Article
Computer Science, Artificial Intelligence
Abdolreza Rashno, Milad Shafipour, Sadegh Fadaei
Summary: This paper introduces a novel multi-objective particle swarm optimization feature selection method. It decodes feature vectors as particles and ranks them in a two-dimensional optimization space. The proposed method incorporates feature ranks to update particle velocity and position during the optimization process. Experimental results demonstrate the effectiveness of the method in finding Pareto Fronts of the best particles in multi-objective optimization space.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Pei Hu, Jeng-Shyang Pan, Shu-Chuan Chu, Chaoli Sun
Summary: In this paper, a multi-surrogate assisted binary particle swarm optimization method is proposed for feature selection on large-scale datasets. Two surrogate models are trained to approximate the fitness values of individuals in two sub-populations, and a new population is generated through communication between the two sub-populations. Additionally, a dynamic transfer function is introduced to balance global and local search for finding optimal solutions with limited computational resources.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Zhi Jiang, Yong Zhang, Jun Wang
Summary: The paper proposes a new ensemble feature selection algorithm, MDEFS, which can handle large-scale data, reduce computational costs, and improve the accuracy of feature selection results.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Software Engineering
Ziqi Xu, Chao Xu, Jing Hu, Zhaopeng Meng
Summary: This study improved the Screened Poisson Surface Reconstruction algorithm by using an adaptive bandwidth Gaussian kernel density estimator, which effectively removes noise and outliers in the reconstruction process.
COMPUTERS & GRAPHICS-UK
(2021)
Article
Multidisciplinary Sciences
Ziyi Wang, Debin Ma, Dongqi Sun, Jingxiang Zhang
Summary: This study accurately identified the functional areas in the central urban area of Hangzhou by combining Open Street Map and Point of Interest data, revealing spatial distribution characteristics and core-periphery differentiation. The results were consistent with the actual situation, providing references for urban planning and management.
Article
Computer Science, Artificial Intelligence
Muhammad Shabbir Abbasi, Harith Al-Sahaf, Masood Mansoori, Ian Welch
Summary: Ransomware is a type of malware that encrypts data and demands ransom. Behavior-based ransomware detection is challenging due to a large number of system calls in the analysis output. This study presents an automated feature selection method using particle swarm optimization for behavior-based ransomware detection and classification.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Xiao-Min Hu, Shou-Rong Zhang, Min Li, Jeremiah D. Deng
Summary: The purpose of feature selection is to eliminate redundant and irrelevant features and leave useful features for classification. Existing algorithms mainly focus on finding one best feature subset, neglecting the fact that the problem may have more than one best feature subset. A novel multimodal niching particle swarm optimization algorithm is proposed to find out all the best feature combinations in a feature selection problem.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
An-Da Li, Bing Xue, Mengjie Zhang
Summary: This paper proposes a feature selection method to identify key quality features in complex manufacturing processes. A multi-objective binary particle swarm optimization algorithm is proposed, which includes three new components to optimize a bi-objective feature selection model. Experimental results show that this method can identify a small number of key quality features with good predictive ability.
INFORMATION SCIENCES
(2023)
Article
Biology
Mohammed A. Awadallah, Mohammed Azmi Al-Betar, Malik Shehadeh Braik, Abdelaziz Hammouri, Iyad Abu Doush, Raed Abu Zitar
Summary: An enhanced binary version of the Rat Swarm Optimizer (RSO) is proposed for Feature Selection (FS) problems, showing superior performance over other methods on some datasets.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Information Systems
Nour Elhouda Chalabi, Abdelouahab Attia, Abderraouf Bouziane, Zahid Akhtar
Summary: This paper introduces an optimized face recognition system with a feature selection method based on Particle Swarm Optimization, which enhances accuracy by selecting blocks instead of individual features. Experimental results show promising performance on a public face database.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Ke Chen, Bing Xue, Mengjie Zhang, Fengyu Zhou
Summary: This article introduces a novel PSO-based feature selection approach that continuously improves population quality and performance through correlation-guided updating and surrogate technique. Experimental results demonstrate its outstanding performance in classification accuracy.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Pradip Dhal, Chandrashekhar Azad
Summary: In this study, a binary version of the hybrid two-phase multi-objective FS approach based on PSO and GWO is proposed. The approach aims to minimize classification error rate and reduce the number of selected features. By utilizing global and local search strategies, the method shows efficient and effective performance in selecting prominent features in high-dimensional data.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Multidisciplinary
Hao Su, Ling Xiang, Aijun Hu, Benfeng Gao, Xin Yang
Summary: The paper proposes a novel hybrid method for diagnosing rolling bearing faults under variable conditions. By extracting fault features from multiple domains and using improved learning machines, the method allows for efficient and automatic fault detection.
Article
Computer Science, Artificial Intelligence
Warda M. Shaban, Asmaa H. Rabie, Ahmed Saleh, M. A. Abo-Elsoud
Summary: COVID-19, a global infectious disease, requires early detection of infected patients for effective treatment and disease control. This paper introduces a new strategy called DBNB, which uses APSO to select informative features for accurate diagnosis of COVID-19 patients. Experimental results show that DBNB outperforms recent COVID-19 diagnose strategies in accuracy and time efficiency.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Information Systems
Awais Mahmood, Muhammad Imran, Aun Irtaza, Qammar Abbas, Habib Dhahri, Esam Mohammed Asem Othman, Arif Jamal Malik, Aaqif Afzaal Abbasi
Summary: This paper proposes a novel approach to improve the performance of image search by using Particle Swarm Optimization and Genetic Algorithm for early iteration and Support Vector Machine for relevance feedback. Experimental results show that this method outperforms existing CBIR approaches.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Biochemistry & Molecular Biology
Mohammad Amin Valizade Hasanloei, Razieh Sheikhpour, Mehdi Agha Sarram, Elnaz Sheikhpour, Hamdollah Sharifi
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
(2018)
Article
Computer Science, Information Systems
Razieh Sheikhpour, Mehdi Agha Sarram, Elnaz Sheikhpour
INFORMATION SCIENCES
(2018)
Article
Telecommunications
Azam Jannesari, Mehdi Agha Sarram, Razieh Sheikhpour
WIRELESS PERSONAL COMMUNICATIONS
(2020)
Article
Automation & Control Systems
Razieh Sheikhpour, Sajjad Gharaghani, Elmira Nazarshodeh
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2020)
Article
Computer Science, Information Systems
Razieh Sheikhpour, Mehdi Agha Sarram, Sajjad Gharaghani, Mohammad Ali Zare Chahooki
INFORMATION SCIENCES
(2020)
Article
Automation & Control Systems
Mohammad Morovvati Sharifabad, Razieh Sheikhpour, Sajjad Gharaghani
Summary: De novo drug discovery is a costly and time-consuming process. Repositioning existing drugs for new applications can reduce the time and cost of finding new drugs. Predicting drug-target interactions (DTIs) can facilitate drug repositioning, but there are challenges due to the diversity of drug descriptors and protein features, as well as the lack of experimentally-confirmed non-interacting drug-target pairs as negative samples. This study presents a modified algorithm for extracting balanced negative samples and a semi-supervised feature selection method, which outperform other methods on benchmark DTI datasets.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Biochemistry & Molecular Biology
Zahra Bastami, Razieh Sheikhpour, Parvin Razzaghi, Ali Ramazani, Sajjad Gharaghani
Summary: Caspases are important enzymes involved in inflammation and cell death processes. This study used Proteochemometrics Modeling to summarize and predict the interactions between caspases and ligands. The ensemble model showed superior performance compared to other models.
MOLECULAR DIVERSITY
(2023)
Article
Pharmacology & Pharmacy
Mohammad Morovvati Sharifabad, Razieh Sheikhpour, Sajjad Gharaghani
Summary: This study proposes a reliable algorithm for selecting negative samples in drug-target interaction prediction, which demonstrates superior performance and highlights the significant improvement in learning process performance by correctly selecting negative samples.
JOURNAL OF PHARMACOLOGICAL AND TOXICOLOGICAL METHODS
(2022)
Article
Soil Science
Ruhollah Taghizadeh-Mehrjardi, Razieh Sheikhpour, Mojtaba Zeraatpisheh, Alireza Amirian-Chakan, Norair Toomanian, Ruth Kerry, Thomas Scholten
Summary: Digital soil mapping can be used to predict soils at unvisited sites, but problems arise when predictions are needed in areas without any soil observations. A new semi-supervised learning approach was found to outperform supervised learning in extrapolating soil classes in target areas, resulting in higher accuracy and lower uncertainty.
Article
Computer Science, Software Engineering
Roohallah Fazli, Hadi Owlia, Razieh Sheikhpour
Summary: A robust algorithm for source number estimation based on the formation of the Hankel covariance matrix is presented. The proposed algorithm can handle both non-coherent and fully coherent sources, and it outperforms competing methods in numerical simulations.
INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Razieh Sheikhpour
Summary: Feature selection is widely used in machine learning applications to select relevant features from data sets. Recently, there has been considerable research interest in semi-supervised sparse feature selection based on graph Laplacian, which uses the correlation between features. This paper proposes a spline regression-based framework for semi-supervised sparse feature selection, which uses mixed convex and non-convex t2,p-norm regularization to select relevant features and considers feature correlation. The framework retains the geometry structure of labeled and unlabeled data using local spline regression and encodes the data distribution. A unified iterative algorithm is presented to solve the framework, and its convergence is theoretically and experimentally proved. Experiments on several data sets demonstrate the effectiveness of the framework in selecting the most relevant and discriminative features.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Pediatrics
Sanaz Mehrabani, Morteza Zangeneh Soroush, Negin Kheiri, Razieh Sheikhpour, Mahshid Bahrami
Summary: This study aimed to predict blood cancer using leukemia gene expression data and a robust l2,p-norm sparsity-based gene selection method. The results showed that this method can correctly classify all samples of acute myeloid leukemia (AML) and lymphoblastic leukemia (ALL), and identified seven important genes, with PRTN3 gene being the most important. This method can be useful for predicting leukemia and examining the expression levels of related genes.
IRANIAN JOURNAL OF PEDIATRIC HEMATOLOGY AND ONCOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Razieh Sheikhpour, Kamal Berahmand, Saman Forouzandeh
Summary: Feature selection aims to eliminate redundant features and choose informative ones. Semi-supervised feature selection becomes important as it utilizes labeled and unlabeled data. We propose two frameworks, one based on Hessian matrix and the other on Hessian-Laplacian combination, for semi-supervised feature selection. Our frameworks utilize regularization and constraint techniques to select informative features and maintain the topological structure of data. Experimental results demonstrate the effectiveness of our frameworks in selecting informative features.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Pediatrics
Razieh Sheikhpour, Roohallah Fazli, Sanaz Mehrabani
Summary: This study identified important genes for the diagnosis of acute myeloid and lymphoblastic leukemia using microarray data and a sparse feature selection method. The results showed that AML and ALL can be accurately diagnosed with high accuracy using machine learning methods. The investigation of selected genes in this study may be helpful for the diagnosis of ALL and AML.
IRANIAN JOURNAL OF PEDIATRIC HEMATOLOGY AND ONCOLOGY
(2021)
Article
Engineering, Multidisciplinary
Mohammad Momeny, Ali Mohammad Latif, Mehdi Agha Sarram, Razieh Sheikhpour, Yu Dong Zhang
Summary: In this paper, a Noise-Robust Convolutional Neural Network (NR-CNN) is proposed to classify noisy images without preprocessing, by adding a noise map layer and an adaptive resize layer, and considering noise in different components of the network. The proposed NR-CNN improves the classification performance of noisy images and network training speed.
RESULTS IN ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)
