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Mediguide:A machine learning based Smart Prescription System for Personalized Healthcare

Mediguide:A Machine Learning based Smart Prescription System...

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Artificial Intelligence and data Engineering (AIDE), International Conference on

作者： Jeyaganesh Kumar K Mithilesh G Monisha K M Nandhini S Nithish Kumar M Department of Artificial Intelligence and Data Science M.Kumarasamy College of Engineering India Department Of Artificial Intelligence and Machine Learning M.Kumarasamy College Of Engineering India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

This project uses a Decision Tree Classifier model which gives accurate health predictions based on the symptoms provided by users. The model has been trained at thousands of symptoms against diseases and thus predicts very accurately for the inputted data provided by the user. The system has an easy-to-use interface that helps users input their symptoms with ease and get instant predictions of potential diseases. In addition to predicting ailments, the application provides extensive information on the detected disease. Users are given a comprehensive breakdown of the disease, recommended medications, precautions, and personalized advice on diet and exercise regimes. It provides health insights that are meant to help users better manage their health and make better lifestyle decisions. With feedback loops, prediction accuracy and user experience can always be improved directly without worrying much about each setup. It seeks to use advanced technology to make practical health advice possible. Combining disease prediction in real time with meaningful health advice creates added value for users and encourages informed health choices. It enables users to own their health by providing them with a complete picture of their possible health problems and possible solutions to address them. This Project provides a powerful tool for more proactive users, with its combo of predictive analytics and health education resources.(And it helps us recognise the sport itself the user-friendly and detailed output that makes it easy and helpful for everyone to understand their health and what they can do about it.) It upskill traditionally trained AI learners to be perpendicular over ruinous health data towards an accessibly governed future, furnished with health facts and outcomes to improvement on the go.

关键词： Accuracy Medical services machine learning Predictive models Prediction algorithms data models User experience Decision trees Medical diagnostic imaging Diseases

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Enhancing Career Development: An AI-Driven Mentorship Platform for Student-Mentor Connections

Enhancing Career Development: An AI-Driven Mentorship Platfo...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Charanya P Nidhish M Prajesh U R Saktheeswar K Sanjana S Pillai Department of Artificial Intelligence and Machine Learning Sri Eshwar College of Engineering Coimbatore India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Thunai is an AI-powered mentorship platform that connects users with personalized mentors for career guidance. Key features include automated calendar booking, video communication with privacy-enhancing dynamic emojis, and AI-driven mentor matching aligned with mentee goals. The AI assistant, GURU, supports task management, resource recommendations, and LinkedIn-based evaluations to foster growth. Thunai offers subscription plans and partnerships with educational and mental health organizations, enhancing engagement, leadership development, and networking. Leveraging AI technologies like sentiment analysis and feedback, Thunai improves mentor matching, user satisfaction, and mentee engagement for career and emotional well-being.

关键词： Sentiment analysis Technological innovation Engineering profession Organizations Mental health Solids Real-time systems Mentoring Artificial intelligence Monitoring

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Domain adaptation under open set label shift 22

Domain adaptation under open set label shift

引用

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Saurabh Garg Sivaraman Balakrishnan Zachary C. Lipton Machine Learning Department Department of Statistics and Data Science Carnegie Mellon University

ISBN: (纸本)9781713871088

We introduce the problem of domain adaptation under Open Set Label Shift (OSLS) where the label distribution can change arbitrarily and a new class may arrive during deployment, but the class-conditional distributions p{x|y) are domain-invariant. OSLS subsumes domain adaptation under label shift and Positive-Unlabeled (PU) learning. The learner's goals here are two-fold: (a) estimate the target label distribution, including the novel class; and (b) learn a target classifier. First, we establish necessary and sufficient conditions for identifying these quantities. Second, motivated by advances in label shift and PU learning, we propose practical methods for both tasks that leverage black-box predictors. Unlike typical open set domain adaptation problems, which tend to be ill-posed and amenable only to heuristics, OSLS offers a well-posed problem amenable to more principled machinery. Experiments across numerous semi-synthetic benchmarks on vision, language, and medical datasets demonstrate that our methods consistently outperform open set domain adaptation baselines, achieving 10-25% improvements in target domain accuracy. Finally, we analyze the proposed methods, establishing finite-sample convergence to the true label marginal and convergence to optimal classifier for linear models in a Gaussian setup.

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Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews 41

Monitoring AI-Modified Content at Scale: A Case Study on the...

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41st International Conference on machine learning, ICML 2024

作者： Liang, Weixin Izzo, Zachary Zhang, Yaohui Lepp, Haley Cao, Hancheng Zhao, Xuandong Chen, Lingjiao Ye, Haotian Liu, Sheng Huang, Zhi McFarland, Daniel A. Zou, James Y. Department of Computer Science Stanford University United States Machine Learning Department NEC Labs America United States Department of Electrical Engineering Stanford University United States Graduate School of Education Stanford University United States Department of Management Science and Engineering Stanford University United States Department of Computer Science UC Santa Barbara United States Department of Biomedical Data Science Stanford University United States Department of Sociology Stanford University United States Graduate School of Business Stanford University United States

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices. Copyright 2024 by the author(s)

关键词： Maximum likelihood

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The map equation goes neural: mapping network flows with graph neural networks 24

The map equation goes neural: mapping network flows with gra...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Christopher Blöcker Chester Tan Ingo Scholtes Data Analytics Group Department of Informatics University of Zurich Switzerland Chair of Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science Julius-Maximilians-Universität Würzburg Germany

ISBN: (纸本)9798331314385

Community detection is an essential tool for unsupervised data exploration and revealing the organisational structure of networked systems. With a long history in network science, community detection typically relies on objective functions, optimised with custom-tailored search algorithms, but often without leveraging recent advances in deep learning. Recently, first works have started incorporating such objectives into loss functions for deep graph clustering and pooling. We consider the map equation, a popular information-theoretic objective function for unsupervised community detection, and express it in differentiable tensor form for optimisation through gradient descent. Our formulation turns the map equation compatible with any neural network architecture, enables end-to-end learning, incorporates node features, and chooses the optimal number of clusters automatically, all without requiring explicit regularisation. Applied to unsupervised graph clustering tasks, we achieve competitive performance against state-of-the-art deep graph clustering baselines in synthetic and real-world datasets.

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Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission Capacity with Optimized Fault Current Limiters and HVDC Circuit Breaker Parameters 3

Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Ramkumar, M. Siva Saranya, N. Gopan, Gokul Baig, Rahmath Ulla Josha Daniel, S. Babu, M. Sns College of Technology Department of Ece India Karpagam College of Engineering Department of Artificial Intelligence and Data Science India Department of Mechanical Engineering Chennai602105 India Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning K R Puram Bangalore560086 India Hindustan College of Engineering and Technology Department of Eee India Hindusthan College of Engineering and Technology Master of Engineering in Embedded Systems India

ISBN: (纸本)9798331509675

The multi-terminal HVDC system relies heavily on circuit breakers (CBs) and fault current limiters (FCLs) for protection and performance reliability. This research describes a new Giant Trevally optimizer-based strategy for improving the performance of 4-terminal HVDC systems by optimizing crucial circuit breakers and fault current limiters. The proposed method uses time domain estimation to correctly capture fault dynamics, such as traveling waves and reflection coefficients and finds the worst-case fault locations. The multi-objective optimization seeks to optimize the overcurrent, overvoltage handling, fault clearance time, and energy absorption in arresters. This technology provides an effective, dependable protection solution that reduces fault recovery time and increases system stability. The output pulse of the HVDC system shows very low THD results 1.98% for the minimum load condition, 0.87% for median load, and 0.04% for peak load conditions. The results established the simulation of the model in a MATLAB environment. © 2025 IEEE.

关键词： MATLAB

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A Review on machine learning Assisted Drying Behaviour Prediction of Agricultural Seeds and Fruits 2

A Review on Machine Learning Assisted Drying Behaviour Predi...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Jawre, Sandip Verma, Prateek Kothare, Chandrakant B. Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Shri Shankarprasad Agnihotri College of Engineering Department of Mechanical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

machine learning approaches have been more popular in recent years across many scientific and engineering fields, with the potential to improve drying behavior and thermal property predictions in terms of accuracy and efficiency. Agricultural seeds and fruits must be handled carefully after harvest because drying has a considerable impact on their quality, shelf life, and overall worth. To optimize drying processes and guarantee product quality, drying kinetics and thermal characteristics must be accurately predicted. This review article outlines the basic principles of drying, emphasizes their significance in determining the ideal drying conditions, and provides a thorough overview of the application of machine learning methods for predicting the drying kinetics and thermal properties of agricultural seeds and fruits. Then, in the context of their applications to drying prediction, a summary of several machine learning approaches, including artificial neural networks, support vector machines, and decision trees have been discussed. © 2024 IEEE.

关键词： Fruits

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Probabilistic Task Modelling for Meta-learning 37

Probabilistic Task Modelling for Meta-Learning

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37th Conference on Uncertainty in Artificial Intelligence, UAI 2021

作者： Nguyen, Cuong C. Do, Thanh-Toan Carneiro, Gustavo Australian Institute for Machine Learning University of Adelaide Australia Department of Data Science and AI Monash University Australia

We propose probabilistic task modelling - a generative probabilistic model for collections of tasks used in meta-learning. The proposed model combines variational auto-encoding and latent Dirichlet allocation to model each task as a mixture of Gaussian distribution in an embedding space. Such modelling provides an explicit representation of a task through its task-theme mixture. We present an efficient approximation inference technique based on variational inference method for empirical Bayes parameter estimation. We perform empirical evaluations to validate the task uncertainty and task distance produced by the proposed method through correlation diagrams of the prediction accuracy on testing tasks. We also carry out experiments of task selection in meta-learning to demonstrate how the task relatedness inferred from the proposed model help to facilitate meta-learning algorithms. © 2021 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021. All Rights Reserved.

关键词： learning algorithms

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Smart Healthcare: Harnessing the Power of machine learning for Predictive Analysis 6

Smart Healthcare: Harnessing the Power of Machine Learning f...

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6th Parul International Conference on Engineering and Technology, PICET 2024

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

ISBN: (纸本)9798350369748

Smart healthcare employs cloud computing, the Internet of Things (IoT), and supercomputers to manage the healthcare system. Medical data storage volume is increasing, necessitating the adoption of machine learning (ML) models for predicting outcomes, particularly in the treatment of chronic illnesses. The trend of care personalization and technological integration is highlighted by the development of ML applications for clinical decision support systems, including real-time predictive analytics. Predictive analytics applications employ ML and Artificial Intelligence (AI) to forecast patient risks, outcomes, and treatment responses. Predictive analytics is more important in healthcare when it comes to detecting Alzheimer's disease, cardiovascular events, radiology, and other conditions. Therefore, the current study highlights the change in illness identification, medical imaging, and personalized medication brought about by ML and Predictive Analytics in Precision Medicine (PM). However, ethical concerns and the deployment of transparent AI models still need more research. This study anticipates a time when healthcare will be quicker, better informed, and more patient-centered by using the insights provided by intelligent healthcare data. By effectively saving patient lives, the use of ML in healthcare ushers in a new era for the industry. © 2024 IEEE.

关键词： Disease Detection machine learning Medical Imaging Predictive Analytics Smart Healthcare

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An Analysis of Light Weight Symmetric Encryption Algorithms for Secure data Transmission in IoT

An Analysis of Light Weight Symmetric Encryption Algorithms ...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Sanjay Kumar C. S. Pillai Department of CSE (Artificial Intelligence & Machine Learning) Don Bosco Institute of Technology Bengaluru India Department of CSE-Data Science ACS College of Engineering Bengaluru India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

The Internet of Things (IoT) consists of a network of resource-constrained devices, sensors, and machines that are interconnected and communicate via the internet. However, these devices often struggle while handling complex attacks during data encryption and after the encrypted data is transmitted. Cipher algorithms are therefore critical to the data security process because they handle the growing complexities in the applications of data environments. This review aims to present the most effective and efficient symmetric cipher algorithms, which use the same components as the ciphers. It demonstrates how the current cipher has developed into a lightweight encryption by lowering the number and impact of each component. Thus, it becomes clearer how the strength of these components varies between symmetric and asymmetric ciphers dependent on their size. Finally, several efficient lightweight symmetric cipher algorithms are selected, and the security, performance, and resource needs of each algorithm's components are reviewed.

关键词： Ciphers Reviews Encryption Sensors Complexity theory Internet of Things data communication Computational intelligence

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