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Local Search-Based Anytime Algorithms for Continuous Distributed Constraint Optimization Problems

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 288-290页

作者： Xin Liao Khoi Hoang Xin Luo the College of Computer and Information Science Southwest University the Department of Computer Science and Engineering Washington University in St.Louis IEEE

Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-world problems like the distributed scheduling [4], sensor network management [5], [6], multi-robot coordination [7], and smart grid [8]. However, DCOPs were not well suited to solve the problems with continuous variables and constraint cost in functional form, such as the target tracking sensor orientation [9], the air and ground cooperative surveillance [10], and the sensor network coverage [11].

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Improvements in Recommendation Systems Using Graph Neural Networks

Improvements in Recommendation Systems Using Graph Neural Ne...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Srivastava, Akhilesh Kumar Verma, Ishanki Garg, Puneet ABES Engineering College Department of Computer Science Engineering Ghaziabad India St. Andrews Institute of Technology and Management Department OfCSE Gurugram India

ISBN: (纸本)9798350376470

The concept of graph neural networks has shown improvements in many applications due to its ability to handle data involving complex relationships. As the population demonstrates a consistent upward trend, concomitant with this demographic expansion is the accrual of an augmented dataset. This project involves the use of graph neural networks in order to handle expanding data. Large dataset requires effective handling and so the concept of multiprocessing is used in order to handle data with maximum utilisation of CPU. Random walk is used for generating node embeddings for the graph. Node embeddings are then utilised for link prediction which is used for assessing the likelihood of relationships between the nodes. There is also an option of leveraging metapath schemes in order to guide the random walk. The key concepts also include variation of N ode2vec algorithm, use of neighbour sampling, use of negative sampling during training and noise contrastive estimation(NCE) as a loss function. © 2024 IEEE.

关键词： Graph neural networks

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Poor and rich squirrel algorithm-based Deep Maxout network for credit card fraud detection

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 123-134页

作者： Paul, Annu Paul, Varghese Department of Computer Science Alphonsa College Kottayam Kerala Pala India Department of Computer Science and Engineering Rajagiri School of Engineering and Technology Kerala Kochi India

This paper proposes a Poor and Rich Squirrel Algorithm (PRSA)-based Deep Maxout network to find fraud data transactions in the credit card system. Initially, input transaction data is passed to the data transformation phase, transforming data using Yeo-Johnson (YJ) transformation. Then, the feature selection procedure is done by the Fisher score for creating the unique and significant features. Next, based on the selected textures, the data augmentation mechanism is done using the oversampling model. At last, the fraud detection is carried out by the Deep Maxout network, which is trained by the proposed PRSA optimisation algorithm, derived by integrating Poor and Rich Optimisation (PRO) and Squirrel Search Algorithm (SSA). The integration of parametric features of the PRSA algorithm effectively trained the classifier to update weights to generate the best solution by considering fitness measures. The proposed method achieved the best accuracy, sensitivity, and specificity measures of 0.96, 0.95 and 0.94, respectively. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： credit card data augmentation data transformation deep learning fraud detection

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Privacy Preserving Electronic Health Records Sharing using Blockchain 1

Privacy Preserving Electronic Health Records Sharing using B...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Kumar, Dhaneshwar Shrivastava, Rajesh Kumar Gupta, Umesh Bennett University Department of Computer Science Gr. Noida India ABES Engineering College Department of Computer Science U.P Ghaziabad India

ISBN: (纸本)9798350353648

A variety of fundamental challenges, such as a lack of data transparency, traceability, immutability, audit, data provenance, flexible access, trust, privacy, and security, confront healthcare information management systems today. Furthermore, the centralized nature of most existing healthcare systems for data management raises the risk of a single point of failure in the event of a natural disaster. Blockchain, a novel and disruptive type of decentralized technology, has the potential to profoundly revolutionize, reshape, and transform the way the healthcare sector manages data. This could have a significant impact on the quality of care that patients receive. We have covered the fundamental capacities and characteristics of blockchain technology that are required to ensure the safety of patient data. We investigate the most significant benefits of utilizing blockchain technology and its potential applications in the medical and healthcare industries. The field of medicine has witnessed a surge in interest in mobile applications, particularly those aimed at patients. However, there are several concerns regarding privacy and safety with the current mechanisms for sharing data from IoT devices and medical records. These concerns relate to the fact that these mechanisms are currently in use. The electronic health record proposes the blockchain technology and the Inter Planetary File System (IPFS) mechanism, both of which are secure and considerate of patients' privacy, to effectively tackle these concerns. In conclusion, we cover a broad spectrum of potential research topics for further investigation. © 2024 IEEE.

关键词： Electronic health record

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Medi-Connect: An End-to-End Intelligent Framework to Facilitate New Era Online Consultations Toward Sustainable Healthcare 5th

Medi-Connect: An End-to-End Intelligent Framework to Facilit...

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5th International Conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

作者： Yadav, Jayash Raj Singh Jain, Paras Sharma, Shanu Dubey, Ashwani Kumar Department of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819716814

The healthcare industry has undergone a significant transformation in recent years, with the integration of information technology and online platforms revolutionizing the way medical services are delivered. One such development is the emergence of online consultations, enabling patients to seek medical advice from doctors without the need for in-person visits. The increasing popularity of online consultations in the healthcare industry has highlighted the need for efficient systems to support remote medical services. In this paper, an intelligent framework is proposed to assist patients and doctors during online consultations. The framework offers end-to-end services starting from patient registration, doctors’ availability, the online appointment of doctors, etc., required during an online consultation. The proposed framework is also capable of assisting the doctors during diagnosis of diseases and drug recommendation through intelligent decision-making capability based on the symptoms provided by the patient. The use of the Naïve Bayes algorithm is explored here to perform classification on patient data and the determination of the probability of specific diseases. In addition to accurate diagnoses, the system recommends appropriate medicinal treatments and provides a database of specialist doctors for further consultation. This integration of technology and health expertise seeks to streamline the healthcare process, saving time and reducing costs for both patients and doctors. By presenting a streamlined framework for online consultations, the main aim of this work is to contribute to the advancement of virtual healthcare services and to bridge the gap between traditional healthcare practices and the evolving digital landscape, ultimately improving healthcare accessibility and outcomes for patients worldwide. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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Deep learning framework for automatic detection and classification of sleep apnea severity from polysomnography signals

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Neural Computing and Applications 2024年第28期36卷 17483-17493页

作者： Raja Brundha, A. Lakshmi Sangeetha, A. Balajiganesh, A. Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India Department of Computer Science Engineering Velammal Engineering College Chennai India

Sleep apnea (SA) is a sleep-related breathing disorder characterized by breathing pauses during sleep. A person’s sleep schedule is significantly influenced by that person’s hectic lifestyle, which may include unhealthy eating habits and their line of work. Polysomnographic (PSG) sleep studies examine sleep-related disorders by recording various biosignals from the human body. However, SA classification methods could be more robust in terms of performance because they rely on feature-engineering strategies or employ a particular signal from PSG recording for diagnosis. This study aims to classify the severity of SA according to the apnea–hypopnea index (AHI) into normal, mild, and moderate-to-severe groups using oxygen saturation (SpO2), electroencephalogram (EEG), and electrocardiogram (ECG) signals. The proposed deep neural network (DNN)-bidirectional long short-term memory (DNN-BiLSTM) framework addresses the issue of low detection accuracy in analysis. The DNN-BiLSTM approach employs features extracted from a multiscale dilation attention 1D convolutional neural network (MSDA-1DCNN) as input for detection and classification purposes. The MSDA-1DCNN network extracts deep features from processed SpO2, EEG, and ECG signals. The developed firefly combined electric fish optimization (FCEFO) algorithm improves performance by optimizing the hidden neuron count of the DNN and the learning rate of the BiLSTM framework. The performance measures proved the effectiveness of the model over conventional machine and deep learning approaches. With the integration of deep features, the proposed DNN-BiLSTM model provides enhanced performance in terms of accuracy and precision. Thus, the proposed approach is progressive and can be used for medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electrocardiograms

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Enhancing epigraphy: a deep learning approach to recognize and analyze Tamil ancient inscriptions

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Neural Computing and Applications 2024年第31期36卷 19839-19861页

作者： Bhuvaneswari, S. Kathiravan, K. Computer Science and Engineering Easwari Engineering College Tamil Nadu Chennai India Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India

This research paper presents a novel algorithmic approach for character recognition and contextual analysis of temple inscriptions, specifically focusing on Tamil ancient script. The methodology combines advanced preprocessing techniques, deep learning models, and contextual analysis to address the challenges posed by noisy images, script variations, and historical context understanding. We compiled a dataset of 100 high-resolution images of temple inscriptions from various regions and periods. The preprocessing phase involves Noise Reduction, Contrast Enhancement, Orientation Correction, and Adaptive Binarization algorithms to enhance the quality of the inscription images. The character recognition stage employs Convolutional Neural Networks with Transfer Learning, further enhanced by the Multi-head Attention mechanism in Vision Transformers (ViT). The character segmentation algorithm used was the Stroke Width Transform. Transfer Learning was incorporated to adapt the pre-trained ViT model to our specific task. This approach significantly improves the model’s ability to recognize characters from diverse scripts and languages. The results demonstrate the effectiveness of the proposed methodology. The character recognition accuracy metrics include a precision of 97.25%, a recall of 95.05%, and an F1-score of 95.17%. Additionally, the model achieved a recognition rate of 98.92% for key terms related to historical events, deities, and rulers. It also demonstrated a 94% recognition rate for context-specific phrases and a 95% recognition rate for historical dates. Contextual analysis results indicate that the model successfully identifies specific terms, phrases, and historical references, contributing to a deeper understanding of the inscriptions. The model's ability to recognize characters from multiple scripts underscores its adaptability to diverse inscriptions. In conclusion, this research provides a comprehensive and efficient solution for character recognition and

关键词： Image segmentation

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Unleashing the potential of ChatGPT in education: Revolutionizing learning and collaboration

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IEEE Potentials 2024年第6期43卷 13-22页

作者： Chandrasekaran, Balaji Nair, Smrithy Girijakumari Sreekantan College of Engineering Guindy Anna University Department of Computer Science and Engineering Chennai600025 India Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and collaboration within educational settings. By leveraging natural language processing and machine learning, ChatGPT offers an interactive and personalized learning experience, enabling students to engage in dynamic conversations and receive instant feedback. Moreover, ChatGPT may facilitate collaborative learning by promoting effective communication and knowledge sharing among students and educators. This article examines the various applications of ChatGPT in education, including virtual tutoring, adaptive learning, and intelligent chatbots. It also discusses the benefits and considerations associated with implementing ChatGPT, such as ethical considerations, data privacy, and potential biases. While acknowledging the limitations and challenges, this article emphasizes the promising potential of ChatGPT in transforming education by enhancing student engagement, fostering critical thinking skills, and expanding access to quality education. © 1988-2012 IEEE.

关键词： Adversarial machine learning

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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HCBiL-DMN: an effective food infestation detection from stored food grains using deep learning model

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Multimedia Tools and Applications 2025年第14期84卷 14047-14070页

作者： Vedavathi, N. Suhas Bharadwaj, R. Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru570002 India Department of Information Science and Engineering The National Institute of Engineering Karnataka Mysuru570008 India

Food Infestation Detection is more important for food safety and health concerns. It is a challenging task to separate the grains into infested or non-infested. It is found that in the existing system, there is no effective approach for the early detection of pest populations and for classifying crops as infested or non-infested. Therefore, the identification and categorization of early-stage grain infestations is in high demand. To address the above problem, this research presents a better technique for detecting and classifying infested and non-infested grains. Initially, the maize, wheat and olive image datasets are collected, which are then pre-processed using data normalization and adaptive wiener filtering (AdwF) approach to improve the excellence of the input images. Image quality helps in achieving better performance and also helps in analyzing the quality of food grains effectively. Feature extraction is done using the Dense Residual Pyramidal Dilated Convolution Network (DR-PDCNet) technique, and feature selection is done using the Linear Discriminant Coati Optimization (LD-CoA) algorithm. The Hybrid Convolutional Bi-LSTM based Deep Maxout Network (HCBiL-DMN) model is used to detect the infected food grains in the preserved food grains with high accuracy. This research explores the potential and future possibilities for using technology to ensure the quality, assessment, and monitoring of nutritional grains during storage. The performance results are achieved with 98.3% accuracy, 99.12% recall, 98.65% precision, 98.02% MCC, and 99.32% F1 score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolution

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