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Delay-Minimization and Back-Off Aware Q-Learning With Advanced Bio-Inspired CH Selection for Multi-Hop Communication in Vehicular Ad-Hoc Networks

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Radio Science 2025年第6期60卷

作者： Sami Abduljabbar Rashid Lukman Audah Mustafa Maad Hamdi Mohammed Salah Abood Ghassan Raad Abbas Bassim Sayed Mohammed Taha A. Elwi Salahuddin Khan Bal S. Virdee Astrit Krasniqi Lida Kouhalvandi Mohammad Alibakhshikenari Biomedical Engineering Research Center University of Anbar Ramadi Iraq Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Department of Computer Science College of Computer Science and IT University of Anbar Ramadi Iraq School of Information and Electronics Beijing Institute of Technology Beijing China Institute of Mathematics and Information Technologies Volgograd State University Volgograd Russia Technical College of Engineering Al-Bayan University Baghdad Iraq Department of Automation and Artificial Intelligence Engineering College of Information Engineering at Al-Nahrain University Baghdad Iraq College of Engineering King Saud University Riyadh Saudi Arabia Center for Communications Technology London Metropolitan University London UK Department of Electrical and Electronics Engineering Dogus University Istanbul Türkiye Electronics Engineering Department University of Rome “Tor Vergata” Rome Italy

The increasing significance of Vehicular Ad-hoc Networks (VANETs) in intelligent transportation systems has introduced challenges related to high mobility, network congestion, and energy efficiency. To address these challenges, this paper proposes a new approach based on Delay-Minimization and Back-Off Aware Q-Learning with Advanced Bio-Inspired Cluster Head (CH) Selection (DBACH) to enhance multi-hop data transmission in VANETs. The DBACH framework features network formation, latency minimization, a back-off Q-learning model, and an improved dragonfly algorithm-based CH selection. This method reduces transmission delay, routing overhead, and power consumption to enhance VANET QoS. DBACH was evaluated against RCDC, DCPA, and WCAM for effectiveness. The simulated vehicle numbers and speeds (km/h) were used to assess energy efficiency, throughput, packet delivery ratio, data loss ratio, computation time, and routing overhead. The DBACH boosts energy efficiency to 85 J, throughput to 160–200 Kbps, and packet delivery ratio to 11%–13%. Data loss drops to 7%–15%, latency is 60–94 ms, and routing overhead is 170–300 packets. When DBACH is a promising option for enhancing VANET communication dependability and energy economy due to its efficiency, communication stability, and success rates. Delay-Minimization and Back-Off Aware Q-Learning with Bio-Inspired CH Selection is concentrated to improve multi-hop data transmission Through VANETs method delay and routing overhead are minimized and power utilization is optimized leading to increase the network's QoS The implementation is performed by considering two variants such as several vehicles and varying speeds (Km/H)

关键词： vehicular ad-hoc network (VANET) delay-minimization multi-hop communication improved dragonfly algorithm CH selection

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Knowledge graph enhanced neural collaborative recommendation

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Expert Systems with Applications 2021年 164卷 113992-113992页

作者： Sang, Lei Xu, Min Qian, Shengsheng Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei 230009 China Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 Australia School of Computer Science and Information Engineering Hefei University of Technology Hefei 230009 China Institute of Automation Chinese Academy of Sciences Beijing 100190 China Mininglamp Academy of Sciences Mininglamp Technology Beijing 100084 China

Existing neural collaborative filtering (NCF) recommendation methods suffer from severe sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem. However, pure NCF models can hardly model the high-order connectivity in KG, and ignores complex pairwise correlations between user/item embedding dimensions. To address these problems, we propose a novel Knowledge graph enhanced Neural Collaborative Recommendation (K-NCR) framework, which effectively combines user–item interaction information and auxiliary knowledge information for recommendation task into three parts: (1) For items, the proposed propagating model learns the representation of item entity. It recursively aggregates information from its multi-hop neighbours in KG, and employs an attention mechanism to discriminate the importance of the relation type to mine users’ potential preferences. (2) For users, another heterogeneous attention weights are leveraged to strengthen the embedding learning of users. (3) The user and item embeddings are then fed into a newly designed two-dimensional interaction map with convolutional hidden layers to model the complex pairwise correlations between their embedding dimensions explicitly. Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our K-NCR framework. © 2020

关键词： Attention mechanism Graph convolutional networks Knowledge graph Neural collaborative filtering Recommendation system

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Adaptive multi-modal fusion hashing via hadamard matrix

arXiv

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arXiv 2020年

作者： Yu, Jun Zhang, Donglin Shu, Zhenqiu Chen, Feng The College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China The Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China The Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China The School of Computer Science and Technology Anhui University of Technology Ma'anshan China

Hashing plays an important role in information retrieval, due to its low storage and high speed of processing. Among the techniques available in the literature, multi-modal hashing, which can encode heterogeneous multi-modal features into compact hash codes, has received particular attention. Most of the existing multi-modal hashing methods adopt the fixed weighting factors to fuse multiple modalities for any query data, which cannot capture the variation of different queries. Besides, many methods introduce hyper-parameters to balance many regularization terms that make the optimization harder. Meanwhile, it is time-consuming and labor-intensive to set proper parameter values. The limitations may significantly hinder their promotion in real applications. In this paper, we propose a simple, yet effective method that is inspired by the Hadamard matrix. The proposed method captures the multi-modal feature information in an adaptive manner and preserves the discriminative semantic information in the hash codes. Our framework is flexible and involves a very few hyper-parameters. Extensive experimental results show the method is effective and achieves superior performance compared to state-of-the-art algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Hash functions

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Model of Optimization of Process Parameters for Machining of Separate Design Elements of the Product

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Applied Sciences 2025年第10期15卷 5395-5395页

作者： Gulnara Zhetessova Irina Khrustaleva Viacheslav Shkodyrev Olga Zharkevich Aliya Kanatova Murat Kozhanov Gulim Tattimbetova Gulnur Abdugaliyeva Department of Technological Equipment Engineering and Standardization Mechanical Engineering Faculty Abylkas Saginov Karaganda Technical University Karaganda 100017 Kazakhstan Graduate School of Mechanical Engineering Institute of Mechanical Engineering Materials and Transportation Peter the Great St. Petersburg Polytechnic University Saint Petersburg 195251 Russia Graduate School of Cyber-Physical Systems Management Institute of Computer Science and Cyber Security Peter the Great St. Petersburg Polytechnic University Saint Petersburg 195251 Russia Department of Communication Systems Technology Faculty of Energy Automation and Telecommunications Abylkas Saginov Karaganda Technical University Karaganda 100017 Kazakhstan Department of Information Technology and Security Faculty of Innovative Technologies Abylkas Saginov Karaganda Technical University Karaganda 100017 Kazakhstan

Optimization of process parameters for manufacturing of individual surfaces and the product as a whole is one of the key tasks of technological preparation of production. Processing of individual design elements of the product is a structurally complex process, which is a clear sequence of actions characterized by a set of parameters. The effectiveness of the optimization process directly depends on the level of detail of the process under study and the optimal choice of targets and control parameters. Thus, for a single design element of the product, the control task is reduced to the structural and parametric optimization of its processing. For this purpose, it is necessary to develop a hierarchical model of structural and parametric optimization of the processing of individual design elements of the product. The paper presents a structural hierarchical model of the design element forming process, describing the sequence of actions and intermediate states of the object at different control levels. The model is based on the structural decomposition of the process and includes three levels: technological processing route, technological transition, and working stroke. For each level, theoretical-multiple models, vector optimization criteria, control parameters, and boundary conditions are defined. The article also demonstrates the application of the model on the example of optimization for the process of shaping a group of threaded holes, where the Pareto-optimization method was used to obtain optimal machining parameters that reduced the labor intensity with an acceptable increase in error and surface roughness. The paper presents the results of controlling the machining process of a group of threaded holes by means of structural optimization of the machining process route and parametric optimization of cutting parameters for each working stroke. Optimal parameters of the machining process for the investigated group of threaded holes are achieved by reducing the num

关键词： forming design element structural hierarchical model Pareto-optimization technological route technological transition working stroke

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Hyperchaos on the dynamics of memristive Tabu learning neuron model under influence of electromagnetic radiation: Application in biomedical data privacy

Franklin Open

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Franklin Open 2025年 10卷

作者： Boya, Bertrand Frederick Boui A Kengne, Jacques Nanfak, Arnaud Muni, Sishu Shankar Nkapkop, Jean de Dieu Kenmoe, Germaine Djuidje Babenko, Lyudmila Klimentyevna Unité de Recherche d'Automatique et d'Informatique Appliquée (UR-AIA) IUT-FV Bandjoun University of Dschang Bandjoun P.O. Box 134 Cameroon Laboratory of Energy-Electric and Electronic Systems Department of Physics Faculty of Science University of Yaoundé I Yaoundé P.O. Box 812 Cameroon Department of Physics Faculty of Science University of Yaoundé 1 Yaoundé Cameroon Institute of Computer Technology and Information Security Southern Federal University P.O. Box 347922 Taganrog Russian Federation Laboratory for electronics electrical engineering automation and telecommunications National Higher Polytechnic School of Douala University of Douala Douala PO Box 2701 Cameroon School of Digital Sciences Digital University Kerala Technocity campus Kerala Mangalapuram India Indian Institute of Information Technology and Management Kerala Technopark road Kerala India Department of Electrical Engineering and Industrial Computing University Institute of Technology Douala P.O. Box 8698 Cameroon

This paper presents a novel biological neural networks based on memristive Tabu learning neuron (MTLN) model influenced by electromagnetic radiation. Despite the model having an unstable equilibrium plane, numerical investigation without the influence of electromagnetic radiation reveal that when increasing the memristor strength induces complex phenomena, including the coexistence of infinite bubbles of bifurcations, offset plane coexistence of various attractors. Furthermore, electromagnetic radiation can significantly impact the system's behavior, giving rise to hyperchaotic dynamics. A decent understanding is seen between the mathematical outcomes, approved through trial execution on an Arduino microcontroller-based. The insights gained from neural dynamics could pave the way for applications in neuromorphic computing, engineering, and brain-machine interfaces. Based on the complex dynamics of the model, a data privacy of biomedical data has been build and reveals good results, and enable to resist to robust attacks. © 2024 The Author(s)

关键词： Data privacy Electromagnetic radiation Hyperchaos Microcontroller design MTLN model Offset plane coexisting behaviors

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CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2020年第2期2卷 182-193页

作者： Zhang, Shifeng Liu, Ajian Wan, Jun Liang, Yanyan Guo, Guodong Escalera, Sergio Escalante, Hugo Jair Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Faculty of Information Technology Macau University of Science and Technology 999078 China Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Institute of Deep Learning Beijing100085 China Óptica y Electrónica Instituto Nacional de Astrofísica Puebla08007 Mexico Computer Science Department CINVESTAV-Zacatenco Mexico City07360 Mexico University of Chinese Academy of Sciences Beijing100049 China Westlake University Hangzhou310024 China

Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://***/***/face-anti-spoofing/welcome/challengecvpr2019?authuser=0. © 2019 IEEE.

关键词： Face recognition

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Robust Leader-Following Output Regulation of Uncertain Multi-Agent Systems With Time-Varying Delay

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IEEE/CAA Journal of Automatica Sinica 2018年第4期5卷 807-817页

作者： Ala Shariati Qing Zhao Automation&Control Research Group Faculty of Electrical and Computer EngineeringKhajeh Nasir Toosi University of TechnologyTehran ***Iran Department of Electrical and Computer Engineering at University of Alberta EdmontonAlberta T6R1Z6Canada

In this paper, the robust analysis and design of leader-following output regulation for multi-agent systems described by general linear models is given in presence of timevarying delay and model uncertainty. To this aim, a new regulation protocol for the closed-loop multi-agent system under a directed graph is proposed. An important specification of the proposed protocol is to guarantee the leader-following output regulation for uncertain multi-agent systems with both stable and unstable agents. Since many signals can be approximated by a combination of the stationary and ramp signals, the presented results work for adequate variety of the leaders. The analysis and design conditions are presented in terms of certain matrix inequalities. The method proposed can be used for both stationary and ramp leaders. Simulation results are presented to show the effectiveness of the proposed method.

关键词： Linear matrix inequality(LMI) multi-agent systems robust leader-following output regulation time-varying delay

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Dynamical multiple polynomial-based neural networks classifier realized with the aid of dropfilter and dual statistical selection

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engineering Applications of Artificial Intelligence 2025年 157卷

作者： Zhen Wang Sung-Kwun Oh Zunwei Fu Seok-Beom Roh Witold Pedrycz Research Center for Big Data and Artificial Intelligence Linyi University Linyi 276005 China School of Automation and Electrical Engineering Linyi University Linyi 276005 Shandong China School of Electrical & Electronic Engineering The University of Suwon 17 Wauan-gil Bongdam-eup Hwaseong-si Gyeonggi-do 18323 South Korea Department of Electronic Engineering Seokyeong University Seoul 02713 South Korea Department of Computer Science The University of Suwon 17 Wauan-gil Bongdam-eup Hwaseong-si Gyeonggi-do 18323 South Korea Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2R3 Canada Systems Research Institute Polish Academy of Sciences 00-901 Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer Istanbul Turkiye

Polynomial neural networks (PNN) have emerged as an effective regression modeling methodology in computational intelligence, relying on its interpretable polynomial nodes to fit complex nonlinear data relationships and the adaptive nature of self-organizing networks. To break the bottleneck of PNN structure in the field of multi-classification, this study designs a dynamical multiple polynomial-based neural networks (DMPNN) classifier, focusing on developing a flexible polynomial network classification methodology that enhances predictive capabilities without sacrificing the advantages of PNN structures. Our approach effectively addresses the challenges of multi-class classification with uncertain class boundaries and reduces computational complexity, which is achieved through the synergy of several proposed techniques. Three key issues underpin the proposed DMPNN: (a) The integration of PNN regression models using the one-against-all strategy can provide effective and scalable solutions to multi-class classification problems, especially for uncertain class boundary issues. (b) The dual statistical selection (DSS) approach aims to eliminate redundant inputs during data processing, reduce the computational burden, and increase the variety of neural network nodes in the model neuron selection stage. (c) The synergy of regularization methods including the ℓ2 norm-based method (ℓ2-LSM) and the DropFilter, is exploited to mitigate potential overfitting in coefficient estimation and enhance the generalization capabilities of the proposed classifier. A series of ablation experiments and parameter analysis were conducted to demonstrate the stability and reliability of the proposed model. Then, we applied DMPNN to 17 publicly available datasets and two engineering applications: Recycling of black plastic wastes and phased resolved partial discharge. The performance results show that the DMPNN model outperforms five classical classifiers and four state-of-the-art (SOTA) class

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From 2015 to 2023: How Machine Learning Aids Natural Product Analysis

arXiv

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arXiv 2024年

作者： Shi, Suwen Huang, Ziwei Gu, Xingxin Lin, Xu Zhong, Chaoying Hang, Junjie Lin, Jianli Zhong, Claire Chenwen Zhang, Lin Li, Yu Huang, Junjie Department of Chemistry Boston University BostonMA02215 United States Department of Physics Boston University BostonMA02215 United States College of Professional Studies Northeastern University BostonMA02215 United States Department of Thoracic Surgery The First Affiliated Hospital School of Medicine Zhejiang University Zhejiang Hangzhou China Department of Electrical Engineering and Automation Guangdong Ocean University Guangdong China Cancer Hospital & Shenzhen Hospital Chinese Academy of Medical Sciences Peking Union Medical College Guangdong China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies Peking University Beijing China The Jockey Club School of Public Health and Primary Care Faculty of Medicine The Chinese University of Hong Kong Hong Kong Suzhou Industrial Park Monash Research Institute of Science and Technology Suzhou China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity and volume of data generated in contemporary research endeavors. Computational methodologies represent robust tools in the field of chemistry, offering the capacity to harness potent machine-learning models to yield insightful analytical outcomes. This review delves into the spectrum of computational strategies available for natural product analysis and constructs a research framework for investigating both qualitative and quantitative chemistry problems. Our objective is to present a novel perspective on the symbiosis of machine learning and chemistry, with the potential to catalyze a transformation in the field of natural product analysis. © 2024, CC BY.

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Corrigendum to “Realistic acceleration of neural networks with fine-grained tensor decomposition” [Neurocomputing 512 (2022) 52–68]

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Neurocomputing 2023年 527卷 47-47页

作者： Rui Lv Dingheng Wang Jiangbin Zheng Yefan Xie Zhao-Xu Yang School of Computer Science Northwestern Polytechnical University Xi’an Shaanxi China Northwest Institute of Mechanical & Electrical Engineering Xianyang Shaanxi China School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an Shaanxi China State Key Laboratory for Strength and Vibration of Mechanical Structures Shaanxi Key Laboratory of Environment and Control for Flight Vehicle and School of Aerospace Engineering Xi’an Jiaotong University Xi’an Shaanxi China

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