Article
Engineering, Multidisciplinary
Xiao-Zhi Gao, Madhu Sudana Rao Nalluri, K. Kannan, Diptendu Sinharoy
Summary: The study introduces a hybrid cat swarm optimization (HCSO) algorithm, which shows better performance in feature subset selection compared to the traditional CSO through testing.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2021)
Article
Computer Science, Information Systems
Amjad Qtaish, Dheeb Albashish, Malik Braik, Mohammad T. T. Alshammari, Abdulrahman Alreshidi, Eissa Jaber Alreshidi
Summary: The rapid expansion of medical data poses challenges for Machine Learning tasks, and the Feature Selection (FS) is critical to pick the most pertinent features. In this study, two intelligent wrapper FS approaches, BSCSO and BMSCSO, are implemented using the SCSO algorithm. While BSCSO lacks a good search strategy, BMSCSO integrates a memory-based strategy to improve the search process. Experimental results on 21 benchmark disease datasets show that BMSCSO outperforms BSCSO in terms of fitness values, accuracy, and number of selected features. BMSCSO efficiently explores the feature domain for optimal feature sets.
Article
Computer Science, Artificial Intelligence
Hugo Siqueira, Clodomir Santana, Mariana Macedo, Elliackin Figueiredo, Anuradha Gokhale, Carmelo Bastos-Filho
Summary: This paper presents a simplified version of the Cat Swarm Optimization algorithm, named SBCSO, with a new position update rule for tracing mode, demonstrating improved performance and reduced computational cost compared to previous versions. The experiments show that SBCSO outperforms other well-known algorithms in various optimization problems.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2021)
Correction
Computer Science, Artificial Intelligence
Ala' M. Al-Zoubi, Mohammad A. Hassonah, Ali Asghar Heidari, Hossam Faris, Majdi Mafarja, Ibrahim Aljarah
Summary: The affiliation information of authors Ali Asghar Heidari and Majdi Mafarja was mistakenly published during typesetting and has been corrected.
Article
Computer Science, Artificial Intelligence
Ala' M. Al-Zoubi, Mohammad A. Hassonah, Ali Asghar Heidari, Hossam Faris, Majdi Mafarja, Ibrahim Aljarah
Summary: This paper aims to enhance the effectiveness of SVM algorithms in classification problems by optimizing parameters and feature weighting. An improved evolutionary variant of CSO is proposed and experimented with to demonstrate its superiority over other optimization methods, particularly in how its crossover mechanism improves classification performance.
Article
Plant Sciences
Rongli Gai, Zhibin Guo
Summary: This paper proposes a river water quality assessment method based on improved grey correlation analysis (ACGRA) and particle swarm optimization multi-classification support vector machine (PSO-MSVM) for assessing river water environment quality. The combination weights of water quality indicators were calculated using Analytic Hierarchy Process (AHP) and Criteria Importance Though Intercrieria Correlation (CRITIC), and then the correlation between water quality indicators was calculated for feature selection. The river water environment assessment methods of ACGRA and PSO-MSVM can evaluate the water environment quality more accurately.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Computer Science, Information Systems
B. Sakthi Karthi Durai, J. Benadict Raja
Summary: The early detection of retinal abnormalities like diabetic retinopathy (DR) can be achieved using computerized analysis of retinal fundus images. This study presents an automated process that employs an optimized SVM classifier and a new feature extraction method for more accurate and efficient detection of DR. The proposed technique is validated using a standard dataset and achieves high sensitivity, specificity, and accuracy.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Asuncion Jimenez-Cordero, Sebastian Maldonado
Summary: Functional Data Analysis (FDA) is important, but classifying hybrid functional data with both functional and static covariates is challenging. This paper proposes an embedded feature selection approach for SVM classification, optimizing bandwidths and SVM parameters to improve classification rates. The methodology outperformed 17 other approaches, demonstrating robustness through sensitivity analysis.
APPLIED INTELLIGENCE
(2021)
Article
Automation & Control Systems
Hui-Ping Yin, Hai-Peng Ren
Summary: A symbol detection method based on genetic algorithm support vector machine is proposed to improve the bit error rate performance and simplify the symbol detection process in chaotic baseband wireless communication systems. By converting symbol decoding into a binary classification process, the proposed method outperforms traditional methods in terms of performance.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Javier Alcaraz, Martine Labbe, Mercedes Landete
Summary: This paper introduces a Support Vector Machine with feature selection and proposes a bi-objective evolutionary algorithm to approximate the Pareto optimal frontier. Extensive computational experiments are conducted to compare the results obtained by different methods, and the properties of points in the Pareto frontier are discussed.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Biology
Song Yang, Lejing Lou, Wangjia Wang, Jie Li, Xiao Jin, Shijia Wang, Jihao Cai, Fangjun Kuang, Lei Liu, Myriam Hadjouni, Hela Elmannai, Chang Cai
Summary: This paper proposes a new algorithm called SCACO, which combines slime mould foraging behavior and collaborative hunting to improve the convergence accuracy and solution quality of ACOR. It also optimizes the ability of ACO to jump out of local optima using an adaptive collaborative hunting strategy. The performance of SCACO is compared with nine basic algorithms and nine variants, demonstrating its effectiveness in classification prediction for the diagnosis of tuberculous pleural effusion.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Tengku Mazlin Tengku Ab Hamid, Roselina Sallehuddin, Zuriahati Mohd Yunos, Aida Ali
Summary: Discovering a hearing disorder early is crucial to reduce its effects, and approaches to improve remaining hearing ability are important for successful human communication development. The complexity posed by explosive dataset features makes it challenging to determine proper treatment. Irrelevant features and improper classifier parameters can significantly impact the accuracy of the audiometry system. This study proposes an ensemble filters feature selection method based on Information Gain, Gain Ratio, Chi-squared, and Relief-F, with optimization using Particle Swarm Optimization and Support Vector Machine. The results demonstrate that this method effectively handles high-dimensional data for hearing disorder prediction, achieving 96.50% accuracy compared to classical SVM.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Maliheh Abbaszadeh, Saeed Soltani-Mohammadi, Ali Najah Ahmed
Summary: This article introduces the application of the support vector classifier in geological modeling and proposes an improved method based on particle swarm optimization to select the best model parameters. Through the application in the modeling process of the Iju porphyry copper deposit, the effectiveness and superiority of this method are demonstrated.
COMPUTERS & GEOSCIENCES
(2022)
Article
Multidisciplinary Sciences
Hichem Rahab, Hichem Haouassi, Mohammed El Habib Souidi, Abdelaali Bakhouche, Rafik Mahdaoui, Maamar Bekhouche
Summary: This study focuses on feature selection in Arabic sentiment analysis using a swarm-based approach. The proposed modified binary rat swarm optimization algorithm overcomes the local optimum and slow convergence problems, achieving good optimization results and demonstrating superiority compared to other swarm-based algorithms.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
An-Da Li, Bing Xue, Mengjie Zhang
Summary: This paper proposes an improved sticky binary PSO algorithm for feature selection problems, which aims to enhance evolutionary performance through new mechanisms such as an initialization strategy, dynamic bits masking, and genetic operations. Experimental results show that ISBPSO achieves higher accuracy with fewer features and reduces computation time compared to benchmark PSO-based FS methods.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Multidisciplinary
Kuan-Cheng Lin, Sih-Yang Chen, Jason C. Hung
MATHEMATICAL PROBLEMS IN ENGINEERING
(2015)
Editorial Material
Computer Science, Information Systems
Neil Y. Yen, Odej Kao, Hai Jiang, Jason C. Hung
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
(2015)
Article
Computer Science, Hardware & Architecture
Kuan-Cheng Lin, Kai-Yuan Zhang, Yi-Hung Huang, Jason C. Hung, Neil Yen
JOURNAL OF SUPERCOMPUTING
(2016)
Article
Computer Science, Artificial Intelligence
Jason C. Hung, Kuan-Cheng Lin, Nian-Xiang Lai
APPLIED SOFT COMPUTING
(2019)
Article
Computer Science, Hardware & Architecture
Jason C. Hung, Chun-Chia Wang
Summary: This study investigates the impact of interface structures on user visual behavior on mobile commerce websites using eye tracking technology. The results show differences in fixation time and sequences in areas like menu icons and navigation menu lists with different presentation methods.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Computer Science, Information Systems
Jia-Wei Chang, Jason C. Hung, Kuan-Cheng Lin
Summary: This study proposes a framework to generate singable lyrics by combining the GPT-2 model with musical style to create lyrics suitable for singing.
COMPUTER COMMUNICATIONS
(2021)
Article
Green & Sustainable Science & Technology
Hsuan-Chu Chen, Chun-Chia Wang, Jason C. Hung, Cheng-Yu Hsueh
Summary: This study used a portable eye tracker to explore the visual search behaviors of existing consumers watching live ecommerce. The findings showed that participants of different sexes had different fixation orders and durations on the live ecommerce platform, but displayed the same level of attention towards the live products. The results of this study can serve as a reference for operators and researchers of live streaming platforms.
Article
Computer Science, Information Systems
Yu-Wei Chan, Feng-Tsun Chien, Min-Kuan Chang, Wei-Chun Ho, Jason C. Hung