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1st International Conference on Expert Clouds and Applications, ICOECA 2023

作者： Jayapradha, J. Prakash, M. Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India

ISBN: (纸本)9789819917440

The distribution of Electronic Health Records is highly needed for various analysis purposes and medical studies. However, the data should be disclosed to the data recipient in such a way that the privacy of the individual is ensured. Privacy-preserving data publishing is a challenging task because the distributed data should be protected against multiple privacy threats. Several privacy models, methods, techniques have been proposed and studied in earlier works, however, the study concerning privacy models considering adversary background knowledge is very limited. In this study, various privacy models and their attack models have been analyzed and depicted, the main focus of the study is the adversary’s background knowledge privacy-preserving data publishing model. Various research challenges in security and privacy have been identified and summarized. Furthermore, the various applications of privacy-preserving data publishing such as Cloud, E-health, Social Network, Agriculture, and Smart City have been studied and the need for privacy is briefed. Finally, the paper highlighted the study's intuitions regarding present unresolved challenges as well as probable future approaches in privacy-preserving data publishing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Records management

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Improving Audience Ratings Prediction of Japanese TV Dramas Using Knowledge-Based Embeddings 23

Improving Audience Ratings Prediction of Japanese TV Dramas ...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Bonnell, Jerry Wuchty, Stefan Ogihara, Mitsunori Frost Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States University of Miami Department of Computer Science Coral GablesFL33146 United States

ISBN: (纸本)9798350374889

Accurate prediction of television (TV) audience ratings is a crucial technology for advertisers and broadcast-ers, which influences advertising costs and indicates program popularity. As for Japanese TV dramas, previous studies have examined factors available prior to broadcast, such as cast com-position, metadata variables (e.g., scheduled time slot, broadcasting station, episode duration), and indicators of actor popularity. However, such studies have been limited in their evaluation of contextual information related to these factors and have not considered additional modalities such as text data. Here, we propose the use of knowledge-based embeddings derived from Japanese Wikipedia to improve audience ratings prediction. The proposed bag-of-entities method is applied to represent dramas in terms of cast, production team, and plot synopsis elements, and is evaluated against conventional metadata features. In an ablation study, supervised forecasting models using Support Vector Machine (SVM) are trained on combinations of the feature groups. These are devised and tested on viewership data collected from the Audience Rating TV (ARTV) database for 574 Japanese TV dramas aired between 2003 and 2021. Our experimental results indicate that a SVM model conditioned on the full feature set outperforms baseline methods, achieving a 77.4 % F1-score. After stratifying predictions based on time slot popularity, the approach is found to be effective for predicting audience ratings in challenging, high-popularity time slots, with improvements in recognition rate up to 19.1 %. © 2024 IEEE.

关键词： Support vector machines

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MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts 41

MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Exp...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Jianan Cao, Zhiguang Wu, Yaoxin Song, Wen Ma, Yining Zhang, Jie Xu, Chi College of Computing and Data Science Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Department of Information Systems Eindhoven University of Technology Netherlands Institute of Marine Science and Technology Shandong University China Singapore

Learning to solve vehicle routing problems (VRPs) has garnered much ***, most neural solvers are only structured and trained independently on a specific problem, making them less generic and *** this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants ***, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in *** further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational ***, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark *** further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution *** source code is available at: https://***/RoyalSkye/Routing-MVMoE. Copyright 2024 by the author(s)

关键词： Vehicle routing

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FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler 41

FedCal: Achieving Local and Global Calibration in Federated ...

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41st International Conference on Machine Learning, ICML 2024

作者： Peng, Hongyi Yu, Han Tang, Xiaoli Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia VancouverBC Canada Vector Institute Canada

Federated learning (FL) enables collaborative machine learning across distributed data owners. However, this approach poses a significant challenge for model calibration due to data heterogeneity. While prior work focused on improving accuracy for non-iid data, calibration remains under-explored. This study reveals existing FL aggregation approaches lead to sub-optimal calibration, and theoretical analysis shows despite constraining variance in clients' label distributions, global calibration error is still asymptotically lower bounded. To address this, we propose a novel Federated Calibration (FedCal) approach, emphasizing both local and global calibration. It leverages client-specific scalers for local calibration to effectively correct output misalignment without sacrificing prediction accuracy. These scalers are then aggregated via weight averaging to generate a global scaler, minimizing the global calibration error. Extensive experiments demonstrate that FedCal significantly outperforms the best-performing baseline, reducing global calibration error by 47.66% on average. Copyright 2024 by the author(s)

关键词： Federated learning

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

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

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Siva Kumar, A. Gopirajan, P.V. Ramasamy, Jayaraj Jackson, Beulah School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur Chennai603203 India School Of Computing Srm Institute Of Science And Technology Department Of Computational Intelligence Kattankulathur Campus Chennai603203 India Botho University Faculty Of Engineering And Technology Department of IT Botswana Vel Tech Rangarajan Dr. Sagunthala R & D Institute Of Science And Technology Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350352689

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. © 2024 IEEE.

关键词： Support vector machines

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Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷 1-16页

作者： Hsu, Chih-Chung Jian, Chih-Yu Tu, Eng-Shen Lee, Chia-Ming Chen, Guan-Lin National Cheng Kung University Institute of Data Science The Department of Statistics The Miin Wu School of Computing Tainan70101 Taiwan National Cheng Kung University Department of Computer Science Tainan70101 Taiwan MobileDrive Artificial Intelligence R&d Department New Taipei City23679 Taiwan

This article addresses the challenges associated with hyperspectral image (HSI) reconstruction from miniaturized satellites, which often suffer from stripe effects and are computationally resource-limited. We propose a real-time compressed sensing (RTCS) network designed to be lightweight and require only relatively few training samples for efficient and robust HSI reconstruction in the presence of the stripe effect and under noisy transmission conditions. The RTCS network features a simplified architecture that reduces the required training samples and allows for easy implementation on integer-8-based encoders, facilitating rapid compressed sensing for stripe-like HSI, which exactly matches the moderate design of miniaturized satellites on push broom scanning mechanism. This contrasts optimization-based models that demand high-precision floating-point operations, making them difficult to deploy on edge devices. Our encoder employs an integer-8-compatible linear projection for stripe-like HSI data transmission, ensuring RTCS. Furthermore, based on the novel two-streamed architecture, an efficient HSI restoration decoder is proposed for the receiver side, allowing for edge-device reconstruction without needing a sophisticated central server. This is particularly crucial as an increasing number of miniaturized satellites necessitates significant computing resources on the ground station. Extensive experiments validate the superior performance of our approach, offering new and vital capabilities for existing miniaturized satellite systems. © 1980-2012 IEEE.

关键词： Real time systems

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Automatic Stress Recognition System with Deep Learning using Multimodal Psychological data

Automatic Stress Recognition System with Deep Learning using...

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2022 International Conference on Electronic Systems and Intelligent computing, ICESIC 2022

作者： Praveenkumar., S. Karthick, T. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9781665483858

There are numerous applications for detecting stress states from physiological inputs. Human stress detection may be used to enhance the human experience as well as monitor and prevent stress-related disorders. Previous research has leveraged existing machine learning techniques for automated stress identification. However, there are also some limitations. Deep learning allows you to discover basic patterns in your body's reactions that are often difficult to notice. This study offers a convolutional network-based approach for detecting human stress utilizing multimodal data sets obtained by smart sensors for physiological and motion, which can aid humans in avoiding health issues caused by stress. Sensor information like triaxial accelerates, heartbeat, body temperature, respiration, electromyographic (EMG) data for physiological states stressed and non-stressed, are included in the WESAD data collection. The use of Long short - term memory method and the DNN method to detect stress in people in real-time is investigated in this study. Our research is based on their physical reactions. The study's findings provide an assessment of both models' ability to predict real-time users' mental expressions based on physiological parameter fluctuation. © 2022 IEEE.

关键词： Deep neural networks

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Analysis of Plant Leaf Diseases Recognition using Image Processing with Machine Learning Techniques

Analysis of Plant Leaf Diseases Recognition using Image Proc...

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2022 IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Vasudevan, N. Karthick, T. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9781665495295

Agriculture plays a foremost role in countries growth. The physical recognition of disease in the plant is more timeconsuming and necessity of expert labor is high. One of the most vital aspect in agriculture field is plant disease that reduces quality andquantity of agricultural crops. The machine learning approaches can easily recognize these diseases compared to physical method. So the machine learning techniques in addition toimage processing is used to identify diseased leaf. These methods will help in recognizing plant diseases thus increasing the yield of plants. This paper concentrates on the previous research work that used the characteristics of machine learning techniques in addition to image processing. It describes the various plant disease types, image processing methods and also various techniques of plant disease systems are analyzed with accuracy. Finally, the various challenges in the recognition of plant leaf diseases are identified and listed. © 2022 IEEE.

关键词： Learning algorithms

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Assessing the feasibility of Blockchain Technology in Automotive Industry 9

Assessing the feasibility of Blockchain Technology in Automo...

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9th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2022

作者： Balachander, S. Murugan, A. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chengalpattu India

ISBN: (数字)9798350332506

ISBN: (纸本)9798350332506

Blockchain is a decentralised, distributed public ledger that has recently garnered enormous traction. The automotive industry, one of the most profitable markets worldwide, is undoubtedly responsible for the transformations in modern civilisation over the past century. This paper primarily focuses on the issue of the complexity of existing technologies in the field and the possibility of applying Blockchain technology as a replacement in the automotive sector. In addition, the researcher has considered compatible precedents for identifying lacunae in the existing technologies. As a result, the adoption of Blockchain technology provides access to a wide range of potential automotive applications that can develop modern business models moreover even transform the established automotive partaking economy. Further, after examining the advantages, limitations, opportunities, and risks, some suggestions are made to guide enterprises and researchers in future advancements in the cyber-resilient automotive sector. © 2022 IEEE.

关键词： Blockchain

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