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Intelligent Systems and Photovoltaic Cells Empowered Topologically by Sudoku Networks

computers, Materials & Continua 2023年第2期74卷 4221-4238页

作者： Khalid Hamid Muhammad Waseem Iqbal MUsman Ashraf Akber Abid Gardezi Shafiq Ahmad Mejdal Alqahtani Muhammad Shafiq Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot51310Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

A graph invariant is a number that can be easily and uniquely calculated through a ***,part of mathematical graph invariants has been portrayed and utilized for relationship ***,no reliable appraisal has been embraced to pick,how much these invariants are associated with a network graph in interconnection networks of various fields of computer science,physics,and *** this paper,the study talks about sudoku networks will be networks of fractal nature having some applications in computer science like sudoku puzzle game,intelligent systems,Local area network(LAN)development and parallel processors interconnections,music composition creation,physics like power generation interconnections,Photovoltaic(PV)cells and chemistry,synthesis of chemical *** networks are generally utilized in disorder,fractals,recursive groupings,and complex *** outcomes are the normal speculations of currently accessible outcomes for specific classes of such kinds of networks of two unmistakable sorts with two invariants K-banhatti sombor(KBSO)invariants,Irregularity sombor(ISO)index,Contraharmonic-quadratic invariants(CQIs)and dharwad invariants with their reduced *** study solved the Sudoku network used in mentioned systems to improve the performance and find irregularities present in *** calculated outcomes can be utilized for the modeling,scalability,introduction of new architectures of sudoku puzzle games,intelligent systems,PV cells,interconnection networks,chemical compounds,and extremely huge scope in very large-scale integrated circuits(VLSI)of processors.

关键词： Sudoku dharwad KBSO indices CQIs network graph intelligent system photovoltaic cells

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Deep learning based water leakage detection for shield tunnel lining

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Frontiers of Structural and Civil engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

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Evolutionary Optimization with a Simplified Helper Task for High-Dimensional Expensive Multiobjective Problems

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-32页

作者： Wu, Xunfeng Lin, Qiuzhen Zhou, Junwei Liu, Songbai Coello Coello, Carlos A. Leung, Victor C. M. College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Mexico City07300 Mexico Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In recent years, surrogate-Assisted evolutionary algorithms (SAEAs) have been sufficiently studied for tackling computationally expensive multiobjective optimization problems (EMOPs), as they can quickly estimate the qualities of solutions by using surrogate models to substitute for expensive evaluations. However, most existing SAEAs only show promising performance for solving EMOPs with no more than 10 dimensions, and become less efficient for tackling EMOPs with higher dimensionality. Thus, this article proposes a new SAEA with a simplified helper task for tackling high-dimensional EMOPs. In each generation, one simplified task will be generated artificially by using random dimension reduction on the target task (i.e., the target EMOPs). Then, two surrogate models are trained for the helper task and the target task, respectively. Based on the trained surrogate models, evolutionary multitasking optimization is run to solve these two tasks so that the experiences of solving the helper task can be transferred to speed up the convergence of tackling the target task. Moreover, an effective model management strategy is designed to select new promising samples for training the surrogate models. When compared to five competitive SAEAs on four well-known benchmark suites, the experiments validate the advantages of the proposed algorithm on most test cases. © 2024 held by the owner/author(s).

关键词： Multitasking

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Enhanced Metaheuristics-Based Clustering Scheme for Wireless Multimedia Sensor Networks

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computers, Materials & Continua 2022年第11期73卷 4179-4192页

作者： R.Uma Mageswari Sara A.Althubiti Fayadh Alenezi E.Laxmi Lydia Gyanendra Prasad Joshi Woong Cho Department of Computer Science and Engineering Vardhaman College of Engineering(Autonomous)HyderabadTelangana501218India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

Traditional Wireless Sensor Networks(WSNs)comprise of costeffective sensors that can send physical parameters of the target environment to an intended *** the evolution of technology,multimedia sensor nodes have become the hot research topic since it can continue gathering multimedia content and scalar from the target *** existence of multimedia sensors,integrated with effective signal processing and multimedia source coding approaches,has led to the increased application of Wireless Multimedia Sensor Network(WMSN).This sort of network has the potential to capture,transmit,and receive multimedia *** energy is a major source in WMSN,novel clustering approaches are essential to deal with adaptive topologies of WMSN and prolonged network *** this motivation,the current study develops an Enhanced Spider Monkey Optimization-based Energy-Aware Clustering Scheme(ESMO-EACS)for *** proposed ESMO-EACS model derives ESMO algorithm by incorporating the concepts of SMO algorithm and quantum *** proposed ESMO-EACS model involves the design of fitness functions using distinct input parameters for effective construction of clusters.A comprehensive experimental analysis was conducted to validate the effectiveness of the proposed ESMO-EACS technique in terms of different performance *** simulation outcome established the superiority of the proposed ESMO-EACS technique to other methods under various measures.

关键词： Wireless multimedia sensor networks clustering spider monkey optimization algorithm energy efficiency metaheuristics quantum computing

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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Secure and Fast Remote Application-Based Authentication Dragonfly Using an LED Algorithm in Smart Buildings 6

Secure and Fast Remote Application-Based Authentication Drag...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Radhi, Batool Mohammed Hussain, Mohammed Abdulridha Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo College of Education for Pure Sciences University of Basrah Department of Computer Science Basrah61004 Iraq Jaramogi Oginga Odinga University of Science and Technology Department of Computer Science and Software Engineering Bondo40601 Kenya

ISBN: (纸本)9798350344349

The proliferation of the internet of things (IoT) has led to the emergence of a wide range of intelligent devices, creating a broad domain with significant security concerns. These concerns impose a high level of security;unfortunately, IoT devices usually have limited resources in terms of little memory, low computing power, and a short battery life. Therefore, IoT application developers must use lightweight cryptographic tools to achieve a trade-off between performance and security. The storage and high computation capacity of cloud computing is often exploited to manage the vast amount of data produced by such gadgets. Some methods still suffer from attacks, and others cannot achieve low complexity. We propose a secure and low-complexity system for smart buildings in transferring data between the local server, the cloud, and users authorized by the owner. The LED encryption algorithm, which is lightweight and requires limited resources and less energy, was used to create a mobile application system characterized by confidentiality, authentication, and privacy. For further security, the owner's biometrics were used and derived as the key to decrypt data from the cloud. We have leveraged Dragonfly authentication technology to transfer data from the local server to the users. The owner can add authorized persons in the cloud database and local server to enjoy using the application. Moreover, we successfully balance security complexity and performance in our work. As a result, we achieve good results with a computation cost of 0.281 s and a communication cost of 1472 bit. © 2024 IEEE.

关键词： Authentication

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Enhancing Secure Development in Globally Distributed software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

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computers, Materials & Continua 2024年第6期79卷 5031-5049页

作者： Marya Iqbal Yaser Hafeez Nabil Almashfi Amjad Alsirhani Faeiz Alserhani Sadia Ali Mamoona Humayun Muhammad Jamal University Institute of Information Technology PMAS-Arid Agriculture UniversityRawalpindiPakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72388Saudi Arabia Department of Mathematics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindiPakistan

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software ***,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity *** paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of *** the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and ***,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient *** experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in *** digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

关键词： Machine Learning variability management cybersecurity digital ecosystems cyber-resilience

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Camouflaged Variational Graph AutoEncoder against Attribute Inference Attacks for Cross-Domain Recommendation

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IEEE Transactions on Knowledge and Data engineering 2025年第07期37卷 3916-3932页

作者： Xiong, Yudi Guo, Yongxin Pan, Weike Yang, Qiang Ming, Zhong Zhang, Xiaojin Yu, Han Lin, Tao Tang, Xiaoying Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China School of Science and Engineering Shenzhen518172 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong WeBank Shenzhen China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Westlake University School of Engineering Hangzhou China

Cross-domain recommendation (CDR) aims to alleviate the data sparsity problem by leveraging the benefits of modeling two domains. However, existing research often focuses on the recommendation performance while ignores the privacy leakage issue. We find that an attacker can infer user attribute information from the knowledge (e.g., user preferences) transferred between the source and target domains. For example, in our experiments, the average inference accuracies of attack models on gender and age attributes are 0.8323 and 0.3897. The best-performing attack model achieves accuracies of 0.8847 and 0.4634, exceeding a random inference by 25.10% and 64.04%. We can see that the leakage of user attribute information may significantly exceed what would be expected from random inference. In this paper, we propose a novel recommendation framework named CVGAE (short for camouflaged variational graph autoencoder), which effectively models user behaviors and mitigates the risk of user attribute information leakage at the same time. Specifically, our CVGAE combines the strengths of VAEs in capturing latent features and variability with the ability of GCNs in exploiting high-order relational information. Moreover, to ensure against attribute inference attacks without sacrificing the recommendation performance, we design a user attribute protection module that fuses user attribute-camouflaged information with knowledge transfer during cross-domain processes. We then conduct extensive experiments on three real-world datasets, and find our CVGAE is able to achieve strong privacy protection while making little sacrifices in recommendation accuracy. © 1989-2012 IEEE.

关键词： Recommender systems

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Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data

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computers, Materials & Continua 2023年第10期77卷 681-697页

作者： Uzair Aslam Bhatti Sibghat Ullah Bazai Shumaila Hussain Shariqa Fakhar Chin Soon Ku Shah Marjan Por Lip Yee Liu Jing College of Information and Communication Engineering Hainan UniversityHaikou570228China Department of Computer Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Department of Computer Science Sardar Bahadur Khan Women’s UniversityQuettaPakistan Department of Computer Science Universiti Tunku Abdul RahmanKampar31900Malaysia Department of Software Engineering Balochistan University of Information TechnologyEngineeringand Management Sciences(BUITEMS)QuettaPakistan Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia

Crop diseases have a significant impact on plant growth and can lead to reduced *** methods of disease detection rely on the expertise of plant protection experts,which can be subjective and dependent on individual experience and *** address this,the use of digital image recognition technology and deep learning algorithms has emerged as a promising approach for automating plant disease *** this paper,we propose a novel approach that utilizes a convolutional neural network(CNN)model in conjunction with Inception v3 to identify plant leaf *** research focuses on developing a mobile application that leverages this mechanism to identify diseases in plants and provide recommendations for overcoming specific *** models were trained using a dataset consisting of 80,848 images representing 21 different plant leaves categorized into 60 distinct *** rigorous training and evaluation,the proposed system achieved an impressive accuracy rate of 99%.This mobile application serves as a convenient and valuable advisory tool,providing early detection and guidance in real agricultural *** significance of this research lies in its potential to revolutionize plant disease detection and management *** automating the identification process through deep learning algorithms,the proposed system eliminates the subjective nature of expert-based diagnosis and reduces dependence on individual *** integration of mobile technology further enhances accessibility and enables farmers and agricultural practitioners to swiftly and accurately identify diseases in their crops.

关键词： Plant disease Inception v3 CNN crop diseases

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Uni4DAL: A Unified Baseline for Multi-dataset 4D Auto-Labeling 27th

Uni4DAL: A Unified Baseline for Multi-dataset 4D Auto-Label...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yang, Zhiyuan Wang, Xuekuan Zhang, Wei Tan, Xiao Lu, Jinchen Wang, Jingdong Ding, Errui Lai, Zhihui Zhao, Cairong Department of Computer Science and Technology Tongji University Shanghai201804 China Baidu Inc. Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783031781124

The 4D auto-labeling system, with its potential to enhance data annotation efficiency for 3D object detection, has garnered significant attention. However, its adoption has been hampered by the high costs associated with temporally annotated long-sequential training data and limited generalization capabilities across diverse scenarios. In this paper, we hypothesize that a multi-dataset approach can address these challenges and, accordingly, first introduce a Unified training pipeline for multi-dataset 4D Auto-Labeling, namely Uni4DAL. We recognize that this is a challenging task, primarily due to data-level variations and feature-level inconsistencies among various datasets. Motivated by this understanding, we initially propose a series of Data-Level Alignment (DLA) operations to mitigate potential discrepancies between diverse datasets and ensure synchronized training progress across samples from multiple datasets. Furthermore, to address feature-level inconsistencies, we introduce the Mixed Expert Models Voxel Feature Encoding (MoE-VFE) module, which aims to extract both domain-specific and domain-generalizable features. Additionally, we employ a Domain-Adaptive Hard Example Mining (DA-HEM) technique to leverage both data-level and feature-level consistencies, ensuring that the model pays enhanced attention to the challenging samples during multi-dataset training. Finally, comprehensive experiments demonstrate that Uni4DAL significantly improves performance on the nuScenes, Argoverse2, and Waymo datasets, and exhibits greater robustness with insufficient training data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： 4D auto-labeling Multi-dataset training Offline 3D object detection

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