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AI-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

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computers, Materials & Continua 2024年第12期81卷 4743-4756页

作者： Praveena Nuthakki Pavan Kumar T. Musaed Alhussein Muhammad Shahid Anwar Khursheed Aurangzeb Leenendra Chowdary Gunnam Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationGuntur522302India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-Si13120Republic of Korea Department of Electronics and Communication Engineering SRM UniversityAmaravati522502India

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the *** these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource ***,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing *** manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place *** approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource *** proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

关键词： Resource utilization smart manufacturing efficiency inter VM communication virtual machine placement cloud computing multi-objective optimization

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Exploring Sleep Paralysis Phenomenon Through Machine Learning: An Analytical Study

Exploring Sleep Paralysis Phenomenon Through Machine Learnin...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Ishaq, Samra Rehman, Attique Ur Ali, Tahir Muhammad Javaid, Sabeen Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait

ISBN: (纸本)9798350349719

Sleep paralysis is when you're awake but powerless to move. Although the majority of occurrences are linked to extreme terror and some potentially clinically significant sufferings are connected with the case;little is known about it. The present state of research on the connection between sleep paralysis and overall sleep will be examined in this study. Several studies have linked an increased risk of sleep paralysis to low-quality sleep. This could occur in between sleeping and waking. The issue is tackled in the context of sleep paralysis prediction using machine learning. There are multiple attributes in the dataset (label Data). Every exam has a unique set of characteristics and expected results. We carried out an accuracy test of posture recognition by the system to evaluate its validity. Predicting sleep paralysis has been done using machine learning techniques. A comparison of sleeping positions with the findings indicated a correlation between paralysis and poor quality of sleep. Poor sleep quality is correlated with sleep paralysis. In the provided dataset, the Random Forest model predicts sleep paralysis with the highest accuracy 92.3%, K-nearest neighbors' classifier has an accuracy rate of 91.0% 91.9%, and 90.4% using Decision Tree and naïve Bayes. © 2024 IEEE.

关键词： Forecasting

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Enhancing Brain Stroke Risk Prediction with Multi-Algorithm Evaluation and Web Interface

Enhancing Brain Stroke Risk Prediction with Multi-Algorithm ...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Ahmed, Muhammad Liaquat, Umer Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait

ISBN: (纸本)9798350349719

Brain stroke is the world's leading cause of death, impacting numerous lives annually. The chances of having a stroke have increased by 50% over one's lifetime, impacting one in four people worldwide. Machine learning may assist in the prior detection of strokes, allowing for more timely diagnosis which can save a person's life and disability. However, existing methods face challenges in integrating ML models with clinical applications to predict patient stroke status with optimal accuracy. This study conducts a comprehensive investigation into brain stroke risk prediction using numerous ML algorithms, including Random Forest, Logistic Regression, KNN, Naive Bayes, XGBoost, and LightGBM. Our main contribution is the development of a web app that predicts stroke based on user inputs with optimal accuracy. The selected dataset contains 5110 records of normal people and 249 records of people having strokes which is highly imbalanced. To ensure model robustness, the dataset undergoes balancing through SMOTE oversampling and KNN Imputation for handling missing values. Custom thresholds for classifiers optimize training, with comprehensive evaluations utilizing key statistics such as confusion matrices, F1-Score, recall, accuracy, and precision. Cross-validation is employed to assess potential overfitting and underfitting risks in machine learning models. Random Forest ranked as the most imposing, reaching a remarkable 99.85% accuracy on the test set with a custom threshold of 0.6 and a random state at 42. Additionally, a user-friendly Web App has been developed to predict stroke status based on user input, enhancing practical usability and accessibility for individuals seeking risk assessments. © 2024 IEEE.

关键词： Forecasting

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Global video object segmentation with spatial constraint module

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Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

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computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 849-862页

作者： Ye‐Qun Wang Jian‐Yu Li Chun‐Hua Chen Jun Zhang Zhi‐Hui Zhan School of Computer Science and Engineering South China University of TechnologyGuangzhouChina School of Software Engineering South China University of TechnologyGuangzhouChina Hanyang University AnsanSouth Korea

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial ***,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally *** tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)***,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best ***,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate ***,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational *** SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

关键词： deep learning evolutionary computation hyperparameter and architecture optimisation neural networks particle swarm optimisation scale‐adaptive fitness evaluation

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Research on Intelligent Voice Broadcast System for Smart Water Conservancy 19

Research on Intelligent Voice Broadcast System for Smart Wat...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Huang, Zhenqian Chen, Lanxin College of Computer Science and Software Engineering Hohai University Nanjing China University of Birmingham Birmingham United Kingdom

ISBN: (纸本)9798331507817

Smart water management imposes higher demands on the real-time capabilities and intelligence of watershed information monitoring systems. Therefore, we propose an intelligent voice broadcast system for monitoring water information, aimed at achieving efficient and accurate dissemination of warning information. This system can be deployed and integrated into existing water information monitoring systems to enable real-time voice broadcasting of water-related information. To ensure the system’s timeliness and effectiveness, we propose a Performance Optimization Mechanism for Data Processing, Priority Control for voice broadcasting, a Content Optimization Mechanism, and a Timeliness Control Mechanism. Comparative experiments demonstrate that the proposed intelligent voice broadcast system significantly enhance the efficiency and accuracy of water information dissemination, providing an innovative solution for real-time monitoring and early warning in water management. © 2025 IEEE.

关键词： Information management

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CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection

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IEEE Transactions on Multimedia 2024年 26卷 6090-6101页

作者： Xiong, Lizhi Xu, Jianhua Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanjing University of Information Science and Technology Software Engineering Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan Fudan University School of Computer Science Shanghai200433 China

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness. © 1999-2012 IEEE.

关键词： Feature extraction

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A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of AI-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for AI-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

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Efficient High-Precision Classification Algorithm for Radar Deceptive Jamming via Array Detection

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Progress in Electromagnetics Research C 2025年 155卷 29-35页

作者： Liu, Wenjie Qin, Wenfang Shen, Mingwei Han, Guodong College of Information Science and Engineering Hohai University Jiangsu Changzhou213200 China College of Computer Science and Software Engineering Hohai University Jiangsu Nanjing211106 China The 54th Research Institute of CETC Hebei Shijiazhuang050081 China

In complex electromagnetic scenarios where multiple deceptive jamming signals are simultaneously aliased in the time frequency domain, conventional single-channel electronic detection systems struggle to effectively separate and classify overlapping jamming sources. To address this limitation, this paper investigates an array detection-based classification scheme for multi-source jamming. First, due to the spatial degrees of freedom offered by array systems, the Direction-of-Arrival (DOA) of each jamming source is precisely estimated using the MUSIC algorithm. Then, the adaptive digital beamforming (ADBF) filters are designed based on the estimated DOAparameters, enabling spatial-domain extraction of individual jamming signals. Finally, the DOA information is integrated into the Pulse Description Word (PDW) of each separated jamming measurement, which can facilitate adaptive K-Means clustering with enhanced class separability. Simulation results demonstrate that the proposed method achieves a 22.4% improvement in classification accuracy compared to existing single-channel detection approaches, while maintaining computational efficiency. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Frequency response

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