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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Revathi, M. Kalaiarasi, P. Jeevitha, S. Akilandasowmya, G. St. Joseph's Institute of Technology Artificial Intelligence & Data Science Chennai India Kalasalingam University Department of Machine Learning Krishnankoil India Jerusalem College of Engineering Information Technology Chennai India Sri Sairam Institute of Technology Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331518097

Brain is an important organ of the human body that helps in the proper functioning of the all the organs. Without brain, the functions of other organs will not happen since they cannot run without the commands of brain. Brain tumor refers to a situation where an abnormal group of cells are found in some part of the brain. There are many types of brain tumor that are identified. The tumors in brain is independent of age and it can grow at anytime. The symptoms that is exhibited by the brain tumor depends on the size of the tumor cell as well as its location. machine learning algorithms helps in the earlier identification of the disease which can be used to give proper treatment. In this paper, a fuzzy c means based segmentation of image is done. Then classification of the segmented image is done by using Support Vector machine. The results shows 98% percentage of accuracy. © 2024 IEEE.

关键词： Support vector machines

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EPR-Net: constructing a non-equilibrium potential landscape via a variational force projection formulation

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National science Review 2024年第7期11卷 135-147页

作者： Yue Zhao Wei Zhang Tiejun Li Center for Data Science Peking University Zuse Institute Berlin Department of Mathematics and Computer Science Freie Universit?t Berlin Laboratory of Mathematics and Applied Mathematics(LMAM)and School of Mathematical Sciences Peking University Center for Machine Learning Research Peking University

We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state ***-Net leverages a nice mathematical fact that the desired negative potential gradient is simply the orthogonal projection of the driving force of the underlying dynamics in a weighted inner-product ***, our loss function has an intimate connection with the steady entropy production rate(EPR),enabling simultaneous landscape construction and EPR estimation. We introduce an enhanced learning strategy for systems with small noise, and extend our framework to include dimensionality reduction and the state-dependent diffusion coefficient case in a unified fashion. Comparative evaluations on benchmark problems demonstrate the superior accuracy, effectiveness and robustness of EPR-Net compared to existing methods. We apply our approach to challenging biophysical problems, such as an eight-dimensional(8D)limit cycle and a 52D multi-stability problem, which provide accurate solutions and interesting insights on constructed landscapes. With its versatility and power, EPR-Net offers a promising solution for diverse landscape construction problems in biophysics.

关键词： high-dimensional potential landscape non-equilibrium system entropy production rate dimensionality reduction deep learning

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S$^\text{3}$Attention: Improving Long Sequence Attention With Smoothed Skeleton Sketching

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IEEE Journal of Selected Topics in Signal Processing 2024年第6期18卷 985-996页

作者： Xue Wang Tian Zhou Jianqing Zhu Jialin Liu Kun Yuan Tao Yao Wotao Yin Rong Jin HanQin Cai Alibaba Group Bellevue WA USA Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Statistics and Data Science University of Central Florida Orlando FL USA Center for Machine Learning Research Peking University Beijing China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai China Meta Menlo Park CA USA Department of Statistics and Data Science and the Department of Computer Science University of Central Florida Orlando FL USA

Attention based models have achieved many remarkable breakthroughs in numerous applications. However, the quadratic complexity of Attention makes the vanilla Attention based models hard to apply to long sequence tasks. Various improved Attention structures are proposed to reduce the computation cost by inducing low rankness and approximating the whole sequence by sub-sequences. The most challenging part of those approaches is maintaining the proper balance between information preservation and computation reduction: the longer sub-sequences used, the better information is preserved, but at the price of introducing more noise and computational costs. In this paper, we propose a smoothed skeleton sketching based Attention structure, coined S $^{3}$ Attention, which significantly improves upon the previous attempts to negotiate this trade-off. S $^{3}$ Attention has two mechanisms to effectively minimize the impact of noise while keeping the linear complexity to the sequence length: a smoothing block to mix information over long sequences and a matrix sketching method that simultaneously selects columns and rows from the input matrix. We verify the effectiveness of S $^{3}$ Attention both theoretically and empirically. Extensive studies over Long Range Arena (LRA) datasets and six time-series forecasting show that S $^{3}$ Attention significantly outperforms both vanilla Attention and other state-of-the-art variants of Attention structures.

关键词： Computational modeling Complexity theory Transformers Task analysis Skeleton Kernel Noise

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Two-sample testing on ranked preference data and the role of modeling assumptions

The Journal of Machine Learning Research

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The Journal of machine learning Research 2022年第1期23卷 10189-10236页

作者： Charvi Rastogi Sivaraman Balakrishnan Nihar B. Shah Aarti Singh Machine Learning Department Carnegie Mellon University Pittsburgh PA Department of Statistics Carnegie Mellon University Pittsburgh PA Machine Learning Department and Computer Science Department Carnegie Mellon University Pittsburgh PA

A number of applications require two-sample testing on ranked preference data. For instance, in crowdsourcing, there is a long-standing question of whether pairwise-comparison data provided by people is distributed identically to ratings-converted-to-comparisons. Other applications include sports data analysis and peer grading. In this paper, we design two-sample tests for pairwise-comparison data and ranking data. For our two-sample test for pairwise-comparison data, we establish an upper bound on the sample complexity required to correctly test whether the distributions of the two sets of samples are identical. Our test requires essentially no assumptions on the distributions. We then prove complementary lower bounds showing that our results are tight (in the minimax sense) up to constant factors. We investigate the role of modeling assumptions by proving lower bounds for a range of pairwise-comparison models (WST, MST, SST, parameter-based such as BTL and Thurstone). We also provide tests and associated sample complexity bounds for partial (or total) ranking data. Furthermore, we empirically evaluate our results via extensive simulations as well as three real-world data sets consisting of pairwise-comparisons and rankings. By applying our two-sample test on real-world pairwise-comparison data, we conclude that ratings and rankings provided by people are indeed distributed differently.

关键词： two-sample testing pairwise-comparisons ranking minimax theory social choice models

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Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research

arXiv

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arXiv 2023年

作者： Freiesleben, Timo König, Gunnar Cluster of Excellence "Machine Learning in Science" Tübingen University Germany Department of Statistics LMU Munich Germany

Despite progress in the field, significant parts of current XAI research are still not on solid conceptual, ethical, or methodological grounds. Unfortunately, these unfounded parts are not on the decline but continue to grow. Many explanation techniques are still proposed without clarifying their purpose. Instead, they are advertised with ever more fancy-looking heatmaps or only seemingly relevant benchmarks. Moreover, explanation techniques are motivated with questionable goals, such as building trust, or rely on strong assumptions about the’concepts’ that deep learning algorithms learn. In this paper, we highlight and discuss these and other misconceptions in current XAI research. We also suggest steps to make XAI a more substantive area of research. © 2023, CC BY.

关键词： learning algorithms

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No Rose for MLE: Inadmissibility of MLE for Evaluation Aggregation Under Levels of Expertise

No Rose for MLE: Inadmissibility of MLE for Evaluation Aggre...

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IEEE International Symposium on Information Theory

作者： Charvi Rastogi Ivan Stelmakh Nihar Shah Sivaraman Balakrishnan Machine Learning Department Carnegie Mellon University Computer Science Department Carnegie Mellon University Department of Statistics and Data Science Carnegie Mellon University

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

A number of applications including crowd-sourced labeling and peer review require aggregation of labels or evaluations sourced from multiple evaluators. There is often additional information available pertaining to the evaluators’ expertise. A natural approach for aggregation is to consider the widely studied Dawid-Skene model (or its extensions incorporating evaluators’ expertise), and employ the standard maximum likelihood estimator (MLE). While MLE is in general widely used in practice and enjoys a number of appealing theoretical guarantees, in this work we provide a negative result for the MLE. Specifically, we prove that the MLE is asymptotically inadmissible for a special case of evaluation aggregation with expertise level information. We show this by constructing an alternative estimator that we show is significantly better than the MLE in certain parameter regimes and at least as good elsewhere. Finally, simulations reveal that our findings may hold in more general conditions than what we theoretically analyze.

关键词： Maximum likelihood estimation Analytical models Labeling Standards Information theory

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A Review on Web data Mining to Detect Online Spread of Terrorism Activity 2

A Review on Web Data Mining to Detect Online Spread of Terro...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Jha, Saurav Kumar Pacharaney, Utkarsha Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine learning Maharashtra Wardha India

ISBN: (纸本)9798331505745

This research study outlines the significant contribution that web data mining can make in discovering and combating the dissemination of online terrorism. With terrorist groups using the internet more and more to deliver propaganda, recruit, and organize, there is a pressing need for efficient monitoring and analysis of web content. This article discusses different data mining methods that can be used to identify and study patterns suggestive of terrorist activity throughout networked digital environments. Specifically, the embedding of machine learning algorithms, i.e., Support Vector machines (SVM), Random Forest and Gradient Boosting, is investigated with regard to its suitability to differentiate between benign and harmful online content. These algorithms can process large volumes of data, where features are extracted, including URL structures, domain ages, and keyword frequencies, to detect anticipate threats. According to the article, feature extraction plays a crucial role in improving detection system accuracy, enabling real-time surveillance of anomalous online activities. In addition, the review discusses the problems that researchers and law enforcement agencies have been encountering in using web data mining for counter-terrorism purposes. Ethical concerns around privacy and civil liberties are addressed and technical challenges, including the quality of data and the dynamic character of online content, are elaborated. Scenarios in which false positives arising in detection systems may occur are also considered, with emphasis on the need for ongoing algorithm and technique refinement. © 2025 IEEE.

关键词： Crop protection

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A Review on Web data Mining to Detect Online Spread of Terrorism Activity

A Review on Web Data Mining to Detect Online Spread of Terro...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Saurav Kumar Jha Utkarsha Pacharaney Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Wardha Maharashtra India Department of Artificial Intelligence and Machine learning Faculty of Engineering and Technology Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

关键词： Support vector machines Uniform resource locators machine learning algorithms Reviews Law enforcement Terrorism Feature extraction Real-time systems data mining Internet of Things

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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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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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