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A General Framework for Clustering and Distribution Matching with Bandit Feedback

IEEE Transactions on Information Theory 2025年第3期71卷 2116-2139页

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y. F. Scarlett, Jonathan CNRS@CREATE DesCartes Program CREATE Tower 138602 Singapore National University of Singapore Department of Computer Science Queenstown119077 Singapore National University of Singapore School of Computing Department of Information Systems and Analytics Queenstown119077 Singapore National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering Queenstown119077 Singapore Institute of Data Science National University of Singapore Department of Computer Science The Department of Mathematics Queenstown119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank-Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ vanishes. © 1963-2012 IEEE.

关键词： Random variables

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Using Blue Whale Technology: An ML Edge Self-Adaptable Vehicle Slowdown Earliest Warning Information System 8th

Using Blue Whale Technology: An ML Edge Self-Adaptable Vehic...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Murali, N. David, D. Beulah Research Scholar Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9789819784219

Human life is challenged by this main work’s ultimate goal of reducing accidents and ensuring life safety due to the enormous growth of vehicles and those based on safety. Here, cases of suspected drunk driving, reckless driving, etc., are present. In this scenario, vehicles are traveling at full speed and irresponsibly using technology on all roads. An early warning system is used to stop auto accidents. In order to accommodate the city's steadily increasing automobile population, intelligent traffic systems must be designed in a resourceful and sustainable manner using all available technology in both light and dark. The advanced ML's work on unexpected traffic flow and all road conditions resulted in significant real-time changes in the flow of traffic for every significant issue that arose. Here, the Blue Whale technology makes use of clever intelligence to quickly identify, detect, and modify warning systems. The primary objective of the study was to train CNN/BWT on the collected data sets in order to operationalize the unsupervised approach for the prediction of critical situations. Additionally, it makes an effort to clarify the situation so that guests can feel more at ease and help them decide on transfers in time-sensitive situations. IOT is a practical way to locate the nearest object using machine learning methods that are accurate and apply received data sets, resulting in significant findings that support object recognition. The paperwork fully focuses on improving driving knowledge and offering new approaches for quick object recognition. The Fast Warning System Initiative, which aims to increase daytime safety and security, is providing a prior warning system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Backward wave tubes

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Harnessing Medical Big data: Integrating Computational Insights for Enhanced Patient Outcomes 24

Harnessing Medical Big Data: Integrating Computational Insig...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Syed Masood, Jafar Ali Ibrahim Vinayagam, Jai Kumar Onteru, Ranjit Kumar Kopparapu, Umamaheswararao Parabathini, Bulah Pushpa Rani Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Centre for Big Data Analytics QIS College of Engineering and Technology Andhra Pradesh Ongole India

ISBN: (纸本)9798400709586

The rapid proliferation of medical big data has opened unprecedented opportunities for enhancing patient outcomes through advanced computational analysis. This paper explores the integration of big data analytics with clinical practices to deliver personalized medicine, optimize treatment protocols, and improve patient care. Our findings demonstrate that a synergistic approach between computational insights and clinical decision-making can significantly advance healthcare outcomes. This study provides a comprehensive framework for harnessing medical big data, addressing key challenges, and proposing solutions to integrate these insights into everyday clinical practice. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Personalized medicine

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TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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Research on Tumor Images Segmentation based on Improved Convolutional Neural Network 24

Research on Tumor Images Segmentation based on Improved Conv...

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5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Wan, Yajie Diao, Su Jiang, Gaozhe Song, Chaoda Department of Computer Science Brown University ProvidenceRI United States Department of Industrial & Systems Engineering Auburn University AuburnAL United States Institute of Operations Research and Analytics National University of Singapore Singapore Singapore School of Engineering Department of Computer and Data Sciences ClevelandOH United States

ISBN: (纸本)9798400717826

Tumors are one of the most common tumors in humans, and the main treatment at present is surgical removal. Preoperative planning of tumors usually requires a professional doctor to manually segment the images, but this process is limited by the doctor’s technical experience, which is time-consuming and labor-intensive. In addition, segmentation is extremely difficult due to the complexity and morphological diversity of tumor lesions. We propose a tumor image segmentation method based on improved Convolutional Neural Network (CNN). The model enhances the feature extraction ability by introducing a deep separable convolutional layer and batch normalization, while using dilated convolution to increase the receptive field, thereby improving the accuracy of segmentation. We also incorporated attention mechanisms into the model to focus more accurately on the tumor area and further optimize the segmentation effect. In order to verify the effectiveness of the model, we conducted experimental analysis on multiple publicly available medical imaging datasets. Experimental results show that the improved convolutional neural network significantly improves the accuracy and efficiency of segmentation compared with traditional methods in tumor image segmentation tasks. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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FEDERATED INVERSE PROBABILITY TREATMENT WEIGHTING FOR INDIVIDUAL TREATMENT EFFECT ESTIMATION

arXiv

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arXiv 2025年

作者： Yin, Changchang Chen, Hong-You Chao, Wei-Lun Zhang, Ping Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Department of Computer Science and Engineering Department of Biomedical Informatics Translational Data Analytics Institute The Ohio State University ColumbusOH United States

Individual treatment effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucial problem in healthcare. Most existing ITE estimation methods are designed for centralized settings. However, in real-world clinical scenarios, the raw data are usually not shareable among hospitals due to the potential privacy and security risks, which makes the methods not applicable. In this work, we study the ITE estimation task in a federated setting, which allows us to harness the decentralized data from multiple hospitals. Due to the unavoidable confounding bias in the collected data, a model directly learned from it would be inaccurate. One well-known solution is Inverse Probability Treatment Weighting (IPTW), which uses the conditional probability of treatment given the covariates to re-weight each training example. Applying IPTW in a federated setting, however, is non-trivial. We found that even with a well-estimated conditional probability, the local model training step using each hospital’s data alone would still suffer from confounding bias. To address this, we propose FED-IPTW, a novel algorithm to extend IPTW into a federated setting that enforces both global (over all the data) and local (within each hospital) decorrelation between covariates and treatments. We validated our approach on the task of comparing the treatment effects of mechanical ventilation on improving survival probability for patients with breadth difficulties in the intensive care unit (ICU). We conducted experiments on both synthetic and real-world eICU datasets and the results show that FED-IPTW outperform state-of-the-art methods on all the metrics on factual prediction and ITE estimation tasks, paving the way for personalized treatment strategy design in mechanical ventilation usage. © 2025, CC BY-NC-SA.

关键词： Inverse problems

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Navigating the Complexities of Municipal Waste Management: Enhancing Cost Prediction for Sustainable Urban Solutions 1st

Navigating the Complexities of Municipal Waste Management: E...

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1st International Research Conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Minu, M.S. Samal, Tushar Bisani, Khushi Tewari, Aishwarya Department of Computer Science and Engineering with Specialisation in Big Data Analytics SRM Institute of Science and Technology Ramapuram Campus Chennai India

ISBN: (纸本)9783031823824

The escalating rates of municipal waste generation in urban areas worldwide present a critical challenge for effective waste management. This paper examines the complexities surrounding municipal waste management, emphasizing the significance of accurate cost prediction for sustainable decision-making. Urbanization, driven by population growth and rising standards of living, intensifies waste generation, while shifts in consumption patterns further complicate waste management efforts. Environmental concerns and regulatory pressures mandate innovative waste reduction strategies. Accurate cost prediction is paramount for budgetary planning and resource allocation, enabling proactive measures to optimize operational efficiency and mitigate financial risks. Emerging methodologies, including data analytics and predictive modeling, offer promising avenues for enhancing cost prediction accuracy by capturing the multifaceted drivers of cost variability. By leveraging these methodologies, decision-makers can forecast municipal waste management costs with precision, informing strategic initiatives to foster sustainable waste management practices in urban areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

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Perspective Chapter: MOOCs in India: Evolution, Innovation, Impact, and Roadmap

arXiv

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arXiv 2025年

作者： Das, Partha Pratim Department of Computer Science Center for Data Science and Analytics Ashoka University Sonepat India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India

With the largest population of the world and one of the highest enrolments in higher education, India needs efficient and effective means to educate its learners. India started focusing on open and digital education in 1980's and its efforts were escalated in 2009 through the NMEICT program of the Government of India. A study by the Government and FICCI in 2014 noted that India cannot meet its educational needs just by capacity building in brick and mortar institutions. It was decided that ongoing MOOCs projects under the umbrella of NMEICT will be further strengthened over its second (2017-21) and third (2021-26) phases. NMEICT now steers NPTEL/SWAYAM (India's MOOCs) and several digital learning projects including Virtual Labs, e-Yantra, Spoken Tutorial, FOSSEE, and National Digital Library on India-the largest digital education library in the world. Further, India embraced its new National Education Policy in 2020 to strongly foster online education. In this chapter, we take a deep look into the evolution of MOOCs in India, its innovations, its current status and impact, and the roadmap for the next decade to address its challenges and grow. AI-powered MOOCs is an emerging opportunity for India to lead MOOCs worldwide. © 2025, CC BY-NC-ND.

关键词：

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Enhancing Breast Cancer Diagnosis with Vision Transformers: A Novel Approach using MaskGuided Attention for Ultrasound Image Classification

Enhancing Breast Cancer Diagnosis with Vision Transformers: ...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Deena Sivakumar Anamika Kumari Divya Dharshini K Samiksha S Hemachandran M Department of Computer Science and Engineering Sri Ramachandra Institute of Higher Education and Research Chennai Department of Computer Science (AI & Data Analytics) Sri Ramachandra Institute of Higher Education and Research Chennai

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Deep learning models, notably convolutional neural networks (CNNs), demonstrate great promise in medical image processing. Nonetheless, CNNs frequently encounter challenges in capture holistic context and long- range dependencies in high-resolution images, which are crucial for accurate medical diagnostics. A new state-of-the-art technique called Vision Transformers (ViTs) uses self-attention mechanisms for gathering global correlations between image patches, thereby addressing some of the inherent limitations of CNNs. This study investigates the application of ViTs for diagnosing breast cancer from ultrasound images of tumor. The findings suggest that ViTs not only achieve competitive accuracy in identifying breast cancer lesions but also offer superior interpretability through attention mechanism, which can aid clinicians in understanding model decisions. This paper highlights the potential of ViTs as a powerful tool in medical imaging, particularly for breast cancer diagnosis, and outlines future research directions for enhancing clinical applicability.

关键词： Deep learning computer vision Accuracy Attention mechanisms Ultrasonic imaging Transformers Breast cancer Convolutional neural networks Medical diagnosis Biomedical imaging

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Empirical Analysis of Nature-Inspired Algorithms for Autism Spectrum Disorder Detection Using 3D Video dataset

arXiv

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arXiv 2025年

作者： Panchal, Aneesh Khan, Kainat Katarya, Rahul Department of Computational and Data Sciences Indian Institute of Science Karnataka Bangalore560012 India Big Data Analytics and Web Intelligence Laboratory Department of Computer Science & Engineering Delhi Technological University Delhi110042 India

Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental disorder symptoms of which includes repetitive behaviour and lack of social and communication skills. Even though these symptoms can be seen very clearly in social but a large number of individuals with ASD remains undiagnosed. In this paper, we worked on a methodology for detection of ASD from 3-dimensional walking video dataset, utilizing supervised machine learning (ML) classification algorithms and using nature-inspired optimization algorithms for feature extraction from dataset. The proposed methodology involve the classification of ASD using supervised ML classification algorithm and extracting important and relevant features from the dataset using nature-inspired optimization algorithms. We also included the ranking coefficients for finding the initial leading particle. This selection of particle significantly reduces the computation time and hence, improving the total efficiency and accuracy for the ASD detection. To evaluate the efficiency of the proposed methodology, we deployed various combination of classification algorithm and nature-inspired algorithms resulting in an outstanding classification accuracy of 100% using random forest classification algorithm and gravitational search algorithm for feature selection. The application of the proposed methodology with different datasets would enhance the robustness and generalizability of the proposed methodology. Due to high accuracy and less total computation time, the proposed methodology will offer a significant contribution to the medical and academia fields, providing a foundation for future research and advancements in ASD diagnosis. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

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