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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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Sentiment Analysis using Optimised Feature Sets in Different Facebook/Twitter Dataset Domains with Big Data

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer Science and Mathematics 2022年第1期3卷 64-70页

作者： Al-Mashhadani, Mohammed Ibrahim Hussein, Kilan M. Khudir, Enas Tariq Ilyas, Muhammad Department of Electrical and Electronics Engineering Altinbas University Istanbul Turkey Diyala University Electronic Computer Center Iraq University of Technology Electrical and Electronics Engineering Altinbas University Turkey

Nowadays, in many real-life applications, sentiment analysis plays a vital role in the automatic prediction of human activities, especially on online social networks (OSNs). Since the last decade, the research on opinion mining and sentiment analysis has been growing with the increase in volume of online reviews available over the social media networks, such as Facebook OSNs. Sentiment analysis falls under the data mining domain research problem. Sentiment analysis is a type of text mining process to determine subjective attitude, such as sentiments from written texts, and hence has become a main research interest in domains of natural language processing and data mining. The main task of sentiment analysis is to classify human sentiments with the objective of classifying the sentiment or emotion of end users based on their specific text on the OSNs. Several research approaches have been designed for sentiment analysis, in which the factors of accuracy, efficiency and speed have been used to evaluate the effectiveness of sentiment analysis methods. The Map-Reduce framework under the domain of big data is used to minimise the speed of execution and efficiency recently with many data mining methods. The sentiment analysis of messages on Facebook OSNs is more challenging compared with those of other sites because of the misspellings and slang words in their Twitter dataset. In this study, the different solutions that have been recently presented are discussed in detail. Then, a new approach for sentiment analysis based on hybrid feature extraction and multi-class support vector machine methods is proposed. The algorithms have been designed using big data techniques in view of optimising the performance of sentiment analysis. © 2022 Iraqi Journal for computer Science and Mathematics. All rights reserved.

关键词： Sentiment analysis

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Machine Vision Based Local Hyacinth Bean Breed Recognition Using Convolutional Neural Network

Machine Vision Based Local Hyacinth Bean Breed Recognition U...

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2024 International Conference on Advances in Computing, Communication, electrical, and Smart Systems, iCACCESS 2024

作者： Khan, Md. Abbas Ali Rahman, Md. Ataur Hossain, Md Liton Habib, Md. Tarek Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Electrical and Electronic Engineering Center for Innovation and Technology University of Liberal Arts Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798350350289

The classification is a significant one. This paper proposes a CNN-based Local Hyacinth Bean Breed Recognition (CNN-LHBR) approach along with a machine vision approach. Among the 52 breeds, we have taken only eight categories, namely Bashpaki Faridpur, Chaina Sada Patla Chela, Gochi, Kajoli, Katla, Lati, Noldub, and Pudi Aishna. There are many works done before about breed detection of different fruits as well as disease recognition. But no research has done such a work as LHBR, especially in Bangladesh. The interest of this research is the reason for the high protein and vitamin B complex;besides, each bean has a separate test, yield, seed, and nutrition level. More importantly, we have implemented 3 CNN models for the experiment of the breed recognition of 8 Hyacinth Bean specimens. For model accuracy, we have considered training, validation, and testing accuracy. As for performance evaluation, the confusion matrix has bean applied. Among the models, the customized CNN model gives the best accuracy. The customized CNN model's accuracy is 97.50%. © 2024 IEEE.

关键词： computer vision

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OFDM with Generalized Cascade Index Modulation for Secure Communications

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China Communications 2024年第10期21卷 210-225页

作者： Hu Zeng Jin Xiaopin Yao Yudong Chen Fangjiong Wan Dehuan Liu Yun College of Information Science and Technology Zhongkai University of Agriculture and EngineeringGuangzhou 510225China College of Information Engineering China Jiliang UniversityHangzhou 310018China Department Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ 07030USA School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Center for Data Science and Artificial Intelligence Guangdong University of FinanceGuangzhou 510521China

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error *** CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital ***, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.

关键词： cascade index modulation low-complexity detector maximum likelihood OFDM physical layer security

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A comparison study between Modbus and IEC 61850 MMS Protocols 33

A comparison study between Modbus and IEC 61850 MMS Protocol...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Johnson, Rayner Krishnamurthy, Senthil Mataifa, Haltor Esmail, Mohammed Cape Peninsula University of Technology Center for Substation Automation and Energy Management Systems Department of Electrical Electronic and Computer Engineering Cape Town7530 South Africa

ISBN: (纸本)9798331535162

The growing adoption of renewable energy primary plants has been seen globally. South Africa has seen a significant development in utility-scale photovoltaic (PV) farms in recent years due to the alleviated regulations stipulated in the latest Electricity Regulation Amendment Act. Connecting the PV plants to the power grid has also undergone a technical evolution by incorporating modern embedded computers known as Intelligent Electronic Devices (IEDs). The International Electrotechnical Committee (IEC) 61850 standard defines a suite of protocols used in modern power systems. This study compares the IEC 61850 Manufacturing Messaging Specification (MMS) with the well-known MODBUS protocol. The comparison is achieved by simulating a solar farm's Power Plant Controller (PPC). © 2025 IEEE.

关键词： Solar power plants

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Hyperbolic Pattern Detection in Ground Penetrating Radar Images Using Faster R-CNN 20

Hyperbolic Pattern Detection in Ground Penetrating Radar Ima...

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20th International Conference on electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Srimuk, Pachara Boonpoonga, Akkarat Kaemarungsi, Kamol Athikulwongse, Krit Torrungrueng, Danai Chantasen, Nattawat King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand National Science and Technology Development Agency National Electronics and Computer Technology Center Pathumthani Thailand Department of Teacher Training in Electrical Engineering Bangkok Thailand Rajamangala University of Technology Phra Nakhon Faculty of Engineering Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350310467

This study presents the application of Faster RCNN, a popular Region Based Convolutional Neural Network, for detecting hyperbolic patterns in Ground Penetrating Radar (GPR) images. GPR is an important tool for subsurface imaging in various fields such as geology, archaeology, and engineering. However, the analysis of GPR images can be challenging due to noise, small objects, and variations in object sizes. To evaluate the performance of the proposed method, 369 simulated GPR B-scan images were generated using GprMax simulation software. These images included single, double, and triple hyperbolic patterns. The results showed that preprocessing improved the detection accuracy and led to higher Intersection over Union (IoU) scores. The experimental results demonstrate that Faster R-CNN is an effective tool for hyperbolic pattern detection in GPR images and provides a promising direction for future research in the field. © 2023 IEEE.

关键词： Ground penetrating radar systems

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Characterizing Memory Access Patterns of Various Convolutional Neural Networks for Utilizing Processing-in-Memory

Characterizing Memory Access Patterns of Various Convolution...

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2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： Jang, Jihoon Kim, Hyun Lee, Hyokeun Research Center for Electrical and Information Technology Seoul National University of Science and Technology Department of Electrical and Information Engineering Seoul Korea Republic of Inter-University Semiconductor Research Center Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350320213

Convolutional neural network (CNN) models require deeper networks and more training data for better performance, which in turn results in greater computational and memory requirements. In this paper, we analyze the memory access patterns that occur in main memory during the training processes of various CNN models. CNN training is a linear procedure consisting of a forward pass (FP) and a backward pass (BP). As a result of the analysis, we found that BP accounted for 83.4% of the total main memory accesses on average. Therefore, CNN training including FP and BP is much more memory-intensive than CNN inference using only FP. This demonstrates that CNN training is a suitable application for near-data processing to reduce memory bottlenecks and conserve computational resources. © 2023 IEEE.

关键词： Convolutional Neural Networks Memory access pattern Near data processing Network training Processing-in-Memory

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Modeling and Simulating more efficient and reliable fuel cell vehicles through enhancing the integration of the 6kW PEM fuel cell stack with DC/DC boost converter and BLDC motor 33

Modeling and Simulating more efficient and reliable fuel cel...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Mtumka, Olona Krishnamurthy, Senthil Cape Peninsula University of Technology Bellville Center for Intelligent Systems and Emerging Technologies Department of Electrical Electronic and Computer Engineering Cape Town7530 South Africa

ISBN: (纸本)9798331535162

This study focuses on enhancing the integration of the PEM fuel cell, DC/DC boost converter, and BLDC motor, which help to develop more efficient and dependable fuel cell vehicles. The development of renewable energy sources for power generation has received much attention due to the depletion of fossil fuel supplies worldwide and economic difficulties. Fuel cells are one of the most efficient and cleanest power generation methods. Polymer electrolyte membrane (PEM) fuel cell is the preferred type of fuel cell for vehicles because of its high energy density, fast start-up, and lack of pollution. This paper aims to model and simulate a PEM fuel cell stack to power a Brushless DC (BLDC) motor for a fuel cell vehicle to reduce emissions in the transportation sectors. A boost converter is implemented to boost the output voltage of the PEM fuel cell stack to 100 V. The paper utilizes the trapezoidal commutation of the BLDC motor to drive the inverter. The trapezoidal commutation uses the six-switching technique to control the inverter's phase currents. The variation of fuel flow rate and drive cycle are included to observe the effect on the operation of the fuel cell stack. The paper presents the modeling and simulation of the PEM Fuel cell stack to improve power conversion, energy management, and vehicle control, helping to make fuel cell vehicles more viable for widespread adoption in the future. © 2025 IEEE.

关键词： Brushless DC motors

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UCR-SSL: Uncertainty-Based Consistency Regularization for Semi-Supervised Learning

UCR-SSL: Uncertainty-Based Consistency Regularization for Se...

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2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： Lee, Seungil Kim, Hyun Chun, Dayoung Seoul National University of Science and Technology Research Center for Electrical and Information Technology Department of Electrical and Information Engineering Seoul Korea Republic of Seoul National University Inter-University Semiconductor Research Center Department of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350320213

Recently, semi-supervised learning methods are being actively developed to increase the performance of neural networks by using large amounts of unlabeled data. Among these techniques, pseudo-labeling methods have the advantage of low computational complexity, but are vulnerable to missing annotations. To solve this problem, we propose a method called uncertainty-based consistency regularization (UCR). UCR models a detection head to obtain different outputs for input images and computes a feature map of each. Subsequently, these feature maps are matched with the original and filtered ground truth (GT), and are classified as positive and negative samples, respectively. In this process, missing samples are generated by the filtered GT;therefore, we use a specialized loss function designed to reduce the logit difference of the samples for robustness against missing annotations. We also use the uncertainty extracted through Gaussian modeling as a criterion for annotation filtering to train the network to focus on reliable results. As a result of experiments with an SSD model on the Pascal VOC dataset, the proposed approach achieved an improvement of 0.7% in terms of mAP compared to a baseline method. © 2023 IEEE.

关键词： Consistency Regularization Convolutional Neural Network Semi-Supervised Object Detection Uncertainty

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Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

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computer Modeling in engineering & Sciences 2024年第5期139卷 1455-1486页

作者： Kolli Ramujee Pooja Sadula Golla Madhu Sandeep Kautish Abdulaziz S.Almazyad Guojiang Xiong Ali Wagdy Mohamed Department of Civil Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India Department of Computer Science LBEF Campus(Asia Pacific University of Technology&InnovationMalaysia)Kathmandu44600Nepal Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmmanJordan

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental *** attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation *** this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering *** achieve this goal,a new approach using convolutional neural networks(CNNs)has been *** study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly *** selection of optimal input parameters is guided by two distinct *** first criterion leverages insights garnered from previous research on the influence of individual features on compressive *** second criterion scrutinizes the impact of these features within the model’s predictive *** to enhancing the CNN model’s performance is the meticulous determination of the optimal *** a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s *** model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score ***,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction *** unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rat

关键词： Class F fly ash compressive strength geopolymer concrete prediction deep learning convolutional neural network

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