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Algorithms 2024年第2期17卷

作者： Juan, Pin-Hung Wu, Ja-Ling Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei106 Taiwan Center for Data Intelligence: Technologies Applications and Systems National Taiwan University Taipei106 Taiwan

In this study, we present a federated learning approach that combines a multi-branch network and the Oort client selection algorithm to improve the performance of federated learning systems. This method successfully addresses the significant issue of non-iid data, a challenge not adequately tackled by the commonly used MFedAvg method. Additionally, one of the key innovations of this research is the introduction of uniformity, a metric that quantifies the disparity in training time amongst participants in a federated learning setup. This novel concept not only aids in identifying stragglers but also provides valuable insights into assessing the fairness and efficiency of the system. The experimental results underscore the merits of the integrated multi-branch network with the Oort client selection algorithm and highlight the crucial role of uniformity in designing and evaluating federated learning systems. © 2024 by the authors.

关键词： Efficiency

Retraction Note: Enabling secure and efficient industry 4.0 transformation through trust-authorized anomaly detection in cloud environments with a hybrid AI approach

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Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Prakash, N. Vignesh, J. Ashwin, M. Ramadass, Sudhir Veeranjaneyulu, N. V. Athawale, Shashikant Ravuri, Ananda Subramanian, Balambigai Department of Information Technology B.S Abdur Rahman Crescent Institute of Science and Technology Vandalur India Department of ISE Jain University Bengaluru India Deparment of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram India Sterck Systems Chennai India Department of Information Technology VFSTR Deemed to Be University Vadlamudi Guntur District India Department of Computer Engineering AISSMS COE Pune India Intel Corporation Hillsboro USA Department of ECE Kongu Engineering College Perundurai India

Medical Diagnostic System using Embedding JADE in jCOLIBRI

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Medical Diagnostic System using Embedding JADE in jCOLIBRI

Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Jagjit Singh Dhatterwal Kiran Malik Purushottam Das Kuldeep Singh Kaswan Department of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science & Engineering (AIML) GL Bajaj Institute of Technology & Management Greater Noida India Department of Computer Science & Engineering Graphic Era Hill University Research Scholar Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Case-based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computer-aided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g., the problems of knowledge acquisition, remembering, robustness and maintenance. This paper discusses the CBR methodology, the research issues and the technical aspects of implementing intelligent medical diagnoses systems. It develops a celebrative framework of MAS-CBR to criticize the medical problem to get the best treatment against dengue disease. This paper also embedding the framework of JADE and jCOLIBRI to improve the quality of decision- making in the medical diagnosis field.

关键词： Knowledge acquisition Knowledge based systems System integration Cognition Robustness Maintenance Medical diagnosis Informatics Medical diagnostic imaging Viruses (medical)

Distributional Robustness Bounds Generalization Errors

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arXiv 2022年

作者： Wang, Shixiong Wang, Haowei The Institute of Data Science National University of Singapore Singapore The Department of Industrial Systems Engineering and Management National University of Singapore Singapore

Bayesian methods, distributionally robust optimization methods, and regularization methods are three pillars of trustworthy machine learning combating distributional uncertainty, e.g., the uncertainty of an empirical distribution compared to the true underlying distribution. This paper investigates the connections among the three frameworks and, in particular, explores why these frameworks tend to have smaller generalization errors. Specifically, first, we suggest a quantitative definition for "distributional robustness", propose the concept of "robustness measure", and formalize several philosophical concepts in distributionally robust optimization. Second, we show that Bayesian methods are distributionally robust in the probably approximately correct (PAC) sense;in addition, by constructing a Dirichlet-process-like prior in Bayesian nonparametrics, it can be proven that any regularized empirical risk minimization method is equivalent to a Bayesian method. Third, we show that generalization errors of machine learning models can be characterized using the distributional uncertainty of the nominal distribution and the robustness measures of these machine learning models, which is a new perspective to bound generalization errors, and therefore, explain the reason why distributionally robust machine learning models, Bayesian models, and regularization models tend to have smaller generalization errors in a unified *** Codes 62F35, 62G35, 62C12 Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

Analysis of Ethical Issues Associated with Wearable Medical Devices 5

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Analysis of Ethical Issues Associated with Wearable Medical ...

5th International Conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Venkatachalam, S. Padmavathi, T. Vinodh, N. Thilagavathi, J. Joshi, Garvita Ramachandran, G. Rajasekaran, B. Karpagam Academy of Higher Education Department of Management Coimbatore India Cvr College of Engineering Department of Ece Telangana India S.A.Engineering College Thiruvalluvar India Arjun College of Technology Artificial Intelligence and Data Science Coimbatore India Mahakal Institute of Pharmaceutical Studies Ujjain India Ece Vinayaka Mission's Kirupananda Variyar Engineering College Tamil Nadu Salem India Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation - Deemed to Be University Department of Electronics and Communication Engineering Salem India

ISBN: (纸本)9781665474672

Medical practice and the delivery of healthcare are changing as a result of the advancement and widespread use of medical wearable technology. The daily vast amount of personal data those results, privacy disclosure, and other ethical difficulties associated with interests are gradually brought to light, though. This paper summarises and analyses the ethical issues that exist in the entire process of health medical wearable equipment serving humans, including the equipment itself, data collection, transmission, management, and data use, from the perspective of medical ethics. It then suggests appropriate solutions and countermeasures from the perspectives of respecting autonomy, informed consent, privacy protection, medical optimization, and beneficial principles. To fully utilize the potential of health care wearables in the field of medical care, advance the development of new intelligent medical care, and contribute to the creation of a healthy India, the concepts of 'prevention before disease, disease prevention, and change prevention,' and 'medicine. © 2023 IEEE.

关键词： Wearable technology

BEHAVIORAL CHANGES DURING THE PANDEMIC WORSENED INCOME DIVERSITY OF URBAN ENCOUNTERS

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arXiv 2022年

作者： Yabe, Takahiro Dong, Xiaowen Bueno, Bernardo García Bulle Pentland, Alex Sandy Moro, Esteban Institute for Data Systems and Society MIT United States Department of Engineering Science University of Oxford United Kingdom Institute for Data Systems and Society & Media Lab MIT United States Institute for Data Systems and Society & Media Lab MIT Departamento de Matemáticas & GISC Universidad Carlos III de Madrid Spain

Diversity of physical encounters and social interactions in urban environments are known to spur economic productivity and innovation in cities, while also to foster social capital and resilience of communities. However, mobility restrictions during the pandemic have forced people to substantially reduce urban physical encounters, raising questions on the social implications of such behavioral changes. In this paper, we study how the income diversity of urban encounters have changed during different periods throughout the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in four large US cities, collected across three years spanning before and during the pandemic. We find that the diversity of urban encounters have substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that while the reduction of outside activities (higher rates of staying at home) was a major factor that contributed to decreased diversity in the early stages of the pandemic, behavioral changes including lower willingness to explore new places and changes in visitation preferences further worsened the long-term diversity of encounters. Our findings suggest that the pandemic could have long-lasting negative effects on urban income diversity, and provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic. © 2022, CC BY.

关键词： COVID-19

eRPCA: Robust Principal Component Analysis for Exponential Family Distributions

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arXiv 2023年

作者： Zheng, Xiaojun Mak, Simon Xie, Liyan Xie, Yao The Department of Statistical Science Duke University United States The School of Data Science Chinese University of H ong Kong Shenzhen China The School of Industrial & Systems Engineering Georgia Institute of Technology United States

Robust Principal Component Analysis (RPCA) is a widely used method for recovering low-rank structure from data matrices corrupted by significant and sparse outliers. These corruptions may arise from occlusions, malicious tampering, or other causes for anomalies, and the joint identification of such corruptions with low-rank background is critical for process monitoring and diagnosis. However, existing RPCA methods and their extensions largely do not account for the underlying probabilistic distribution for the data matrices, which in many applications are known and can be highly non-Gaussian. We thus propose a new method called Robust Principal Component Analysis for Exponential Family distributions (eRPCA), which can perform the desired decomposition into low-rank and sparse matrices when such a distribution falls within the exponential family. We present a novel alternating direction method of multiplier optimization algorithm for efficient eRPCA decomposition. The effectiveness of eRPCA is then demonstrated in two applications: the first for steel sheet defect detection, and the second for crime activity monitoring in the Atlanta metropolitan area. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

ON RESOLUTION OF 1-NORM MINIMIZATION VIA A TWO-METRIC ADAPTIVE PROJECTION METHOD

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arXiv 2025年

作者： Wu, Hanju Xie, Yue Department of Mathematics The University of Hong Kong Hong Kong Department of Mathematics and Musketeers Foundation Institute of Data Science The University of Hong Kong Hong Kong

The two-metric projection method is a simple yet elegant algorithm proposed by Bertsekas to address bound/box-constrained optimization problems. The algorithm’s low per-iteration cost and potential for using Hessian information make it a favorable computation method for this problem class. Inspired by this algorithm, we propose a two-metric adaptive projection method for solving the 1-norm regularization problem that inherits these advantages. We demonstrate that the method is theoretically sound - it has global convergence. Furthermore, it is capable of manifold identification and has superlinear convergence rate under the error bound condition and strict complementarity. Therefore, given sparsity in the solution, the method enjoys superfast convergence in iteration while maintaining scalability, making it desirable for large-scale problems. We also equip the algorithm with competitive complexity to solve nonconvex problems. Numerical experiments are conducted to illustrate the advantages of this algorithm implied by the theory compared to other competitive methods, especially in large-scale scenarios. In contrast to the original two-metric projection method, our algorithm directly solves the 1-norm minimization problem without resorting to the intermediate reformulation as a bound-constrained problem, so it circumvents the issue of numerical instability. Copyright © 2025, The Authors. All rights reserved.

关键词： Convergence of numerical methods

Machine Learning and OpenCV: A Powerful Combination for Driver Drowsiness Detection

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Machine Learning and OpenCV: A Powerful Combination for Driv...

International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： A Siva Kumar Pv Gopirajan Jayaraj Ramasamy Beulah Jackson Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of IT Faculty of Engineering and Technology Botho University Botswana-Africa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Driver fatigue is a significant contributor to road accidents worldwide, and there is a need for efficient driver drowsiness detection systems to prevent such accidents. Computer vision techniques like OpenCV have recently been utilized to develop such strategies. This proposed work investigates the use of machine learning models and OpenCV Python package for driver drowsiness detection. The proposed system uses a camera to continuously capture the driver’s face and applies image processing techniques to identify drowsiness. The system detects blinks by monitoring the driver’s eyes and measuring the frequency and duration of eyelid closures. The real-time experiments conducted with the proposed method showed high accuracy in detecting drowsiness, and the system can be implemented in various settings, including cars and heavy vehicles. Ultimately, the system has some potential to reduce accidents caused by driver fatigue, thereby enhancing road safety.

关键词： Support vector machines Face recognition Machine learning Feature extraction Fatigue Real-time systems Vectors

Deep Learning for Emoji Generation: From Faces and Gestures to Digital Emotion

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Deep Learning for Emoji Generation: From Faces and Gestures ...

Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： R. Nareshkumar K. Nimala M. K. Mohamed Faizal P. Sasikumar S. Karthigeyan Srikanth Cherukuvada Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu India Department of Artificial Intelligence and Data Science K. Ramakrishnan College of Engineering Samayapuram Tamil Nadu India Department of AIML Sphoorthy Engineering College Hyderabad Tamilnadu India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Hyderabad Telangana India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

The objective of this project is to create a reliable and effective system for identifying facial expressions and hand movements and translating them into suitable emojis. Emotions are vital in human communication, and precisely understanding them may significantly improve user experiences in several applications, such as social media, virtual assistants, and human-computer interaction. Through the creation of an extensive dataset and the use of a robust CNN, the system is capable of precisely categorizing facial expressions, even when faced with different lighting conditions and a wide range of facial characteristics. The suggested methodology entails the acquisition of live video data, which is then subjected to preprocessing to enhance the extraction of relevant features. Subsequently, CNN models are used to accurately recognize and interpret facial emotions and movements. These acknowledged phrases are then converted into equivalent emojis, offering an instinctive and smooth method to communicate emotions via digital communication. Initial findings indicate that the system attains a notable level of precision in identifying a diverse array of facial emotions, showcasing its capacity for practical use in real-life scenarios. This study enhances the field of human-computer interaction by facilitating a more authentic and articulate form of communication via emojis. It effectively narrows the divide between human emotions and digital interfaces. Subsequent efforts will concentrate on augmenting the system's functionalities to include a wider array of motions and improving its efficiency in more demanding settings.

关键词： Human computer interaction Hands Deep learning Emotion recognition Social networking (online) Virtual assistants Feature extraction Digital communication Real-time systems Emojis