Article
Computer Science, Interdisciplinary Applications
S. A. R. Sheik Masthan, G. Kanagaraj, Vincent F. Yu
Summary: Inverse kinematics is a complex problem in robotics, and this paper proposes a hybrid algorithm based on the gravitational search algorithm to solve it. Experimental results show that the proposed algorithm outperforms the conventional GSA in terms of efficiency and convergence rate.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Energy & Fuels
Yasir Muhammad, Muhammad Asif Zahoor Raja, Muhammad Abid Ali Shah, Saeed Ehsan Awan, Farman Ullah, Naveed Ishtiaq Chaudhary, Khalid Mehmood Cheema, Ahmad H. Milyani, Chi-Min Shu
Summary: The protection system plays a vital role in different subsystems of the power network. The research focuses on improving the overall performance by optimizing the operational time of directional over current relays (DOCRs) through a new optimization strategy. The results show that the synergy of fractional calculus, particle swarm optimization (PSO) and gravitational search algorithm (GSA) enhances the performance of the optimizer.
Article
Engineering, Aerospace
Khurram Shahzad Sana, Weiduo Hu
Summary: This paper introduces a novel hybrid algorithm called FPSOGSA for trajectory planning of hypersonic lifting reentry flight vehicles, achieving smoother trajectories and enforcement of path constraints by adjusting bank angles and other means. Simulation results demonstrate the superior convergence and computational efficiency of the FPSOGSA method compared to standard PSO and GSA methods.
CHINESE JOURNAL OF AERONAUTICS
(2021)
Article
Automation & Control Systems
Mohamed Siddiq Zatout, Amar Rezoug, Abdellah Rezoug, Khalifa Baizid, Jamshed Iqbal
Summary: This study applied three metaheuristic methods to optimize fuzzy logic controllers for quadrotor attitude stabilisation, and found that BAT algorithm outperformed PSO and CS in terms of performance, computation time, and fitness. The BAT-based fuzzy controller exhibited superior performance compared with other algorithms in stabilising the quadrotor.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Automation & Control Systems
Dikshit Chauhan, Anupam Yadav
Summary: This paper presents a dynamic comprehensive multi-swarm gravitational search algorithm with non-uniform mutation (cdGSA-2m) for optimizing hybrid active power filter (HAPF) parameters. The algorithm divides the population into two sub-populations with different learning mechanisms, focusing on exploitation and exploration respectively. The effectiveness of the implemented mechanisms is analyzed through experiments, and the algorithm is tested on benchmark problems and HAPF case studies. Results show that cdGSA-2m outperforms other state-of-the-art algorithms in terms of accuracy, convergence rate, search capability, and stability.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Physics, Multidisciplinary
Zhonghua Yang, Yuanli Cai, Ge Li
Summary: The gravitational search algorithm is a global optimization algorithm with the advantages of a swarm intelligence algorithm. In order to address the issues of accuracy and local optimal solutions, an improved gravitational search algorithm based on an adaptive strategy is proposed. By enhancing the information interaction between particles and improving the exploration and exploitation capacity, the algorithm shows significant improvements in solving local extrema and finding globally optimal solutions.
Article
Automation & Control Systems
Sinan Unsal, Ibrahim Aliskan
Summary: This study proposes a hybrid algorithm called H-GA-GSA by combining the advantages of Genetic Algorithm (GA) and Gravitational Search Algorithm (GSA) to optimize the output membership functions of fuzzy logic controllers (FLCs) for better speed control performance of permanent magnet synchronous motors (PMSM).
Article
Agronomy
Ozgur Kisi, Payam Khosravinia, Salim Heddam, Bakhtiar Karimi, Nazir Karimi
Summary: The study investigated a new method to improve the accuracy of estimating wetting front dimensions in drip irrigation systems, showing significant improvements in both horizontal and vertical redistribution accuracy. Various evaluation indices and methods were utilized, highlighting the superiority of the new method in estimating wetting front dimensions of drip irrigation systems.
AGRICULTURAL WATER MANAGEMENT
(2021)
Article
Chemistry, Multidisciplinary
Jian Ni, Jing Tang, Rui Wang
Summary: This study combines the beetle antenna search algorithm and the artificial fish swarm algorithm, using a mutation and multi-step detection strategy to improve the optimization accuracy of the algorithm. Experimental results show that the hybrid algorithm performs better in terms of convergence speed and optimization capacity compared to other algorithms.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Sajad Ahmad Rather, P. Shanthi Bala
Summary: The study examines the application of CPSOGSA to three engineering design problems and demonstrates its superior performance compared to other competing algorithms.
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Meijin Lin, Zhenyu Wang, Weijia Zheng
Summary: This paper proposes a hybrid particle swarm-differential evolution algorithm (HPSDE) to address the shortcomings of premature and slow convergence in traditional differential evolution. The HPSDE algorithm improves optimization performance through a modified particle-swarm mutation strategy and enhanced control parameter adaptation. It also increases population diversity with DE/rand-to-rand/1 mutation strategy and combines both strategies in a random mutation framework for improved convergence and stability.
Article
Computer Science, Artificial Intelligence
Hisham A. Shehadeh
Summary: The study introduces a new hybrid optimization algorithm, HSSOGSA, which combines the strengths of GSA and SSO algorithms to achieve better performance and convergence rate in a variety of optimization problems.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Malik Braik, Hussein Al-Zoubi, Mohammad Ryalat, Alaa Sheta, Omar Alzubi
Summary: Crow Search Algorithm (CSA) is a promising meta-heuristic method that mimics the intelligent behavior of crows in nature. By combining it with Particle Swarm Optimization (PSO), the Memory based Hybrid CSA (MHCSA) achieves a stronger diversity ability and a better balance between exploration and exploitation, effectively overcoming the early convergence and imbalance issues. Test results have demonstrated the superiority of MHCSA over other methods in terms of accuracy and stability.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Environmental Sciences
Rana Muhammad Adnan, Zaher Mundher Yaseen, Salim Heddam, Shamsuddin Shahid, Aboalghasem Sadeghi-Niaraki, Ozgur Kisi
Summary: Reliable modeling of river sediments transport is crucial for the economic viability of dams, durability of hydroelectric equipment, river pollution susceptibility, navigation suitability, aesthetics, and fish habitat potential. This study investigates the capability of a new machine learning model, ANFIS-FCM-PSOGSA, in improving the estimation accuracy of river suspended sediment loads (SSLs). The results demonstrate that the proposed model significantly enhances the prediction performance compared to other models.
INTERNATIONAL JOURNAL OF SEDIMENT RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Jeremiah Osei-kwakye, Fei Han, Alfred Adutwum Amponsah, Qing-Hua Ling, Timothy Apasiba Abeo
Summary: In this study, a hybrid particle swarm optimization and crow search algorithm with a clustering initialization strategy (HPSOCSA-CIS) is proposed to enhance the exploration capability of feature selection. The clustering technique ensures an even distribution of the initial population over the feature space and includes more promising features. Additionally, the crow search algorithm helps in exploring unexplored regions within the search space. Experimental results on 15 standard UCI datasets demonstrate the superiority of the proposed method in feature selection tasks.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Meera Ramadas, Ajith Abraham
Summary: Air pollution is a global issue that can cause major health hazards. Satellite remote sensing is an effective way to monitor the atmosphere and improve understanding of complex images through clustering and segmentation techniques. The novel DiDE algorithm showed superior outcomes compared to traditional approaches, and its application in multi-level thresholding significantly reduced computational delay and improved image quality.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Rishav Pramanik, Payel Pramanik, Ram Sarkar
Summary: Breast cancer is a leading cause of premature death among women globally, but early detection and diagnosis can save lives. Hence, computer scientists are working to develop reliable models to tackle this disease. A proposed lightweight model combines transfer learning-based deep learning (DL) with feature selection to detect abnormalities in breast thermograms. This model performs well in detecting and differentiating malignant and healthy breasts.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Analytical
Bhaskar Kapoor, Bharti Nagpal, Praphula Kumar Jain, Ajith Abraham, Lubna Abdelkareim Gabralla
Summary: This paper proposes a hybrid optimization-controlled ensemble classifier to automatically analyze EEG signal dataset for epileptic seizure prediction, combining signal processing and machine learning. The proposed technique shows high accuracy, sensitivity, and specificity in early seizure prediction.
Article
Computer Science, Information Systems
E. Rajalakshmi, R. Elakkiya, V. Subramaniyaswamy, L. Prikhodko Alexey, Grif Mikhail, Maxim Bakaev, Ketan Kotecha, Lubna Abdelkareim Gabralla, Ajith Abraham
Summary: A novel vison-based hybrid deep neural net methodology is proposed in this study for recognizing Indian and Russian sign gestures. The proposed framework aims to establish a single framework for tracking and extracting multi-semantic properties, such as non-manual components and manual co-articulations. By using a 3D deep neural net with atrous convolutions for spatial feature extraction, attention-based Bi-LSTM for temporal and sequential feature extraction, modified autoencoders for abstract feature extraction, and a hybrid attention module for discriminative feature extraction, the proposed sign language recognition framework yields better results than other state-of-the-art frameworks.
Article
Computer Science, Artificial Intelligence
Benkuan Cui, Kun Ma, Leping Li, Weijuan Zhang, Ke Ji, Zhenxiang Chen, Ajith Abraham
Summary: Despite the benefits provided by the Internet and social media, the proliferation of fake news has had negative effects on society and individuals. This paper proposes a Chinese fake news detection model using a Third-order Text Graph Tensor and Information Propagation Network. Data augmentation and a novel text graph tensor representation are employed to address the challenges of feature sparsity and capturing context information. The model outperforms existing methods in fake news detection according to experimental results on four public datasets.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Sk Mohiuddin, Samir Malakar, Munish Kumar, Ram Sarkar
Summary: Video plays a critical role in conveying authenticity in various fields such as surveillance, medicine, journalism, and social media. However, the trust in videos is diminishing due to the ease of video forgery using accessible editing tools. This article comprehensively discusses the initiatives and recent trends in video forgery detection research worldwide.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Avirup Bhattacharyya, Avigyan Bhattacharya, Sourajit Maity, Pawan Kumar Singh, Ram Sarkar
Summary: Designing an automatic vehicle detection system that caters to the requirements of the traffic management system is important. This research develops a still image database, JUVDsi v1, for designing an automated traffic management system in India. The database addresses the shortcomings of existing databases and is evaluated using state-of-the-art deep learning architectures.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Ritam Guha, Kushal Kanti Ghosh, Suman Kumar Bera, Ram Sarkar, Seyedali Mirjalili
Summary: This paper proposes a binary adaptation of Equilibrium Optimizer (EO) called Discrete EO (DEO) for solving binary optimization problems. DEOSA algorithm, combining DEO with Simulated Annealing (SA) as a local search procedure, is applied to various datasets and outperforms other algorithms. The scalability and robustness of DEOSA are also tested on high-dimensional Microarray datasets and Knapsack problems, showing its superiority.
JOURNAL OF COMPUTATIONAL SCIENCE
(2023)
Article
Multidisciplinary Sciences
Mincheol Shin, Mucheol Kim, Geunchul Park, Ajith Abraham
Summary: High-performance computing supports advancements in various scientific disciplines by providing computing power and insights. This paper proposes an adaptive variable sampling model for performance analysis in high-performance computing environments. The model automatically selects optimal variables for performance prediction without requiring expert knowledge. Experiments show that the model improves speed by at least 24.25% and up to 58.75% without sacrificing accuracy.
Article
Engineering, Electrical & Electronic
Ankit Rajpal, Subodh Kumar, Neeraj Kumar Sharma, Ajith Abraham, Anurag Mishra, Naveen Kumar
Summary: This paper proposes a chest X-ray image watermarking scheme (CXRmark) using an online sequential reduced kernel extreme learning machine (OS-RKELM). The scheme segments the lung area into the region of non-interest (RONI) and region of interest (ROI) using U-Net, and modulates the approximation coefficients using OS-RKELM with different embedding strengths for ROI and RONI. Experimental results on 461 CXR images demonstrate that CXRmark outperforms other schemes in terms of perceptual quality and robustness.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2023)
Review
Automation & Control Systems
Shreyas Gawde, Shruti Patil, Satish Kumar, Pooja Kamat, Ketan Kotecha, Ajith Abraham
Summary: Industry 4.0 is the era of smart manufacturing, which relies heavily on machinery. Maintaining critical rotating machines is the top priority for engineers to minimize unplanned shutdowns and increase their useful life. This paper aims to provide a systematic literature review on the data-driven approach for multi-fault diagnosis of industrial rotating machines, highlighting the foundational work, comparative study, major challenges, and research gaps in this field.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Gargi Joshi, Ananya Srivastava, Bhargav Yagnik, Mohammed Hasan, Zainuddin Saiyed, Lubna A. Gabralla, Ajith Abraham, Rahee Walambe, Ketan Kotecha
Summary: Web Information Processing (W.I.P.) has had a significant impact on modern society as many people rely on the internet for information. Social Media platforms provide both a means of disseminating information and a breeding ground for misinformation. Machine learning models have been used to detect misinformation, but the development of generalized and explainable detectors remains a challenge. Integrating domain adaptation and explainable A.I. approaches can address these challenges.
Article
Computer Science, Information Systems
Mayur Wankhade, Chandra Sekhara Rao Annavarapu, Ajith Abraham
Summary: Sentiment classification is a crucial task in natural language processing. This research investigates the impact of text preprocessing techniques on sentiment classification and proposes a novel framework called CBMAFM that leverages the synergistic power of CNN and BiLSTM through a multi-attention fusion mechanism. The framework preserves both local and global context dependencies, resulting in improved performance compared to other state-of-the-art methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Behzad Saemi, Ali Asghar Rahmani Hosseinabadi, Azadeh Khodadadi, Seyedsaeid Mirkamali, Ajith Abraham
Summary: The task scheduling problem in Mobile Cloud Computing (MCC) is a difficult problem to solve, and this study proposes a non-dominated multi-objective strategy based on the Harris Hawks Optimization (HHO) technique to address this issue. By comparing with other algorithms, it is found that the proposed method performs better in terms of job completion time and energy savings.
Article
Computer Science, Information Systems
Deepali Arun Bhanage, Ambika Vishal Pawar, Ketan Kotecha, Ajith Abraham
Summary: This paper proposes a semantic log analysis model that utilizes three log features to capture the essence of the log message. By employing the BERT pre-trained model and an attention-based OLSTM classifier, the proposed model is able to detect failures in different infrastructures. The evaluation results demonstrate that the system delivers improved and stable results across various IT infrastructures.
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)