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Strategies for classifying water quality in the Cauvery River using a federated learning technique

International Journal of Cognitive Computing in engineering 2023年第1期4卷 187-193页

作者： J, Vellingiri K, Kalaivanan P, Gopinath M C, Gobinath Subramaniam, Prabhakar Rontala Rangarajan, Sarathkumar School of Information Technology and Engineering Vellore Institute of Technology Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Information System and Technology University of KwaZulu-Natal Durban South Africa Victorian Institute of Technology Melbourne Australia

Artificial intelligence methods are emerging techniques used in the field of environmental protection, especially in the analysis of air, water, and soil quality. AI analyzes vast amounts of environmental data to predict pollution and provide decision-makers with the information they need to develop efficient policies. One of the most important problems in environmental analysis is data security, and many organizations are actively working to ensure the secure collection, storage, and utilization of sensitive environmental data. In addition, organizations are focusing on developing strategies to protect their data from malicious attacks, such as cyber-attacks, as well as from accidental misuses, like unauthorized access. For this purpose, we have introduced a novel water quality prediction using the Federated Learning Technique. Federated learning enables multiple parties to collaborate and train a model on their local data without sharing it with others, thereby preserving data privacy. The proposed method is applied to a Cauvery River dataset of water quality parameters, and the results demonstrate that the PSO-optimized federated learning process achieves better prediction accuracy of 87%, a precision of 85%, a recall of 93%, and an 89% F1 score. © 2023

关键词： Machine learning

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A Modeling and Probabilistic Reasoning Method of Dynamic Uncertain Causality Graph for Industrial Fault Diagnosis

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International Journal of Automation and computing 2014年第3期11卷 288-298页

作者： Chun-Ling Dong Qin Zhang Shi-Chao Geng Department of Geographical Information System Shandong Normal University School of Computer Science and Engineering Beihang University Institute of Nuclear and New Energy Technology Tsinghua University

Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient ***, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.

关键词： Fault diagnosis causality model probabilistic graphical model uncertain knowledge representation weighted logic inference.

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Reusing Service Process Fragments with a Consensus Between Service Providers and Users

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Chinese Journal of Electronics 2016年第4期25卷 648-657页

作者： YANG Rong LI Bing WANG Jian LI Zengyang HU Yan College of Computer Science and Technology Hubei University of Science and Technology International School of Software Wuhan University State Key Laboratory of Software Engineering & School of Computer Wuhan University Science and Technology on Integrated Information System Laboratory Institute of SoftwareUniversity of Chinese Academy of Sciences

Recent years have witnessed a rapid growth in using Web services for data publishing and sharing among *** improve the efficiency of software development and economize on human and material resources,service reuse is viewed as a powerful means which will not only reuse atomic services,but also reuse arbitrary granularities of Service process fragments(SPFs).However,effectively reusing arbitrary granularities of SPFs has not been solved yet,let alone taking diverse Qo S preferences of service providers and users into *** this paper,we propose a novel method of SPF reuse,named SCKY,based on the Cocke-Kasami-Younger(CKY) *** first present an extended CKY to do *** we address how to do SPF-query by a probability CKY,i.e.,return a SPF with maximum emergence ***,we explore the SPF-query with a consensus of Qo S preferences between service providers and *** a set of experiments,the effectiveness and robustness of our approach are evaluated.

关键词： Cocke-Kasami-Younger algorithm Service reuse Service process fragment Qo S preferences

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Social Media-Based Surveillance systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

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computer Modeling in engineering & Sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

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AI-optimized electrochemical aptasensors for stable, reproducible detection of neurodegenerative diseases, cancer, and coronavirus

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Heliyon 2025年第1期11卷 e41338页

作者： Tayfour Ahmed, Amira Elsir Dhahi, Th.S. Attia, Tahani A. Elhassan Ali, Fawzia Awad Elobaid, Mohamed Elshaikh Adam, Tijjani Gopinath, Subash C.B. Department of Information System College of Science & Arts King Khalid University Asser Mohyel Saudi Arabia Health and Medical Technicals College Southern Technical University Basrah Iraq Department of Computer Engineering College of Computer Science and Engineering University of Ha'il Saudi Arabia DEEE Faculty of Engineering University of Khartoum Sudan Imam Abdulrahman Bin Faisal University Applied College Computer Department Saudi Arabia Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Perlis Arau 02600 Malaysia Institute of Nano Electronic Engineering Universiti Malaysia Perlis Perlis Kangar 01000 Malaysia Micro System Technology Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia Center for Global Health Research Saveetha Medical College & Hospital Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Tamil Nadu Chennai 602 105 India Faculty of Chemical Engineering & Technology Universiti Malaysia Perlis (UniMAP) Perlis Arau 02600 Malaysia

AI-optimized electrochemical aptasensors are transforming diagnostic testing by offering high sensitivity, selectivity, and rapid response times. Leveraging data-driven AI techniques, these sensors provide a non-invasive, cost-effective alternative to traditional methods, with applications in detecting molecular biomarkers for neurodegenerative diseases, cancer, and coronavirus. The performance metrics outlined in the comparative table illustrate the significant advancements enabled by AI integration. Sensitivity increases from 60 to 75 % in ordinary aptasensors to 85–95 %, while specificity improves from 70-80 % to 90–98 %. This enhanced performance allows for ultra-low detection limits, such as 10 fM for carcinoembryonic antigen (CEA) and 20 fM for mucin-1 (MUC1) using Electrochemical Impedance Spectroscopy (EIS), and 1 pM for prostate-specific antigen (PSA) with Differential Pulse Voltammetry (DPV). Similarly, Square Wave Voltammetry (SWV) and potentiometric sensors have detected alpha-fetoprotein (AFP) at 5 fM and epithelial cell adhesion molecule (EpCAM) at 100 fM, respectively. AI integration also enhances reproducibility, reduces false positives and negatives (from 15-20 % to 5–10 %), and significantly decreases response times (from 10-15 s to 2–3 s). These advancements improve data processing speeds (from 10 to 20 min per sample to 2–5 min) and calibration accuracy (<2 % margin of error compared to 5–10 %), while expanding application scope to multi-target biomarker detection. This review highlights how these advancements position AI-optimized electrochemical aptasensors as powerful tools for personalized treatment, point-of-care testing, and continuous health monitoring. Despite a higher cost ($500-$1,500/unit), their enhanced portability and diagnostic performance promise to revolutionize healthcare, environmental monitoring, and food safety, ultimately improving public health outcomes. © 2024

关键词： AI Aptasensors Cancer Coronavirus Electrochemical Integrated biosensor Molecular cells Neurodegenerative

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Collaborative Service Provisioning for UAV-Assisted Mobile Edge Computing

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Chinese Journal of Electronics 2024年第6期33卷 1504-1514页

作者： Yuben QU Zhenhua WEI Zhen QIN Tao WU Jinghao MA Haipeng DAI Chao DONG The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Colllege of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Xi'an Research Institute of High Technology Department of Information and Communication Noncommissioned Officer Academy of PAP National University of Defense Technology Department of Computer Science and Technology Nanjing University

Unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC), as a way of coping with delaysensitive and computing-intensive tasks, is considered to be a key technology to solving the challenges of terrestrial MEC networks. In this work, we study the problem of collaborative service provisioning(CSP) for UAV-assisted MEC. Specifically, taking into account the task latency and other resource constraints, this paper investigates how to minimize the total energy consumption of all terrestrial user equipments, by jointly optimizing computing resource allocation, task offloading, UAV trajectory, and service placement. The CSP problem is a non-convex mixed integer nonlinear programming problem, owing to the complex coupling of mixed integral variables and non-convexity of CSP. To address the CSP problem, this paper proposes an alternating optimization-based solution with the convergence guarantee as follows. We iteratively deal with the joint service placement and task offloading subproblem, and UAV movement trajectory subproblem, by branch and bound and successive convex approximation, respectively,while the closed form of the optimal computation resource allocation can be efficiently obtained. Extensive simulations validate the effectiveness of the proposed algorithm compared to three baselines.

关键词： Energy consumption Multi-access edge computing Collaboration Programming Autonomous aerial vehicles Minimization Trajectory Resource management Optimization Convergence

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The Relation of Department Components and Activity in Spatial information Infrastructure

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Wuhan University Journal of Natural Sciences 2001年第Z1期6卷 460-466页

作者： Yi Shan zhen 1, Zhou Li zhu 1,Cheng Ji cheng 2, Li Qi 2 1.Department of computer Science and technology,Tsinghua University, Beijing 100084, China 2.institute of Remote Sensing and Geographical information system,Peking University,Beijing 1008 Department of Computer Science and Technology Tsinghua University Beijing China Institute of Remote Sensing and Geographical Information System Peking University Beijing China

Spatial information Infrastructure (SII) facilitates the sharing, interoperability and integration of geographical information among department components of a region or a country. The SII is developed and shared by different department components. The relation of department components is a fundament for collaborating tasks and information exchange in SII. There are two kinds of department components, one is the provider which produces geospatial data of SII, other is the consumer which uses geospatial data of SII. The consumer includes two kinds of user, one is the user which only uses and does not produce geospatial data in SII, other is the special user which not only uses but also produces geospatial data in SII. The provider includes different hierarchies corresponding to different kinds of geospatial data in SII. All providers in the hierarchies form provider actor set PA. All consumers also form consumer actor set CA. The sender recipient relation SR and SRI are defined on Cartesian product of PA and CA. Five tasks and information flow in SII demonstrate the geospatial data acquisition, information production, management, and application among department components. The tasks and their sub tasks are subdivided activities. Each activity corresponds to the relation SR or RSI. The activity along with the relation of provider and consumer forms a scheme of geographical information exchange between department components.

关键词： geographical information exchange relation activity information infrastructure

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Multi-directional feature difference enhancement network for remote sensing change detection

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International Journal of Remote Sensing 2025年

作者： Xu, Chuan Liang, Yabin Hu, Xinglong Wang, Jin Gao, Linyao Xia, Jiali Ye, Zhiwei Du, Wenying Mei, Liye The School of Computer Science Hubei University of Technology Wuhan China State Grid Hubei Electric Power Co. Ltd. Huanggang Power Supply Company Huanggang China State Grid Hubei Electric Power Co. Ltd Wuhan China The National Engineering Research Center for Geographic Information System China University of Geosciences Wuhan China The Institute of Technological Sciences Wuhan University Wuhan China

Monitoring surface changes using change detection technology is significant for sustainable development and environmental protection. However, existing change detection methods do not deeply analyse inter-class differences and fail to fully understand the textural, shape, and spatial distribution characteristics of the changed objects. This inadequacy affects the subsequent comparison of information differences between the two temporal features, leading to increased false changes and inaccurate detection. We propose a Multi-Directional Feature Difference Enhancement Network (MDENet) method for change detection. This method effectively extracts information of bitemporal change features and enhances their differences, thereby improving the model’s accuracy in detecting various complex changes. We employ Learnable Feature Selection Residual Blocks (LFSB) for feature extraction, incorporating a Multi-directional Depth Convolution Module (MDCM). This module enhances the model’s representational capacity by using depth convolutions at different directional scales, thereby improving the extraction of change features. Additionally, we introduce a Feature Difference Attention Enhancement Module (FDAM), which selectively aggregates corresponding position features based on the weighted comparison between bitemporal images. This module models the local spatial dependencies at any location, enhancing the model’s perception and recognition capabilities. The experimental results indicate this method improves accuracy on LEVIR-CD, WHU-CD, CL-CD, and SYSU-CD datasets, outperforming other mainstream change detection methods. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Change detection

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An IPTV Video Stream Transmission Algorithm Based on Multicast and QoS 19

An IPTV Video Stream Transmission Algorithm Based on Multica...

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19th IEEE International Conference on Communication technology, ICCT 2019

作者： Tao, Jun Du, Min Zhang, Yunling Ma, Lixiang Anhui Institute of Information Technology Computer and Soft Engineer Department Wuhu China Anhui Institute of Information Technology College of Electrical and Electronic Engineering Wuhu China

ISBN: (纸本)9781728105352

This paper introduces the basic concepts of IPTV and multicast technology, and proposes a USDP algorithm to overcome the shortcomings of MSDP algorithm. After the simulation experiment has achieved good results, according to the USDP algorithm, a scheme of IPTV bearer network based on multicast and QOS technology is proposed. After the implementation of the scheme, the network data are collected and analyzed. It is verified that the scheme reduces the network traffic load, reduces the transmission delay and ensures the normal operation of key services. © 2019 IEEE.

关键词： IPTV

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A Multi Access VPDN Network Model 22

A Multi Access VPDN Network Model

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22nd IEEE International Conference on Communication technology, ICCT 2022

作者： Tao, Jun Chen, Huan She, XingXing Liu, HuaMin Anhui Institute of Information Technology Computer and Soft Engineer Department Wuhu China College of Electrical and Electronic Engineering Anhui Institute of Information Technology Wuhu China

ISBN: (纸本)9781665470674

VPN network is widely used at present, among which L2TP VPDN can better solve the problem of decentralized users accessing private networks, but users accessing through a variety of network types and multiple ISP will have higher time prices and occupy more bandwidth resources. Aiming at these drawbacks, a multi access L2TP VPDN network model named ILP is proposed, which has the characteristics of security and low cost. The simulation experiment of the new model is carried out, and the experimental results are analyzed by Waxman mathematical model, which shows that the results of the network simulation experiment are excellent. Finally, according to ILP model, the actual network is constructed, and all parameters operate normally. © 2022 IEEE.

关键词： Virtual private networks

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