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A Global Best-guided Firefly Algorithm for engineering Problems

Journal of Bionic engineering 2023年第5期20卷 2359-2388页

作者： Mohsen Zare Mojtaba Ghasemi Amir Zahedi Keyvan Golalipour Soleiman Kadkhoda Mohammadi Seyedali Mirjalili Laith Abualigah Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahrom***FrasIran Department of Electronics and Electrical Engineering Shiraz University of TechnologyShiraz***Iran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehran***Iran Department of Computer Engineering Sari BranchIslamic Azad UniversitySari***Iran Department of Electrical Engineering Urmia BranchIslamic Azad UniversityUrmia571696896Iran Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan School of Computer Sciences Universiti Sains Malaysia11800George TownPulau PinangMalaysia University Research and Innovation Center Obuda University1034BudapestHungary School of Engineering and Technology Sunway University MalaysiaPetaling Jaya27500Malaysia Applied science research center Applied science private universityAmman11931Jordan

The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when *** article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA's movement *** proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best ***,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm's main ***,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original *** GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha ***,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced *** all cases,GbFA provides the optimal result compared to other *** that the source code of the GbFA algorithm is publicly available at https://***/projects/gbfa.

关键词： Firefly algorithm New movement vector Global best-guided firefly algorithm Global optimization engineering design

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Quantum Algorithms and Lower Bounds for Finite-Sum Optimization 41

Quantum Algorithms and Lower Bounds for Finite-Sum Optimizat...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Yexin Zhang, Chenyi Fang, Cong Wang, Liwei Li, Tongyang School of Electronics Engineering and Computer Science Peking University China Computer Science Department Stanford University United States National Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Center on Frontiers of Computing Studies Peking University China School of Computer Science Peking University China

Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, *** this paper, we initiate the study of solving finite-sum optimization problems by quantum ***, let f1, ..., fn : d → be -smooth convex functions and ψ: d → be a µ-strongly convex proximal *** goal is to find an ϵ-optimal point for F(x) = n1 Pni=1 fi(x) + ψ(x).We give a quantum algorithm with complexity Õ(Equation presented) 1 improving the classical tight bound (Equation presented).We also prove a quantum lower bound Ω˜(n + n3/4(/µ)1/4) when d is large *** our quantum upper and lower bounds can extend to the cases where ψ is not necessarily strongly convex, or each fi is Lipschitz but not necessarily *** addition, when F is nonconvex, our quantum algorithm can find_an ϵ-critial point using (Equation presented) queries. Copyright 2024 by the author(s)

关键词： Quantum computers

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Robust Wireless Power Transfer by Self-Oscillating Controlled Inverter and Circular Pads 3

Robust Wireless Power Transfer by Self-Oscillating Controlle...

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3rd International Conference on Electrical Machines and Drives, ICEMD 2023

作者： Eikani, Alireza Amirkhani, Mohammad Jafari, Hossein Vaez-Zadeh, Sadegh Mirsalim, Mojtaba Khaburi, Davood Arab University of Tehran Advanced Motion Systems Research Lab School of Electrical and Computer Engineering College of Engineering Tehran Iran Department of Electrical Engineering Tehran Iran Iran University of Science and Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350393743

Inductive wireless power transfer (WPT) faces significant challenges in achieving high efficiency and stable output power for charging mobile devices like EVs. Using circular pads (CP) as the transmitter (Tx) and receiver (Rx) and a self-oscillating controlled inverter, this paper demonstrates a robust WPT system that maintains high constant transfer efficiency and output power in a defined region regardless of coupling coefficient variations without communication between Tx and Rx or control circuits for the receiver side. In contrast to couplers with non-identical Tx and Rx pads and multiple decoupled coils in the Rx pad, the compact CP-CP magnetic coupler with a single identical coil on the Tx and Rx is more robust at a wider transfer distance (air gap) and lateral and rotational misalignment between the Tx and Rx. The proposed 1-kW robust WPT system with up to a 20 cm air gap between the Tx and Rx and up to 12 cm lateral misalignment in all directions at a 15 cm air gap and all degrees of rotational misalignment can tolerate coupling variations and achieve constant output power with 93% constant transfer efficiency. © 2023 IEEE.

关键词： Energy transfer

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Multimodal Classification of Software Issue Reports Using CLIP: A Fine-Tuned Approach for Bug and Feature Detection

Multimodal Classification of Software Issue Reports Using CL...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Yasir Arafah Sumaiya Shejin Shamim Akhter Md Arafat Alam Khandaker Department of Computer Science and Engineering AISIP Lab Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Efficiently identifying software bugs is critical to accelerate software development processes. Traditional methods, which frequently depend on manual review or constraint-driven mechanisms, require a significant amount of time, susceptible to mistakes made by humans, and unable to scale effectively with the growing complexity of modern software systems. With texts, code snippets, and images from well-known repositories like VS Code, Flutter, Roslyn, and Kubernetes in total comprised of 4304 samples, this paper presents a new multimodal approach that uses Contrastive Language-Image Pretraining (CLIP) and its ViT-B/32 variant to effectively address this problem. We experimented with several CLIP variants, including ViT-B/32, RN50, RN101, RN50x4, ViT-B/16, ViT-L/14, and RN50x64, and observed that ViT-B/32 performed well under zero-shot settings, with an accuracy of 56.6%, precision of 54.08%, recall of 53.96% and F1-score of 54.02%, showing the usefulness of general-purpose models in task-specific situations. We performed k-fold cross-validation and hyperparameter tuning on the ViT-B/32 variant, which improved the model’s ability to classify software issues with an 18.8% improvement in accuracy, 19.1% in precision, 20.14% in recall, and 19.6% in F1-score over its zero-shot performance. These findings demonstrate a scalable framework for multimodal classification that goes beyond bugs and features. This framework has potential uses in issue prioritization, automated software maintenance, and productivity tools. These findings emphasize the benefits of using multimodal AI models, such as CLIP, to tackle practical software engineering problems, providing new insights and a solid basis for further developments in this field.

关键词： Solid modeling Software maintenance Accuracy Codes computer bugs Software systems Software Tuning Software engineering Software development management

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An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

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computers, Materials & Continua 2022年第8期72卷 3529-3548页

作者： Ghalib H.Alshammri Amani K.Samha Ezz El-Din Hemdan Mohammed Amoon Walid El-Shafai Department of Computer Science Community CollegeKing Saud UniversityRiyadh28095Saudi Arabia Deanship of Scientific Research Saudi Electronic UniversityRiyadhSaudi Arabia Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh28095Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electronics and Electrical Communications Eng.Depart. Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic *** recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable ***,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN ***,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five ***,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)***,the utilized dataset is clustered into normal and four major attack categories via the clustering ***,a deep learning method is projected for building an efficient SDN-based intrusion detection *** results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD ***,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing *** example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

关键词： Deep neural network DL WEKA network traffic intrusion and anomaly detection SDN clustering and classification KDD dataset

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Variance-Guided Structured Sparsity in Deep Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1714-1723页

作者： Pandit, Mohammad Khalid Banday, Mahroosh Indian Institute of Technology Delhi Department of Computer Science and Engineering New Delhi110016 India Indian Institute of Technology Delhi Samsung Innovation Lab Bharti School of Telecommunication Technology and Management New Delhi110016 India

The success of deep neural networks, especially convolutional neural networks in various applications, has greatly been possible by the presence of an enormous number of learnable parameters. These parameters increase the learning capacity of the model, but at the same time, it also significantly increases the computational and memory costs. This severely hinders the scalability of these models to limited resource environments, such as IoT devices. The majority of the network weights are known to be redundant and can be removed from the network. This article introduces a regularization scheme, which is the combination of structured sparsity regularization and variance regularization. It simultaneously helps to obtain computationally sparse models by making the majority of parameter groups zero and increasing the variance of nonzero groups to compensate for the accuracy drop. We use sparse group lasso, group sparsity variant of 1 (lasso) regularization, which removes redundant connections and unnecessary neurons from the network. For variance regularization, the KL divergence of the current parameter distribution and the target distribution is minimized, which aims to have the concentration of weights toward zero and a high variance of nonzero weights (skewed distribution). To check the effectiveness of the proposed regularizer, the experiments are performed on various benchmark datasets and it is noticed that variance regularization helps to reduce the accuracy drop caused by sparsity regularization. On MNIST, the trainable parameters are reduced from 331 984 (baseline model) to 57 327 and managed to obtain better accuracy than the baseline (99.6%). Also, on Fashion-MNIST, Cifar-10, and ImageNet the proposed scheme achieved state-of-the-art sparsity with almost no drop in accuracy. © 2020 IEEE.

关键词： Deep neural networks

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Enhancing Soil Carbon Content Classification Using Transfer Learning and FrFT on Millimeter Wave Radar Array Signals with Varied Backgrounds 6

Enhancing Soil Carbon Content Classification Using Transfer ...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： An, Di Zhang, Patrick Chen, YangQuan University of California Merced Electrical Engineering & Computer Science Merced United States University of California Merced Mesa Lab Merced United States University of California Merced Department of Mechanical Engineering Merced United States

ISBN: (纸本)9798350356618

The precise quantification of soil carbon content is crucial for understanding soil health and its impact on climate change. This study investigates the classification of soil carbon content using millimeter wave radar array signals, focusing on a comparative analysis of Support Vector Machines (SVM), Continuous Wavelet Transform (CWT), and Fractional Fourier Transform (FrFT) methods. Furthermore, raw time and CWT-processed signals using SVM are used as baseline to establish the fundamental classification performance. The key contribution of this research is the innovative use of FrFT, where the transformed signals are converted into images and subsequently fed into GoogLeNet through transfer learning. This approach leverages the powerful feature extraction capabilities of GoogleNet to enhance classification accuracy. Experiments were conducted on a dataset comprising millimeter wave signals reflecting varied backgrounds with the primary focus on one specific distance measurement. Results provided show that high resolution millimeter wave signals achieved up to 100% validation accuracy at multiple fractional orders. The improved accuracy highlights the potential of integrating advanced signal transformation methods with deep learning models for soil carbon content classification. © 2024 IEEE.

关键词： Support vector machines

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Identifying Candidate Classes for Unit Testing Using Deep Learning Classifiers: An Empirical Validation 22

Identifying Candidate Classes for Unit Testing Using Deep Le...

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4th World Symposium on Software engineering, WSSE 2022

作者： Matcha, Wyao Toure, Fadel Badri, Mourad Badri, Linda Department of Mathematics and Computer Science Lab of Software Engineering University of Quebec at Trois-Rivieres Canada

ISBN: (纸本)9781450396950

This paper aims at investigating the use of deep learning to suggest (prioritize) classes to be tested rigorously during unit testing of object-oriented systems. We relied on software unit testing information history and source code metrics. We conducted an empirical study using data collected from two Apache open-source Java software systems (POI and ANT). For each software system, we extracted the source code of five different versions. For each version, we collected various metrics from the source code of the Java classes. Then, for all software classes, we extracted testing coverage measures at instruction and method levels of granularity. We used the existing JUnit test cases developed for these systems. Based on the different datasets we collected, we trained several deep neural network models. We validated the obtained classifiers using four validation techniques: (1) CV: Cross Version validation, (2) CPV: Combined Previous Version validation, (3) CSPV: Combined System and Previous Version validation, and (4) LOSO: Leave One System Out validation. The obtained results in terms of classifiers' performance vary between 70% and 80% of accuracy and strongly support the viability of our approach. © 2022 ACM.

关键词： Deep neural networks

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Efficient Deep CNN Model for COVID-19 Classification

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computers, Materials & Continua 2022年第3期70卷 4373-4391页

作者： Walid El-Shafai Amira A.Mahmoud El-Sayed M.El-Rabaie Taha E.Taha Osama F.Zahran Adel S.El-Fishawy Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in *** Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death *** X-Ray(CXR)and computerized Tomography(CT)screening of infected persons are essential in diagnosis *** are numerous ways to identify positive COVID-19 *** of the fundamental ways is radiology imaging through CXR,or CT *** comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high ***,automated classification techniques are required to facilitate the diagnosis *** Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical *** deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image *** this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is *** activation functions as well as three optimizers are tested and compared for this *** proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train *** performance is tested and investigated on the CT and CXR *** activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch *** optimizers are studied with different batch sizes and a constant learning ***,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is ***,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early *** proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR an

关键词： COVID-19 image classification CNN DL activation functions optimizers

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Toward a personalized autonomous transportation system:Vision,challenges,and solutions

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The Innovation 2024年第6期5卷 47-49页

作者： Linlin You Mai Hao Jian Sun Yunpeng Wang Chunming Rong Chau Yuen Paolo Santi Carlo Ratti School of Intelligent Systems Engineering Sun Yat-Sen UniversityShenzhen 518107China Department of Traffic Engineering Tongji UniversityShanghai 200092China School of Transportation Science and Engineering Beihang UniversityBeijing 100083China Department of Electrical Engineering and Computer Science University of Stavanger4036 StavangerNorway School of Electrical and Electronics Engineering Nanyang Technological UniversitySingapore 639798Singapore Senseable City Lab Massachusetts Institute of TechnologyCambridgeMA 02139USA

The fragmented design of intelligent transportation systems creates isolated intelligent *** competition and information gaps are fierce and widespread,worsening traffic issues and degrading overall service ***,empowered by advanced technologies,an evolution toward an autonomous transportation system(ATS)is *** evolution aims to develop a collaborative and sustainable ecosystem,prompting interoperability within the cloud-edge-device continuum.

关键词： autonomous system prompt

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