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Revolutionising Chrono Nutrition Using AI: A Review for Personalized Dietary Recommendation

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Revolutionising Chrono Nutrition Using AI: A Review for Pers...

Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Vidhi Wankhede Meher Langote Prateek Verma Artificial Intelligence and Data Science Faculty of Engineering And Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi(M) Wardha India Artificial Intelligence and Machine Learning Faculty of Engineering And Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi(M) Wardha India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Chrono nutrition is the science of analysing foods' effects and consumption patterns on the outer-infra-clock organism's circadian rhythms. As such, it can provide valuable possibilities for tailored health promotion and disease prevention efforts. With Artificial Intelligence (AI), there is an opportunity to change dietary advice and make it accurate, dynamic and personalised. This review discusses how AI can improve chrononutrition, analyse large datasets, determine the optimal circadian feeding intervals, and deliver individualised interventions based on chronobiological and genetic data. Limitations include the absence of cross-sectional analysis and inadequate consideration of socio-cultural factors in AI models. AI can significantly contribute to optimising chronotherapeutic or chrono-dietary interventions in metabolic health and disease. Furthermore, a multisectoral approach in which nutritionists, data scientists, and other actors in the healthcare sector must work together is crucial within the present state and future practical application of these advances. In extending the discourse of this paper, it is also essential to establish that much more work needs to be done for AI to make a tangible difference in chronic nutrition and translate it into an optimal solution to the problem of practical dietary advice and disease prevention.

关键词： Analytical models Sentiment analysis Reviews Prevention and mitigation Collaboration Medical treatment Genetics Synchronization Artificial intelligence Diseases

A Comprehensive Review of Supervised learning Algorithms in Healthcare Applications

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A Comprehensive Review of Supervised Learning Algorithms in ...

Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Aditya Barhate Abhay Tale Nayan Jikar Prateek Verma Praveen Kumar Prajyot Yesankar Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Supervised learning has revolutionized the concept of personalization in treatment with the development of Precision Medicine. This review aims to provide a systematic analysis of the utilization of supervised learning in healthcare and its functions in diagnostics and prognoses, recommendations for treatment, and patient surveillance. Classification algorithms such as Logistic Regression, Support Vector machines, and Decision Trees. Regression techniques such as Linear Regression, Polynomial Regression, and Neural Networks. Ensemble methods such as Bagging, Boosting, and Stacking. Deep learning techniques such as Convolutional Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory are discussed for their efficiency and applicability in various healthcare applications. There are strengths highlighted by the review including enhanced diagnostic capabilities and patient-targeted therapy alongside weaknesses such as quality of data, interpretability as well as compatibility with existing healthcare systems. This review gives a more generalized approach to how supervised learning algorithms help to enhance healthcare results and identifies the likely topic that requires more research in the future.

关键词： Industries Deep learning Ethics Systematics Reviews Precision medicine Supervised learning Medical services Classification algorithms Tuning

Application of machine learning Algorithms for Analyzing Sentiments Using Twitter dataset

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Application of Machine Learning Algorithms for Analyzing Sen...

Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Bhairavi Rewatkar Aditya Barhate Prateek Verma Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Sentiment analysis is a Natural Language Processing (NLP) approach used to determine whether a piece of text is negative, positive, or neutral based on the emotions it expresses. Despite advancements, several challenges persist in this field, including handling sarcasm, context ambiguity, and the presence of noisy data. Recent techniques, such as deep learning models and transformer-based approaches, have improved accuracy but still face limitations in computational efficiency and real-time analysis. This article employs machine learning (ML) techniques to analyze text and determine the sentiments of reviews shared on Twitter, aiming to address these challenges. The proposed system extracts attributes from the text, such as the sentiment polarity (whether the sentiment is negative or positive) and the topic or theme of the discussion. A comparison with prior work on sentiment analysis in tweets is presented, along with a comprehensive study of feature extraction and representation methods. The study evaluates the performance of six classifiers: Naïve Bayes, Decision Tree, Logistic Regression, Support Vector machine (SVM), Gradient Boosting, and Random Forest, based on different feature extraction techniques. Using the Bag of Words technique, SVM achieved the highest accuracy of 94.58%, while using term frequency, Random Forest showed the highest accuracy of 94.99%. The developed algorithms for sentiment analysis in tweets are useful for analyzing social feedback, product surveys, and social media comments. Combining neural networks and deep learning improves the accuracy of sentiment analysis and provides opportunities to analyze various types of emotions, enabling proactive responses to public sentiment.

关键词： Support vector machines Deep learning Sentiment analysis Accuracy machine learning algorithms Social networking (online) Reviews

Making all pairwise comparisons in multi-arm clinical trials without control treatment

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arXiv 2024年

作者： Burnett, T. Jaki, T. Department of Mathematical Sciences University of Bath BathBA2 7AY United Kingdom MRC Biostatistics Unit University of Cambridge CambridgeCB2 0SR United Kingdom Department of Machine Learning and Data Science University of Regensburg Regensburg Germany

The standard paradigm for confirmatory clinical trials is to compare experimental treatments with a control, for example the standard of care or a placebo. However, it is not always the case that a suitable control exists. Efficient statistical methodology is well studied in the setting of randomised controlled trials. This is not the case if one wishes to compare several experimental with no control arm. We propose hypothesis testing methods suitable for use in such a setting. These methods are efficient, ensuring the error rate is controlled at exactly the desired rate with no conservatism. This in turn yields an improvement in power when compared with standard methods one might otherwise consider using, such as a Bonferroni adjustment. The proposed testing procedure is also highly flexible. We show how it may be extended for use in multi-stage adaptive trials, covering the majority of scenarios in which one might consider the use of such procedures in the clinical trials setting. With such a highly flexible nature, these methods may also be applied more broadly outside of a clinical trials setting. Copyright © 2024, The Authors. All rights reserved.

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AI-Optimized Placement and Routing for PCB Design 9

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AI-Optimized Placement and Routing for PCB Design

9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Sathyaraj, P. Arulkumar, S. Ajina, A. Beshir, Kedir Umasankar, L. Arularasan, A.N. Rmk College of Engineering And Technology Department of Electronics And Communication Engineering Tamil Nadu Puduvoyal601206 India Wachemo University Department of Electrical And Computer Engineering Ethiopia M S Ramaiah Institute of Technology Department of Artificial Intelligence And Machine Learning Bangalore560054 India S.A. Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Tiruvallur600077 India Panimalar Engineering College Department of Artificial Intelligence And Data Science Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

Rapid electronic device development requires more complicated and densely packed PCB designs. These systems need properly placed and connected electrical components for best performance and reliability. Complexity and time restrictions are rendering manual methods ineffective. Therefore, AI-based PCB insertion and routing automation methods have been developed. This research suggests using AI to optimize PCB component arrangement and routing. The system uses machine learning and optimization to improve design efficiency. The AI system creates layouts that match performance requirements, decrease signal interference, and meet manufacturing limits using previous design data and user preferences. A learning module that updates its knowledge base, an algorithm that chooses between design objectives, and real-time user input are examples. Signal integrity, thermal issues, and manufacturability are considered while modifying design requirements. Evaluation of the suggested methodology shows considerable gains in design efficiency, time-to-market, and electronic system performance. This research improves AI-automated PCB design, enabling more complex and effective electrical systems. AI in PCB design will revolutionize the industry by solving complicated design problems and speeding up the development of cutting-edge electronics. © 2024 IEEE.

关键词： Reinforcement learning

Sampling Strategies for Compressive Imaging Under Statistical Noise

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Sampling Strategies for Compressive Imaging Under Statistica...

2023 International Conference on Sampling Theory and Applications, SampTA 2023

作者： Hoppe, Frederik Krahmer, Felix Verdun, Claudio Mayrink Menzel, Marion I. Rauhut, Holger Rwth Aachen University Chair of Mathematics of Information Processing Aachen Germany Technical University of Munich Department of Mathematics Munich Germany Munich Center for Machine Learning Munich Germany Technical University of Munich Munich Data Science Institute Germany Technische Hochschule Ingolstadt AImotion Bavaria Ingolstadt Germany Technical University of Munich Department of Physics Garching Germany Ge Healthcare Munich Germany

ISBN: (纸本)9798350328851

Most of the compressive sensing literature in signal processing assumes that the noise present in the measurement has an adversarial nature, i.e., it is bounded in a certain norm. At the same time, the randomization introduced in the sampling scheme usually assumes an i.i.d. model where rows are sampled with replacement. In this case, if a sample is measured a second time, it does not add additional information. For many applications, where the statistical noise model is a more accurate one, this is not true anymore since a second noisy sample comes with an independent realization of the noise, so there is a fundamental difference between sampling with and without replacement. Therefore, a more careful analysis must be performed. In this short note, we illustrate how one can mathematically transition between these two noise models. This transition gives rise to a weighted LASSO reconstruction method for sampling without replacement, which numerically improves the solution of high-dimensional compressive imaging problems. © 2023 IEEE.

关键词： Compressed sensing

Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph

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arXiv 2024年

作者： Roth, Andreas Bause, Franka Kriege, Nils M. Liebig, Thomas Faculty of Computer Science TU Dortmund University Dortmund Germany Faculty of Computer Science University of Vienna Vienna Austria UniVie Doctoral School Computer Science University of Vienna Vienna Austria Research Network Data Science University of Vienna Vienna Austria Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany

The ability of message-passing neural networks (MPNNs) to fit complex functions over graphs is limited as most graph convolutions amplify the same signal across all feature channels, a phenomenon known as rank collapse, and oversmoothing as a special case. Most approaches to mitigate over-smoothing extend common message-passing schemes, e.g., the graph convolutional network, by utilizing residual connections, gating mechanisms, normalization, or regularization techniques. Our work contrarily proposes to directly tackle the cause of this issue by modifying the message-passing scheme and exchanging different types of messages using multi-relational graphs. We identify a sufficient condition to ensure linearly independent node representations. As one instantion, we show that operating on multiple directed acyclic graphs always satisfies our condition and propose to obtain these by defining a strict partial ordering of the nodes. We conduct comprehensive experiments that confirm the benefits of operating on multi-relational graphs to achieve more informative node representations. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks

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arXiv 2024年

作者： Bonfanti, Andrea Bruno, Giuseppe Cipriani, Cristina BMW AG Basque Center for Applied Mathematics University of the Basque Country Digital Campus Munich Spain BMW AG Digital Campus Munich Germany Technical University of Munich Munich Center for Machine Learning Munich Data Science Institute Germany

The Neural Tangent Kernel (NTK) viewpoint is widely employed to analyze the training dynamics of overparameterized Physics-Informed Neural Networks (PINNs). However, unlike the case of linear Partial Differential Equations (PDEs), we show how the NTK perspective falls short in the nonlinear scenario. Specifically, we establish that the NTK yields a random matrix at initialization that is not constant during training, contrary to conventional belief. Another significant difference from the linear regime is that, even in the idealistic infinite-width limit, the Hessian does not vanish and hence it cannot be disregarded during training. This motivates the adoption of second-order optimization methods. We explore the convergence guarantees of such methods in both linear and nonlinear cases, addressing challenges such as spectral bias and slow convergence. Every theoretical result is supported by numerical examples with both linear and nonlinear PDEs, and we highlight the benefits of second-order methods in benchmark test cases. © 2024, CC BY.

关键词： Numerical methods

On the Origins of Linear Representations in Large Language Models

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arXiv 2024年

作者： Jiang, Yibo Rajendran, Goutham Ravikumar, Pradeep Aragam, Bryon Veitch, Victor Department of Computer Science University of Chicago United States Machine Learning Department Carnegie Mellon University United States Booth School of Business University of Chicago United States Department of Statistics University of Chicago United States Data Science Institute University of Chicago United States

Recent works have argued that high-level semantic concepts are encoded "linearly" in the representation space of large language models. In this work, we study the origins of such linear representations. To that end, we introduce a simple latent variable model to abstract and formalize the concept dynamics of the next token prediction. We use this formalism to show that the next token prediction objective (softmax with cross-entropy) and the implicit bias of gradient descent together promote the linear representation of concepts. Experiments show that linear representations emerge when learning from data matching the latent variable model, confirming that this simple structure already suffices to yield linear representations. We additionally confirm some predictions of the theory using the LLaMA-2 large language model, giving evidence that the simplified model yields generalizable insights. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting