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How to Write a Machine Learning Research Proposal

Introduction, what is a machine learning research proposal, the structure of a machine learning research proposal, tips for writing a machine learning research proposal, how to get started with writing a machine learning research proposal, the importance of a machine learning research proposal, why you should take the time to write a machine learning research proposal, how to make your machine learning research proposal stand out, the bottom line: why writing a machine learning research proposal is worth it, further resources on writing machine learning research proposals.

If you want to get into machine learning, you first need to get past the research proposal stage. We’ll show you how.

Checkout this video:

A machine learning research proposal is a document that summarizes your research project, methods, and expected outcomes. It is typically used to secure funding for your project from a sponsor or institution, and can also be used to assessment your project by peers. Your proposal should be clear, concise, and well-organized. It should also provide enough detail to allow reviewers to assess your project’s feasibility and potential impact.

In this guide, we will cover the basics of what you need to include in a machine learning research proposal. We will also provide some tips on how to create a strong proposal that is more likely to be funded.

A machine learning research proposal is a document that describes a proposed research project that uses machine learning algorithms and techniques. The proposal should include a brief overview of the problem to be tackled, the proposed solution, and the expected results. It should also briefly describe the dataset to be used, the evaluation metric, and any other relevant details.

There is no one-size-fits-all answer to this question, as the structure of a machine learning research proposal will vary depending on the specific research question you are proposing to answer, the methods you plan to use, and the overall focus of your proposal. However, there are some general principles that all good proposals should follow.

In general, a machine learning research proposal should include:

-A summary of the problem you are trying to solve and the motivation for solving it -A brief overview of previous work in this area, including any relevant background information -A description of your proposed solution and a discussion of how it compares to existing approaches -An evaluation plan outlining how you will evaluate the effectiveness of your proposed solution -A discussion of any potential risks or limitations associated with your proposed research

Useful tips for writing a machine learning research proposal:

-Your proposal should address a specific problem or question in machine learning.

-Before writing your proposal, familiarize yourself with the existing literature in the field. Your proposal should build on the existing body of knowledge and contribute to the understanding of the chosen problem or question.

-Your proposal should be clear and concise. It should be easy for non-experts to understand what you are proposing and why it is important.

-Your proposal should be well organized. Include a brief introduction, literature review, methodology, expected results, and significance of your work.

-Make sure to proofread your proposal carefully before submitting it.

A machine learning research proposal is a document that outlines the problem you want to solve with machine learning, the methods you will use to solve it, the data you will use, and the anticipated results. This guide provides an overview of what should be included in a machine learning research proposal so that you can get started on writing your own.

1. Introduction 2. Problem statement 3. Methodology 4. Data 5. Evaluation 6. References

A machine learning research proposal is a document that outlines the rationale for a proposed machine learning research project. The proposal should convince potential supervisors or funding bodies that the project is worthwhile and that the researcher is competent to undertake it.

The proposal should include:

– A clear statement of the problem to be addressed or the question to be answered – A review of relevant literature – An outline of the proposed research methodology – A discussion of the expected outcome of the research – A realistic timeline for completing the project

A machine learning research proposal is not just a formal exercise; it is an opportunity to sell your idea to potential supervisors or funding bodies. Take advantage of this opportunity by doing your best to make your proposal as clear, concise, and convincing as possible.

Your machine learning research proposal is your chance to sell your project to potential supervisors and funders. It should be clear, concise and make a strong case for why your project is worth undertaking.

A well-written proposal will convince others that you have a worthwhile project and that you have the necessary skills and experience to complete it successfully. It will also help you to clarify your own ideas and focus your research.

Writing a machine learning research proposal can seem like a daunting task, but it doesn’t have to be. If you take it one step at a time, you’ll be well on your way to writing a strong proposal that will get the support you need.

In order to make your machine learning research proposal stand out, you will need to do several things. First, make sure that your proposal is well written and free of grammatical errors. Second, make sure that your proposal is clear and concise. Third, make sure that your proposal is organized and includes all of the necessary information. Finally, be sure to proofread your proposal carefully before submitting it.

The benefits of writing a machine learning research proposal go beyond helping you get funding for your project. A good proposal will also force you to think carefully about your problem and how you plan to solve it. This process can help you identify potential flaws in your approach and make sure that your project is as strong as possible before you start.

It can also be helpful to have a machine learning research proposal on hand when you’re talking to potential collaborators or presenting your work to a wider audience. A well-written proposal can give people a clear sense of what your project is about and why it’s important, which can make it easier to get buy-in and find people who are excited to work with you.

In short, writing a machine learning research proposal is a valuable exercise that can help you hone your ideas and make sure that your project is as strong as possible before you start.

Here are some further resources on writing machine learning research proposals:

– How to Write a Machine Learning Research Paper: https://MachineLearningMastery.com/how-to-write-a-machine-learning-research-paper/

– 10 Tips for Writing a Machine Learning Research Paper: https://blog.MachineLearning.net/10-tips-for-writing-a-machine-learning-research-paper/

Please also see our other blog post on writing research proposals: https://www.MachineLearningMastery.com/how-to-write-a-research-proposal/

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Research Topics & Ideas

Artifical Intelligence (AI) and Machine Learning (ML)

If you’re just starting out exploring AI-related research topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of research topics and ideas , including examples from past studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan  to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

AI-Related Research Topics & Ideas

Below you’ll find a list of AI and machine learning-related research topics ideas. These are intentionally broad and generic , so keep in mind that you will need to refine them a little. Nevertheless, they should inspire some ideas for your project.

• Developing AI algorithms for early detection of chronic diseases using patient data.
• The use of deep learning in enhancing the accuracy of weather prediction models.
• Machine learning techniques for real-time language translation in social media platforms.
• AI-driven approaches to improve cybersecurity in financial transactions.
• The role of AI in optimizing supply chain logistics for e-commerce.
• Investigating the impact of machine learning in personalized education systems.
• The use of AI in predictive maintenance for industrial machinery.
• Developing ethical frameworks for AI decision-making in healthcare.
• The application of ML algorithms in autonomous vehicle navigation systems.
• AI in agricultural technology: Optimizing crop yield predictions.
• Machine learning techniques for enhancing image recognition in security systems.
• AI-powered chatbots: Improving customer service efficiency in retail.
• The impact of AI on enhancing energy efficiency in smart buildings.
• Deep learning in drug discovery and pharmaceutical research.
• The use of AI in detecting and combating online misinformation.
• Machine learning models for real-time traffic prediction and management.
• AI applications in facial recognition: Privacy and ethical considerations.
• The effectiveness of ML in financial market prediction and analysis.
• Developing AI tools for real-time monitoring of environmental pollution.
• Machine learning for automated content moderation on social platforms.
• The role of AI in enhancing the accuracy of medical diagnostics.
• AI in space exploration: Automated data analysis and interpretation.
• Machine learning techniques in identifying genetic markers for diseases.
• AI-driven personal finance management tools.
• The use of AI in developing adaptive learning technologies for disabled students.

AI & ML Research Topic Ideas (Continued)

• Machine learning in cybersecurity threat detection and response.
• AI applications in virtual reality and augmented reality experiences.
• Developing ethical AI systems for recruitment and hiring processes.
• Machine learning for sentiment analysis in customer feedback.
• AI in sports analytics for performance enhancement and injury prevention.
• The role of AI in improving urban planning and smart city initiatives.
• Machine learning models for predicting consumer behaviour trends.
• AI and ML in artistic creation: Music, visual arts, and literature.
• The use of AI in automated drone navigation for delivery services.
• Developing AI algorithms for effective waste management and recycling.
• Machine learning in seismology for earthquake prediction.
• AI-powered tools for enhancing online privacy and data protection.
• The application of ML in enhancing speech recognition technologies.
• Investigating the role of AI in mental health assessment and therapy.
• Machine learning for optimization of renewable energy systems.
• AI in fashion: Predicting trends and personalizing customer experiences.
• The impact of AI on legal research and case analysis.
• Developing AI systems for real-time language interpretation for the deaf and hard of hearing.
• Machine learning in genomic data analysis for personalized medicine.
• AI-driven algorithms for credit scoring in microfinance.
• The use of AI in enhancing public safety and emergency response systems.
• Machine learning for improving water quality monitoring and management.
• AI applications in wildlife conservation and habitat monitoring.
• The role of AI in streamlining manufacturing processes.
• Investigating the use of AI in enhancing the accessibility of digital content for visually impaired users.

Recent AI & ML-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic in AI, they are fairly generic and non-specific. So, it helps to look at actual studies in the AI and machine learning space to see how this all comes together in practice.

Below, we’ve included a selection of AI-related studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• An overview of artificial intelligence in diabetic retinopathy and other ocular diseases (Sheng et al., 2022)
• HOW DOES ARTIFICIAL INTELLIGENCE HELP ASTRONOMY? A REVIEW (Patel, 2022)
• Editorial: Artificial Intelligence in Bioinformatics and Drug Repurposing: Methods and Applications (Zheng et al., 2022)
• Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities, and Challenges (Mukhamediev et al., 2022)
• Will digitization, big data, and artificial intelligence – and deep learning–based algorithm govern the practice of medicine? (Goh, 2022)
• Flower Classifier Web App Using Ml & Flask Web Framework (Singh et al., 2022)
• Object-based Classification of Natural Scenes Using Machine Learning Methods (Jasim & Younis, 2023)
• Automated Training Data Construction using Measurements for High-Level Learning-Based FPGA Power Modeling (Richa et al., 2022)
• Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (Manickam et al., 2022)
• Critical Review of Air Quality Prediction using Machine Learning Techniques (Sharma et al., 2022)
• Artificial Intelligence: New Frontiers in Real–Time Inverse Scattering and Electromagnetic Imaging (Salucci et al., 2022)
• Machine learning alternative to systems biology should not solely depend on data (Yeo & Selvarajoo, 2022)
• Measurement-While-Drilling Based Estimation of Dynamic Penetrometer Values Using Decision Trees and Random Forests (García et al., 2022).
• Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls (Patil et al., 2022).
• Automated Machine Learning on High Dimensional Big Data for Prediction Tasks (Jayanthi & Devi, 2022)
• Breakdown of Machine Learning Algorithms (Meena & Sehrawat, 2022)
• Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device (Carolan et al., 2021)
• Machine Learning in Tourism (Rugge, 2022)
• Towards a training data model for artificial intelligence in earth observation (Yue et al., 2022)
• Classification of Music Generality using ANN, CNN and RNN-LSTM (Tripathy & Patel, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, in order for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

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• Office Hours

One of CS230's main goals is to prepare you to apply machine learning algorithms to real-world tasks, or to leave you well-qualified to start machine learning or AI research. The final project is intended to start you in these directions.

Instructors

Time and Location

Wednesday 9:30AM-11:20AM Zoom

Getting Started

Project starter package.

The teaching team has put together a

• github repository with project code examples, including a computer vision and a natural language processing example (both in Tensorflow and Pytorch).
• A series of posts to help you familiarize yourself with the project code examples, get ideas on how to structure your deep learning project code, and to setup AWS. The code examples posted are optional and are only meant to help you with your final project. The code can be reused in your projects, but the examples presented are not complex enough to meet the expectations of a quarterly project.
• A sheet of resources to get started with project ideas in several topics

Project Topics

This quarter in CS230, you will learn about a wide range of deep learning applications. Part of the learning will be online, during in-class lectures and when completing assignments, but you will really experience hands-on work in your final project. We would like you to choose wisely a project that fits your interests. One that would be both motivating and technically challenging.

Most students do one of three kinds of projects:

• Application project. This is by far the most common: Pick an application that interests you, and explore how best to apply learning algorithms to solve it.
• Algorithmic project. Pick a problem or family of problems, and develop a new learning algorithm, or a novel variant of an existing algorithm, to solve it.
• Theoretical project. Prove some interesting/non-trivial properties of a new or an existing learning algorithm. (This is often quite difficult, and so very few, if any, projects will be purely theoretical.) Some projects will also combine elements of applications and algorithms.

Many fantastic class projects come from students picking either an application area that they’re interested in, or picking some subfield of machine learning that they want to explore more. So, pick something that you can get excited and passionate about! Be brave rather than timid, and do feel free to propose ambitious things that you’re excited about. (Just be sure to ask us for help if you’re uncertain how to best get started.) Alternatively, if you’re already working on a research or industry project that deep learning might apply to, then you may already have a great project idea.

Project Hints

A very good CS230 project will be a publishable or nearly-publishable piece of work. Each year, some number of students continue working on their projects after completing CS230, submitting their work to a conferences or journals. Thus, for inspiration, you might also look at some recent deep learning research papers. Two of the main machine learning conferences are ICML and NeurIPS . You may also want to look at class projects from previous years of CS230 ( Fall 2017 , Winter 2018 , Spring 2018 , Fall 2018 ) and other machine learning/deep learning classes ( CS229 , CS229A , CS221 , CS224N , CS231N ) is a good way to get ideas. Finally, we crowdsourced and curated a list of ideas that you can view here , and an older one here , and a (requires Stanford login).

Once you have identified a topic of interest, it can be useful to look up existing research on relevant topics by searching related keywords on an academic search engine such as: http://scholar.google.com . Another important aspect of designing your project is to identify one or several datasets suitable for your topic of interest. If that data needs considerable pre-processing to suit your task, or that you intend to collect the needed data yourself, keep in mind that this is only one part of the expected project work, but can often take considerable time. We still expect a solid methodology and discussion of results, so pace your project accordingly.

Notes on a few specific types of projects:

• Computation power. Amazon Web Services is sponsoring the CS230 projects by providing you with GPU credits to run your experiments! We will update regarding how to retrieve your GPU credits. Alternatively Google Cloud and Microsoft Azure offer free academic units which you can apply to.
• Preprocessed datasets. While we don’t want you to have to spend much time collecting raw data, the process of inspecting and visualizing the data, trying out different types of preprocessing, and doing error analysis is often an important part of machine learning. Hence if you choose to use preprepared datasets (e.g. from Kaggle, the UCI machine learning repository, etc.) we encourage you to do some data exploration and analysis to get familiar with the problem.
• Replicating results. Replicating the results in a paper can be a good way to learn. However, we ask that instead of just replicating a paper, also try using the technique on another application, or do some analysis of how each component of the model contributes to final performance.

Project Deliverables

This section contains the detailed instructions for the different parts of your project.

Groups: The project is done in groups of 1-3 people; teams are formed by students.

Submission: We will be using Gradescope for submission of all four parts of the final project. We’ll announce when submissions are open for each part. You should submit on Gradescope as a group: that is, for each part, please make one submission for your entire project group and tag your team members.

Evaluation: We will not be disclosing the breakdown of the 40% that the final project is worth amongst the different parts, but the video and final report will combine to be the majority of the grade. Attendance and participation during your TA meetings will also be considered. Projects will be evaluated based on:

• The technical quality of the work. (I.e., Does the technical material make sense? Are the things tried reasonable? Are the proposed algorithms or applications clever and interesting? Do the authors convey novel insight about the problem and/or algorithms?)
• Significance. (Did the authors choose an interesting or a “real” problem to work on, or only a small “toy” problem? Is this work likely to be useful and/or have impact?)
• The novelty of the work. (Is this project applying a common technique to a well-studied problem, or is the problem or method relatively unexplored?)

In order to highlight these components, it is important you present a solid discussion regarding the learnings from the development of your method, and summarizing how your work compares to existing approaches.

Deadline: April 19, Wednesday 11:59 PM

First, make sure to submit the following Google form so that we can match you to a TA mentor. In the form you will have to provide your project title, team members and relevant research area(s).

In the project proposal, you’ll pick a project idea to work on early and receive feedback from the TAs. If your proposed project will be done jointly with a different class’ project, you should obtain approval from the other instructor and approval from us. Please come to the project office hours to discuss with us if you would like to do a joint project. You should submit your proposals on Gradescope. All students should already be added to the course page on Gradescope via your SUNet IDs. If you are not, please create a private post on Ed and we will give you access to Gradescope.

In the proposal, below your project title, include the project category. The category can be one of:

• Computer Vision
• Natural Language Processing
• Generative Modeling
• Speech Recognition
• Reinforcement Learning
• Others (Please specify!)

Your project proposal should include the following information:

• What is the problem that you will be investigating? Why is it interesting?
• What are the challenges of this project?
• What dataset are you using? How do you plan to collect it?
• What method or algorithm are you proposing? If there are existing implementations, will you use them and how? How do you plan to improve or modify such implementations?
• What reading will you examine to provide context and background? If relevant, what papers do you refer to?
• How will you evaluate your results? Qualitatively, what kind of results do you expect (e.g. plots or figures)? Quantitatively, what kind of analysis will you use to evaluate and/or compare your results (e.g. what performance metrics or statistical tests)?

Presenting pointers to one relevant dataset and one example of prior research on the topic are a valuable (optional) addition. We link one past example of a good project proposal here and a latex template .

Deadline: May 19, Friday 11:59 PM

The milestone will help you make sure you’re on track, and should describe what you’ve accomplished so far, and very briefly say what else you plan to do. You should write it as if it’s an “early draft” of what will turn into your final project. You can write it as if you’re writing the first few pages of your final project report, so that you can re-use most of the milestone text in your final report. Please write the milestone (and final report) keeping in mind that the intended audience is Profs. Ng and Katanforoosh and the TAs. Thus, for example, you should not spend two pages explaining what logistic regression is. Your milestone should include the full names of all your team members and state the full title of your project. Note: We will expect your final writeup to be on the same topic as your milestone. In order to help you the most, we expect you to submit your running code. Your code should contain a baseline model for your application. Along with your baseline model, you are welcome to submit additional parts of your code such as data pre-processing, data augmentation, accuracy matric(s), and/or other models you have tried. Please clean your code before submitting, comment on it, and cite any resources you used. Please do not submit your dataset . However, you may include a few samples of your data in the report if you wish.

Submission Deadline: June 7, Wednesday 11:59 PM (No late days allowed)

Your video is required to be a 3-4 minute summary of your work. There is a hard limit of 4 minutes, and TAs will not watch a video beyond the 4 minute mark. Include diagrams, figures and charts to illustrate the highlights of your work. The video needs to be visually appealing, but also illustrate technical details of your project.

If possible, try to come up with creative visualizations of your project. These could include:

• System diagrams
• More detailed examples of data that don’t fit in the space of your report
• Live demonstrations for end-to-end systems

We recommend searching for conference presentation sessions (AAAI, Neurips, ECCV, ICML, ICLR etc) and following those formats.

You can find a sample video from a previous iteration of the class here

Final Report

Deadline: June 7, Wednesday 11:59 PM (No late days allowed)

The final report should contain a comprehensive account of your project. We expect the report to be thorough, yet concise. Broadly, we will be looking for the following:

• Good motivation for the project and an explanation of the problem statement
• A description of the data
• Any hyperparameter and architecture choices that were explored
• Presentation of results
• Analysis of results
• Any insights and discussions relevant to the project

After the class, we will post all the final writeups online so that you can read about each other’s work. If you do not want your write-up to be posted online, then please create a private Piazza post.

Office Address

• #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
• [email protected]
• +91- 81240 01111

Social List

Trending topics for phd research proposal in machine learning.

Machine learning techniques have prompted at the forefront over the last few years due to the advent of big data. Machine learning is a precise subfield of artificial intelligence (AI) that seeks to analyze the massive data chunks and facilitate the system to learn the data automatically without the explicit support of programming. The machine learning algorithms attempt to reveal the fine-grained patterns from the unprecedented data under multiple perspectives and to build an accurate prediction model as never before. For the purpose of learning, the machine learning algorithm is sub-categorized into four broad groups include supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning. Whenever the new unseen data is fed as input to the machine learning algorithm, it automatically learns and predicts the forthcoming by exploiting the previous experience over time. Machine learning is continually liberating its potency in a broad range of applications, including the Internet of Things (IoT), computer vision, natural language processing, speech processing, online recommendation system, cyber security, neuroscience, prediction analytics, fraud detection, and so on.

• Guidelines for Preparing a Phd Research Proposal

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• Research Proposal on Attention-based interpretation of neural networks
• Research Proposal on Dialogue Systems
• Research Proposal on Temporal Consistency Restoration in Videos using Deep Learning
• Research Proposal on Meta-Reinforcement Learning
• Research Proposal on Multimodal Representation Learning
• Research Proposal on Real-time Image Denoising with Deep Learning
• Research Proposal on Multi-Modal and Cross-Lingual Word Embeddings
• Research Proposal on Face Recognition using Deep Learning
• Research Proposal on Deep learning for blood pressure prediction in low-resource settings
• Research Proposal on Attention Mechanisms in Convolutional Neural Networks
• Research Proposal on Image Captioning using Deep Learning
• Research Proposal on Named entity recognition in a multilingual context
• Research Proposal on Graph-based pattern recognition
• Research Proposal on Named Entity Recognition
• Research Proposal on Deep learning for palmprint recognition
• Research Proposal on Action recognition in videos
• Research Proposal on Pharmacogenomics using Deep Learning
• Research Proposal on Deep learning for detecting abnormalities in medical images in radiology
• Research Proposal on Deep Learning for Cell Segmentation and Tracking
• Research Proposal on Action Recognition using Deep Learning
• Research Proposal on Transfer learning in Convolutional Neural Networks
• Research Proposal on Multi-Modal and Multi-Task Ensemble Learning
• Research Proposal on Microscopic Image Analysis using Deep Learning
• Research Proposal in Real-time analytics on big data streams
• Research Proposal on Augmentation for object detection and segmentation
• Research Proposal on Facial Expression Recognition using Deep Learning
• Research Proposal on Attention for visual data processing
• Research Proposal on Domain Adaptation for Health Record Analysis
• Research Proposal on Radiology using Deep Learning
• Research Proposal on Large-scale parallel hyperparameter optimization
• Research Proposal on Multi-modal Fusion for Facial Expression Recognition
• Research Proposal on Bioinformatics using Deep Learning
• Research Proposal on Neural Machine Translation with semantic representation
• Research Proposal on Cross-lingual Text Summarization
• Research Proposal on Text Summarization
• Research Proposal on Task-Oriented Dialogue Systems
• Research Proposal on Adversarial attacks and defenses in medical image analysis
• Research Proposal on Semantic Analysis
• Research Proposal on Image Captioning with Attention Mechanisms
• Research Proposal on Named entity recognition in low-resource languages using deep learning
• Research Proposal on Radiotherapy using Deep Learning
• Research Proposal on Deep Learning for Quantitative Image Analysis in Microscopy
• Research Proposal on Deep learning for improved classification of multi-view sequential data
• Research Proposal on Image Super Resolution Using Deep Learning
• Research Proposal on Deep Learning for Facial Emotion Recognition from Speech
• Research Proposal on Incorporating Background Knowledge in Topic Modeling
• Research Proposal on Neural Rendering
• Research Proposal on Deep learning in radiation therapy planning and optimization
• Research Proposal on Multi-modal Text Summarization
• Research Proposal on Biometric Recognition using Deep Learning
• Research Proposal on Neural rendering for improved video game graphics
• Research Proposal on Time-series Regression with Recurrent Neural Networks
• Research Proposal on Medical image analysis in resource-limited settings with deep learning
• Research Proposal on Meta-learning for few-shot multi-class classification
• Research Proposal on Automated evaluation of radiotherapy outcomes using deep learning
• Research Proposal on Domain-specific entity embeddings
• Research Proposal on Neural rendering for virtual and augmented reality applications
• Research Proposal on Semantic Segmentation using FCN
• Research Proposal on Improved gene expression analysis with deep learning
• Research Proposal on Multi-modal data analysis for disease prediction
• Research Proposal on Multi-level Deep Network for Image Denoising
• Research Proposal on Deep learning for image-based diagnosis in radiology
• Research Proposal on Video Inpainting with Deep Learning
• Research Proposal on Deep learning for predicting blood pressure response to treatment
• Research Proposal on Multi-agent Reinforcement Learning with Evolutionary Algorithms
• Research Proposal on Decentralized Multi-agent Reinforcement Learning
• Research Proposal on Deep Learning for Compressed Sensing Reconstruction
• Research Proposal on Deep Reinforcement Learning for Supply Chain Optimization
• Research Proposal on Multi-modal medical image analysis in radiology with deep learning
• Research Proposal on Deep Learning for Multimodal Fusion
• Research Proposal on Adversarial attacks and defenses in biometric recognition using deep learning
• Research Proposal on Deep Learning for Compressed Sensing in Wireless Communications
• Research Proposal on Human-in-the-loop Neural Architecture Search
• Research Proposal on Agricultural Resource Management with Deep Learning
• Research Proposal on Generative Models for Semi-Supervised Learning
• Research Proposal on Deep learning for predicting cancer treatment response
• Research Proposal on Graph Generative Models
• Research Proposal on Deep generative models for image super-resolution
• Research Proposal on Deep Learning for Drug Response Prediction
• Research Proposal on Transfer Learning for Face Recognition
• Research Proposal on Deep Reinforcement Learning for Facial Expression Recognition
• Research Proposal on Neural rendering for photorealistic image synthesis
• Research Proposal on Prediction of treatment response using deep learning
• Research Proposal on Deep Learning for Plant Species Identification
• Research Proposal on Deep transfer learning for medical image analysis
• Research Proposal on Improved drug discovery in neglected diseases using deep learning
• Research Proposal on Interpretability and Explainability of Convolutional Neural Networks
• Research Proposal on Cross-lingual semantic analysis
• Research Proposal on Deep learning for predicting genetic interactions
• Research Proposal on Deep Reinforcement Learning for Plant Disease Detection
• Research Proposal on Fine-grained named entity recognition
• Research Proposal on Transfer learning for sentiment analysis
• Research Proposal on Deep learning for predicting protein-protein interactions
• Research Proposal on Object detection with active learning
• Research Proposal on Deep learning for improving drug discovery through in silico experimentation
• Research Proposal on Cross-lingual Image Captioning
• Research Proposal on Deep Learning for Food Safety Prediction in Agriculture
• Research Proposal on Improved epigenetic analysis using deep learning
• Research Proposal on Deep Learning for Route Optimization in Logistics
• Research Proposal on Deep Learning for Predictive Maintenance in Supply Chain
• Research Proposal on Multi-modal Representation Learning for Sentiment Analysis
• Research Proposal on Plant Leaf Recognition with Computer Vision
• Research Proposal on Cross-lingual named entity recognition
• Research Proposal on Deep learning for semantic-aware image super-resolution
• Research Proposal on Generative Adversarial Networks with Convolutional Neural Networks
• Research Proposal on Attention in reinforcement learning
• Research Proposal on Multi-objective optimization for deep learning hyperparameters
• Research Proposal on Multi-modal entity embeddings
• Research Proposal on Dynamic Graph Neural Networks
• Research Proposal on Image Captioning with Visual and Language Context
• Research Proposal on Deep Learning for Portfolio Diversification and Optimization
• Research Proposal on Motion Compensation for Video Restoration using Deep Learning
• Research Proposal on Multi-modal deep learning for multi-view sequential data analysis
• Research Proposal on Deep learning for cancer diagnosis from medical images
• Research Proposal on Deep transfer learning for radiology image analysis
• Research Proposal on Deep learning for improved iris recognition
• Research Proposal on Processing high-velocity and high-volume data streams
• Research Proposal on Causal inference for multi-class classification
• Research Proposal on Deep Extreme Learning Machines
• Research Proposal on Meta-representation learning
• Research Proposal on Data augmentation in Neural Machine Translation
• Research Proposal on Fairness and Bias in Health Record Analysis
• Research Proposal on Multi-omics Integration for Personalized Medicine
• Research Proposal on Deep Learning for Micro-expression Recognition
• Research Proposal on Deep Learning for Compressed Sensing in Compressed Speech
• Research Proposal on Transfer learning for feature engineering
• Research Proposal in Sentiment analysis on multimodal data
• Research Proposal on Online Extreme Learning Machines
• Research Proposal on Deep Learning for Face Anti-spoofing
• Research Proposal on Domain adaptation and transfer learning for multi-class classification
• Research Proposal on Reinforcement Learning for Natural Language Processing
• Research Proposal on Transfer Learning for Word Embeddings
• Research Proposal on Multi-head attention
• Research Proposal on Model-agnostic interpretation methods
• Research Proposal on Deep Generative Models for Microscopy Image Synthesis
• Research Proposal on Deep learning for quality assurance in radiotherapy
• Research Proposal on Low-light image super-resolution with deep learning
• Research Proposal on Fine-tuning Pre-trained Transformer Models for Image Captioning
• Research Proposal on Deep Learning for Facial Expression Recognition in the Wild
• Research Proposal on Multi-modal semantic analysis
• Research Proposal on Deep learning for gait recognition
• Research Proposal on Graph Reinforcement Learning
• Research Proposal on Gradient-based optimization for deep learning hyperparameters
• Research Proposal on Object detection with transformers
• Research Proposal on Transfer learning for multimedia classification
• Research Proposal on Generative adversarial networks for representation learning
• Research Proposal on Representation Learning with Graphs for Word Embeddings
• Research Proposal on Deep Learning for Motion Anomaly Detection in Videos
• Research Proposal on Deep Reinforcement Learning for Face Recognition
• Research Proposal on Deep Learning for Microscopy Image Restoration and Denoising
• Research Proposal on Deep Reinforcement Learning for Text Summarization
• Research Proposal on Deep learning for medical image registration
• Research Proposal on Improved computer-aided diagnosis in radiology with deep learning
• Research Proposal on Multi-modal cancer diagnosis using deep learning
• Research Proposal on Improved single image super-resolution using deep learning
• Research Proposal on Image Captioning in the Wild
• Research Proposal on Graph Convolutional Networks
• Research Proposal on Deep Learning for Small-Molecule Property Prediction
• Research Proposal on Real-time image super-resolution using deep learning
• Research Proposal on Deep learning for improved image quality assessment in radiology
• Research Proposal on Quantum reinforcement learning
• Research Proposal on Adaptive attention
• Research Proposal on Transfer Ensemble Learning
• Research Proposal on Multi-Task and Multi-Modal Learning with Convolutional Neural Networks
• Research Proposal on Two-stage object detection using Faster R-CNN
• Research Proposal on Face Attribute Prediction and Analysis
• Research Proposal on Deep learning for medical image synthesis and augmentation
• Research Proposal on Weather Forecasting for Agriculture using Deep Learning
• Research Proposal on Deep Learning for Video Compression and Restoration
• Research Proposal on Non-Parametric Topic Modeling
• Research Proposal on Deep Learning for Demand Forecasting in Supply Chain Management
• Research Proposal on Soil Moisture Prediction using Deep Learning
• Research Proposal on Deep Learning for Predictive Portfolio Management
• Research Proposal on Plant Disease Image Analysis with Deep Learning
• Research Proposal on Inventory Optimization with Deep Learning
• Research Proposal on Attention-based Image Denoising
• Research Proposal on Deep Generative Models for Drug Repurposing
• Research Proposal on Deep Learning for Compressed Sensing in Compressed Video
• Research Proposal on Transfer Learning for Topic Modeling
• Research Proposal on Representation learning for graph-structured data
• Research Proposal on Federated Learning for Recommendation System
• Research Proposal on Adversarial Ensemble Learning
• Research Proposal on Graph-based Natural Language Processing
• Research Proposal on Cross-domain sentiment analysis
• Research Proposal on Unsupervised feature learning using Belief Networks
• Research Proposal on Quantum neural networks
• Research Proposal on Representation learning for speech data
• Research Proposal on Object detection with semantic segmentation
• Research Proposal on Zero-shot Neural Machine Translation
• Research Proposal on Dialogue State Tracking
• Research Proposal on Image Captioning with Semantic Segmentation
• Research Proposal on Deep Learning for Image Registration and Stitching
• Research Proposal on Text Summarization with Sentiment Analysis
• Research Proposal on Deep learning for radiation therapy-related toxicity prediction
• Research Proposal on Improved image quality assessment in medical imaging using deep learning
• Research Proposal on Scene synthesis and manipulation using neural rendering
• Research Proposal on Multi-modal biometric recognition using deep learning
• Research Proposal on Named entity recognition for multi-modal data
• Research Proposal on Improved lung cancer diagnosis using deep learning
• Research Proposal on Multi-view sequential data analysis in low-resource settings using deep learning
• Research Proposal on Deep Learning for Video Denoising
• Research Proposal on Multi-agent Reinforcement Learning for Dynamic Environments
• Research Proposal on Deep Learning for Quality Control in Supply Chain
• Research Proposal on Object detection with domain adaptation
• Research Proposal on Plant Disease Segmentation and Recognition with Deep Learning
• Research Proposal on Adversarial Attacks and Defences in Face Recognition
• Research Proposal on Anomaly Detection in Videos with Deep Reinforcement Learning
• Research Proposal on Integrating Electronic Health Records and Genomics for Personalized Medicine
• Research Proposal on Adversarial attacks and defenses in radiology using deep learning
• Research Proposal on Deep Generative Models for Synthetic Facial Expression Data
• Research Proposal on Transfer Learning for Text Summarization
• Research Proposal on Extractive Text Summarization
• Research Proposal on Multi-modal Face Recognition
• Research Proposal on Multi-frame super-resolution using deep learning
• Research Proposal on Spatial-Temporal Graph Convolutional Networks
• Research Proposal on Real-time neural rendering for interactive environments
• Research Proposal on Convolutional Neural Networks for Object Detection and Segmentation
• Research Proposal on Transfer learning for named entity recognition
• Research Proposal on Transfer Learning for Semi-Supervised Learning
• Research Proposal on Deep learning for early cancer detection
• Research Proposal on Imitation Learning and Inverse Reinforcement Learning
• Research Proposal on Deep reinforcement learning for multi-view sequential data analysis
• Research Proposal on Attention for sequential reasoning
• Research Proposal on Deep learning for drug repurposing and de-novo drug discovery
• Research Proposal on Generative adversarial networks for domain adaptation
• Research Proposal on Crop Growth Monitoring using Deep Learning
• Research Proposal in Opinion mining on social media
• Research Proposal on Deep Learning for Video Frame Interpolation
• Research Proposal on Multi-lingual entity embeddings
• Research Proposal on Multi-agent Reinforcement Learning with Communication
• Research Proposal on Semantic augmentation
• Research Proposal on Deep Learning for Supplier Selection in Supply Chain
• Research Proposal on Domain adaptation in Neural Machine Translation
• Research Proposal on Deep Learning for Plant Leaf Disease Diagnosis
• Research Proposal on Multi-Turn conversational Dialogue Systems
• Research Proposal on Attention-based Multimodal Representation Learning
• Research Proposal on Deep Generative Models for Face Synthesis
• Research Proposal on Fine-grained Plant Disease Recognition with Deep Learning
• Research Proposal on Deep Reinforcement Learning for Drug Discovery
• Research Proposal on Deep Learning for Compressed Sensing in Medical Imaging
• Research Proposal on Transfer Learning for Microscopy Image Analysis
• Research Proposal on Multi-object Anomaly Detection in Videos with Deep Learning
• Research Proposal on Adversarial Training for Text Summarization
• Research Proposal on Human-in-the-loop Active Learning
• Research Proposal on Contextual word embeddings for semantic analysis
• Research Proposal on Scalable Neural Architecture Search for large-scale datasets and hardware accelerators
• Research Proposal on Deep learning for super-resolution of microscopy images
• Research Proposal on Causal inference and causal feature engineering
• Research Proposal on Improved 3D object rendering using deep neural networks
• Research Proposal on Convolutional Neural Networks (CNN) for Computer Vision tasks
• Research Proposal on Deep transfer learning for bioinformatics analysis
• Research Proposal on Self-training and Co-training for Semi-Supervised Learning
• Research Proposal on Video Super-resolution using Deep Learning
• Research Proposal on Meta-Learning for Few-shot Semi-Supervised Learning
• Research Proposal on Fertilizer Recommendation System using Deep Learning
• Research Proposal on Non-Linear Regression with Gaussian Processes
• Research Proposal on Adversarial Training for Image Denoising
• Research Proposal on Active and Semi-Supervised Ensemble Learning
• Research Proposal on Improved blood pressure prediction in cardiovascular disease patients using deep learning
• Research Proposal on Privacy-preserving Natural Language Processing
• Research Proposal on Named entity disambiguation using deep learning
• Research Proposal on Continuous Learning and Adaptation for Word Embeddings
• Research Proposal on Deep reinforcement learning in medical imaging and radiology
• Research Proposal on Incremental and online machine learning for data streams
• Research Proposal on Adversarial training for image super-resolution
• Research Proposal on Attention in federated learning
• Research Proposal on Multi-modal Microscopy Image Analysis
• Research Proposal Topic on Attention Mechanism for Natural Language Processing with Deep Learning
• Research Proposal on Mode collapse and stability in generative adversarial networks
• Research Proposal on Image Captioning with Scene Graphs
• Research Proposal Topics on Convolutional Neural Networks Research Challenges and Future Impacts
• Research Proposal on Adversarial attacks and defenses in sentiment analysis
• Research Proposal on Multi-person Motion Analysis
• Research Proposal on Graph Neural Network for Graph Analytics
• Research Proposal on Sentiment polarity detection
• Research Proposal on Transformer-based Neural Machine Translation
• Research Proposal on Deep Reinforcement Learning Methods for Active Decision Making
• Research Proposal on Cross-modal correspondence learning
• Research Proposal on Graph Transformer Networks
• Research Proposal on Deep Learning based Medical Imaging
• Research Proposal on Representation learning for pattern recognition
• Research Proposal on Mixup and cutmix data augmentation
• Research Proposal On Pre-trained Word embedding for Language models
• Research Proposal on Multi-modal data analysis using Belief Networks
• Research Proposal on Object detection with instance segmentation
• Research Proposal on Medical Machine Learning for Healthcare Analysis
• Research Proposal on Multi-modal data analysis using Extreme Learning Machines
• Research Proposal on Graph-based entity embeddings
• Research Proposal on Generative Adversarial Network
• Research Proposal on Quantum clustering algorithms
• Research Proposal on Sentiment analysis for low-resource languages
• Research Proposal on Hyperparameter Optimization and Fine-Tuning in Deep Neural Network
• Research Proposal on Transfer learning for hyperparameter optimization
• Research Proposal on Self-attention for sequential data
• Research Proposal on Deep Learning Models for Epilepsy Detection
• Research Proposal on Geometrical transformations for data augmentation
• Research Proposal on Meta-Learning for Word Embeddings
• Research Proposal Topics on Deep Learning Models for Epilepsy Detection
• Research Proposal on Anchor-free object detection
• Research Proposal on Multi-Task and Multi-lingual Natural Language Processing
• Research Proposal on Machine Learning in Alzheimer-s Disease Detection
• Research Proposal on Graph Autoencoders
• Research Proposal on Graph-based Semi-Supervised Learning
• Research Proposal on Machine Learning in Cancer Diagnosis
• Research Proposal on Human Pose Estimation
• Research Proposal on Adversarial Reinforcement Learning
• Research Proposal on Machine Learning in Covid-19 Diagnosis
• Research Proposal on Medication Recommendation
• Research Proposal in Light-weight and Efficient Convolutional Neural Networks for deployment on edge devices
• Research Proposal on Machine Learning in Diagnosis of Diabetes
• Research Proposal on Cross-age Face Recognition
• Research Proposal on Adversarial robustness in pattern recognition
• Research Proposal on Machine Learning in Heart Disease Diagnosis
• Research Proposal on Image Captioning with Transfer Learning
• Research Proposal on Representation learning for time series data
• Research Proposal on Machine Learning in Parkinson-s Diagnosis
• Research Proposal on Deep Learning for Toxicology and Safety Assessment
• Research Proposal on Instance Segmentation using Mask R-CNN
• Research Proposal on Deep Learning Models for Epileptic Focus Localization
• Research Proposal on Adversarial Training for Robust Microscopy Image Analysis
• Research Proposal on Dialogue Generation using Generative Models
• Research Proposal on Preprocessing Methods for Epilepsy Detection
• Research Proposal on Neural Text Summarization
• Research Proposal on Convolutional Deep Belief Networks
• Research Proposal on Human-in-the-loop Deep Reinforcement Learning
• Research Proposal on Multi-modal data analysis for multimedia classification
• Research Proposal on Interactive Machine Learning with Human Feedback
• Research Proposal on Online and Stream-based Regression
• Research Proposal on Deep Learning for Compressed Sensing in Image and Video Processing
• Research Proposal on Multi-view and multi-modal fusion for multi-class classification
• Research Proposal on Deep Learning for Risk Management in Portfolio Optimization
• Research Proposal on Sparse and Low-Rank Regression
• Research Proposal on Epilepsy Prediction
• Research Proposal on Deep Learning for Early Disease Detection in Plants
• Research Proposal on Compressed Sensing with Deep Autoencoders
• Research Proposal on Deep Learning for Multi-modal Representation in Healthcare
• Research Proposal on Deep Learning for Algorithmic Trading and Portfolio Optimization
• Research Proposal on Deep Learning for Predictive Sourcing in Supply Chain Management
• Research Proposal on Cross-modal Representation Learning with Deep Learning
• Research Proposal on Multi-agent Reinforcement Learning for Resource Allocation
• Research Proposal on Cooperative Multi-agent Reinforcement Learning
• Research Proposal on Plant Leaf Segmentation and Recognition with Deep Learning
• Research Proposal on Multi-topic Modeling with Deep Learning
• Research Proposal on Deep Learning for Topic Modeling
• Research Proposal on Supply Chain Risk Management with Deep Learning
• Research Proposal on Video Color Correction with Deep Learning
• Research Proposal on Fine-grained Plant Leaf Recognition with Deep Learning
• Research Proposal on Self-Supervised Image Denoising
• Research Proposal on Multi-class Plant Disease Recognition with Deep Learning
• Research Proposal on Deep Learning for Pest and Disease Detection in Crops
• Research Proposal on Topic Modeling with Graph-based Approaches
• Research Proposal on Video Restoration with Generative Adversarial Networks
• Research Proposal on Precision Irrigation Scheduling with Deep Learning
• Research Proposal on Deep learning for improved representation of multi-view sequential data
• Research Proposal on Deep learning for predicting drug efficacy and toxicity
• Research Proposal on Improved analysis of large-scale genomics data with deep learning
• Research Proposal on Deep learning for summarization and visualization of multi-view sequential data
• Research Proposal on Personalized cancer diagnosis using deep learning
• Research Proposal on Deep transfer learning for cancer diagnosis
• Research Proposal on Improved biometric recognition in low-resource settings using deep learning
• Research Proposal on Deep learning for improved facial recognition
• Research Proposal on Improved voice recognition with deep learning
• Research Proposal on Deep learning for patient-specific dose modeling
• Research Proposal on Neural rendering for product visualization in e-commerce
• Research Proposal on Deep learning for computer-aided diagnosis
• Research Proposal on Deep learning for improved medical image interpretation in radiology
• Research Proposal on Summarization with Pre-trained Language Models
• Research Proposal on Image super-resolution with attention mechanisms in deep learning
• Research Proposal on Deep Learning for Cross-cultural Facial Expression Recognition
• Research Proposal on Deep learning for dose prediction in radiotherapy
• Research Proposal on Deep Learning for Drug-Drug Interaction Prediction
• Research Proposal on Multi-modality medical image analysis with deep learning
• Research Proposal on Adversarial Training for Fair and Robust Drug Response Prediction
• Research Proposal on Improved segmentation of anatomical structures in radiotherapy using deep learning
• Research Proposal on Transfer Learning for Facial Expression Recognition
• Research Proposal on Abstractive Text Summarization
• Research Proposal on Deep Learning for Livestock Health Monitoring
• Research Proposal on Adversarial Training for Robust Facial Expression Recognition
• Research Proposal on Multi-class Plant Leaf Recognition with Deep Learning
• Research Proposal on Deep Learning for Object Detection and Classification in Microscopy Images
• Research Proposal on Multi-modal Representation Learning for Image and Text
• Research Proposal on Transfer Learning for Drug Response Prediction
• Research Proposal on Deep Learning for Event Detection in Video Surveillance
• Research Proposal on Time Series Data Analysis
• Research Proposal on Face Detection and Landmark Localization
• Research Proposal on Human-in-the-loop Anomaly Detection
• Research Proposal on Machine Learning for Pattern Recognition
• Research Proposal on Cross-Lingual Dialogue Systems
• Research Proposal on Deep Learning for Facial Action Unit Detection
• Research Proposal on Regression Model for Machine Learning
• Research Proposal on Medical Concept Embedding
• Research Proposal on Semantic parsing and question answering
• Research Proposal on Deep learning Algorithms and Recent advancements
• Research proposal on Natural Language Processing using Deep Learning
• Research Proposal on Predictive Analytics to forecast future outcomes
• Research proposal on Deep Learning-based Contextual Word Embedding for Text Generation
• Research Proposal on Discourse Representation-Aware Text Generation using Deep Learning Model
• Research Proposal on Deep Autoencoder based Text Generation for Natural Language
• Research Proposal on Reinforcement Learning
• Research Proposal Topics on Conversational Recommendation Systems
• Research Proposal on Pre-trained Deep Learning Model based Text Generation
• Research Proposal on Text Sequence Generation with Deep Transfer Learning
• Research Proposal in Modeling Deep Semi-Supervised Learning for Non-Redundant Text Generation
• Research Proposal in Utterances and Emoticons based Multi-Class Emotion Recognition
• Research Proposal in Negation Handling with Contextual Representation for Sentiment Classification
• Research Proposal on Deep Learning-based Emotion Classification
• Research Proposal in Sentiment Classification in Social Media with Deep Contextual Embedding
• Research Proposal in Deep Learning-based Emotion Classification in Conversational Text
• Research Proposal on Attention Mechanism-based Argument Mining using Deep Neural Network
• Research Proposal in Adaptive Deep Learning with Topic Extraction for Argument Mining
• Research Proposal on Context-aware Argument Mining with Deep Semi-supervised Learning
• Research Proposal in Deep Transfer Learning-based Sequential Keyphrase Generation
• Research Proposal on Deep Bi-directional Text Analysis for Sarcasm Detection
• Research Proposal in Emotion Transition Recognition with Contextual Embedding in Sarcasm Detection
• Research Topic on Attention-based Sarcasm Detection with Psycholinguistic Sources
• Research Proposal in Deep Attentive Model based Irony Text and Sarcasm Detection
• Research Proposal Topic on Discourse Structure and Opinion based Argumentation Mining
• Research Proposal in Sarcasm Detection using Syntactic and Semantic Feature Representation
• Research Proposal in Multi-Class Behavior Modeling in Deep Learning-based Sarcasm Detection
• Research Proposal on Deep Transfer Learning for Irony Detection
• Research Proposal in Deep Neural Network-based Sarcasm Detection with Multi-Task Learning
• Research Proposal in Deep Learning-Guided Credible User Identification using Social Network Structure and User-Generated Content
• Research Proposal in Semi-supervised Misinformation Detection in Social Network
• Research Proposal in Deep Contextualized Word Representation for Fake News Classification
• Research Proposal on Self-Attentive Network-based Rumour Classification in Social Media
• Research Proposal in Multi-Modal Rumour Classification with Deep Ensemble Learning
• Research Proposal in Hybrid Deep Learning Model based Fake News Detection in Social Network
• Research Proposal on Anomaly Detection by Applying the Machine Learning Technique
• Research Proposal on Transformer based Opinion Mining Approach for Fake News Detection
• Research Proposal in Data Augmentation for Deep Learning-based Plant Disease Detection
• Research Proposal in Multi-Class Imbalance Handling with Deep Learning in Plant Disease Detection
• Research Proposal on Incremental Learning-based Concept Drift Detection in Stream Classification
• Research Proposal in Class-Incremental Learning for Large-Scale IoT Prediction
• Research Proposal in Time-series Forecasting using Weighted Incremental Learning
• Research Proposal on Deep Reinforcement Learning based Time Series Prediction
• Research Proposal in Federated Learning for Intelligent IoT Healthcare System
• Research Proposal on Deep Learning based Stream Data Imputation for IoT Applications
• Research Proposal on Deep Incremental Learning-based Cyber Security Threats Prediction
• Research Proposal in Aspect based Opinion Mining for Personalized Recommendation
• Research Proposal in Personalized Recommendation with Contextual Pre-Filtering
• Research Proposal in Temporal and Spatial Context-based Group Recommendation
• Research Proposal on Session based Recommender System with Representation learning
• Research Proposal in Serendipity-aware Product Recommendation
• Research Proposal in Deep Preference Prediction for Novelty and Diversity-Aware Top-N Recommendation
• Research Proposal on Personalized Recommendation with Neural Attention Model
• Research Proposal in Cross-domain Depression Detection in Social Media
• Research Proposal in Emotional Feature Extraction in Depression Detection
• Research Proposal in Contextual Recommendation with Deep Reinforcement Learning
• Research Proposal on Deep Neural Network-based Cross-Domain Recommendation
• Research Proposal in Multimodal Extraction for Depression Detection
• Research Proposal on Early Depression Detection with Deep Attention Network
• Research Proposal in Proactive Intrusion Detection in Cloud using Deep Reinforcement Learning
• Research Proposal in Context Vector Representation of Text Sequence for Depression Detection
• Research Proposal in Sparsity Handling in Recommender System with Transfer Learning
• Research Proposal on Artificial Intelligence and Lexicon based Suicide Attempt Prevention
• Research Proposal in Modeling Deep Neural Network for Mental Illness Detection from Healthcare Data
• Research Proposal in Deep Learning-based Domain Adaptation for Recommendation
• Research proposal on Emotion Classification using Deep Learning Models
• Research Proposal in Topic Modeling for Personalized Product Recommendation
• Research Proposal in Deep Reinforcement Learning based Resource Provisioning for Container-based Cloud Environment
• Research Proposal in Energy and Delay-Aware Scheduling with Deep Learning in Fog Computing
• Research Proposal in Spammer Detection in Social Network from the Advertiser Behavior Modeling
• Research Proposal in Social Information based People Recommendation
• Research Proposal in Deep Learning-based Advertiser Reliability Computation in Social Network
• Research Proposal in Artificial Neural Network-based Missing Value Imputation in Disease Detection
• Research Proposal in Deep Transfer Learning-based Disease Detection
• Research Proposal in Aspect based Depressive Feature Extraction for Multi-Class Depression Classification
• Research Proposal in Environmental Data-Aware Behavior Modeling and Depression Detection
• Research Proposal on User Credibility Detection in Social Networks using Deep Learning Models
• Research Proposal in Incremental Clustering Model for Malware Classification
• Research Proposal in Deep Feature Extraction with One-class Classification for Anomaly Detection
• Research Proposal in Fake News Dissemination Detection with Deep Neural Network
• Research Proposal in Deep Learning-based Prediction for Mobile Cloud Offloading Decision-Making
• Research Proposal in Hybrid Deep Learning-based Traffic Congestion Prediction in VANET
• Research Proposal in Deep Ensemble Learning-based Android Malware Detection
• Research Proposal in Proactive Prediction of Insider Threats using Deep Neural Network
• Research Proposal in Delay-constrained Computation Offloading with Deep Learning in Fog Environment
• Research Proposal in Latency-aware IoT Service Placement in Fog through Deep Learning Prediction
• Research Proposal in Proactive Management of Edge Resources with Federated Learning
• Research Proposal in Reinforcement Learning with Incentive Mechanism for Offloading Decision in Edge Computing
• Research Proposal on Multi-Agent Offloading Decision in Fog Computing using Deep Reinforcement Learning
• Research Proposal in Energy-efficient Edge Computation with Light-weight Deep Learning Model
• Research Proposal Topic on Integrating Deep Learning with Rule-based Model for Sarcasm Detection
• Research Proposal in Deep Incremental Learning based Intrusion Detection in IoT Environment
• Research Proposal for Deep Learning based Explainable Recommendation Systems
• Research Proposal in Federated Learning-based Cooperative Management of Fog Computation Resources
• Research Proposal in Mobility-aware Collaborative Resource Management in Edge Intelligence System
• Research Proposal in Deep learning-driven Computation Offloading in Fog-Cloud Environment
• Research Proposal on Zero-day Attack Detection using Deep Neural Network
• Research Proposal in Deep Learning-based Dynamic Workload Prediction in Cloud
• Research Proposal in Federated Learning-based Dynamic Resource Scheduling in Edge-Cloud Environment
• Research Proposal in Anomaly-aware Cloud Security Model with Deep Transfer Learning
• Research Proposal in Federated Transfer Learning for Smart Decision-Making in Cloud Environment
• Research Proposal on Explicit and Implicit Feedback-Aware Top-N Recommendation
• Research Proposal in Disease Diagnosis using Unsupervised Deep Learning
• Research Proposal for Hybrid Deep Learning based Recommendation
• Research Proposal in Privacy Preservation using Deep Neural Network in Cloud Computing
• Research Proposal in Machine learning Techniques for Social Media Analytics
• Adversarial-aware Modeling of Federated Learning with Personalization
• Research Proposal in Adversarial-aware Modeling of Federated Learning with Personalization
• Research Proposal in Energy-efficient Federated Learning for Resource-Constrained Edge Environment
• Research Proposal in Deep Learning-based Task Offloading for Dynamic Edge Computing
• Research Proposal in Dynamic Joint Resource Allocation for Federated Edge Learning
• Research Proposal in Deep Ensemble Learning-based Housing Price Prediction with Self-Attention Mechanism
• Research Proposal in Pre-trained One-Class Anomaly Detection with Unsupervised Learning
• Research Proposal in Unsupervised Deep Learning-based Anomaly Detection in IoT Environment
• Research Proposal in Deep Federated Learning-based IoT Malware Detection
• Research Proposal in Deep Learning-based Contextual Text Generation for Conversational Text
• Research Proposal in Designing Intelligent Internet of Things with Deep Reinforcement Learning for Mobile Edge Computing
• Research Proposal on Adaptive IoT Traffic Prediction with Deep Neural Network
• Research Proposal in Deep Learning-based Congestion-aware Dynamic Routing in IoT Network
• Machine Learning Based intelligent Trust Computational Model for IoT Applications
• Research proposal in Ensemble Machine Learning for Big Data Stream Processing
• One-Shot Learning
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Machine Learning Project Proposal Scaling Up Deep Networks Melissa Queen and Piotr Teterwak

Proposal: Deep Neural Networks (DNNs) are used in a variety of applications, such as object recognition in images and acoustic processing for speech recognition [1]. There is significant motivation to use large training sets, as performance depends highly on input size. Banko and Brill [2] found "that simple, classical models can outperform newer, more complex models, just because the simple models can be tractably learnt using orders of magnitude more input data." However, training DNNs is a time intensive process, so the size of the training set is often limited by resources. In [3], Raina et al., noted that "parameter learning can take weeks using a conventional implementation on a single CPU." Dean et al., [1] have investigated ways to use distributed networks to reduce the training time, and thus allow the use of much larger training sets leading to much more effective networks. We believe that by exploited possible parallelism, we can improve the performance of a traditional DNN.

Methods: We will implement a deep neural network on both a cluster of computers and a sequential machine, and compare performance on varying sizes of training data. We will rely heavily on the work done by Dean et al., [1] in our development of a distributed algorithm. In particular, they outline methods of distributing stochastic gradient descent (SGD) and limited-memory BFGS. We expect to implement and test only one of these procedures. A traditional, sequential deep network will be run as a control. Comparisions between the two will be based on both object recognition accuracy and the time taken to train the network.

Dataset: We will use images from ImageNet ( http://www.image-net.org/ ) to train and test the neural networks. Image recognition is a common task for deep neural networks, and we expect the large data set available from ImageNet to highlight potential advantages of the distributed algorithm.

Milestone: We will have a traditional DNN working by the milestone deadline. We will also have the parallel algorithm coded and be in the process of debugging and optimizing. We expect to be very close to running data on the parallel network.

Reference: [1] Dean, Jeffrey, et al. Large Scale Distributed Deep Networks. NIPS, 2012. [2] Banko, M., & Brill, E. (2001). Scaling to very very large corpora for natural language disambiguation. Annual Meeting of the Association for Computational Linguistics (pp. 26 - 33). [3] R. Raina, A. Madhavan, and A. Y. Ng. Large-scale deep unsupervised learning using graphics processors. In ICML , 2009

Sources for background learning: [1] Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai, Caroline Suen. UFLDL Tutorial, http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial [2] Hinton, Geoffrey. Video Lecture: http://videolectures.net/jul09_hinton_deeplearn/

• 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.

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

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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Machine Learning Project Proposal Template

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Machine learning projects can be complex and time-consuming, requiring careful planning and organization. But fear not! ClickUp's Machine Learning Project Proposal Template is here to simplify the process and set you up for success.

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Collect relevant data that will be used for your machine learning project. This can include structured data, unstructured data, or even data from external sources. Clean and preprocess the data to ensure its quality and suitability for your project.

Use the Table view in ClickUp to organize and analyze your data, and create custom fields to track data quality and preprocessing steps.

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Create tasks in ClickUp to research and evaluate different machine learning algorithms, and assign team members to explore and choose the most appropriate one.

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Deep transfer learning for tool condition monitoring under different processing conditions

• ORIGINAL ARTICLE
• Published: 16 May 2024

Cite this article

• Yongqing Wang 1 ,
• Mengmeng Niu 1 ,
• Kuo Liu   ORCID: orcid.org/0000-0001-5530-9951 1 ,
• Haibo Liu 1 ,
• Bo Qin 1 &
• Yiming Cui 1  

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Deep learning methods have developed rapidly in the field of tool condition monitoring, but due to the complexity and diversity of working conditions, it is difficult to ensure the high accuracy, strong generalization performance, and wide applicability of monitoring models. Therefore, this article proposes a deep transfer learning method with center loss for tool condition monitoring under different processing conditions. Firstly, Deep Extreme Learning Machine (DELM) is used to extract sample features and tool condition monitoring, and deep coral is integrated into the last feature extraction layer of DELM for domain adaptation. Secondly, the center loss is introduced into the transfer learning model to improve the intra-class compactness by minimizing the center loss, thereby obtaining a broader decision boundary. A tool wear experiment was conducted on a milling machine. Research shows that the proposed method can achieve the tool condition monitoring under different processing conditions. The introduction of central loss is beneficial for promoting the separation of samples from different categories in the target domain and effectively improves the model’s applicability. Compared with other domain adaptation methods, this method has better accuracy and generalization ability.

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This paper was funded by the Key Science and Technology Projects of Liaoning Province (2020JH1/10100016 and 2021JH1/10400102).

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State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, Dalian, 116024, China

Yongqing Wang, Mengmeng Niu, Kuo Liu, Haibo Liu, Bo Qin & Yiming Cui

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All authors contributed to the study conception and design. Material preparation and data collection were performed by Kuo Liu, Yiming Cui, and Bo Qin. Data analysis was performed by Haibo Liu and Yongqing Wang. The first draft of the manuscript was written by Mengmeng Niu, and all authors have commented on the first few versions of the manuscript. All authors have read and approved the final manuscript.

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Wang, Y., Niu, M., Liu, K. et al. Deep transfer learning for tool condition monitoring under different processing conditions. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-024-13713-6

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Published : 16 May 2024

DOI : https://doi.org/10.1007/s00170-024-13713-6

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Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes

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Background: Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes. Methods: This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites. Results: During a median follow-up of 13.6 years, 2,525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver operating characteristic curve [AUROC], 1-year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5-year: 0.830 vs 0.798, 10-year: 0.801 vs 0.776, all P <0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes. Conclusions: Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006).

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study was approved by the Northwest Multicenter Research Ethics Committee (REC reference for UK Biobank 11/NW/0382), and all participants provided informed consent.

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Google helped make an exquisitely detailed map of a tiny piece of the human brain

A small brain sample was sliced into 5,000 pieces, and machine learning helped stitch it back together.

• Cassandra Willyard archive page

A team led by scientists from Harvard and Google has created a 3D, nanoscale-resolution map of a single cubic millimeter of the human brain. Although the map covers just a fraction of the organ—a whole brain is a million times larger—that piece contains roughly 57,000 cells, about 230 millimeters of blood vessels, and nearly 150 million synapses. It is currently the highest-resolution picture of the human brain ever created.

To make a map this finely detailed, the team had to cut the tissue sample into 5,000 slices and scan them with a high-speed electron microscope. Then they used a machine-learning model to help electronically stitch the slices back together and label the features. The raw data set alone took up 1.4 petabytes. “It’s probably the most computer-intensive work in all of neuroscience,” says Michael Hawrylycz, a computational neuroscientist at the Allen Institute for Brain Science, who was not involved in the research. “There is a Herculean amount of work involved.”

Many other brain atlases exist, but most provide much lower-resolution data. At the nanoscale, researchers can trace the brain’s wiring one neuron at a time to the synapses, the places where they connect. “To really understand how the human brain works, how it processes information, how it stores memories, we will ultimately need a map that’s at that resolution,” says Viren Jain, a senior research scientist at Google and coauthor on the paper, published in Science on May 9 . The data set itself and a preprint version of this paper were released in 2021 .

Brain atlases come in many forms. Some reveal how the cells are organized. Others cover gene expression. This one focuses on connections between cells, a field called “connectomics.” The outermost layer of the brain contains roughly 16 billion neurons that link up with each other to form trillions of connections. A single neuron might receive information from hundreds or even thousands of other neurons and send information to a similar number. That makes tracing these connections an exceedingly complex task, even in just a small piece of the brain..  

To create this map, the team faced a number of hurdles. The first problem was finding a sample of brain tissue. The brain deteriorates quickly after death, so cadaver tissue doesn’t work. Instead, the team used a piece of tissue removed from a woman with epilepsy during brain surgery that was meant to help control her seizures.

Once the researchers had the sample, they had to carefully preserve it in resin so that it could be cut into slices, each about a thousandth the thickness of a human hair. Then they imaged the sections using a high-speed electron microscope designed specifically for this project. 

Next came the computational challenge. “You have all of these wires traversing everywhere in three dimensions, making all kinds of different connections,” Jain says. The team at Google used a machine-learning model to stitch the slices back together, align each one with the next, color-code the wiring, and find the connections. This is harder than it might seem. “If you make a single mistake, then all of the connections attached to that wire are now incorrect,” Jain says. 

“The ability to get this deep a reconstruction of any human brain sample is an important advance,” says Seth Ament, a neuroscientist at the University of Maryland. The map is “the closest to the  ground truth that we can get right now.” But he also cautions that it’s a single brain specimen taken from a single individual. 

The map, which is freely available at a web platform called Neuroglancer , is meant to be a resource other researchers can use to make their own discoveries. “Now anybody who’s interested in studying the human cortex in this level of detail can go into the data themselves. They can proofread certain structures to make sure everything is correct, and then publish their own findings,” Jain says. (The preprint has already been cited at least 136 times .) 

The team has already identified some surprises. For example, some of the long tendrils that carry signals from one neuron to the next formed “whorls,” spots where they twirled around themselves. Axons typically form a single synapse to transmit information to the next cell. The team identified single axons that formed repeated connections—in some cases, 50 separate synapses. Why that might be isn’t yet clear, but the strong bonds could help facilitate very quick or strong reactions to certain stimuli, Jain says. “It’s a very simple finding about the organization of the human cortex,” he says. But “we didn’t know this before because we didn’t have maps at this resolution.”

The data set was full of surprises, says Jeff Lichtman, a neuroscientist at Harvard University who helped lead the research. “There were just so many things in it that were incompatible with what you would read in a textbook.” The researchers may not have explanations for what they’re seeing, but they have plenty of new questions: “That’s the way science moves forward.” 

Biotechnology and health

How scientists traced a mysterious covid case back to six toilets.

When wastewater surveillance turns into a hunt for a single infected individual, the ethics get tricky.

An AI-driven “factory of drugs” claims to have hit a big milestone

Insilico is part of a wave of companies betting on AI as the "next amazing revolution" in biology

• Antonio Regalado archive page

There is a new most expensive drug in the world. Price tag: $4.25 million

But will the latest gene therapy suffer the curse of the costliest drug?

Beyond Neuralink: Meet the other companies developing brain-computer interfaces

Companies like Synchron, Paradromics, and Precision Neuroscience are also racing to develop brain implants

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AI Is Everybody’s Business

This briefing presents three principles to guide business leaders when making AI investments: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects. The principles are supported by the MIT CISR data monetization research, and the briefing illustrates them using examples from the Australia Taxation Office and CarMax. The three principles apply to any kind of AI, defined as technology that performs human-like cognitive tasks; subsequent briefings will present management advice distinct to machine learning and generative tools, respectively.

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Author Barb Wixom reads this research briefing as part of our audio edition of the series. Follow the series on SoundCloud.

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Today, everybody across the organization is hungry to know more about AI. What is it good for? Should I trust it? Will it take my job? Business leaders are investing in massive training programs, partnering with promising vendors and consultants, and collaborating with peers to identify ways to benefit from AI and avoid the risk of AI missteps. They are trying to understand how to manage AI responsibly and at scale.

Our book Data Is Everybody’s Business: The Fundamentals of Data Monetization describes how organizations make money using their data.[foot]Barbara H. Wixom, Cynthia M. Beath, and Leslie Owens, Data Is Everybody's Business: The Fundamentals of Data Monetization , (Cambridge: The MIT Press, 2023), https://mitpress.mit.edu/9780262048217/data-is-everybodys-business/ .[/foot] We wrote the book to clarify what data monetization is (the conversion of data into financial returns) and how to do it (by using data to improve work, wrap products and experiences, and sell informational solutions). AI technology’s role in this is to help data monetization project teams use data in ways that humans cannot, usually because of big complexity or scope or required speed. In our data monetization research, we have regularly seen leaders use AI effectively to realize extraordinary business goals. In this briefing, we explain how such leaders achieve big AI wins and maximize financial returns.

Using AI in Data Monetization

AI refers to the ability of machines to perform human-like cognitive tasks.[foot]See Hind Benbya, Thomas H. Davenport, and Stella Pachidi, “Special Issue Editorial: Artificial Intelligence in Organizations: Current State and Future Opportunities , ” MIS Quarterly Executive 19, no. 4 (December 2020), https://aisel.aisnet.org/misqe/vol19/iss4/4 .[/foot] Since 2019, MIT CISR researchers have been studying deployed data monetization initiatives that rely on machine learning and predictive algorithms, commonly referred to as predictive AI.[foot]This research draws on a Q1 to Q2 2019 asynchronous discussion about AI-related challenges with fifty-three data executives from the MIT CISR Data Research Advisory Board; more than one hundred structured interviews with AI professionals regarding fifty-two AI projects from Q3 2019 to Q2 2020; and ten AI project narratives published by MIT CISR between 2020 and 2023.[/foot] Such initiatives use large data repositories to recognize patterns across time, draw inferences, and predict outcomes and future trends. For example, the Australian Taxation Office (ATO) used machine learning, neural nets, and decision trees to understand citizen tax-filing behaviors and produce respectful nudges that helped citizens abide by Australia’s work-related expense policies. In 2018, the nudging resulted in AUD$113 million in changed claim amounts.[foot]I. A. Someh, B. H. Wixom, and R. W. Gregory, “The Australian Taxation Office: Creating Value with Advanced Analytics,” MIT CISR Working Paper No. 447, November 2020, https://cisr.mit.edu/publication/MIT_CISRwp447_ATOAdvancedAnalytics_SomehWixomGregory .[/foot]

In 2023, we began exploring data monetization initiatives that rely on generative AI.[foot]This research draws on two asynchronous generative AI discussions (Q3 2023, N=35; Q1 2024, N=34) regarding investments and capabilities and roles and skills, respectively, with data executives from the MIT CISR Data Research Advisory Board. It also draws on in-progress case studies with large organizations in the publishing, building materials, and equipment manufacturing industries.[/foot] This type of AI analyzes vast amounts of text or image data to discern patterns in them. Using these patterns, generative AI can create new text, software code, images, or videos, usually in response to user prompts. Organizations are now beginning to openly discuss data monetization initiative deployments that include generative AI technologies. For example, used vehicle retailer CarMax reported using OpenAI’s ChatGPT chatbot to help aggregate customer reviews and other car information from multiple data sets to create helpful, easy-to-read summaries about individual used cars for its online shoppers. At any point in time, CarMax has on average 50,000 cars on its website, so to produce such content without AI the company would require hundreds of content writers and years of time; using ChatGPT, the company’s content team can generate summaries in hours.[foot]Paula Rooney, “CarMax drives business value with GPT-3.5,” CIO , May 5, 2023, https://www.cio.com/article/475487/carmax-drives-business-value-with-gpt-3-5.html ; Hayete Gallot and Shamim Mohammad, “Taking the car-buying experience to the max with AI,” January 2, 2024, in Pivotal with Hayete Gallot, produced by Larj Media, podcast, MP3 audio, https://podcasts.apple.com/us/podcast/taking-the-car-buying-experience-to-the-max-with-ai/id1667013760?i=1000640365455 .[/foot]

Big advancements in machine learning, generative tools, and other AI technologies inspire big investments when leaders believe the technologies can help satisfy pent-up demand for solutions that previously seemed out of reach. However, there is a lot to learn about novel technologies before we can properly manage them. In this year’s MIT CISR research, we are studying predictive and generative AI from several angles. This briefing is the first in a series; in future briefings we will present management advice specific to machine learning and generative tools. For now, we present three principles supported by our data monetization research to guide business leaders when making AI investments of any kind: invest in practices that build capabilities required for AI, involve all your people in your AI journey, and focus on realizing value from your AI projects.

Principle 1: Invest in Practices That Build Capabilities Required for AI

Succeeding with AI depends on having deep data science skills that help teams successfully build and validate effective models. In fact, organizations need deep data science skills even when the models they are using are embedded in tools and partner solutions, including to evaluate their risks; only then can their teams make informed decisions about how to incorporate AI effectively into work practices. We worry that some leaders view buying AI products from providers as an opportunity to use AI without deep data science skills; we do not advise this.

But deep data science skills are not enough. Leaders often hire new talent and offer AI literacy training without making adequate investments in building complementary skills that are just as important. Our research shows that an organization’s progress in AI is dependent on having not only an advanced data science capability, but on having equally advanced capabilities in data management, data platform, acceptable data use, and customer understanding.[foot]In the June 2022 MIT CISR research briefing, we described why and how organizations build the five advanced data monetization capabilities for AI. See B. H. Wixom, I. A. Someh, and C. M. Beath, “Building Advanced Data Monetization Capabilities for the AI-Powered Organization,” MIT CISR Research Briefing, Vol. XXII, No. 6, June 2022, https://cisr.mit.edu/publication/2022_0601_AdvancedAICapabilities_WixomSomehBeath .[/foot] Think about it. Without the ability to curate data (an advanced data management capability), teams cannot effectively incorporate a diverse set of features into their models. Without the ability to oversee the legality and ethics of partners’ data use (an advanced acceptable data use capability), teams cannot responsibly deploy AI solutions into production.

It’s no surprise that ATO’s AI journey evolved in conjunction with the organization’s Smarter Data Program, which ATO established to build world-class data analytics capabilities, and that CarMax emphasizes that its governance, talent, and other data investments have been core to its generative AI progress.

Capabilities come mainly from learning by doing, so they are shaped by new practices in the form of training programs, policies, processes, or tools. As organizations undertake more and more sophisticated practices, their capabilities get more robust. Do invest in AI training—but also invest in practices that will boost the organization’s ability to manage data (such as adopting a data cataloging tool), make data accessible cost effectively (such as adopting cloud policies), improve data governance (such as establishing an ethical oversight committee), and solidify your customer understanding (such as mapping customer journeys). In particular, adopt policies and processes that will improve your data governance, so that data is only used in AI initiatives in ways that are consonant with your organization's values and its regulatory environment.

Principle 2: Involve All Your People in Your AI Journey

Data monetization initiatives require a variety of stakeholders—people doing the work, developing products, and offering solutions—to inform project requirements and to ensure the adoption and confident use of new data tools and behaviors.[foot]Ida Someh, Barbara Wixom, Michael Davern, and Graeme Shanks, “Configuring Relationships between Analytics and Business Domain Groups for Knowledge Integration, ” Journal of the Association for Information Systems 24, no. 2 (2023): 592-618, https://cisr.mit.edu/publication/configuring-relationships-between-analytics-and-business-domain-groups-knowledge .[/foot] With AI, involving a variety of stakeholders in initiatives helps non-data scientists become knowledgeable about what AI can and cannot do, how long it takes to deliver certain kinds of functionality, and what AI solutions cost. This, in turn, helps organizations in building trustworthy models, an important AI capability we call AI explanation (AIX).[foot]Ida Someh, Barbara H. Wixom, Cynthia M. Beath, and Angela Zutavern, “Building an Artificial Intelligence Explanation Capability,” MIS Quarterly Executive 21, no. 2 (2022), https://cisr.mit.edu/publication/building-artificial-intelligence-explanation-capability .[/foot]

For example, at ATO, data scientists educated business colleagues on the mechanics and results of models they created. Business colleagues provided feedback on the logic used in the models and helped to fine-tune them, and this interaction helped everyone understand how the AI made decisions. The data scientists provided their model results to ATO auditors, who also served as a feedback loop to the data scientists for improving the model. The data scientists regularly reported on initiative progress to senior management, regulators, and other stakeholders, which ensured that the AI team was proactively creating positive benefits without neglecting negative external factors that might surface.

Given the consumerization of generative AI tools, we believe that pervasive worker involvement in ideating, building, refining, using, and testing AI models and tools will become even more crucial to deploying fruitful AI projects—and building trust that AI will do the right thing in the right way at the right time.

Principle 3: Focus on Realizing Value From Your AI Projects

AI is costly—just add up your organization’s expenses in tools, talent, and training. AI needs to pay off, yet some organizations become distracted with endless experimentation. Others get caught up in finding the sweet spot of the technology, ignoring the sweet spot of their business model. For example, it is easy to become enamored of using generative AI to improve worker productivity, rolling out tools for employees to write better emails and capture what happened in meetings. But unless those activities materially impact how your organization makes money, there likely are better ways to spend your time and money.

Leaders with data monetization experience will make sure their AI projects realize value in the form of increased revenues or reduced expenses by backing initiatives that are clearly aligned with real challenges and opportunities. That is step one. In our research, the leaders that realize value from their data monetization initiatives measure and track their outcomes, especially their financial outcomes, and they hold someone accountable for achieving the desired financial returns. At CarMax, a cross-functional team owned the mission to provide better website information for used car shoppers, a mission important to the company’s sales goals. Starting with sales goals in mind, the team experimented with and then chose a generative AI solution that would enhance the shopper experience and increase sales.

Figure 1: Three Principles for Getting Value from AI Investments

The three principles are based on the following concepts from MIT CISR data research: 1. Data liquidity: the ease of data asset recombination and reuse 2. Data democracy: an organization that empowers employees in the access and use of data 3. Data monetization: the generation of financial returns from data assets

Managing AI Using a Data Monetization Mindset

AI has and always will play a big role in data monetization. It’s not a matter of whether to incorporate AI, but a matter of how to best use it. To figure this out, quantify the outcomes of some of your organization’s recent AI projects. How much money has the organization realized from them? If the answer disappoints, then make sure the AI technology value proposition is a fit for your organization’s most important goals. Then assign accountability for ensuring that AI technology is applied in use cases that impact your income statements. If the AI technology is not a fit for your organization, then don’t be distracted by media reports of the AI du jour.

Understanding your AI technology investments can be hard if your organization is using AI tools that are bundled in software you purchase or are built for you by a consultant. To set yourself up for success, ask your partners to be transparent with you about the quality of data they used to train their AI models and the data practices they relied on. Do their answers persuade you that their tools are trustworthy? Is it obvious that your partner is using data compliantly and is safeguarding the model from producing bad or undesired outcomes? If so, make sure this good news is shared with the people in your organization and those your organization serves. If not, rethink whether to break with your partner and find another way to incorporate the AI technology into your organization, such as by hiring people to build it in-house.

To paraphrase our book’s conclusion: When people actively engage in data monetization initiatives using AI , they learn, and they help their organization learn. Their engagement creates momentum that initiates a virtuous cycle in which people’s engagement leads to better data and more bottom-line value, which in turn leads to new ideas and more engagement, which further improves data and delivers more value, and so on. Imagine this happening across your organization as all people everywhere make it their business to find ways to use AI to monetize data.

This is why AI, like data, is everybody’s business.

© 2024 MIT Center for Information Systems Research, Wixom and Beath. MIT CISR Research Briefings are published monthly to update the center’s member organizations on current research projects.

Related Publications

Talking Points

Ai, like data, is everybody's business.

Working Paper: Vignette

The australian taxation office: creating value with advanced analytics.

Research Briefing

Building advanced data monetization capabilities for the ai-powered organization.

Building AI Explanation Capability for the AI-Powered Organization

What is Data Monetization?

About the researchers.

Barbara H. Wixom, Principal Research Scientist, MIT Center for Information Systems Research (CISR)

Cynthia M. Beath, Professor Emerita, University of Texas and Academic Research Fellow, MIT CISR

Mit center for information systems research (cisr).

Founded in 1974 and grounded in MIT's tradition of combining academic knowledge and practical purpose, MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.

MIT CISR Associate Members

MIT CISR wishes to thank all of our associate members for their support and contributions.

MIT CISR's Mission Expand

MIT CISR helps executives meet the challenge of leading increasingly digital and data-driven organizations. We provide insights on how organizations effectively realize value from approaches such as digital business transformation, data monetization, business ecosystems, and the digital workplace. Founded in 1974 and grounded in MIT’s tradition of combining academic knowledge and practical purpose, we work directly with digital leaders, executives, and boards to develop our insights. Our consortium forms a global community that comprises more than seventy-five organizations.