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Observation points classifier ensemble for high-dimensional imbalanced classification

CAAI Transactions on Intelligence Technology 2023年第2期8卷 500-517页

作者： Yulin He Xu Li Philippe Fournier‐Viger Joshua Zhexue Huang Mianjie Li Salman Salloum College of Computer Science&Software Engineering Shenzhen UniversityShenzhenChina Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen UniversityShenzhenChina Faculty of Information Technology Macao University of Science and TechnologyMacaoChina School of Computing National University of SingaporeSingaporeSingapore

In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction ***,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as ***,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data ***,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown *** experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data *** results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC *** demonstrates that OPCE is a viable algorithm to deal with HDIC problems.

关键词： classifier ensemble feature transformation high-dimensional data classification imbalanced learning observation point mechanism

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IoT Authentication Protocols: Classification, Trend and Opportunities

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 515-533页

作者： N.alsheavi, Amar Hawbani, Ammar Wang, Xingfu Othman, Wajdy Zhao, Liang Liu, Zhi Alsamhi, Saeed Hamood Al-Qaness, Mohammed A. A. University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China Nankai University Haihe Laboratory of ITAI School of Cyber Science Tianjin300459 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan University of Galway Insight Centre for Data Analytics Galway Ireland IBB University Faculty of Engineering Ibb Yemen Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions. © 2016 IEEE.

关键词： Authentication Protocol

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Sequence-to-Sequence Knowledge Graph Completion Based on Gated Attention Unit 29

Sequence-to-Sequence Knowledge Graph Completion Based on Gat...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Yi, Fengge Wei, Xiumei Liu, Xiaojing Jiang, Xuesong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (纸本)9798350330717

We present GauKGT5, a sequence-to-sequence model proposed for knowledge graph completion (KGC). Our research extends the KGT5 model, a recent sequence-to-sequence link prediction (LP) model. GauKGT5 takes advantage of textual characteristics inherent in the knowledge graph, exhibiting a small model size. However, KGT5's proficiency in link prediction necessitates the ensemble with a knowledge graph embedding model, which itself poses challenges due to its substantial size and expense. By integrating the Gated Attention Unit into the KGT5 model and directly applying it to the encoder-decoder structure, we achieve improved contextual dependency capturing within the sequence, resulting in enhanced prediction accuracy, accelerated training speed, and enhanced computational efficiency. At the same time, we introduce parallel computing as a means to enhance the efficiency of model training and inference within the XPU distributed computing environment. © 2023 IEEE.

关键词： gated attention unit knowledge graph completion sequence-to-sequence model

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Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

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Multimedia Tools and Applications 2025年第16期84卷 15735-15761页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

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Heterogeneous Edge Service Deployment for Cyber Physical Social Intelligence in Internet of Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-13页

作者： Meng, Ke Liu, Zhongjian Xu, Xiaolong Xia, Xiaoyu Tian, Hao Qi, Lianyong Zhou, Xiaokang School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Software Nanjing University of Information Science and Technology Nanjing China School of Computing Technologies RMIT University Melbourne Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Qingdao China Faculty of Data Science Shiga University Otsu Japan

Cyber Physical Social Intelligence (CPSI) integrates the social intelligence and cyber-physical systems, enabling machines to better interact and respond to human social behaviors. Under CPSI, the Internet of Vehicles (IoV) has given rise to an increasing number of latency-sensitive services. Edge computing, as a distributed computing paradigm, enhances data processing capabilities, reduces data transmission latency, and minimizes bandwidth consumption. However, due to the limited resources of edge servers, striking a balance between service latency and deployment costs remains a highly challenging issue in the process of service deployment. In this paper, we propose a heterogeneous edge service deployment method for CPSI in IoV. Firstly, considering the heterogeneity of IoV services and edge servers, communication model, computational model, and heterogeneous service deployment cost model are constructed. Secondly, to maximize service deployment efficiency and minimize communication latency, a distance and workload-based edge server cluster division method is proposed. Subsequently, heterogeneous service deployment is performed in different clusters based on service category prioritization and minimal deployment quantity prioritization principles. Furthermore, an Analytic hierarchy process-based Heterogeneous edge Service dePloyment algorithm for CPSI in IoV, named AHSP, has been designed to determine optimal service deployment strategies. Finally, extensive numerical experimental results demonstrate the effectiveness of AHSP. IEEE

关键词： Computation theory

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A Review of vision-based Abnormal Human Activity Analysis for Elderly Emergency Detection 17

A Review of Vision-based Abnormal Human Activity Analysis fo...

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17th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2023

作者： Nouisser, Raoudha Jarraya, Salma Kammoun Hammami, Mohamed University of Sfax Miracl Laboratory Faculty of Economics and Management Tunisia King Abdulaziz University Faculty of Computing and Information Technology Computer Science Department Jeddah Saudi Arabia University of Sfax Miracl Laboratory Faculty of Sciences Tunisia

ISBN: (纸本)9798350338904

As life expectancy increases, people can live a longer life. However, ageing is associated with various health problems. As a result, older people are vulnerable to emergencies. To ensure the security of older people, it is necessary to analyse abnormal activities. Accordingly, vision sensors serve in elderly care and assisted living. Hence, this study allows the researchers to better understand the field of elderly emergency detection through abnormal activity analysis. In this work, we investigate and compare existing visual approaches and their performance. In addition, we explore available and interesting datasets. Moreover, we discuss existing approaches and datasets to improve real-world applications. We also present challenges to be tackled for future studies in monitoring applications. © 2023 IEEE.

关键词： information analysis

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A Hybrid Security Framework for Medical Image Communication

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computers, Materials & Continua 2022年第11期73卷 2713-2730页

作者： Walid El-Shafai Hayam A.Abd El-Hameed Ashraf A.M.Khalaf Naglaa F.Soliman Amel A.Alhussan Fathi E.Abd El-Samie Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMinia61111Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

Authentication of the digital image has much attention for the digital *** image authentication can be verified with image watermarking and image encryption *** schemes are widely used to protect images against forgery attacks,and they are useful for protecting copyright and rightful *** on the desirable applications,several image encryption and watermarking schemes have been proposed to moderate this *** framework presents a new scheme that combines a Walsh Hadamard Transform(WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding(DRPE).First,on the sender side,the secret medical image is encrypted using *** the encrypted image is watermarking based on *** combination between watermarking and encryption increases the security and robustness of transmitting an *** performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index(SSIM),Peak Signal-to-Noise Ratio(PSNR),Normalized cross-correlation(NC),and Feature Similarity Index(FSIM).

关键词： Walsh hadamard transform watermarking encryption double random phase encoding structural similarity index

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The Impact Of Quantum Computing In Evolutionary Algorithms

The Impact Of Quantum Computing In Evolutionary Algorithms

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2023 International Conference on Decision Aid sciences and Applications, DASA 2023

作者： Belkebir, Djalila Lati, Abdelhai Belkebir, Malak Bp 511 Faculty of New Information and Communication Technologies Ouargla30000 Algeria University of Oum El Bouaghi Research Laboratory on Computer Science's Complex Systems Dept. Mathematics and Computer Science Oum El Bouaghi Algeria

ISBN: (纸本)9798350342055

Genetic algorithms are widely used for solving optimization problems. As the search space of the problem is augmented, the probability of falling into local minima will be augmented as well. Previous works showed that the adjusted phase (i.e. required modification per generation), may enhance badly the optimality reaching. Moreover, the search for suitable probabilities on crossover and mutation results in huge computational complexity. Recent developments in quantum technology have shown that quantum computers can provide a dramatic advantage over classical computers. This paper will be oriented toward computer scientists interested in the quantum genetic algorithm (QGA). Therefore, we will present a discussion, of the pros, and cons of classical GA. We survey the 'state of the art' of the most known techniques for evolving evolutionary algorithms and quantum computing. We intend in this work to help researchers and application developers in gaining insights into the power of quantum computing mechanisms. This science can inherently treat the balance between exploration and exploitation of the search space. This property is due to the use of Q-bits. The orientation toward the best solution is due to the use of Q-gates. © 2023 IEEE.

关键词： Genetic algorithms

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Real-Time Sensor Fault Tolerant Control of DC-DC Converters in DC Microgrids Using a Switching Unknown Input Observer

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IEEE Access 2025年 13卷 95837-95850页

作者： Ouahabi, Mohammed Said Benyounes, Abdelhafid Barkat, Said Ihammouchen, Syphax Rekioua, Toufik Rabehi, Abdelaziz El-Kenawy, El-Sayed M. Alharbi, Amal H. University of M’sila Electrical Engineering Laboratory Faculty of Technology M’sila28000 Algeria University of Bejaia Laboratoire de Technologie industrielle et de l’Information Bejaia06000 Algeria University of Djelfa Telecommunications and Smart Systems Laboratory PO Box 3117 Djelfa17000 Algeria PO Box 33349 Isa Town Bahrain Applied Science Private University Applied Science Research Center Amman Jordan Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences P.O. Box 84428 Riyadh11671 Saudi Arabia

DC Microgrids are rapidly developing, driven by the increasing integration of renewable sources and dynamic loads. Their growing adoption in critical applications raises the need for operational safety and reliability, particularly under fault conditions. This paper presents a sensor fault-tolerant control (FTC) for DC-DC power converters in DC Microgrids designed to maintain resilient and reliable operation. The proposed FTC leverages a single Switching Unknown Input Observer (SW-UIO) at the bottom control level to estimate arbitrary sensor faults with a high accuracy (0.3% fault estimation error), allowing a real-time identification and precise control reconfiguration to accommodate sensor faults with minimal computational burden. The approach is validated under diverse conditions, including time-varying loads, complete sensor failure, noise abnormality, and simultaneous faults/failure, achieving a consistent fault accommodation of 1μs from fault occurrence to compensation. Real-time simulation has been performed in the OPAL-RT environment to assess the efficacy and superiority of the proposed sensor fault tolerant strategy in comparison to reference FTC strategies. © 2013 IEEE.

关键词： Identification (control systems)

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Project oriented teaching approach of decentralised applications for undergraduate students 23

Project oriented teaching approach of decentralised applicat...

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Proceedings of the 15th International Conference on Education Technology and computers

作者： Sandi Gec Petar Kochovski Vlado Stankovski Dejan Lavbic Laboratory for Data Technologies Faculty of Computer and Information Science University of Ljubljana Slovenia

ISBN: (纸本)9798400709111

The emergence of new technologies and software engineering methodologies in the decentralised Web, referred to as Web 3.0, often requires using new approaches to supplement conventional development concepts such as object-oriented programming and design patterns. Therefore, it is essential to understand the blockchain fundamentals and modular development of distributed systems built from multiple components. This paper proposes a project-oriented teaching approach of smart contracts-based distributed applications (dApps) integrated into existing (Web 2.0) Web applications. First, we present the main overview of the Web programming course for a bachelor study program, prior knowledge requirements, and the characteristics of the lectures. Second, we define the dApp functional requirements for student projects that intend to consolidate the dApp theoretical concepts with a practical approach to complement the Web 3.0 concepts. The study of our teaching approach is based on an experiment where we thoroughly analyse performance on the course level, projects and individual students. The results indicate a high correlation between students' performance in Web 2.0 and Web 3.0 development, and a higher degree of iterative process is also associated with better-performing students. Moreover, the student project outcomes also provide a basis for possible future course improvements.

关键词： decentralised application smart contract teaching

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