Article
Computer Science, Information Systems
D. P. Acharjya, R. Rathi
Summary: This article conducts a comparative study on statistical, rough computing, and hybridized computing approaches using a financial bankruptcy dataset of Polish companies. The results show that the rough hybridization of the binary-coded genetic algorithm achieves an accuracy of 98.3%, outperforming other descriptive and rough computing techniques.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Amirhossein Javanshir, Thanh Thi Nguyen, M. A. Parvez Mahmud, Abbas Z. Kouzani
Summary: This paper proposes a novel metaheuristic-based supervised learning method for spiking neural networks (SNNs) to overcome the challenges of training SNNs with backpropagation-based supervised learning methods. Experimental results show that the proposed algorithm outperforms other experimental algorithms in solving four classification benchmark datasets, with Cuckoo Search (CS) reporting the best performance.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Xi-Ao Ma
Summary: This paper explores the computational formulations of two types of attribute reducts in three-way probabilistic rough set models based on fuzzy entropies, constructing monotonic measures from which the computational formulations of the two types of attribute reducts can be obtained. Algorithms are developed for finding the two types of attribute reducts based on addition-deletion method or deletion method. Experimental results confirm the monotonicity of the proposed measures with respect to set inclusion of attributes and show that class-specific attribute reducts are more effective for attribute reduction with respect to a particular decision class compared to classification-based attribute reducts.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Review
Computer Science, Artificial Intelligence
Wanting Ji, Yan Pang, Xiaoyun Jia, Zhongwei Wang, Feng Hou, Baoyan Song, Mingzhe Liu, Ruili Wang
Summary: Feature selection is a key method for data preprocessing in data mining tasks, aiming to select a feature subset based on evaluation criteria. Fuzzy rough set theory has been proven to be ideal for dealing with uncertain information in feature selection. This article provides a comprehensive review of fuzzy rough set theory and its applications, discussing challenges in feature selection methods.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Computer Science, Artificial Intelligence
Bichitrananda Behera, G. Kumaravelan
Summary: The fuzzy rough set (FRS) and FRS-RNN based on robust nearest neighbor perform well in handling real-valued datasets, but have not been studied for text document classification. A modified CNN structure is proposed for text document classification and feature extraction. Experimental results show that the proposed FRS-RNN model outperforms traditional classification models.
Article
Computer Science, Artificial Intelligence
Aysenur Ozden, Ismail Iseri
Summary: In recent years, significant advancements have been made in artificial neural network models, which have been applied to various real-world problems. However, these models can get stuck in local minima during training due to the use of gradient descent-based techniques, impacting their generalization performance. This study proposes a new hybrid artificial neural network model called COOT-ANN that utilizes a metaheuristic-based approach to avoid local minima during training. Results demonstrate that the COOT-ANN model outperforms gradient descent, scaled conjugate gradient, and Levenberg-Marquardt optimization techniques in terms of accuracy, cross-entropy, F1-score, and Cohen's Kappa metrics.
KNOWLEDGE AND INFORMATION SYSTEMS
(2023)
Article
Materials Science, Textiles
Niharendu Bikash Kar, Anindya Ghosh, Subhasis Das, Debamalya Banerjee
Summary: The study focuses on using rough set theory to generate decision rules for predicting silk quality, with a main emphasis on eliminating redundant data sets to ensure accuracy of the generated rules.
JOURNAL OF THE TEXTILE INSTITUTE
(2022)
Article
Engineering, Biomedical
Mohamed Amine Tahiri, Fatima Zohra Hlouli, Ahmed Bencherqui, Hicham Karmouni, Hicham Amakdouf, Mhamed Sayyouri, Hassan Qjidaa
Summary: The structural characteristics of white blood cells (WBCs) can provide important information about human health status. In this study, a new method combining the hybrid discrete moment quaternion approach with deep learning is proposed to classify white blood cells into four types. The method includes a preprocessing phase and a classification phase, utilizing image moments and optimizing local parameters. Experimental results show high accuracy rates in determining the cell types.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Yen-Liang Chen, Fang-Chi Chi
Summary: In the rough set theory, the concept of reduct plays a crucial role in preserving the partition of the data universe. Simplification helps summarize the original data table but could potentially overwhelm users with excessive information.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Muhammad Riaz, Nawazish Ali, Bijan Davvaz, Muhammad Aslam
Summary: This paper introduces the concepts of SRqROFS and qROPFSRS based on soft rough set and fuzzy soft relation, respectively, and discusses fundamental operations and properties of these models. The suggested models are more efficient in handling vagueness in MCDM problems and algorithms are developed for their construction and application. The notions of upper reduct and lower reduct based on variations of decision attributes are proposed, and the idea of core is used to find a unanimous optimal decision.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jianli Gao
Summary: This study constructed a comprehensive performance evaluation model for collaborative logistics in manufacturing industry using BP neural network and rough set. The study successfully screened and optimized evaluation indicators using rough set attribute reduction theory to improve the prediction of key performance indicators. The method proposed in this study has shown certain effects based on case analysis.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Mathematics, Applied
R. Mareay, Radwan Abu-Gdairi, M. Badr
Summary: This research paper introduces a new approximation structure based on topological near open sets in the approximation space of a rough set. It also presents concepts of topological near open sets and rough concepts, and discusses the properties of the new approximation structure. The paper includes an algorithm for COVID-19 detection based on its side effects, which is believed to be helpful for future detection.
Article
Computer Science, Information Systems
Iftikhar Ul Haq, Tanzeela Shaheen, Hamza Toor, Tapan Senapati, Sarbast Moslem
Summary: The dominance-based rough set approach is crucial to the advancement of rough set theory. This study extends this method to a Pythagorean fuzzy setting and determines the lower and upper approximations using the constructive technique. Reductions are prescribed in four distinct manners by combining Approximate Distribution Reductions with a Pythagorean fuzzy dominance-based rough set. These findings are all Pythagorean fuzzy generalizations or extensions of the conventional rough set method relying on dominance.
Article
Energy & Fuels
Minghua Wei, Zhihong Zheng, Xiao Bai, Ji Lin, Farhad Taghizadeh-Hesary
Summary: Combining rough set theory and artificial neural network for fault diagnosis of hydraulic turbine conversion improves diagnostic rate and reduces training time. Compared to a neural network based on rough set, an adaptive neural-fuzzy inference system offers advantages in terms of network topology simplicity, diagnostic accuracy, and shorter training time.
FRONTIERS IN ENERGY RESEARCH
(2021)
Article
Mathematics
Fernando Chacon-Gomez, M. Eugenia Cornejo, Jesus Medina
Summary: This paper investigates different methods for classifying new objects using decision rules in decision-making processes. These methods determine the best possible decision based on various indicators associated with the decision rules.
Article
Mathematics, Applied
A. Abirami, P. Prakash, K. Thangavel
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS
(2018)
Article
Computer Science, Artificial Intelligence
K. Sasirekha, K. Thangavel
NEURAL COMPUTING & APPLICATIONS
(2019)
Article
Computer Science, Artificial Intelligence
P. S. Raja, K. Thangavel
Article
Computer Science, Artificial Intelligence
P. S. Raja, K. Sasirekha, K. Thangavel
NEURAL COMPUTING & APPLICATIONS
(2020)
Article
Multidisciplinary Sciences
R. Sekhar, K. Sasirekha, P. S. Raja, K. Thangavel
Summary: This study introduces a new intrusion detection technique using Deep Autoencoder with Fruitfly Optimization, filling missing values with the Fuzzy C-Means Rough Parameter method, extracting robust features, and classifying attacks through Deep Autoencoder and BPN. Neurons in the hidden layers of Deep Autoencoder are optimized with population based Fruitfly Optimization algorithm. Experiments on NSL_KDD and UNSW-NB15 datasets show the computational results of the proposed intrusion detection system compared to other algorithms.
SN APPLIED SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
K. Sasirekha, K. Thangavel
INTERNATIONAL JOURNAL OF BIOMETRICS
(2020)
Article
Computer Science, Artificial Intelligence
K. Sasirekha, K. Thangavel
INTERNATIONAL JOURNAL OF BIOMETRICS
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
K. Thangavel, K. Sasirekha
DIGITAL CONNECTIVITY - SOCIAL IMPACT
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
K. Thangavel, A. Kaja Mohideen
DIGITAL CONNECTIVITY - SOCIAL IMPACT
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
P. S. Raja, K. Thangavel
DIGITAL CONNECTIVITY - SOCIAL IMPACT
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
K. Sasirekha, K. Thangavel
COMPUTATIONAL INTELLIGENCE, CYBER SECURITY AND COMPUTATIONAL MODELS, ICC3 2015
(2016)
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)
