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
Computer Science, Artificial Intelligence
Khalid Y. Aram, Sarah S. Lam, Mohammad T. Khasawneh
Summary: This article introduces the Alternated Sorting Method Genetic Algorithm (ASMGA), which is a hybrid wrapper-filter algorithm for simultaneous feature selection and model selection for Support Vector Machine (SVM) classifiers. ASMGA approximates a set of Pareto optimal feature subsets based on three objectives: cost-sensitive error rate, feature subset size, and Max-Margin Feature Selection (MMFS)-based estimates of feature relevance and redundancy. The proposed algorithm outperforms canonical GA and NSGA-II on benchmark datasets, showing the potential of ASMGA in cost-sensitive feature selection.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Essam H. Houssein, Diaa Salama Abdelminaam, Hager N. Hassan, Mustafa M. Al-Sayed, Emad Nabil
Summary: This study investigates the importance of gene selection in cancer classification, proposing a BMO-SVM algorithm for selecting the most predictive and informative genes. Experimental comparisons with traditional metaheuristic optimization algorithms validate the high efficiency of the proposed algorithm.
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
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
Biology
Mingjing Wang, Yingqi Liang, Zhongyi Hu, Siyuan Chen, Beibei Shi, Ali Asghar Heidari, Qian Zhang, Huiling Chen, Xiaowei Chen
Summary: This research aims to build a framework for discriminating between different types of lupus nephritis using real clinical data. By combining a hybrid stochastic optimizer moth-flame algorithm with support vector machine, a more stable and effective computer-assisted technique for analyzing systemic lupus erythematosus nephritis is developed.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Theory & Methods
Xialin Zhang, Lingkun Lian, Fukang Zhu
Summary: The study improves the accuracy and automation of variogram fitting models through a hybrid algorithm, demonstrating stronger optimization ability and higher precision compared to traditional methods.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Correction
Computer Science, Artificial Intelligence
Ala' M. Al-Zoubi, Mohammad A. Hassonah, Ali Asghar Heidari, Hossam Faris, Majdi Mafarja, Ibrahim Aljarah
Summary: During the typesetting process, there was an error in the publication of the authors' affiliations, which has since been corrected.
Article
Automation & Control Systems
Nebojsa Bacanin, Timea Bezdan, Fadi Al-Turjman, Tarik A. Rashid
Summary: This study proposes a hybridized artificial flora optimization algorithm named genetically guided best artificial flora, achieved by combining genetic algorithms' uniform crossover and mutation operators. The hybrid algorithm shows excellent performance in artificial neural network training and feature selection, striking a better balance between exploration and exploitation compared to the original version.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Swarnajyoti Patra, Barnali Barman
Summary: A novel feature selection technique based on rough set theory is proposed in this work to reduce the dimensionality of hyperspectral images. The technique defines a new criterion by combining relevance and significance measures, and adopts a first order incremental search to select the most informative bands, showing better results compared to existing techniques. The proposed dependency measure definition is completely parameter free and computationally very cheap.
APPLIED SOFT COMPUTING
(2021)
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
A. Ramirez-Morales, J. U. Salmon-Gamboa, Jin Li, A. G. Sanchez-Reyna, A. Palli-Valappil
Summary: This paper presents experimental studies on ensembles of binary classifiers based on individual support vector machines. The proposed GenBoost-SVM method uses an adaptive boosting algorithm to construct these ensembles. Genetic algorithms are used for pre-selections to reduce training times and address imbalanced data challenges. Diversity and early stopping are also considered in the ensembles to reduce generalization error. The study proposes 56 different types of ensembles that vary in support vector machine kernels, genetic selections, and diversity. The results show that the ensembles with genetic selections and diversity perform competitively compared to popular classifiers, and they outperform most of them for imbalanced data. The study also demonstrates that using different support vector machine kernels leads to enhanced performances. This is the first study to combine adaptive boosted ensembles, genetic selections, and support vector machines.
APPLIED INTELLIGENCE
(2023)
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, Information Systems
Kuan-Cheng Lin, Yi-Hung Huang, Jason C. Hung, Yung-Tso Lin
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