to solve optimization or machine learning problems where the decision variable (or target label) is a set of unknown cardinality?

Optimization and machine learning have proved remarkably successful in applications requiring the choice of single vectors. Some tasks, in particular many inverse problems, call for the design, or estimation, of sets of objects. When the size of these sets is a priori unknown, directly applying optimization or machine learning techniques designed for single vectors appears difficult. The work in this dissertation shows that a very old idea for transforming sets into elements of a vector space (namely, a space of measures), a common trick in theoretical analysis, generates effective practical algorithms.

4. A Geometric Perspective on Some Topics in Statistical Learning

Modern science and engineering often generate data sets with a large sample size and a comparably large dimension which puts classic asymptotic theory into question in many ways. Therefore, the main focus of this dissertation is to develop a fundamental understanding of statistical procedures for estimation and hypothesis testing from a non-asymptotic point of view, where both the sample size and problem dimension grow hand in hand. A range of different problems are explored in this thesis, including work on the geometry of hypothesis testing, adaptivity to local structure in estimation, effective methods for shape-constrained problems, and early stopping with boosting algorithms. The treatment of these different problems shares the common theme of emphasizing the underlying geometric structure.

5. Essays on Random Forest Ensembles

A random forest is a popular machine learning ensemble method that has proven successful in solving a wide range of classification problems. While other successful classifiers, such as boosting algorithms or neural networks, admit natural interpretations as maximum likelihood, a suitable statistical interpretation is much more elusive for a random forest. The first part of this dissertation demonstrates that a random forest is a fruitful framework in which to study AdaBoost and deep neural networks. The work explores the concept and utility of interpolation, the ability of a classifier to perfectly fit its training data. The second part of this dissertation places a random forest on more sound statistical footing by framing it as kernel regression with the proximity kernel. The work then analyzes the parameters that control the bandwidth of this kernel and discuss useful generalizations.

6. Marginally Interpretable Generalized Linear Mixed Models

A popular approach for relating correlated measurements of a non-Gaussian response variable to a set of predictors is to introduce latent random variables and fit a generalized linear mixed model. The conventional strategy for specifying such a model leads to parameter estimates that must be interpreted conditional on the latent variables. In many cases, interest lies not in these conditional parameters, but rather in marginal parameters that summarize the average effect of the predictors across the entire population. Due to the structure of the generalized linear mixed model, the average effect across all individuals in a population is generally not the same as the effect for an average individual. Further complicating matters, obtaining marginal summaries from a generalized linear mixed model often requires evaluation of an analytically intractable integral or use of an approximation. Another popular approach in this setting is to fit a marginal model using generalized estimating equations. This strategy is effective for estimating marginal parameters, but leaves one without a formal model for the data with which to assess quality of fit or make predictions for future observations. Thus, there exists a need for a better approach.

This dissertation defines a class of marginally interpretable generalized linear mixed models that leads to parameter estimates with a marginal interpretation while maintaining the desirable statistical properties of a conditionally specified model. The distinguishing feature of these models is an additive adjustment that accounts for the curvature of the link function and thereby preserves a specific form for the marginal mean after integrating out the latent random variables. 

7. On the Detection of Hate Speech, Hate Speakers and Polarized Groups in Online Social Media

The objective of this dissertation is to explore the use of machine learning algorithms in understanding and detecting hate speech, hate speakers and polarized groups in online social media. Beginning with a unique typology for detecting abusive language, the work outlines the distinctions and similarities of different abusive language subtasks (offensive language, hate speech, cyberbullying and trolling) and how we might benefit from the progress made in each area. Specifically, the work suggests that each subtask can be categorized based on whether or not the abusive language being studied 1) is directed at a specific individual, or targets a generalized “Other” and 2) the extent to which the language is explicit versus implicit. The work then uses knowledge gained from this typology to tackle the “problem of offensive language” in hate speech detection. 

8. Lasso Guarantees for Dependent Data

Serially correlated high dimensional data are prevalent in the big data era. In order to predict and learn the complex relationship among the multiple time series, high dimensional modeling has gained importance in various fields such as control theory, statistics, economics, finance, genetics and neuroscience. This dissertation studies a number of high dimensional statistical problems involving different classes of mixing processes. 

9. Random forest robustness, variable importance, and tree aggregation

Random forest methodology is a nonparametric, machine learning approach capable of strong performance in regression and classification problems involving complex data sets. In addition to making predictions, random forests can be used to assess the relative importance of feature variables. This dissertation explores three topics related to random forests: tree aggregation, variable importance, and robustness. 

10. Climate Data Computing: Optimal Interpolation, Averaging, Visualization and Delivery

This dissertation solves two important problems in the modern analysis of big climate data. The first is the efficient visualization and fast delivery of big climate data, and the second is a computationally extensive principal component analysis (PCA) using spherical harmonics on the Earth’s surface. The second problem creates a way to supply the data for the technology developed in the first. These two problems are computationally difficult, such as the representation of higher order spherical harmonics Y400, which is critical for upscaling weather data to almost infinitely fine spatial resolution.

I hope you enjoyed learning about these compelling machine learning dissertations.

Editor’s note: Interested in more data science research? Check out the Research Frontiers track at ODSC Europe this September 17-19 or the ODSC West Research Frontiers track this October 27-30.

phd research proposal on deep learning

Daniel Gutierrez, ODSC

Daniel D. Gutierrez is a practicing data scientist who’s been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, “Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R.” Daniel holds a BS in Mathematics and Computer Science from UCLA.

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Get your deep learning proposal work from high end trained professionals. The passion of your areas of interest will be clearly reflected in your proposal. Chose an expert to provide you with custom research proposal work. To interpret the real-time process of the art, historical context and future scopes we have made a literature survey in Deep Learning (DL).

  • Define Objectives:
  • Clearly sketch what we need to execute with our comprehensive view.
  • Take transformers in Natural Language Processing (NLP) as an example and note its specific tasks and issues.
  • Primary Sources:
  • Research Databases: We can use the fields such as Google Scholar, arXIv, PubMed (for biomedical papers), IEEE Xplore, and others.
  • Conference: Here NeurIPS, ICML, ICLR, CVPR, ICCV, ACL, EMNLP are the basic conferences in DL.
  • Journal: The Journal of Machine Learning Research (JMLR) and Neural Computation are the papers frequently establish DL related studies.
  • Start by Reviews and Surveys:
  • Find the latest survey and review papers on our area of interest which gives a literature outline and frequently see the seminal latest works.
  • Begin with Convolutional Neural Networks (CNNs) architecture survey paper if we search for CNN.
  • Reading Papers:
  • Skim: Begin with reading abstracts, introductions, conclusions, and figures.
  • Deep Dive: When a study shows high similar to our work, then look in-depth to its methodology, experiments, and results.
  • Take Notes: Look down the basic plans, methods, datasets, Evaluation metrics, and open issues described in the paper and note it.
  • Forward and Backward Search:
  • Forward: We can detect how the area is emerging using the tools such as Google Scholar’s “Cited by” feature to find latest papers in our research.
  • Backward : We can track the improvement of designs by seeing the reference which is gives more knowledge in our study.  
  • Organize and Combine:
  • Classify the papers by its themes, methodologies and version.
  • We have to analyze the trends, patterns, and gaps in the literature.
  • Keep Updates:
  • We need to stay update with notifications on fields such as Google Scholar and arXiv for keywords similar to our title with the recent publications, because Dl is a fast-emerging area.
  • Tools and Platforms:
  • Utilize the tools such as Mendeley, Zotero and EndNote for maintaining and citing papers.
  • We find similar papers with AI-driven suggestions from Semantic Scholar platform.
  • Engage with the Community:
  • Join into mailing lists, social media groups and online conference to get related with DL. Websites such as Reddit’s r/Machine Learning or the AI Alignment Forum frequently gather latest papers.
  • By attending the webinars, workshops and meetings often can help us to gain skills from recent techniques and find knowledge of what the group seems essential.
  • Report and Share:
  • If we want to establish the paper make annotated bibliographies, presentations, and review papers based on our motive and file the research.
  • We can our scope to help others and publish us a skilled person in this topic.

            The objective of this review is to crucially recognize and integrate the real-time content in the area. Though it is a time-consuming work, it will be useful for someone aims to make research and latest works in DL.

Deep Learning project face recognition with python OpenCV

            Designing a face remembering system using Python and OpenCV is an amazing work that introduces us into the world of computer vision and DL. The following are the step-by-step guide to construct a simple face recognition system:

  • Install Necessary Libraries

Make sure that we have the required libraries installed:

pip install opencv-python opencv-python-headless

  • Capture Faces

We require a dataset for training. We utilize the pre-defined dataset and capture our own using OpenCV.

cam = cv2.VideoCapture(0)

detector = cv2.CascadeClassifier( + ‘haarcascade_frontalface_default.xml’)

id = input(‘Enter user ID: ‘)

sampleNum = 0

while True:

    ret, img =

    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    faces = detector.detectMultiScale(gray, 1.3, 5)

    for (x,y,w,h) in faces:

        sampleNum += 1

        cv2.imwrite(f”faces/User.{id}.{sampleNum}.jpg”, gray[y:y+h,x:x+w])

        cv2.rectangle(img, (x,y), (x+w, y+h), (255,0,0), 2)


    cv2.imshow(‘Capture’, img)


    if sampleNum > 20: # capture 20 images




  • Training the Recognizer

OpenCV has a built-in face recognizer. For this example, we’ll use the LBPH (Local Binary Pattern Histogram) face recognizer.

import numpy as np

from PIL import Image

path = ‘faces’

recognizer = cv2.face.LBPHFaceRecognizer_create()

def getImagesAndLabels(path):

    imagePaths = [os.path.join(path,f) for f in os.listdir(path)]    


    ids = []

    for imagePath in imagePaths:

        PIL_img =‘L’)

        img_numpy = np.array(PIL_img,’uint8′)

        id = int(os.path.split(imagePath)[-1].split(“.”)[1])

        faces = detector.detectMultiScale(img_numpy)

        for (x,y,w,h) in faces:



    return faceSamples, np.array(ids)

faces,ids = getImagesAndLabels(path)

recognizer.train(faces, ids)‘trainer/trainer.yml’)

  • Recognizing Faces‘trainer/trainer.yml’)

cascadePath = + “haarcascade_frontalface_default.xml”

faceCascade = cv2.CascadeClassifier(cascadePath)


minW = 0.1*cam.get(3)

minH = 0.1*cam.get(4)

    faces = faceCascade.detectMultiScale(




        minSize=(int(minW), int(minH)),

        id, confidence = recognizer.predict(gray[y:y+h,x:x+w])

        if (confidence < 100):

            confidence = f”  {round(100 – confidence)}%”


            id = “unknown”

        cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)

        cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1) 

    cv2.imshow(‘Face Recognition’,img)

    if cv2.waitKey(1) & 0xFF == ord(‘q’):

We have proper directories (faces and trainer) to design. It will be a basic face recognition system and can strengthen with DL models for better accuracy and robustness against various states in real-time. To achieve better accuracy in real-time conditions, we discover latest DL based techniques like FaceNet or pre-trained models from DL frameworks.

Deep learning MS Thesis topics

Have a conversation with our faculty members to get the best topics that matches with your interest. Some of the unique topic ideas are shared below …. contact us for more support.


  • Modulation Recognition based on Incremental Deep Learning
  • Fast Channel Analysis and Design Approach using Deep Learning Algorithm for 112Gbs HSI Signal Routing Optimization
  • Deep Learning of Process Data with Supervised Variational Auto-encoder for Soft Sensor
  • Methodological Principles for Deep Learning in Software Engineering
  • Recent Trends in Deep Learning for Natural Language Processing and Scope for Asian Languages
  • Adding Context to Source Code Representations for Deep Learning
  • Weekly Power Generation Forecasting using Deep Learning Techniques: Case Study of a 1.5 MWp Floating PV Power Plant
  • A Study of Deep Learning Approaches and Loss Functions for Abundance Fractions Estimation
  • A Trustless Federated Framework for Decentralized and Confidential Deep Learning
  • Research on Financial Data Analysis Based on Applied Deep Learning in Quantitative Trading
  • A Deep Learning model for day-ahead load forecasting taking advantage of expert knowledge
  • Locational marginal price forecasting using Transformer-based deep learning network
  • H-Stegonet: A Hybrid Deep Learning Framework for Robust Steganalysis
  • Comparison of Deep Learning Approaches for Sentiment Classification
  • An Unmanned Network Intrusion Detection Model Based on Deep Reinforcement Learning
  • Indoor Object Localization and Tracking Using Deep Learning over Received Signal Strength
  • Analysis of Deep Learning 3-D Imaging Methods Based on UAV SAR
  • Research and improvement of deep learning tool chain for electric power applications
  • Hybrid Intrusion Detector using Deep Learning Technique
  • Non-Trusted user Classification-Comparative Analysis of Machine and Deep Learning Approaches

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Our Editor-in-Chief has Website Ownership who control and deliver all aspects of PhD Direction to scholars and students and also keep the look to fully manage all our clients.

Our world-class certified experts have 18+years of experience in Research & Development programs (Industrial Research) who absolutely immersed as many scholars as possible in developing strong PhD research projects.

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Solid works delivering by young qualified global research team. "References" is the key to evaluating works easier because we carefully assess scholars findings.

Detailed Videos, Readme files, Screenshots are provided for all research projects. We provide Teamviewer support and other online channels for project explanation.

Worthy journal publication is our main thing like IEEE, ACM, Springer, IET, Elsevier, etc. We substantially reduces scholars burden in publication side. We carry scholars from initial submission to final acceptance.

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Machine Learning - CMU

PhD Dissertations

PhD Dissertations

[all are .pdf files].

Reliable and Practical Machine Learning for Dynamic Healthcare Settings Helen Zhou, 2023

Automatic customization of large-scale spiking network models to neuronal population activity (unavailable) Shenghao Wu, 2023

Estimation of BVk functions from scattered data (unavailable) Addison J. Hu, 2023

Rethinking object categorization in computer vision (unavailable) Jayanth Koushik, 2023

Advances in Statistical Gene Networks Jinjin Tian, 2023 Post-hoc calibration without distributional assumptions Chirag Gupta, 2023

The Role of Noise, Proxies, and Dynamics in Algorithmic Fairness Nil-Jana Akpinar, 2023

Collaborative learning by leveraging siloed data Sebastian Caldas, 2023

Modeling Epidemiological Time Series Aaron Rumack, 2023

Human-Centered Machine Learning: A Statistical and Algorithmic Perspective Leqi Liu, 2023

Uncertainty Quantification under Distribution Shifts Aleksandr Podkopaev, 2023

Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023

Comparing Forecasters and Abstaining Classifiers Yo Joong Choe, 2023

Using Task Driven Methods to Uncover Representations of Human Vision and Semantics Aria Yuan Wang, 2023

Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023

Applied Mathematics of the Future Kin G. Olivares, 2023



Principled Machine Learning for Societally Consequential Decision Making Amanda Coston, 2023

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Maxwell B. Wang

Long term brain dynamics extend cognitive neuroscience to timescales relevant for health and physiology Darby M. Losey, 2023

Calibrated Conditional Density Models and Predictive Inference via Local Diagnostics David Zhao, 2023

Towards an Application-based Pipeline for Explainability Gregory Plumb, 2022

Objective Criteria for Explainable Machine Learning Chih-Kuan Yeh, 2022

Making Scientific Peer Review Scientific Ivan Stelmakh, 2022

Facets of regularization in high-dimensional learning: Cross-validation, risk monotonization, and model complexity Pratik Patil, 2022

Active Robot Perception using Programmable Light Curtains Siddharth Ancha, 2022

Strategies for Black-Box and Multi-Objective Optimization Biswajit Paria, 2022

Unifying State and Policy-Level Explanations for Reinforcement Learning Nicholay Topin, 2022

Sensor Fusion Frameworks for Nowcasting Maria Jahja, 2022

Equilibrium Approaches to Modern Deep Learning Shaojie Bai, 2022

Towards General Natural Language Understanding with Probabilistic Worldbuilding Abulhair Saparov, 2022

Applications of Point Process Modeling to Spiking Neurons (Unavailable) Yu Chen, 2021

Neural variability: structure, sources, control, and data augmentation Akash Umakantha, 2021

Structure and time course of neural population activity during learning Jay Hennig, 2021

Cross-view Learning with Limited Supervision Yao-Hung Hubert Tsai, 2021

Meta Reinforcement Learning through Memory Emilio Parisotto, 2021

Learning Embodied Agents with Scalably-Supervised Reinforcement Learning Lisa Lee, 2021

Learning to Predict and Make Decisions under Distribution Shift Yifan Wu, 2021

Statistical Game Theory Arun Sai Suggala, 2021

Towards Knowledge-capable AI: Agents that See, Speak, Act and Know Kenneth Marino, 2021

Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods Po-Wei Wang, 2021

Bridging Language in Machines with Language in the Brain Mariya Toneva, 2021

Curriculum Learning Otilia Stretcu, 2021

Principles of Learning in Multitask Settings: A Probabilistic Perspective Maruan Al-Shedivat, 2021

Towards Robust and Resilient Machine Learning Adarsh Prasad, 2021

Towards Training AI Agents with All Types of Experiences: A Unified ML Formalism Zhiting Hu, 2021

Building Intelligent Autonomous Navigation Agents Devendra Chaplot, 2021

Learning to See by Moving: Self-supervising 3D Scene Representations for Perception, Control, and Visual Reasoning Hsiao-Yu Fish Tung, 2021

Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe Collin Politsch, 2020

Causal Inference with Complex Data Structures and Non-Standard Effects Kwhangho Kim, 2020

Networks, Point Processes, and Networks of Point Processes Neil Spencer, 2020

Dissecting neural variability using population recordings, network models, and neurofeedback (Unavailable) Ryan Williamson, 2020

Predicting Health and Safety: Essays in Machine Learning for Decision Support in the Public Sector Dylan Fitzpatrick, 2020

Towards a Unified Framework for Learning and Reasoning Han Zhao, 2020

Learning DAGs with Continuous Optimization Xun Zheng, 2020

Machine Learning and Multiagent Preferences Ritesh Noothigattu, 2020

Learning and Decision Making from Diverse Forms of Information Yichong Xu, 2020

Towards Data-Efficient Machine Learning Qizhe Xie, 2020

Change modeling for understanding our world and the counterfactual one(s) William Herlands, 2020

Machine Learning in High-Stakes Settings: Risks and Opportunities Maria De-Arteaga, 2020

Data Decomposition for Constrained Visual Learning Calvin Murdock, 2020

Structured Sparse Regression Methods for Learning from High-Dimensional Genomic Data Micol Marchetti-Bowick, 2020

Towards Efficient Automated Machine Learning Liam Li, 2020

LEARNING COLLECTIONS OF FUNCTIONS Emmanouil Antonios Platanios, 2020

Provable, structured, and efficient methods for robustness of deep networks to adversarial examples Eric Wong , 2020

Reconstructing and Mining Signals: Algorithms and Applications Hyun Ah Song, 2020

Probabilistic Single Cell Lineage Tracing Chieh Lin, 2020

Graphical network modeling of phase coupling in brain activity (unavailable) Josue Orellana, 2019

Strategic Exploration in Reinforcement Learning - New Algorithms and Learning Guarantees Christoph Dann, 2019 Learning Generative Models using Transformations Chun-Liang Li, 2019

Estimating Probability Distributions and their Properties Shashank Singh, 2019

Post-Inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Willie Neiswanger, 2019

Accelerating Text-as-Data Research in Computational Social Science Dallas Card, 2019

Multi-view Relationships for Analytics and Inference Eric Lei, 2019

Information flow in networks based on nonstationary multivariate neural recordings Natalie Klein, 2019

Competitive Analysis for Machine Learning & Data Science Michael Spece, 2019

The When, Where and Why of Human Memory Retrieval Qiong Zhang, 2019

Towards Effective and Efficient Learning at Scale Adams Wei Yu, 2019

Towards Literate Artificial Intelligence Mrinmaya Sachan, 2019

Learning Gene Networks Underlying Clinical Phenotypes Under SNP Perturbations From Genome-Wide Data Calvin McCarter, 2019

Unified Models for Dynamical Systems Carlton Downey, 2019

Anytime Prediction and Learning for the Balance between Computation and Accuracy Hanzhang Hu, 2019

Statistical and Computational Properties of Some "User-Friendly" Methods for High-Dimensional Estimation Alnur Ali, 2019

Nonparametric Methods with Total Variation Type Regularization Veeranjaneyulu Sadhanala, 2019

New Advances in Sparse Learning, Deep Networks, and Adversarial Learning: Theory and Applications Hongyang Zhang, 2019

Gradient Descent for Non-convex Problems in Modern Machine Learning Simon Shaolei Du, 2019

Selective Data Acquisition in Learning and Decision Making Problems Yining Wang, 2019

Anomaly Detection in Graphs and Time Series: Algorithms and Applications Bryan Hooi, 2019

Neural dynamics and interactions in the human ventral visual pathway Yuanning Li, 2018

Tuning Hyperparameters without Grad Students: Scaling up Bandit Optimisation Kirthevasan Kandasamy, 2018

Teaching Machines to Classify from Natural Language Interactions Shashank Srivastava, 2018

Statistical Inference for Geometric Data Jisu Kim, 2018

Representation Learning @ Scale Manzil Zaheer, 2018

Diversity-promoting and Large-scale Machine Learning for Healthcare Pengtao Xie, 2018

Distribution and Histogram (DIsH) Learning Junier Oliva, 2018

Stress Detection for Keystroke Dynamics Shing-Hon Lau, 2018

Sublinear-Time Learning and Inference for High-Dimensional Models Enxu Yan, 2018

Neural population activity in the visual cortex: Statistical methods and application Benjamin Cowley, 2018

Efficient Methods for Prediction and Control in Partially Observable Environments Ahmed Hefny, 2018

Learning with Staleness Wei Dai, 2018

Statistical Approach for Functionally Validating Transcription Factor Bindings Using Population SNP and Gene Expression Data Jing Xiang, 2017

New Paradigms and Optimality Guarantees in Statistical Learning and Estimation Yu-Xiang Wang, 2017

Dynamic Question Ordering: Obtaining Useful Information While Reducing User Burden Kirstin Early, 2017

New Optimization Methods for Modern Machine Learning Sashank J. Reddi, 2017

Active Search with Complex Actions and Rewards Yifei Ma, 2017

Why Machine Learning Works George D. Montañez , 2017

Source-Space Analyses in MEG/EEG and Applications to Explore Spatio-temporal Neural Dynamics in Human Vision Ying Yang , 2017

Computational Tools for Identification and Analysis of Neuronal Population Activity Pengcheng Zhou, 2016

Expressive Collaborative Music Performance via Machine Learning Gus (Guangyu) Xia, 2016

Supervision Beyond Manual Annotations for Learning Visual Representations Carl Doersch, 2016

Exploring Weakly Labeled Data Across the Noise-Bias Spectrum Robert W. H. Fisher, 2016

Optimizing Optimization: Scalable Convex Programming with Proximal Operators Matt Wytock, 2016

Combining Neural Population Recordings: Theory and Application William Bishop, 2015

Discovering Compact and Informative Structures through Data Partitioning Madalina Fiterau-Brostean, 2015

Machine Learning in Space and Time Seth R. Flaxman, 2015

The Time and Location of Natural Reading Processes in the Brain Leila Wehbe, 2015

Shape-Constrained Estimation in High Dimensions Min Xu, 2015

Spectral Probabilistic Modeling and Applications to Natural Language Processing Ankur Parikh, 2015 Computational and Statistical Advances in Testing and Learning Aaditya Kumar Ramdas, 2015

Corpora and Cognition: The Semantic Composition of Adjectives and Nouns in the Human Brain Alona Fyshe, 2015

Learning Statistical Features of Scene Images Wooyoung Lee, 2014

Towards Scalable Analysis of Images and Videos Bin Zhao, 2014

Statistical Text Analysis for Social Science Brendan T. O'Connor, 2014

Modeling Large Social Networks in Context Qirong Ho, 2014

Semi-Cooperative Learning in Smart Grid Agents Prashant P. Reddy, 2013

On Learning from Collective Data Liang Xiong, 2013

Exploiting Non-sequence Data in Dynamic Model Learning Tzu-Kuo Huang, 2013

Mathematical Theories of Interaction with Oracles Liu Yang, 2013

Short-Sighted Probabilistic Planning Felipe W. Trevizan, 2013

Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms Lucia Castellanos, 2013

Approximation Algorithms and New Models for Clustering and Learning Pranjal Awasthi, 2013

Uncovering Structure in High-Dimensions: Networks and Multi-task Learning Problems Mladen Kolar, 2013

Learning with Sparsity: Structures, Optimization and Applications Xi Chen, 2013

GraphLab: A Distributed Abstraction for Large Scale Machine Learning Yucheng Low, 2013

Graph Structured Normal Means Inference James Sharpnack, 2013 (Joint Statistics & ML PhD)

Probabilistic Models for Collecting, Analyzing, and Modeling Expression Data Hai-Son Phuoc Le, 2013

Learning Large-Scale Conditional Random Fields Joseph K. Bradley, 2013

New Statistical Applications for Differential Privacy Rob Hall, 2013 (Joint Statistics & ML PhD)

Parallel and Distributed Systems for Probabilistic Reasoning Joseph Gonzalez, 2012

Spectral Approaches to Learning Predictive Representations Byron Boots, 2012

Attribute Learning using Joint Human and Machine Computation Edith L. M. Law, 2012

Statistical Methods for Studying Genetic Variation in Populations Suyash Shringarpure, 2012

Data Mining Meets HCI: Making Sense of Large Graphs Duen Horng (Polo) Chau, 2012

Learning with Limited Supervision by Input and Output Coding Yi Zhang, 2012

Target Sequence Clustering Benjamin Shih, 2011

Nonparametric Learning in High Dimensions Han Liu, 2010 (Joint Statistics & ML PhD)

Structural Analysis of Large Networks: Observations and Applications Mary McGlohon, 2010

Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy Brian D. Ziebart, 2010

Tractable Algorithms for Proximity Search on Large Graphs Purnamrita Sarkar, 2010

Rare Category Analysis Jingrui He, 2010

Coupled Semi-Supervised Learning Andrew Carlson, 2010

Fast Algorithms for Querying and Mining Large Graphs Hanghang Tong, 2009

Efficient Matrix Models for Relational Learning Ajit Paul Singh, 2009

Exploiting Domain and Task Regularities for Robust Named Entity Recognition Andrew O. Arnold, 2009

Theoretical Foundations of Active Learning Steve Hanneke, 2009

Generalized Learning Factors Analysis: Improving Cognitive Models with Machine Learning Hao Cen, 2009

Detecting Patterns of Anomalies Kaustav Das, 2009

Dynamics of Large Networks Jurij Leskovec, 2008

Computational Methods for Analyzing and Modeling Gene Regulation Dynamics Jason Ernst, 2008

Stacked Graphical Learning Zhenzhen Kou, 2007

Actively Learning Specific Function Properties with Applications to Statistical Inference Brent Bryan, 2007

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields Pradeep Ravikumar, 2007

Scalable Graphical Models for Social Networks Anna Goldenberg, 2007

Measure Concentration of Strongly Mixing Processes with Applications Leonid Kontorovich, 2007

Tools for Graph Mining Deepayan Chakrabarti, 2005

Automatic Discovery of Latent Variable Models Ricardo Silva, 2005

phd research proposal on deep learning

  • Our Promise
  • Our Achievements
  • Our Mission
  • Proposal Writing
  • System Development
  • Paper Writing
  • Paper Publish
  • Synopsis Writing
  • Thesis Writing
  • Assignments
  • Survey Paper
  • Conference Paper
  • Journal Paper
  • Empirical Paper
  • Journal Support
  • Deep Learning Research Proposal

Various deep learning project proposals development process includes describing a research-based queries and problems, demonstrating its importance and suggesting an approach to overcome this. Below we discussed about the common flow of deep learning-based project proposals:

  • Our title should be effective, clear and brief.
  • Introduction:
  • Background: In background study, we offer various research related ideas. What’s the current state of deep learning in the area of our interest?
  • Problem statement: Specific problems and issues that we are intended to overcome are properly described in our study. Why is it important?
  • Objective: We have to discuss about the major goal of our research.
  • Literature Survey:
  • For our specified problem, we described some previous research solutions and techniques.
  • We have to point out the research gaps in some techniques or concepts that need further enhancement.
  • To inspire our research, initial experimental analysis and results are examined by us.
  • Research Queries and Hypotheses:
  • Demonstrate particular queries that we ensure about answer and we will examine hypotheses.
  • Methodology:
  • Data: Utilized datasets and the processes involved such as gathering of data, preprocessing and data augmentation are investigated by us.
  • Framework: In our study, we discussed about the utilized or constructed neural network framework.
  • Training: We investigated about the process involved like model training, loss functions, optimization and regularization methods etc.
  • Evaluation: Discussed about how we are going to examine the model’s efficiency. What metrics will we use?
  • Software and Hardware: Utilized libraries, tools and computing resources have to be mentioned in our research.
  • Preliminary Findings (if any):
  • We can discuss about the basic experimental analysis and results that may inspire our research or it may show its efficiency.
  • Significance and Impact:
  • Important objective of our research are investigated by us. Who will benefit from it? How it might it advance the field?
  • Time utilization for various stages of our project is described in our study.
  • Budget (if applicable):
  • We discussed about the calculated cost like data acquisition, computing resources and software licenses.
  • Potential Challenges and Overcoming Plans:
  • The defined problems are highlighted and the strategies to overcome are also described by us.
  • Conclusion:
  • In conclusion, we explained about the essential aspects of our proposal and its significance is restated.
  • References:
  • We should point out all the cited references which include articles, papers and resources.


  • Clarity: We need to check whether our project is understandable and free from jargon or not. Even for the people who are not well versed in our research field, it should be interpretable.
  • Rigour: The exactness of our proposed techniques and concept is very important.
  • Feedback: Look for reviews from professionals, associations, and staffs to confirm about model’s efficiency is an appreciable one before publishing our project.

Often, we consider particular needs and conditions of the association, institution or some research groups in which we are going to submit your project proposals. We follow the rules and instructions properly. Plagiarism free paper will be provided in leading tools like Turnitin we detect plagiarism to assure you success.

Which of these are the research areas covered by deep learning?

            Deep learning has been utilized in various research concepts. We listed out deep learning based innovative fields and sub-fields that we frequently work out and achieve success are listed below:

  • Natural Language Processing (NLP):
  • Speech Recognition and Generation
  • Question-Answering
  • Language Modeling (for instance: Transformers)
  • Named Entity Recognition
  • Sentiment Analysis
  • Text Summarization
  • Machine Translation
  • Healthcare and Biomedical :
  • Drug Findings
  • Predictive Analytics for patient Care
  • Medical Image Analysis
  • Genomic Sequence Analysis
  • Algorithmic Trading
  • Credit Scoring
  • Fraud Detection
  • Computer Vision :
  • Image Categorization
  • Facial Recognition
  • Object Identification and Segmentation
  • Image Generation (For example: GANs)
  • Image to Image Translation
  • Super-Resolution
  • Art and Creativity:
  • Art Generation
  • Music Composition
  • Style Transfer
  • Anomaly Identification :
  • Network Security
  • Industrial Defect Identification
  • Multimodal Learning :
  • Combining Information from Various Sources
  • Cross-modal Transfer Learning
  • Robot Navigation
  • Manipulation Tasks
  • Ethics and Fairness:
  • Bias Identification in Frameworks
  • Interpretability & Explainability of deep Models
  • Generative Models :
  • Generative Adversarial Networks (GANs)
  • Variational Autoencoders (VAEs)
  • Agriculture :
  • Identification of Crop Disease
  • Precision Agriculture
  • Reinforcement Learning:
  • Robotics Control
  • Game Playing (For instance: AlphaGo, OpenAI Five)
  • Optimization Issues
  • Neuroscience :
  • Neural Signal processing
  • Brain-Computer Interfaces
  • Time Series Analysis:
  • Weather Prediction
  • Stock Price Forecasting
  • Audio and Speech processing:
  • Speech Recognition
  • Speech Synthesis
  • Audio Categorization
  • Music Generation
  • Automatic System:
  • Drone Navigation
  • Self-Driving Cars
  • IoT and Edge Devices:
  • Activity Recognition
  • On-Device ML for Smart Devices

These various concepts are the subdivisions of several deep learning-based research topics. From this, we state that, the deep learning methods can be employed in a wide area and we can make use of its efficiency and ability.

Where can I find online deep learning projects?

If you are reading this page means you are in deep learning research help. Taught provoking research assistance will be given by online no matter where you are. We assist globally as our framework is reliable for all. Thesis topics and thesis ideas will be shared by our professional experts hurry up.

  • Recognition and classification of mathematical expressions using machine learning and deep learning methods
  • Prediction of Subscriber VoLTE using Machine Learning and Deep Learning
  • Light-Weight Design and Implementation of Deep Learning Accelerator for Mobile Systems
  • Multifarious Face Attendance System using Machine Learning and Deep Learning
  • A bert model for sms and twitter spam ham classification and comparative study of machine learning and deep learning technique
  • A Comparative Analysis for Leukocyte Classification Based on Various Deep Learning Models Using Transfer Learning
  • Machine Learning Based Real-Time Industrial Bin-Picking: Hybrid and Deep Learning Approaches
  • Insight on Human Activity Recognition Using the Deep Learning Approach
  • A Comprehensive Survey of Trending Tools and Techniques in Deep Learning
  • The Advance of the Combination Method of Machine Learning and Deep Learning
  • Research and Discussion on Image Recognition and Classification Algorithm Based on Deep Learning
  • An Intelligent Anti-jamming Decision-making Method Based on Deep Reinforcement Learning for Cognitive Radar
  • Conv2D Xception Adadelta Gradient Descent Learning Rate Deep learning Optimizer for Plant Species Classification
  • Beyond the Bias Variance Trade-Off: A Mutual Information Trade-Off in Deep Learning
  • Machine Learning and Deep Learning framework with Feature Selection for Intrusion Detection
  • Transfer Learning with Shapeshift Adapter: A Parameter-Efficient Module for Deep Learning Model
  • Deep Learning Network for Object Detection Under the Poor Lighting Condition
  • Sign Language Recognizer: A Deep Learning Approach
  • Ensemble Deep Learning Applied to Predict Building Energy Consumption
  • Will Deep Learning Change How Teams Execute Big Data Projects?

MILESTONE 1: Research Proposal

Finalize journal (indexing).

Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.

Research Subject Selection

As a doctoral student, subject selection is a big problem. has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.

Research Topic Selection

We helping you with right and perfect topic selection, which sound interesting to the other fellows of your committee. For e.g. if your interest in networking, the research topic is VANET / MANET / any other

Literature Survey Writing

To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)

Case Study Writing

After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

Problem Statement

Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.

Writing Research Proposal

Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.

Tools/Plan Approval

We get the approval for implementation tool, software, programing language and finally implementation plan to start development process.

Pseudocode Description

Our source code is original since we write the code after pseudocodes, algorithm writing and mathematical equation derivations.

Develop Proposal Idea

We implement our novel idea in step-by-step process that given in implementation plan. We can help scholars in implementation.


We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.

Graphs, Results, Analysis Table

We evaluate and analyze the project results by plotting graphs, numerical results computation, and broader discussion of quantitative results in table.

Project Deliverables

For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

MILESTONE 3: Paper Writing

Choosing right format.

We intend to write a paper in customized layout. If you are interesting in any specific journal, we ready to support you. Otherwise we prepare in IEEE transaction level.

Collecting Reliable Resources

Before paper writing, we collect reliable resources such as 50+ journal papers, magazines, news, encyclopedia (books), benchmark datasets, and online resources.

Writing Rough Draft

We create an outline of a paper at first and then writing under each heading and sub-headings. It consists of novel idea and resources

Proofreading & Formatting

We must proofread and formatting a paper to fix typesetting errors, and avoiding misspelled words, misplaced punctuation marks, and so on

Native English Writing

We check the communication of a paper by rewriting with native English writers who accomplish their English literature in University of Oxford.

Scrutinizing Paper Quality

We examine the paper quality by top-experts who can easily fix the issues in journal paper writing and also confirm the level of journal paper (SCI, Scopus or Normal).

Plagiarism Checking

We at is 100% guarantee for original journal paper writing. We never use previously published works.

MILESTONE 4: Paper Publication

Finding apt journal.

We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.

Lay Paper to Submit

We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.

Paper Submission

We upload paper with submit all prerequisites that are required in journal. We completely remove frustration in paper publishing.

Paper Status Tracking

We track your paper status and answering the questions raise before review process and also we giving you frequent updates for your paper received from journal.

Revising Paper Precisely

When we receive decision for revising paper, we get ready to prepare the point-point response to address all reviewers query and resubmit it to catch final acceptance.

Get Accept & e-Proofing

We receive final mail for acceptance confirmation letter and editors send e-proofing and licensing to ensure the originality.

Publishing Paper

Paper published in online and we inform you with paper title, authors information, journal name volume, issue number, page number, and DOI link

MILESTONE 5: Thesis Writing

Identifying university format.

We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

Gathering Adequate Resources

We collect primary and adequate resources for writing well-structured thesis using published research articles, 150+ reputed reference papers, writing plan, and so on.

Writing Thesis (Preliminary)

We write thesis in chapter-by-chapter without any empirical mistakes and we completely provide plagiarism-free thesis.

Skimming & Reading

Skimming involve reading the thesis and looking abstract, conclusions, sections, & sub-sections, paragraphs, sentences & words and writing thesis chorological order of papers.

Fixing Crosscutting Issues

This step is tricky when write thesis by amateurs. Proofreading and formatting is made by our world class thesis writers who avoid verbose, and brainstorming for significant writing.

Organize Thesis Chapters

We organize thesis chapters by completing the following: elaborate chapter, structuring chapters, flow of writing, citations correction, etc.

Writing Thesis (Final Version)

We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.

How deal with significant issues ?

1. novel ideas.

Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.

2. Plagiarism-Free

To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

3. Confidential Info

We intended to keep your personal and technical information in secret and it is a basic worry for all scholars.

  • Technical Info: We never share your technical details to any other scholar since we know the importance of time and resources that are giving us by scholars.
  • Personal Info: We restricted to access scholars personal details by our experts. Our organization leading team will have your basic and necessary info for scholars.


4. Publication

Most of the PhD consultancy services will end their services in Paper Writing, but our is different from others by giving guarantee for both paper writing and publication in reputed journals. With our 18+ year of experience in delivering PhD services, we meet all requirements of journals (reviewers, editors, and editor-in-chief) for rapid publications. From the beginning of paper writing, we lay our smart works. PUBLICATION IS A ROOT FOR PHD DEGREE. WE LIKE A FRUIT FOR GIVING SWEET FEELING FOR ALL SCHOLARS.

5. No Duplication

After completion of your work, it does not available in our library i.e. we erased after completion of your PhD work so we avoid of giving duplicate contents for scholars. This step makes our experts to bringing new ideas, applications, methodologies and algorithms. Our work is more standard, quality and universal. Everything we make it as a new for all scholars. INNOVATION IS THE ABILITY TO SEE THE ORIGINALITY. EXPLORATION IS OUR ENGINE THAT DRIVES INNOVATION SO LET’S ALL GO EXPLORING.

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A Novel Proposal for Deep Learning-Based Diabetes Prediction: Converting Clinical Data to Image Data

Associated data.

The data utilized in this work can be found at (accessed on 8 June 2022).

Diabetes, one of the most common diseases worldwide, has become an increasingly global threat to humans in recent years. However, early detection of diabetes greatly inhibits the progression of the disease. This study proposes a new method based on deep learning for the early detection of diabetes. Like many other medical data, the PIMA dataset used in the study contains only numerical values. In this sense, the application of popular convolutional neural network (CNN) models to such data are limited. This study converts numerical data into images based on the feature importance to use the robust representation of CNN models in early diabetes diagnosis. Three different classification strategies are then applied to the resulting diabetes image data. In the first, diabetes images are fed into the ResNet18 and ResNet50 CNN models. In the second, deep features of the ResNet models are fused and classified with support vector machines (SVM). In the last approach, the selected fusion features are classified by SVM. The results demonstrate the robustness of diabetes images in the early diagnosis of diabetes.

1. Introduction

The most prevalent chronic non-communicable disease in the world is diabetes, also known as diabetes mellitus. Diabetes is fatal or drastically lowers quality of life and affects more women than men [ 1 ]. Diabetes is particularly risky for pregnant women, and unborn children are likely to be affected by this disease. Generally, if the glucose level in the blood rises above the normal value, the person is considered diabetic. This is due to the inability of the pancreas in the human body to fully perform its task. The person’s blood sugar rises if the pancreas cannot utilize the insulin it produces or does not create enough of it. Diabetes can cause long-term damage to different organs such as the eyes, heart, kidneys and blood vessels [ 2 ]. There are three different types of diabetes: type 1, type 2 and gestational. In type 1 diabetes, the pancreas produces little or no insulin. Insulin therapy is needed. It is usually seen in young individuals (age < 30) or children. Type 2 is usually caused by insulin resistance and is more common in older (age > 65) and obese patients [ 3 , 4 , 5 ]. Gestational diabetes is hyperglycemia that occurs during pregnancy. In addition, after pregnancy, the risk of type 2 diabetes is higher in women, and in this case, babies are also at risk [ 6 , 7 ].

It is known that diabetes is a public health problem that affects 60% of the world’s population [ 8 ]. Although the main cause of diabetes is unknown, scientists think it is related to genetic factors and environmental conditions. There are currently 425 million diabetics worldwide, according to the International Diabetes Federation, and 625 million will develop the disease in the next 23 years [ 9 , 10 ]. It is essential to identify the disease at an early stage in order to stop this rise. Only early detection can stop the growth of the disease because there is no cure for diabetes, which is a lifetime condition. With the right treatment, regular nutrition and drugs, the disease can be managed after early diagnosis. [ 11 , 12 ]. However, a delayed diagnosis might result in heart conditions and serious harm to many organs. For the early diagnosis of diabetes, clinical (plasma glucose concentration, serum insulin, etc.) and physical data (for example, body mass index (BMI), age) are often used [ 13 ]. According to these data, a doctor carries out the diagnosis of the disease. However, making a medical diagnosis is a very difficult task for the doctor and can take a very long time. In addition, the decisions made by the doctor may be erroneous and biased. For this reason, the fields called data mining and machine learning are frequently used as a decision support mechanism for the rapid and accurate detection of diseases according to data [ 11 , 14 , 15 ].

Recent advances in computer technologies have led to the emergence of algorithms that allow human tasks to be performed faster and more automatically by computers. Tools such as data mining, machine learning and deep learning, which are generally referred to as artificial intelligence, have shown remarkable performance in interpreting existing data. Especially in the medical field, artificial-intelligence-based methods are used in the diagnosis or treatment of many different diseases as they provide fast and powerful results. Examples of these are diagnostic studies of cancer [ 16 ], diabetes [ 17 ], COVID-19 [ 18 ], heart diseases [ 19 ], brain tumors [ 20 ], Alzheimer’s [ 21 ], etc. For more comprehensive information on the applications of artificial intelligence in the medical field, research studies by Kaur et al. [ 22 ] and Mirbabaie et al. [ 23 ] can be reviewed. Artificial intelligence is very useful for the medical field. Thanks to the superior success of artificial intelligence in medical studies so far, it has recently become common to record medical big data in hospitals. Considering that each patient is a real data point, much numerical data such as electrocardiograms (ECG), electromyograms (EMG), clinical data, blood values or a large number of image data such as X-ray, magnetic resonance imaging (MRI) or computed tomography (CT) can be produced after medical records. In this sense, such medical records constitute an important part of big data in the medical field [ 24 ].

Machine learning algorithms are generally used to interpret (regression, classification or clustering) big data based on artificial intelligence. Thanks to these algorithms, the relationship between them is learned based on samples and observations of the data. Machine learning methods that are frequently used in this sense are artificial neural networks (ANN), support vector machines (SVM), k-nearest neighbors (k-NN), decision trees (DT) and naïve Bayes (NB). These methods directly learn the correlation between input and target data. However, with the developments in artificial intelligence and computer processors in the last decade, ANN has been further deepened, and deep learning, which applies both feature extraction and classification together, has come to the fore. Especially in big data applications, deep learning has given a great advantage over traditional machine learning methods [ 25 ]. The most frequently used model in deep-learning-based medical diagnosis/detection applications is convolutional neural network (CNN). CNN models are very popular due to both their deep architecture and high-level feature representation. Since the architecture designed for CNN is end to end, raw data are given as input and classes are obtained as output. Therefore, the designed architecture is very important for the performance of the CNN model [ 26 ]. Recently, however, researchers have adopted transfer learning applications and used popular CNN architectures such as ResNet [ 27 ], GoogleNet [ 28 ], Inception [ 29 ], Xception [ 30 ], VGGNet [ 31 ], etc. In different data-driven studies [ 32 ], the direct use of pre-trained or pre-designed CNN architectures has provided advantages in terms of both performance and convenience

1.1. Previous Artificial Intelligence Based Studies on Diabetes Prediction

This study performs deep-learning-based diabetes prediction using the PIMA dataset. In general, studies developed for diabetes prediction are based on machine learning or deep learning.

Some of the studies that applied diabetes prediction to the PIMA dataset using machine learning methods are as follows. Zolfaghari [ 33 ] performed diabetes detection based on an ensemble of SVM and feedforward neural network. For this, the results obtained from the individual classifiers were combined using the majority voting technique. The ensemble approach provided a better result than the individual classifiers with 88.04% success. Sneha and Gangil [ 34 ] performed diabetes prediction using many machine learning methods such as naïve Bayes (NB), SVM and logistic regression. The best accuracy was obtained with SVM with 77.37%. In addition, the authors applied feature selection for the PIMA dataset. The features with low correlation were removed. Edeh et al. [ 35 ] compared four machine learning algorithms, Bayes, decision tree (DT), SVM and random forest (RF), on two different datasets for diabetes prediction. In the experimental results with PIMA, the highest accuracy was obtained with SVM at 83.1%. Chen et al. [ 36 ] reorganized the PIMA data with preprocessing and removed the misclassified data with the k-means algorithm (data reduction). They then classified the reduced data with DT. As a result of the study, diabetes was predicted with an accuracy of 90.04%. Dadgar and Kaardaan [ 37 ] proposed a hybrid technique for diabetes prediction. First, feature selection was performed with the UTA algorithm. Then, the selected features were given to the two-layer neural network (NN) whose weights were updated by genetic algorithm (GA). As a result, diabetes estimation was provided with an accuracy of 87.46%. Zou et al. [ 38 ] used DT, RF, and NN models for diabetes prediction. They also used principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) to reduce dimensionality. As a result, RF performed more successful predictions than the others, with 77.21% accuracy. For other proposed studies based on machine learning, studies by Choudhury and Gupta [ 39 ] and Rajeswari and Prabhu [ 40 ] can be examined.

The following are some studies that use the PIMA dataset with deep learning models: For diabetes prediction, Ashiquzzaman et al. [ 41 ] created a network with an input layer, fully connected layers, dropouts and an output layer architecture. It fed the PIMA dataset features directly into this designed MLP and achieved an accuracy of 88.41% at the end of the application. Massaro et al. [ 42 ] created artificial records and classified these data with long short-term memory (LSTM) (LSTM-AR). The LSTM-AR classification result, which was stated as 89%, was superior to both LSTM and the multi-layer perceptron (MLP) with cross validation previously performed. Kannadasan et al. [ 43 ] designed a deep neural network that extracts features with stacked autoencoders and performs diabetes classification with softmax. The designed deep architecture provided 86.26% accuracy. Rahman et al. [ 44 ] presented a model based on convolutional LSTM (Conv-LSTM). They also experimented with traditional LSTM and CNN to compare the results. They applied grid search algorithm for hyperparameter optimization in deep models. For all models, the input layer was one dimensional (1D). After training and test separation, Conv-LSTM for test data outperformed other models, with 91.38% accuracy. Alex et al. [ 45 ] designed a 1D CNN architecture for diabetes prediction. However, missing values were corrected by outlier detection. Then, they preprocessed the data with synthetic minority oversampling technique (SMOTE), and the imbalance in the data were removed. They then fed the processed data into the 1D CNN architecture and achieved 86.29% accuracy. For other applications based on deep learning for diabetes prediction, the studies presented by Zhu et al. [ 46 ] and Fregoso-Aparicio et al. [ 47 ] can be examined.

Previous studies show that the PIMA dataset is often used for machine learning, 1D-CNN and LSTM structures. The numerical nature of the PIMA dataset has limited the feature extraction and classification algorithms that researchers can use. In this study, this limitation is overcome by converting numerical data to images. Thus, the PIMA numerical dataset will be applicable with popular CNN models such as ResNet, VGGNet and GoogleNet.

1.2. The Structure, Purpose, Differences and Contribution of the Study

Examining the previous studies mentioned in Section 1.1 . reveals that various machine learning and deep-learning-based applications predict diabetes quite successfully for the PIMA dataset containing clinical data records. Similar to the PIMA dataset, many clinical data in the medical field are composed of numerical values. Using numerical values directly with conventional machine learning techniques is more typical because studies involving machine learning models such as SVM, NB, RF, DT, etc. feed raw data or data with small preprocessing directly to the model and give target (0 (negative)–1 (positive)) values to the output. Studies that design deep architecture using the same data feed the PIMA features either to the 1D convolution layer or to the fully connected layers. The study by Massaro, Maritati, Giannone, Convertini and Galiano [ 42 ] processed the PIMA dataset containing 1D data with a recurrent-neural-network (RNN)-based LSTM. Nevertheless, LSTM was designed for sequential data, whereas the PIMA dataset contains independent data.

Traditional machine learning techniques have been surpassed in many respects by deep learning, which has become more popular in recent years [ 48 , 49 ]. With the high-level capabilities they offer, particularly deep CNN models, they have shown greater performance, notably in computer vision applications. However, the PIMA dataset’s inclusion of numeric values has thus far prompted researchers to create 1D CNN models. Popular CNN models are created for computer vision, and therefore the input layer only accepts 2D data. These models are employed in transfer learning applications. As a result, feature extraction using well-known CNN models and a diabetes prediction using these models have not yet been established from this PIMA dataset containing independent numerical data. Therefore, in order to provide more successful diagnoses, transformation can be applied to the raw data in accordance with popular CNN models.

This study converts each sample in the PIMA dataset to images (diabetes images) to overcome this limitation. Each diabetes image has cells representing features in the PIMA dataset. The ReliefF feature selection algorithm [ 50 , 51 , 52 ] was also used to make the feature with high correlation more dominant in the image. After each feature is placed on the image according to its importance, data augmentation is applied for these images. In fact, the easy application of data augmentation for diabetes data is one of the important contributions of this study because compared to numerical data, data augmentation for images is an easier and more common technique. The augmented image data are then fed to the ResNet18 and ResNet50 CNN models and diabetes prediction is performed. In order to improve these current results, the features of both models are then fused and classified with SVM (CNN-SVM). Finally, feature selection is made with the ReliefF algorithm, among many fusion features, and these selected features are classified by SVM. At the end of the study, all these results are compared. According to the results, the CNN-SVM structure with selected fusion features provides more successful diabetes prediction than others. In addition, the results of the proposed method are compared with those of previous studies, and the method is proven to be effective. The contributions of the proposed method can be stated as follows:

  • An application with an end-to-end structure is suggested for diabetes prediction.
  • PIMA dataset with numeric values is converted to images.
  • It is provided to use numerical diabetes data together with popular CNN models.
  • During the conversion to the image, the importance of the features is taken into account.
  • The proposed method is superior to most previous studies.

2. PIMA Indians Diabetes Dataset

In this study, the PIMA Indians Diabetes dataset, which is taken from the Kaggle data repository and is frequently preferred for diabetes prediction, is used. The access link is (Access Date: 8 June 2022). The National Institute of Diabetes and Digestive and Kidney Diseases provided the source data for this dataset. The dataset’s goal is to diagnose whether or not a patient has diabetes based on certain diagnostic metrics provided in the collection. All patients here, in particular, are PIMA Indian women over the age of 21.

The dataset includes the following measurements and ranges of clinical and physical characteristics. Pregnancies (number, [0–17]), glucose (value, [0–199]), blood pressure (mm Hg, [0–122]), skin thickness (mm, [0–99]), insulin (mu U/mL, [0–846]), BMI (kg/m 2 , [0–67.1]), diabetes pedigree function (PDF) (value, [0.078–2.42]), age (years, [21–81]), and outcome (Boolean- 0, 1). The data are entirely numerical and comprise a total of 8 features and 768 samples. Table 1 shows a few samples from the dataset.

Some examples of Pima Indians Diabetes dataset.

3. Methodology

The methods used to determine the diabetes status of patients will be outlined in detail in this section. The steps of the proposed method are shown in Figure 1 . The feature selection method initially selects the most useful features from the numerical data, as shown in Figure 1 . The boundaries of all features are then adjusted for the numeric-to-image conversion stage once the numerical data has been normalized. The numerical to image conversion process is applied in such a way that the most effective features determined by the feature selection algorithm are dominant. The classification success of deep ResNet models is then increased by the use of data augmentation techniques. The three ResNet-based approaches suggested in this study are used to classify data in the final stage. Below, we go over each of these processes in more detail.

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Application steps of proposed methods.

3.1. ReliefF Feature Selection Algorithm

To improve classification capability, a variety of feature reduction strategies have been explored in the literature [ 53 ]. In the literature, ReliefF is one of the distance-based feature selectors. ReliefF, developed by Kira and Rendell [ 52 ] in 1992, is one of the most successful feature filtering methods.

Dimension reduction strategies aid in the removal of superfluous attributes from a data set. These technologies aid in data compression, which saves storage space. It also shortens the time required for computational complexity and reduces the amount of time it takes to attain the same goal [ 54 ].

Kononenko [ 55 ] improved the algorithm for multi-class issues in 1994. With the help of this algorithm, feature selection can be performed successfully. The ReliefF algorithm is highly efficient and does not impose any restrictions on the data kinds’ features. The ReliefF method assists in the solution of many classes of issues by selecting the nearest neighboring samples from each sample in each category [ 56 ].

ReliefF seeks to expose the connections and consistency found in the dataset’s properties. Furthermore, by constructing a model that addresses the proximity to samples of the same class and distance to samples of different classes, it is feasible to discover the significant features in the dataset. Between samples of distinct qualities, the ReliefF model chooses neighboring attributes that are closest to each other [ 54 ]. The dataset is divided into two components in this model: training and test data. R i random samples are chosen from the training set, and the difference function d i f f is used to calculate the nearest neighbors of the same and different classes to identify the nearest neighbors to the selected R i sample, as illustrated in Equation (1). When identifying nearest neighbors, the d i f f function is also utilized to compute the distance between instances. The total distance is simply the sum of all attribute differences (i.e., Manhattan distance) [ 51 ].

Equation (1) is used to determine the difference between two separate I 1 and I 2 samples for the attribute A and to discover the closest distance between samples. The nearest neighbor H from the same class and the nearest neighbor M from a different class are chosen. The distance of adjacent sample A f in the class and between the classes is compared based on the values of R i , M , H , and the dataset’s weighting vector. The W A f weight is calculated as a result of the comparison by giving less weight to the distant attributes [ 57 ]. These processes are performed m times for each attribute, and the weight values are calculated for each attribute. The weights are updated using Equation (2) [ 55 , 58 ].

As a result of applying the ReliefF feature selection method described above to the PIMA dataset features, the importance weight of each feature is shown in Figure 2 . The number of nearest neighbors was also determined as 10. As seen in Figure 2 , the most effective features from the PIMA numerical data were determined by the ReliefF algorithm.

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Importance weight of features in the PIMA dataset.

3.2. Normalization of Data

In artificial intelligence studies, normalizing data containing many features is a known process. Because different features have different limits. Setting features to the same or similar range, i.e., normalization, improves learning performance. The PIMA dataset also has different lower and upper bound values, as seen in Table 1 . In this sense, normalization of these values is necessary. In addition, normalization is vital for the numeric-to-image conversion process in the proposed implementation because the value of each feature must be located on the image that represents that sample. According to the amplitude of the feature, the cell in the corresponding image has a brighter color. Therefore, the maximum and minimum values for all features must be the same.

The preferred method for normalization is feature scaling. With this method, feature values are rescaled to a certain range. The feature scaling method used in this study is the min – max normalization method. In this method, the new sample value ( x ^ ) is determined according to the maximum ( x m a x ) and minimum ( x m i n ) values of the features. As a result of normalization, all features are distributed between 0–1. In the application phase, normalization is applied for eight features in the PIMA dataset. Figure 3 shows that after this normalization, the glucose [0–199] and blood pressure [0–122] values range from 0–1. Equation (3) shows the formula for the min–max normalization method.

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Min – max normalization of PIMA dataset.

3.3. Conversion of Numeric Data to Image Data

Although the number of image data in the medical field has increased considerably recently, there is still a large amount of numerical data available. Although numerical values are easily and cheaply obtained, the interpretation of these data is usually performed by machine learning methods. Recently proposed deep architecture studies prefer 1D CNN structures that take these numerical values as input because popular CNN models, which provide significant improvements in computer vision, cannot be used directly for such data. Because for these models, 2D data should be given as input to the input layer. CNN models such as ResNet, VGGNet, GoogleNet, etc., have an architecture designed for image data. Therefore, the inability to analyze datasets containing 1D samples with these powerful models is a major disadvantage in terms of both application diversity and prediction performance. This section discusses the conversion of numeric data to images to overcome this limitation in the PIMA dataset, which is a numeric dataset.

In the process of converting PIMA data to images, the principle of determining the brightness of a specific region (cell) in the image according to the amplitude of each feature is adopted. In fact, each feature can be viewed as a piece of the sample image’s puzzle. For each sample in the PIMA dataset, the 120 × 120 image structure shown in Figure 4 is used. The index on each cell corresponds to the feature index in the PIMA dataset. That is, Figure 4 shows feature locations in a sample image. In Figure 4 , the location and size of features are determined not randomly but based on feature importance. As seen in Figure 2 as a result of the ReliefF algorithm, the order of importance of features is 2-8-6-1-7-3-5-4. Therefore, a larger cell is assigned for the more important feature. Each cell is colored according to the amplitude of the corresponding feature value. Because all data were previously normalized, each feature value ranges from 0 to 1. Each feature value is multiplied by 255, resulting in images with cells with brightness values between 0 and 255. Therefore, the resulting images are in gray spaces. Some sample diabetes images are shown in Figure 4 .

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Conversion selected features to image (numeric to image).

As a result of applying the aforementioned image conversion method on the PIMA dataset, one image for each sample (that is, 768 images in total) is formed. These images, with all features included, can now be used in CNN models that require 2D data input. Furthermore, image data augmentation methods are easily applicable to these image data. For this purpose, the image structure in Figure 4 is designed asymmetrically because in the data augmentation stage, all images must be reproduced differently from each other.

3.4. Data Augmentaion

The number of samples directly influences the success of deep learning approaches. However, accessing a significant volume of data is not always possible. As a result, researchers artificially increase the size of a training dataset by producing modified versions of the images in the dataset. These techniques, which are applied to raw images for this purpose, are known as data augmentation techniques.

In this study, the diabetic data contains 768 numerical samples in total, and hence 768 images are created during the conversion from numerical to image data. Data augmentation techniques are used because this amount is insufficient for a deep learning implementation. To ensure data diversity and robust training, four different data augmentation techniques (rotation, scale, reflection and translation) are applied to all images produced, as in Figure 4 . Table 2 shows the lower and upper limit values for these data augmentation techniques. Additionally, Figure 5 shows the new diabetes images produced as a result of data augmentation techniques.

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Data augmentation methodologies and sample augmented images.

Approaches to data augmentation’s lower and upper limitations.

After data augmentation, each original diabetes image is reproduced in four different ways. As a result, a total of five artificial samples is obtained from one sample. The sample numbers of the classes before and after the data augmentation stage are shown in Table 3 . As a result of the data augmentation, the total number of images reached 3840.

Examination of all data before and following data augmentation.

3.5. Diabetes Prediction via ResNet Models

After data augmentation, images separated into 80% training and 20% testing are fed to the CNN model. In this study, diabetes estimation is provided with the ResNet18 and ResNet50 models, which are frequently used for comparison purposes. Many studies apply ResNet models widely because of the advantages they provide [ 59 ]. What makes ResNet preferable is that it transmits residual values to the next layers to avoid the vanishing gradients problem, for which it uses residual blocks. There are ResNet models with different depths. The depths of the ResNet18 and ResNet50 models used in this study are 18 and 50, respectively.

This study performs diabetes detection with existing models instead of designing a new CNN architecture. With only minor modifications (fine-tuning), existing ResNet models are adapted to our work. For both models, the last two layers of existing models are removed and replaced with two fully connected output layers and a classification (softmax) layer. In addition, while the diabetes images produced are 120 × 120, the input size for ResNet models should be 224 × 224. Therefore, all diabetes images are resized before and during training (see Figure 6 ). Information about the results obtained after the training and testing phases will be discussed in the results section.

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Classification of diabetes images as diabetic (1) and nondiabetic (0) with ResNet models.

3.6. Deep Feature Extraction, Feature Selection and Classification

While the previous section directly uses fine-tuning of ResNet models, this section describes the CNN-SVM structure. In other words, CNN is used for feature extraction and SVM is used for classification. This approach has been frequently preferred recently to increase the classification accuracy [ 60 ]. Two different experimental applications are presented at this stage. The features obtained with the CNN models in the previous stage are combined and fed to the SVM. In the previous step, 512 deep features were extracted from the ResNet18 model, and 2048 deep features were extracted from the ResNet50 model. Then, these features were combined to obtain a total of 2560 deep features, with 80% and 20% of these deep features being divided into two groups for training and testing. In the first experimental stage, these deep features are classified by the SVM machine learning algorithm. For this classification, linear, quadratic, cubic and Gaussian SVM kernel functions are used and results are obtained. In the second stage, the most effective 500 features from a total of 2560 features extracted from ResNet models are selected using the ReliefF feature selection algorithm. These 500 selected features are classified by the SVM machine learning algorithm. Similarly, at this stage, classification is made for SVM using linear, quadratic, cubic and Gaussian SVM kernel functions. All results are then compared. Figure 7 shows the proposed CNN-SVM structure.

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Implementation steps of the proposed CNN-SVM approach.

The results of the experimental studies are discussed in Section 4 . Experimental application in the last step provided the most successful results. The flow graph containing the applications of this step is shown in Figure 8 .

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Application flow chart in the last step.

4. Results and Discussion

In this section, the results of the proposed approach are discussed. All deep learning applications for diabetes prediction were performed using a laptop with Intel Core i7-7700HG processor, NVIDIA GeForce GTX 1050 4 GB graphics card and 16 GB RAM. Applications are developed in Matlab environment. Figure 8 can be taken as reference for the software design or code implementation of the proposed approach. The algorithm of the method was created as in Figure 8 . Toolbox and libraries used directly during coding prevented software complexity. Toolboxes used in this context are Machine Learning Toolbox, Deep Learning Toolbox and Image Processing Toolbox.

In order to demonstrate the superiority of the proposed method, results are produced with three different approaches. Methodological information about these three approaches has been shared in detail in the previous section. In the first approach, classification is performed with fine-tuned ResNet models to perform the diabetes prediction using diabetes images after data augmentation. For this, the ResNet18 and ResNet50 models are fine-tuned, and the output layer is changed according to the two classes. Then, 3840 diabetes images are divided into two groups as 80% and 20% training data and test data, respectively. While the models are trained using the training data, the performance of the network is obtained using the test data. In the second approach, deep features extracted from two fine-tuned ResNet models are combined and these fusion features (2560) are classified by the SVM machine learning method. The performance of SVM differs according to the kernel function used. Therefore, in the second approach, classification accuracies are obtained by using linear, quadratic, cubic and Gaussian kernel functions and compared with each other. In the last approach, namely the proposed method, the most important 500 features from a total of 2560 fusion features extracted from fine-tuned ResNet models are selected with the ReliefF feature selection algorithm. In this way, we aimed to achieve similar success with fewer features. These features are classified with SVM as in the second approach. Classification results are obtained with linear, quadratic, cubic and Gaussian kernel functions, and the results are compared with other approaches.

ResNet models are trained once for all the approaches mentioned above. In other words, as a result of the three approaches, the features obtained with the ResNet models are the same. The parameters used for training ResNet models are: Mini Batch Size: 32; Max Epochs: 5; Learn Rate Drop factor: 0.1; Learn Rate Drop period: 20; Initial Learn Rate: 0.001. In addition, the optimizer used to update the weights during the training process is Stochastic Gradient Descent with Momentum.

After training is performed in the first approach, the features obtained in the first approach are used in other approaches. The accuracy and loss graph of the first approach, obtained during the training and testing phase of the ResNet18 and ResNet50 models, is shown in Figure 9 . It is clear that overfitting does not occur during the training phase. In the second and third approaches, the CNN model is not trained, and 512 and 2048 features are extracted from ResNet18 and ResNet50, respectively, through fully connected layers used directly. The confusion matrixes obtained as a result of the classification of these features with SVM are shown in Figure 10 and Figure 11 . Figure 10 shows the application results obtained with the second approach using all the fusion features. Figure 11 shows the final application results that classify the selected features from the fusion features.

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Training and loss graphics of ResNet models. ( a ) ResNet18. ( b ) ResNet50.

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Confusion matrices obtained as a result of classification of all fused features with SVM.

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Confusion matrices obtained as a result of classification of selected features with SVM.

The confusion matrix structure that enables the calculation of these metrics is shown in Figure 12 . The performance of the system is measured with the t p , t n , f p and f n values in this matrix. Using these values, accuracy, specificity, precision, sensitivity, F1-score and MCC performance metrics are calculated with the help of the formulas between Equations (4) and (9). Table 4 shows the performance metrics obtained as a result of the three approaches.

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Structure of confusion matrices.

Performance metrics for the three approaches.

According to Table 4 , the highest accuracy in the first approach is obtained with the fine-tuned ResNet18 model. The accuracy rates obtained with ResNet18 and Resnet50 are 80.86% and 80.47%, respectively. In the second approach, in the classification made with SVM using 2560 features, the highest accuracy is calculated as 91.67% with the quadratic kernel function. In the last approach, in the classification made with the 500 most effective features selected by the feature selection algorithm, the highest accuracy is calculated with the SVM/cubic kernel function of 92.19%. The results of the first approach showed that converting diabetes data from numeric to image is an effective technique because these images were successfully classified with ResNet models. The second approach shows that fusing the features of different CNN models highly affects the success. In addition, SVM also showed a successful classification performance. The last approach showed that higher achievement can be achieved with fewer features. The results obtained with the last approach are compared with previous studies using the PIMA dataset, as shown in Table 5 . As can be seen, the method proposed in our study outperformed many previous studies. Considering the methodological knowledge of previous studies, the numerical nature of the PIMA dataset has led researchers to use algorithms fed with numerical data such as traditional machine learning, 1D-CNN and LSTM. This study, unlike previous studies, transformed the PIMA dataset into image data and thus made the PIMA dataset suitable for popular CNN models.

Comparative analysis with previous works.

5. Conclusions, Discussion and Future Works

Diabetes is a chronic disease that limits people’s daily activities, reduces their quality of life and increases the risk of death. In the past, machine learning and DNN solutions have been developed using clinical data and various diabetes prediction studies have been carried out. Despite the encouraging results of these studies, the numerical nature of clinical registry data has limited the use of popular CNN models. In this study, popular CNN models were used to determine the diagnosis of diabetes. Since these CNN models require two-dimensional data input, numerical clinical patient data (PIMA dataset) were first converted to images in this study. In this way, each feature was included in the sample image. This process was not performed randomly, and the most effective feature was made to stand out more in the image. During this process, the ReliefF feature selection method was used to determine the most effective features. After the number of generated images was increased by data augmentation and their size was adjusted for the ResNet model, diabetes prediction was carried out with three different approaches.

Diabetes images were successfully classified with the first approach using the fine-tuned ResNet18 and ResNet50 models. In the second approach, SVM was used to classify a total of 2560 deep features extracted from the fully connected layers of both ResNet models. In the last approach, the most effective 500 of these deep features were selected using the ReliefF feature selection algorithm, and the selected features were classified by SVM. The most successful prediction was obtained with the third approach. The accuracy of the classification using the SVM/cubic model with 500 selected features was 92.19%. All these classifications were performed on the image data. The conversion to image data removed the algorithm limitation that can be used for the PIMA dataset. In this way, the PIMA dataset or similar numerical data can be analyzed with different CNN models capable of extracting high-level and complex features. An application containing image data can be analyzed more diversely and comprehensively than an application containing numerical data because the different artificial intelligence combinations that can be applied to the image data are very rich. The results obtained with the ResNet18 and ResNet50 models in this study, therefore, outperform previous studies. For example, the number and variety of features can be increased with different CNN models. Based on all this, the experimental results have shown that converting clinical data into images is an effective technique.

The method proposed in this study can also be applied for different numerical data. Deep-learning-based studies have reduced the dependency on features and the designed architecture has come to the fore. However, the method proposed in this study is valuable in that deep and comprehensive architectures can also be used for numerical data. This application may involve more processing steps than studies using raw data directly. However, the generation of image data paves the way for further improvement of diabetes prediction performance because CNN models in many different architectures are now applicable to numerical data. Moreover, data augmentation can now be easily applied to diabetes images. In addition, the application results show that the fusion features used in the CNN-SVM architecture greatly increase the success. Additionally, using selected features, CNN-SVM is less costly and provides more accurate predictions. Based on these situations, the important trend of experimental simulation studies can be explained as follows: selected fusion features increase the performance of the system, although they are fewer in number. In addition, the CNN-SVM structure is quite effective. Different applications with fewer, more effective and more diverse features increase the classification accuracy of the system.

In future studies, it is planned to use of different CNN models and feature selection methods to improve diabetes prediction performance. A greater variety of features will be obtained by using more CNN models. In this case, it is expected that the classification accuracy will increase. In addition, future studies plan to apply the produced diabetes images with transformer-based networks.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, M.F.A. and K.S.; methodology, M.F.A.; software, M.F.A. and K.S.; validation, M.F.A.; formal analysis, M.F.A. and K.S.; investigation, M.F.A.; writing—original draft preparation, M.F.A.; writing—review and editing, M.F.A. and K.S.; visualization, M.F.A. and K.S.; supervision, K.S. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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  • Research Proposal Topics on Deep learning Algorithms and Recent advancements

phd research proposal on deep learning

   Deep learning is a trending research topic in machine learning, which utilizes multiple layers of data representation to perform nonlinear processing to understand the levels of data abstractions. Deep learning can generate high-level data representations from large volumes of structured and unstructured data. The efficiency of deep learning algorithms depends on the good representation of the input data to build powerful computational models. The explosive growth of data in recent times and the remarkable advancement of low-cost hardware technologies have led to the emergence of new deep learning models rapidly. Deep learning has delivered powerful methods that enable remarkable achievement in many research fields, leading to groundbreaking advancements in deep learning applications. Many deep learning techniques have demonstrated promising state-of-the-art results across many interdisciplinary applications. Deep Learning Approaches: Deep learning is a universal learning approach that is not task-specific and capable of solving almost all sorts of problems in diverse application domains. Types of deep learning are categorized as deep supervised learning, deep semi-supervised learning, deep unsupervised learning, and deep reinforcement learning.    • Supervised deep learning creates great impact as such, most of the deep learning models, namely Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU) follow supervised technique, utilizes labeled data for training a model and iteratively modify the network parameters for a good approximation of the desired outcomes.    • Deep semi-supervised learning efficiently employs deep learning algorithms under consistency regularization, generative models, graph-based methods, and holistic approaches based on partially labeled datasets.    • Deep unsupervised learning is well in clustering and nonlinear dimensionality reduction without the presence of data labels and exploits Auto-Encoders (AE), Restricted Boltzmann Machines (RBM), and Generative Adversarial Networks (GAN) and RNN for unsupervised learning in many application domains.    • Deep reinforcement learning is a fast developing approach and is remarkably used in an unknown environment and complex real-world applications, for instance, intelligent transport systems, communication, networking, and robotics. Deep Learning Algorithms: Deep learning has been expanding rapidly, with many new networks and new models emerging regularly. Many potential deep learning algorithms are playing significant roles in information processing, such as Recursive Neural Network (RvNN), RNN, GAN, CNN, AE, RBM, Deep Belief Network (DBN), Deep Boltzmann Machine (BBM), Graph Neural Network (GNN), Graph Convolutional Networks (GCN), Deep Stacking Network (DSN), LSTM, GRU Network, and model transfers have completely altered our perception of information processing.    • RvNN represents a hierarchical structure to make predictions and classification with compositional vectors. The successful application of RvNN is Natural language Processing (NLP) to handle different modalities such as natural images and natural language sentences.    • RNN is a popularly applied deep learning algorithm, especially in NLP and speech processing. The sequential information in the RNN network conveys useful knowledge in many applications, and Deep RNN was also developed to reduce the difficult learning in deep networks. LSTM and GRU are variants of RNN that emerged for short-memory problems, and comparatively, GRU is more efficient in its execution.    • GAN is a unique network architecture exploited to generate novel data and make more accurate predictions in many image and signal processing applications through its generative invention.    • CNN is the commonly utilized deep learning architecture, and its applicability includes image-processing, computer vision, and medical imaging with better accuracy and improved performance.    • AE is an unsupervised deep learning algorithm capable of high dimensional data operations and representation of a set of data through dimensionality reduction. Several progressive variants of AE are developed with stack layered representation to produce a deep learning network.    • DBNs are evolved to solve slow learning, poor parameter selection, and many training dataset requirements in neural networks. DBN and RBM are extensively applied for data encoding, news clustering, image segmentation, and cybersecurity.    • GNN and GCN are state-of-the-art models for deep learning on graphs and produce superior performance in various graph-related problems. GCN is the special type of GNN that uses convolutional aggregations in computer vision and NLP domains.    • DSN are evolved to solve complex classification with many deep individual networks and work superior to DBNs due to their suitable network architecture. Applications of Deep Learning: The target deep learning approach is to resolve the sophisticated aspects of the input with multiple levels of representation and strong learning ability. The applicability of deep learning reaches tremendous success and covers significant breadth and depth of research, such works not only across the particular field but also over the broad range of multi-disciplinary fields. Nowadays, deep learning models impart state-of-the-art performance in various application domains, including image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others.    • Deep learning has recently demonstrated several high impactful attempts in achieving high accuracy in natural language processing tasks, and areas of such tasks include sentiment analysis, machine translation, paraphrase identification, summarization, and question answering.    • In visual data processing, deep learning methods attain successful outcomes on various tasks, including image classification, object detection and semantic segmentation, and video processing.    • For speech processing, most of the current deep learning research works focus on speech emotion recognition, speech enhancement, and speaker separation.    • Deep learning has shown promising performance in social network analysis, adopted for semantic evaluation, link prediction, and crisis response.    • Deep learning greatly impacts information retrieval, for instance, document retrieval and web search with the help of deep structured semantic modeling and a deep stacking network.    • Deep learning architectures are applied for transportation network congestion evolution, destination prediction, traffic signal control, demand prediction, traffic flow prediction, transportation mode, and combinatorial optimization in the intelligent transportation system with low computational resources.    • Currently, a huge number of companies that are engrossed in self-driving automotive technologies that utilize deep learning models for autonomous driving systems were categorized into robotics approaches and behavioral cloning approaches in order to facilitate high-level driving decisions.    • Deep learning is a highly progressive research field. It accomplishes more complicated bio-medicine tasks by discovering new knowledge and revealing things undetectable by human beings.    • The explosive use of deep learning in NLP is high by concentrating on several core linguistic processing issues and more applications of computational linguistics such as sentiment analysis, machine translation, and question answering.    • Deep learning in disaster information management is still in its early stages that need to focus on time-sensitive data and provide the most accurate assistance in a nearly real-time manner.    • The tendency of big data analytics requires new and sophisticated algorithms, which are accomplished by deep learning techniques by using hybrid learning and training mechanisms to process data in real-time with high accuracy, speed and efficiency.    • Deep learning is explored for various Internet of Things (IoT) scenarios including, Industrial Internet of Things, Internet of Vehicles, smart grid, smart home, and smart medical    • Deep learning on edge computing requires sustainable computational resources for combining end devices, edge servers, and the cloud across multiple edge devices with privacy, bandwidth efficiency, and scalability. Research Challenges in Deep learning: Even though deep learning approaches are proving their finest and have been solving a variety of complicated applications with multiple layers and a high level of abstraction. There are still several issues that oblige to be contented in the future of deep learning due to either their challenging nature or lack of data availability for the general public.     Lack of innovation in model structure - There is no complete implementation of the depth of the advantages of deep learning technology, which needs to realize the development of a new depth of learning model for effective integration.     Update training methods - Many training methods focus on supervised training; there is no real sense to achieve complete unsupervised training combined with supervised training.    Deep learning has many parameters learning bottlenecks with learning rate, local optima, saddle points, vanishing, and exploding gradients.     Reduce training time - As the complexity of the problem is bigger, the amount of information processed is essential, which means that there is a demand for more and more training time for the deep learning model to improve the accuracy and the speed of data processing     Online learning - The current training in deep learning does not contribute to the realization of online learning, and it is necessary to enhance the online learning ability based on an innovative deep learning model.     Overcome adversarial sample - a big problem in the current deep learning is the adversarial sample; the long-term development of deep learning is needed to solve the problem of precision and avoid the potential security problem.     Data dimensionality issue - It is another landmark challenge faced by deep learning due to the critical information and overfitting problem in classification, especially in medical domain applications.     Insufficient data samples problem - Currently, deep learning models are requisite to focus on data sparsity, missing data, and messy data conducive to obtaining the approximated information through observations rather than training. However, data of some applications suffer from incompleteness, heterogeneity, and unlabeled data are challenging for deep learning is a relevant problem. Future Scopes of Deep learning: The rapid utilization of deep learning algorithms in different fields shows its success and versatility, clearly accentuating the growth of deep learning and the tendency for future advancement and research. Some of the future aspects of deep learning are listed below:    • Deep neural networks with a sophisticated and non-static noisy framework and with multiple noises are need enhancement    • The improvement of feature diversity in deep learning models will raise the performance of deep networks.    • Compatible deep neural networks need to be introduced in the unsupervised learning online environment.    • Huge future direction relies on enhancement in deep reinforcement learning.    • The upcoming deep neural network should be designed by considering inferences, efficiency, and the accuracies and maintenance of a wide repository of data.    • For developing deep generative models, superior and advanced temporal modeling abilities will be instigated for the parametric speech recognition system.    • In the medical domain, automatically assess Electrocardiogram (ECG) with deep learning methods required to be improved.    • The deep learning model with fully autonomous driving is a succeeding opportunity in the technology of self-driving cars.    • Other emerging research trends in deep learning are acceleration and optimization, distributed deep learning in IoT and Cyber-Physical Systems (CPS), network management and control, and security.

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  • PhD Guidance and Support Enquiry
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PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.


The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.


We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

PhD Proposal: Interpretable Deep Learning for Time Series Prediction and Forecasting


Time series data emerges in applications across many domains including neuroscience, medicine, finance, economics, and meteorology. Deep learning has revolutionized many machine learning including natural language processing and computer vision; however, its applications to time series data has been limited. In this work, we investigate both interpretability and accuracy of deep neural networks when applied to time series data.We start by analyzing saliency-based interpretability for Recurrent Neural Networks (RNNs). We show that RNN saliency vanishes over time, biasing detection of salient features only to later time steps and are, therefore, incapable of reliably detecting important features at arbitrary time intervals. To address this, we propose a novel RNN cell structure (input-cell attention), which can extend any RNN cell architecture. At each time step, instead of only looking at the current input vector, input-cell attention uses a fixed-size matrix embedding, each row of the matrix attending to different inputs from current or previous time steps. We show that the saliency map produced by the input-cell attention RNN is able to faithfully detect important features regardless of their occurrence in time.Next, we create an evaluation framework based on time series data for interpretability methods and neural architectures. We propose and report multiple metrics as an empirical determination for the performance of a specific saliency method for detecting feature importance over time. We apply our framework to different saliency-based methods including Gradient, Input X Gradient, Integrated Gradients, SHAP values, DeepLIFT, DeepLIFT with SHAP and SmoothGrad, across diverse models including LSTMs, LSTMs with Input-Cell Attention, Temporal Convolutional Networks and Transformers. We find that, architecture has a strong effect on saliency quality over the choice of saliency measurement method.In addition to interpretability, we explore the challenges of long-horizon forecasting using RNNs. We show that the performance of these methods decays as the forecasting horizon extends beyond few time steps. We then propose expectation-biasing, an approach motivated by Dynamic Belief Networks, as a solution to improve long-horizon forecasting using RNNs.In light of our findings, we propose creating a benchmark comparing neural architectures performance when changing the forecasting horizon and investigating the effect of forecasting horizon has on model interpretability. Finally, we set out to build inherently interpretable neural architectures specifically transformers that allows saliency methods to faithfully capture feature importance across time.Examining Committee:

Chair: Dr. Héctor Corrada Bravo Co-Chair: Dr. Soheil Feizi Dept rep: Dr. Marine Carpuat Members: Dr. Thomas Goldstein

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How to write a PhD research proposal on ‘deep learning’

Due to the complexity of the concepts, it is essential to know how to write a PhD research proposal on ‘deep learning’ early enough before the actual writing.

Deep learning is a collection of algorithms in use for machine learning to model the high-level concepts in data using model architectures that are a composition of various nonlinear transformations. It is part of the methods to learn representations of data.  An algorithm is ‘deep’ if the input of data passes through a series of nonlinear transformations before it has become an output.

Deep learning allows computational models with processing layers to learn data representations with various levels of abstraction without the need for manual identification of features. It relies on the available Write my literature review training process to discover the critical patterns in input examples. An example of how to implement deep learning is when online service provider such as Netflix uses it to predict what a customer is going to order.

Organizing a Ph.D. proposal on deep learning

 Begin by familiarizing with deep learning algorithms before writing your organize your ideas in these sections.

Literature review 

A literature review should provide a survey of relating works to clarify the sphere of your work.   Find contemporary publications that relate to your research.  Determine the reason why there is no modern data on the problem if all that you find is old.   Deep learning has many uses, and it could be that some of the relating work might appear in another field. Many people have more interest in using the concepts than addressing it, and it is a challenge for many researchers in this area.

Problem statement

The purpose of this section is to explain the problem to solve in the study. It requires much learning on the current state of deep learning to determine the problems on which the people are focusing. Identify the specific problem to address.

The significance of study

The purpose of this part is to explain the problem and importance of tackling it.  This section should present arguments supporting the relevance of the problem and reasons why anyone should care. It puts the project into context.

Explains the difficulties that you will tackle for you to solve the problem.   These challenges can be show stoppers for the project or be irrelevant because they focus on a different aspect. Explain the reason for acknowledging the difficulty but decided to avoid addressing it explicitly.   Describe the ways of resolving or working around all other challenges.

A background of a Ph.D. research proposal provides a summary that helps readers to understand the approach.


 A methodology is the method you intend to use for solving, provides a sound validation of a verification method for the approach.  You should be concise on the steps to follow, what you want to achieve, the scope and timeline.

Limitations and delimitations

Explains limitations, delimitations, and assumptions about the way you used when narrowing down the problem. Gauge all the efforts of past researchers in solving the questions using the methods you propose.

Preliminary experiments and results

 The purpose of this section is to explain all the tests, observations and conclusions. It should also analyze the results.


 A bibliography is the last section that lists the related works and the cited references. You include all of them on this section.

A research proposal is provisional and might change as your study continues. It is still essential to ensure that it is error-free and in a consistent structure before sending it to evaluators.

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    PhD Project Proposals ... Data-Driven Modelling, Deep Learning, Digital Twins, Resilient Cyber Physical Systems, ... More specifically, the PhD project will research current methods and limitations of developing . empirical models of value-chain systems using sensor data. It will then design methods for developing

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    Abstract. I am looking for an ambitious PhD candidate in the area of Deep Learning Systems Security and Robustness.The objectives of this project are to develop new robust deep learning models ...

  9. deep learning PhD Projects, Programmes & Scholarships

    Faculty of Science URSA PhD project: Multi-Modal Manipulation of Visual Contents based on Deep Learning. University of Bath Department of Computer Science. The University of Bath is inviting applications for the following PhD project commencing in September 2024 under the supervision of Dr Yongliang Yang in the Department of Computer Science.

  10. PhD Proposal: A Reconciliation of Deep Learning and the Brain ...

    In 1958, Frank Rosenblatt conceived of the Perceptron, in an effort to fulfill the dream of connectionism, to explain and recreate brain phenomena such as learning and behavior through simple learning rules of simple neurons. After his tragic death and the A.I. winter, and the resurgence that followed, his more brain-focused network was distilled into the more standardized feed-forward deep ...


    Stinson, Derek L. M.S.E.C.E., Purdue University, May 2020. Deep Learning with Go. Major Professor: Zina Ben Miled. Current research in deep learning is primarily focused on using Python as a sup-port language. Go, an emerging language, that has many bene ts including native support for concurrency has seen a rise in adoption over the past few ...

  12. PhD Proposal: PhD Preliminary: Pruning for Efficient Deep Learning

    My research focuses on developing novel model pruning techniques and efficient architecture search methods for deep computer vision models for visual recognition and generative modeling. Despite the impressive achievements of deep learning models in computer vision domains, their tremendous memory and computational requirements make their cloud deployment costly for private companies and ...


    The main three chapters of the thesis explore three recursive deep learning modeling choices. The rst modeling choice I investigate is the overall objective function that crucially guides what the RNNs need to capture. I explore unsupervised, supervised and semi-supervised learning for structure prediction (parsing), structured sentiment

  14. How to Write Research Proposal Deep Learning

    Reading Papers: Skim: Begin with reading abstracts, introductions, conclusions, and figures. Deep Dive: When a study shows high similar to our work, then look in-depth to its methodology, experiments, and results. Take Notes: Look down the basic plans, methods, datasets, Evaluation metrics, and open issues described in the paper and note it.

  15. PhD Dissertations

    PhD Dissertations [All are .pdf files] Probabilistic Reinforcement Learning: Using Data to Define Desired Outcomes, and Inferring How to Get There Benjamin Eysenbach, 2023. Data-driven Decisions - An Anomaly Detection Perspective Shubhranshu Shekhar, 2023. METHODS AND APPLICATIONS OF EXPLAINABLE MACHINE LEARNING Joon Sik Kim, 2023. Applied Mathematics of the Future Kin G. Olivares, 2023

  16. Best Deep Learning Research Proposal Ideas

    Research and Discussion on Image Recognition and Classification Algorithm Based on Deep Learning. An Intelligent Anti-jamming Decision-making Method Based on Deep Reinforcement Learning for Cognitive Radar. Conv2D Xception Adadelta Gradient Descent Learning Rate Deep learning Optimizer for Plant Species Classification.

  17. A Novel Proposal for Deep Learning-Based Diabetes Prediction

    A Novel Proposal for Deep Learning-Based Diabetes Prediction: Converting Clinical Data to Image Data. Muhammet Fatih Aslan, ... N.R.J. Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction. Health Technol. 2021; 11:693-731. doi: 10.1007/s12553-021-00555-5.

  18. Phd Research Proposal Topics in Deep Learning Algorithms

    Research Proposal Topics on Deep learning Algorithms and Recent advancements. Deep learning is a trending research topic in machine learning, which utilizes multiple layers of data representation to perform nonlinear processing to understand the levels of data abstractions. Deep learning can generate high-level data representations from large ...

  19. (PDF) A Proposal for a Deep Learning Model to Enhance ...

    Abstract. Despite attempts to improve the university's training offer, dropout rates are constantly increasing. This paper is an attempt to optimize university's training offer that would ...

  20. PhD Programme in Advanced Machine Learning

    The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato, Carl Rasmussen, Richard E. Turner, Adrian Weller, Hong Ge and David Krueger. ... The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your ...

  21. PhD Proposal: Interpretable Deep Learning for Time Series ...

    Time series data emerges in applications across many domains including neuroscience, medicine, finance, economics, and meteorology. Deep learning has revolutionized many machine learning including natural language processing and computer vision; however, its applications to time series data has been limited. In this work, we investigate both interpretability and accuracy of deep neural ...

  22. Novel Research Proposal Deep Learning

    Research Proposal Deep Learning. In general, deep learning is the latest and growing technology that used for supporting the prediction, classification, and analysis of any real-time tasks. Deep learning technology makes use of neural networks to enhance the systems in the forms of automated thinking and adjusting according to the tasks.

  23. A review of deep learning techniques used in agriculture

    Deep learning (DL) is a robust data-analysis and image-processing technique that has shown great promise in the agricultural sector. In this study, 129 papers that are based on DL applications used in agriculture are discussed, categorizing them into five areas: crop yield prediction, plant stress detection, weed and pest detection, disease detection, and smart farming.

  24. P.h.D Research Proposal for solving such a problem using Deep Learning

    I'm looking for some suggestions from the expert and experienced people in Deep Learning and Machine Learning fields to write a P.h.D research proposal. I'm thinking of choosing a problem in the ...

  25. How to write a PhD research proposal on 'deep learning'

    Organizing a Ph.D. proposal on deep learning . Begin by familiarizing with deep learning algorithms before writing your organize your ideas in these sections. Literature review . A literature review should provide a survey of relating works to clarify the sphere of your work. Find contemporary publications that relate to your research.