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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Zuo, Huixiang Xv, Yaosong Liaoning Technical University Faculty of Electrical and Control Engineering Huludao China

ISBN: (纸本)9798350382426

Based on the low accuracy of transformer fault diagnosis and less complexity, a research algorithm based on IAVOA is proposed. Firstly, obtain relevant data through gas decomposition in oil (DGA) and form 24-dimensional features, integrate the core principal component analysis to solve the problem of the compressor. Secondly, improve the african vulture optimization algorithm (IAVOA), improve its optimization ability, avoid the local optimization, to optimize the parameters and weights of the kernel function in the hybrid core limit learning machine (HKELM) to increase the classification accuracy and improve the generalization ability and accuracy. The test results showed that the IAVOA-HKELM model achieved 96% accuracy, which improved by 10% over the ABC-HKELM model, 8% and 7%, respectively. © 2023 IEEE.

关键词： african vulture optimization algorithm convex lens reverse learning Fault diagnosis firefly algorithm limit learning machine

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Cost optimization of Steel Beam-to-Column Connections using AVOA

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Journal of Architectural Research and Development 2024年第2期8卷 18-23页

作者： Ziyu Wang Zhaoyang Ren School of Civil Engineering University of BirminghamBirmingham B152TTUnited Kingdom Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266000China

The joint-bolt-african vulture optimization algorithm(AVOA)model is proposed for the design of building connections to improve the stability of steel beam-to-column *** this algorithm,the type of steel is first determined,and the number of bolts needed by the corresponding steel type is referenced in Eurocode ***,the bearing capacity of the joint can be *** joint-bolt-AVOA model is established by substituting the bolt number required by the steel into the algorithm to obtain the optimal bolt number required while ensuring joint *** results show that the number of bolts required by the joint-bolt-AVOA model based on the stability of steel is lower than that calculated by Eurocode ***,AVOA can effectively optimize the number of bolts needed in building connections and save resources.

关键词： Steel connections african vulture optimization algorithm optimization of bolts

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A novel hybrid optimization algorithm for depression detection using MRI and speech signal

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年 86卷

作者： Patil, Minakshee Mukherji, Prachi Wadhai, Vijay MKSSSs Cummins Coll Engn Women Elect & Telecommunicat Cummins Coll Rd Pune 411052 Maharashtra India Astute Career Counselling Acad Off 4&51st Floor Aditya Ceneteegra FC Rd Pune 411044 Maharashtra India

Depression is defined as a psychiatric disorder, which has many adverse effects on the person, family, and so-ciety. In this paper, the multimodalities are utilized as an input for depression detection that includes both speech signal and brain MRI for better detection. Here, the median filter and Gaussian filter are utilized to remove the clamour exist in MRI and speech signal, respectively. State of depression is classified from both MRI and speech signal using a Deep Maxout Network (DMN), where the weight of the deep learning network is trained by a novel approach of Exponential african vulture optimization algorithm (ExpAVOA). The ExpAVOA is formed by the merging of the Exponentially Weighted Moving Average (EWMA) and african vultures optimization algorithm (AVOA). Finally, the classified outcomes of the MRI and speech signals are fused by the RV coefficient. Thus, the classified outcome is either a healthy person or a depressed person. The unique optimal deep learning technique attained maximum testing accuracy, sensitivity, and specificity of 0.959, 0.942, and 0.968 respectively.

关键词： Depression detection Exponential weighted moving average african vulture optimization algorithm Gaussian filter Median filter Deep Maxout Network

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A robust FRF damage indicator combined with optimization techniques for damage assessment in complex truss structures

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CASE STUDIES IN CONSTRUCTION MATERIALS 2022年 17卷

作者： Khatir, Samir Tiachacht, Samir Thanh, Cuong-Le Tran-Ngoc, Hoa Mirjalili, Seyedali Wahab, Magd Abdel Univ Ghent Fac Engn & Architecture Soete Lab Technol Pk Zwijnaarde 903 B-9052 Zwijnaarde Belgium Mouloud Mammeri Univ Tizi Ouzou Lab Mech Struct & Energet LMSE BP 17 RP Tizi Ouzou 15000 Algeria Ho Chi Minh City Open Univ Fac Civil Engn Ho Chi Minh City Vietnam Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Van Lang Univ Fac Mech Elect & Comp Engn Sch Engn & Technol Ho Chi Minh City Vietnam

Vibration-based damage detection tools are frequently employed because of their advantages over other non-destructive techniques. This paper presents an improved Frequency Response Function (FRF) indicator for damage identification in complex structures. To verify the effectiveness of the improved damage indicator, different structures are used, namely a 20-Bar Planar Truss (2D), a 28-Bar Space Truss (3D), a 72-Bar Space Truss, and a 600-bar Space Truss. The numerical models of all these structures are built in MATLAB using Finite Element Method (FEM). The improved indicator detects and localises single and multiple damages in the first stage. Next, after eliminating the healthy elements, it is used in a new objective function to solve the damage quantification problem using recent optimization techniques, including Artificial Gorilla Troops Optimizer (GTO), Dingo optimization algorithm (DOA), african vulture optimization algorithm (AVOA), and Gradient-Based Optimizer (GBO). The results demonstrate that all optimization techniques can accurately predict the exact level of damage. However, GTO is the most efficient in terms of convergence. To study the effectiveness of this indicator in case of noisy data, different levels of noise are considered in the damage assessment exercises.

关键词： Damage detection Frequency response function GBO DOA Inverse problem 2D and 3D complex truss structures Artificial Gorilla Troops Optimizer african vulture optimization algorithm

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Impact of solid oxide FC-RFB and IPFC on a renewable multi-area power system using combined PI and FOPD controllers optimized by the african vulture algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-28页

作者： Saha, Arindita Bhaskar, Mahajan Sagar Elmorshedy, Mahmoud F. Almakhles, Dhafer J. Padmanaban, Sanjeevikumar Regent Educ & Res Fdn Grp Inst Dept Elect Engn Kolkata 700121 West Bengal India Prince Sultan Univ Coll Engn Renewable Energy Lab Riyadh Saudi Arabia Tanta Univ Fac Engn Elect Power & Machines Engn Dept Tanta 31733 Egypt Univ South Eastern Norway Dept Elect Engn IT & Cybernet Porsgrunn Norway

The expanding complexity of modern energy systems and the increasing integration of renewable sources make stable load frequency control (LFC) in interconnected power networks a continuing issue. Traditional controllers, such as proportional-integral (PI), proportional-integral-derivative (PID), and other subordinate control methods, frequently fail to control frequency adequately, especially in multi-source generating systems. Furthermore, standard optimization techniques may exhibit sluggish convergence and inefficient tuning, limiting their usefulness in real-time applications. To address these problems, this study suggest an enhanced LFC framework for a three-area power system that includes thermal-biodiesel (Area-1), thermal (Area-2), and hydro-thermal (Area-3) components. The african vulture optimization algorithm (AVOA) is used to improve a novel PI(FOPD) controller that combines integer-order PI with fractional-order Proportional Derivative (FOPD). According to a comparative investigation, the AVOA-augmented PI(FOPD) controller outperforms conventional I, PI, and PID controllers in terms of transient responsiveness, stability, and convergence. Additionally, AVOA outperforms optimization approaches such as Cuckoo Search, Particle Swarm optimization, and the Firefly algorithm. The integration of a Dish-Stirling solar thermal system, a Flexible AC Transmission System (FACTS) device, and an energy storage component improves system robustness. The results show that the AVOA-optimized PI(FOPD) controller greatly enhances LFC performance, making it a promising alternative for current power networks.

关键词： Automatic generation control Biodiesel Interline power flow controller Redox flow battery Solid oxide fuel cell african vulture optimization algorithm Cascaded controller

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A novel AVOA optimized DNN-BiLSTM-attention model for improved gesture classification using electromyography signal

Results in Engineering

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Results in Engineering 2025年 27卷

作者： Gautam Shah Abhinav Sharma Deepak Joshi Ajit Singh Rathor Department of Electrical and Electronics Engineering University of Petroleum and Energy Studies Dehradun Uttarakhand India Centre for Biomedical Engineering Indian Institute of Technology Delhi India

Identification of hand gestures using wearable interfaces has gained significant attention in areas such as human–computer interaction, gaming, sign language recognition, rehabilitation and assistive robotics. This study presents a hybrid deep learning architecture that integrates convolutional layers with BiLSTM and attention mechanisms to classify upper limb movements in healthy individuals using four distinct feature sets (F1, F2, F3, F4). A key contribution of this work is the optimization of convolutional hyperparameters, such as the number of filters, using various nature-inspired metaheuristic algorithms, thereby eliminating the need for manual tuning. Extensive experiments conducted on EMAHA-DB1 dataset demonstrate the effectiveness of the proposed method. Comparative evaluations against several state-of-the-art ML models, namely Cubic SVM, LDA, Decision Tree, and Deep Neural Network, reveal that our model chieves superior performance across all four distinct feature sets. Specifically, the AVOA-optimized DNN-BiLSTM-Attention model achieved classification accuracies of 99.35 % for F1, 99.72 % for F2, 94.39 % for F3, and 99.19 % for F4 feature set. Furthermore, statistical analysis confirms the model’s robustness and significant improvements across all feature sets, highlighting its superiority.

关键词： sEMG Feature sets AVOA optimized-DNN-BiLSTM-attention model Hand gesture classification AVOA african vulture optimization algorithm STO Siberian Tiger optimization AOA Arithmetic optimization algorithm WaOA Walrus optimization algorithm IMU Inertial Measurement Unit SVM Support Vector Machine DT Decision Tree HGR Hand Gesture Recognition TPR True Positive Rate FPR False Positive Rate CA Classification Accuracy MAV Mean Average Value ZC Zero Crossing TD Time Domain AUC Area Under the Curve MCC Mathew Correlation Coefficient CWT Continuous Wavelet Transform DLPR Deep Learning Pattern Recognition EMAHA-DB Electromyography Analysis of Hand Activity-Database LDA Liner Discriminant Analysis DNN Deep Neural Network BiLSTM Bidirectional Long Short-Term Memory Network sEMG Surface Electromyography FMG Force Myography ANN Artificial Neural Network CNN Convolutional Neural Network TCN Temporal Convolutional Network NPV Negative Predictive Value FNR False Negative Rate ML Machine Learning WL Waveform Length RMS Root Mean Square Value FD Frequency Domain VMD Variable mode Decomposition TFR Time-Frequency Representation ANNOVA Analysis of Variance FAABOS Functional Arm Activity Behavioural Observation System

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Evolutionary algorithm with Graph Neural Network Driven Cyberbullying Detection on Low Resource Asian Languages

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ACM Transactions on Asian and Low-Resource Language Information Processing 1000年

作者： Hussein Ali Rasool Firas Aldolaimy Forat Falih Hasan Ali H. Alsalamy Munqith Saleem Ahmed Hussein Alkhayyat Moolchand Sharma Altoosi University College Najaf Iraq Department of Mathematics College of Education Al-zahraa University for women Karbala Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna 66002 Iraq Medical technical college Al-Farahidi University Baghdad Iraq College of technical engineering The Islamic University Najaf Iraq Assistant Professor Department of Computer Science & Engineering Maharaja Agrasen Institute of Technology India

ICT is widely adopted by Asian youth and is utilized by people of all ages across the continent. Despite its many advantages, unethical ICT usage can lead to many complications. A harmful application of ICT for social communication and engagement is cyberbullying. Simply adhering to the generally accepted norms and guidelines for cybersecurity will not protect you from cybercrime. Even well-known social media stages like Twitter are safe from this attack. Natural language processing (NLP) research on cyberbullying detection has become popular recently. Even though old-style NLP procedures have become highly cyberbullying, there are still hurdles to overcome. These include the limited character count allowed by social media platforms, an imbalance among comments, ambiguity, and unnecessary use of slang. Models based on (CNNs), Multilayer Perceptrons (MLPs), and (RNNs), have recently shown encouraging results in a variety of NLP tasks. With this motivation, this research develops an african vulture optimization algorithm with a graph neural network-based cyberbullying detection and classification (AVOAGNN-CBDC) model. The proposed AVOAGNN-CBDC technique mainly intends to detect and classify cyberbullying. The AVOAGNN-CBDC technique undergoes data preprocessing in different stages and a FastText-based word embedding process to achieve this. Besides, the AVOAGNN-CBDC technique employs the GNN model for cyberbullying detection and classification. Finally, the AVOA is used for the optimal parameter selection of the GNN model, which helps achieve improved classification performance. The experimental result investigation of the AVOAGNN-CBDC technique is tested on the cyberbullying dataset, and the outcomes highlighted the supremacy of the AVOAGNN-CBDC technique in terms of several measures.

关键词： african vulture optimization algorithm Graph neural network FastText-based word embedding Cyberbullying detection Natural language processing

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