Article
Computer Science, Information Systems
Yong Zhang, Xinyue Li, Li Wang, Shurui Fan, Lei Zhu, Shuhao Jiang
Summary: This paper focuses on real-time dynamic clustering analysis of power load data using the dynamic conditional score (DCS) model. The proposed autocorrelation increment fuzzy C-means clustering algorithm based on the DCS model addresses the issue of current power load clustering methods neglecting variance characteristics and the handling of data stream clustering problems with time series characteristics. The method is validated using power load time series data from a Chinese power supply company, demonstrating high clustering accuracy and good performance.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Daiwei Li, Haiqing Zhang, Tianrui Li, Abdelaziz Bouras, Xi Yu, Tao Wang
Summary: Two algorithms, JFCM-VQNNI and JFCM-FVQNNI, have been proposed in this research to achieve effective data imputation by considering clustering and uncertain information extraction when predicting missing values. Experimental results show that these two algorithms have higher imputation performance and reliability compared to traditional parameter-based imputation algorithms.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yong Zhang, Xinyue Li, Shuhao Jiang, Ming-Lang Tseng, Li Wang, Shurui Fan
Summary: In this study, a dynamic conditional score model is constructed to analyze and extract statistical characteristic parameters of a time series and calculate the autocorrelation value of the parameter series. A weighted fuzzy C-mean clustering analysis is performed, and the obtained data weight information is used for incremental clustering to improve clustering accuracy. Experimental results show that the proposed algorithm achieves satisfactory clustering and improves performance.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Liming Fang, Xinyu Yun, Changchun Yin, Weiping Ding, Lu Zhou, Zhe Liu, Chunhua Su
Summary: The article introduces the automatic NXDomain classification system (ANCS) to automatically identify and classify nonexistent domains as benign or malicious by studying features. ANCS uses online, incremental, and fuzzy rough sets machine learning to improve detection process efficiency. Experimental evaluation shows ANCS has high accuracy with low false positive and false negative rates, as well as good generalization performance.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Information Systems
Yasemin Eryoldas, Alptekin Durmusoglu
Summary: Metaheuristic algorithms are developed to find near-optimal solutions to optimization problems within acceptable times. Fine-tuning specific parameters of these algorithms can improve their performance. In this paper, a novel algorithm configuration method based on Latin Hypercube Hammersley Sampling and Fuzzy C-means Clustering is proposed and evaluated against state-of-the-art automatic parameter tuning methods in two experiments and four cases.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Sirisup Laohakiat, Vera Sa-ing
Summary: The FIDC is an incremental density-based clustering framework that utilizes a one-pass scheme to effectively process large datasets with reduced computation time and memory usage. By employing fuzzy local clustering and a modified valley seeking algorithm, FIDC improves clustering performance and simplifies parameter selection process.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Huanrong Ren, Wei Guo, Pingyu Jiang, Xu Wan
Summary: The paper introduces a new architecture based on active incremental fine-tuning, SegNet, and CRF, which integrates these elements to improve running speed, reduce model size, and achieve high precision with small sample training. The proposed architecture has the potential to reach an average accuracy rate of about 88% on a small dataset, while reducing the number of learning parameters.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Automation & Control Systems
Pradipta Maji, Partha Garai
Summary: This study introduces a novel clustering algorithm that can effectively handle natural groups present in a dataset, demonstrating superior performance and shorter computation time compared to existing algorithms.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Engineering, Electrical & Electronic
Tong Xiao, Yiliang Wan, Jianjun Chen, Wenzhong Shi, Jianxin Qin, Deping Li
Summary: An improved rough-fuzzy possibilistic c-means clustering algorithm combined with multiresolution scales information is proposed to reduce classification uncertainty.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Chemistry, Multidisciplinary
Yiming Tang, Rui Chen, Bowen Xia
Summary: Currently, most fuzzy clustering algorithms are sensitive to initialization results and have limited capability in handling high-dimensional data. To address these issues, we developed the viewpoint-driven subspace fuzzy c-means (VSFCM) algorithm. The VSFCM algorithm incorporates a new cut-off distance and an initialization method called cut-off distance-induced clustering initialization (CDCI). Moreover, it achieves better clustering efficiency and convergence speed by utilizing knowledge and data-driven fuzzy clustering strategy.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Wanli Huang, Yanhong She, Xiaoli He, Weiping Ding
Summary: This article presents an incremental feature selection approach for hierarchical classification in the era of big data. By employing the sibling strategy, theoretical analysis, and algorithmic design, two incremental algorithms are proposed and demonstrated to be effective and feasible.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Adnan Theerens, Chris Cornelis
Summary: Classical (fuzzy) rough sets are sensitive to noise, and we improve on this issue by introducing fuzzy quantifier-based fuzzy rough sets (FQFRS). We propose an intuitive fuzzy rough approximation operator that utilizes general unary and binary quantification models, and conduct a theoretical study of their properties. We also apply them to classification problems.
FUZZY SETS AND SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
R. S. Rajkumar, A. Grace Selvarani
Summary: This paper proposes an unsupervised clustering technique for automatic clustering of Diabetic Retinopathy. The method utilizes deep learning and fuzzy rough c-means clustering for training and updating. Experimental results show that the proposed model improves the accuracy of Diabetic Retinopathy diagnosis compared to other algorithms.
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Meenakshi Kaushal, Harish Garg, Q. M. Danish Lohani
Summary: The paper proposes a novel approach that utilizes Atanassov intuitionistic fuzzy sets (AIFS) to cluster datasets and identify outliers. It introduces a new function called the typicality function for outlier detection and parameter tuning to improve clustering. To optimize the tuning process and reduce time complexity, a global error search approach (k-GESA) is introduced. The paper also presents a new clustering algorithm called Global Intuitionistic Fuzzy Weighted C-Ordered Means (Global-IFWCOM), which improves clustering results using k-GESA. The effectiveness of the proposed approach is evaluated against various C-ordered means algorithms on synthetic datasets with outliers, and compared to the Fuzzy Weighted C-Ordered Means (FWCOM) algorithm on a dataset with noise and outliers.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Theory & Methods
Ling Wang, Peipei Xu, Qian Ma
Summary: Clustering is a popular data mining method for analyzing time series, and the incremental fuzzy clustering algorithm (IFCTS) proposed in this paper shows good clustering accuracy and efficiency for both equal-length and unequal-length time series.
FUZZY SETS AND SYSTEMS
(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)