Article
Computer Science, Information Systems
Elham S. Kashani, Saeed Bagheri Shouraki, Yaser Norouzi
Summary: This paper proposes a novel, fully-online, density-based method for handling evolving data streams, addressing the issue of parameter selection in existing methods. The method has the ability to identify clusters with arbitrary shapes, is robust to noise, and provides high accuracy and efficiency in both low and high dimensions.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Alessio Bechini, Francesco Marcelloni, Alessandro Renda
Summary: This article presents a novel fuzzy clustering algorithm TSF-DBSCAN, which shows competitive performance in handling streaming data. The algorithm deals with outliers and evolution of data streams by introducing fuzziness and a fading model, while ensuring computational and memory efficiency.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Son T. Mai, Jon Jacobsen, Sihem Amer-Yahia, Ivor Spence, Nhat-Phuong Tran, Ira Assent, Quoc Viet Hung Nguyen
Summary: This paper introduces IncAnyDBC, a unique parallel incremental data clustering approach to effectively update clustering results when the database is frequently changed. It can process changes in bulks, keeps an underlying cluster structure, and uses it to incrementally update clusters. It selects the most meaningful objects to produce accurate clustering results or approximate results. By processing objects in blocks, it can be efficiently parallelized on multicore CPUs, scaling well with multiple threads.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(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)
Review
Computer Science, Information Systems
Mustafa Tareq, Elankovan A. Sundararajan, Aaron Harwood, Azuraliza Abu Bakar
Summary: Clustering data streams, especially evolving data streams, presents challenges for conventional density grid-based clustering algorithms. In this study, a systematic literature review was conducted to summarize existing grid-based clustering algorithms, their limitations, and the challenges they face in handling evolving data streams. The findings revealed a variety of active research studies on density grid-based clustering.
Article
Computer Science, Artificial Intelligence
Preeti Jha, Aruna Tiwari, Neha Bharill, Milind Ratnaparkhe, Neha Nagendra, Mukkamalla Mounika
Summary: In this paper, a scalable incremental fuzzy consensus clustering (SIFCC) algorithm is proposed for big data clustering, implemented on the Apache Spark cluster framework. SIFCC not only facilitates efficient big data clustering, but also improves cluster quality, storage space optimization, and time complexity during clustering. Comparison experiments show that SIFCC algorithm outperforms the existing scalable fuzzy consensus clustering (SFCC) in clustering Big Data.
Article
Computer Science, Information Systems
Na Fang, Xianwen Fang, Ke Lu, Esther Asare
Summary: The proposed real-time incremental mining algorithm based on trusted behavior interval improves the quality and efficiency of process mining by analyzing online event streams to update the reference model.
Article
Computer Science, Information Systems
Ali Degirmenci, Omer Karal
Summary: This paper presents a novel parameter-free method called iLDCBOF, which combines incremental versions of local outlier factor (LOF) and density-based spatial clustering of applications with noise (DBSCAN) to efficiently detect outliers in data streams. The iLDCBOF method has several advantages over previous iLOF-based studies, including the use of a newly developed core k-nearest neighbor concept, an algorithm that automatically adjusts the number of neighbors parameter, and the use of the Mahalanobis distance metric. Extensive experiments on 16 real world datasets show that the iLDCBOF method significantly outperforms benchmark methods.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Rajesh Dwivedi, Aruna Tiwari, Neha Bharill, Milind Ratnaparkhe, Rishabh Soni, Rahul Mahbubani, Saket Kumar
Summary: With the advancements in big data and bioinformatics, there has been a significant increase in both the quantity and quality of raw data in the past two decades. To handle these complex, diverse, and vast datasets, a real-time clustering method is proposed. By considering multiple objectives and utilizing incremental clustering, this method outperforms other state-of-the-art methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Can Atilgan, Baris Tekin Tezel, Efendi Nasiboglu
Summary: This study discusses the efficient implementation of a fuzzy density-based clustering algorithm, introduces a specific algorithm and its parallel version, and conducts experimental tests, showing a wide variety of differences in relative speed-ups.
INFORMATION SCIENCES
(2021)
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
Engineering, Chemical
Shuo Hu, Yonglin Pang, Yong He, Yuan Yang, Henian Zhang, Linmeng Zhang, Beiyi Zheng, Caiyun Hu, Qing Wang
Summary: With the continuous enrichment of big data technology application scenarios, the clustering analysis of a data stream has become a research hotspot. However, the existing data stream clustering algorithms usually have some defects, such as inability to cluster arbitrary shapes, difficulty determining some important parameters, and static clustering. In this study, a novel algorithm called MDDSDB-GC is proposed, which effectively overcomes these conventional defects and achieves better overall performance in clustering analysis of data streams.
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
Ali Mahmoudabadi, Marjan Kuchaki Rafsanjani, Mohammad Masoud Javidi
Summary: This paper introduces an online fuzzy approach for clustering data streams based on the growing neural gas algorithm, with more restrictive criteria for selecting winner nodes in the topological graph, showing improvements over existing clustering methods when tested on public datasets.
Article
Computer Science, Artificial Intelligence
David P. Hofmeyr
Summary: An efficient unsupervised method for obtaining low-density hyperplane separators is proposed, which is based on a modified stochastic gradient descent applied on a convolution of the empirical distribution function with a smoothing kernel. Multiple hyperplanes can be combined in a hierarchical model to obtain a complete clustering solution. Experiments show that the proposed method is highly competitive in terms of both speed and accuracy when compared with relevant benchmarks.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Sirisup Laohakiat, Suphakant Phimoltares, Chidchanok Lursinsap
KNOWLEDGE-BASED SYSTEMS
(2016)
Article
Computer Science, Information Systems
Sirisup Laohakiat, Suphakant Phimoltares, Chidchanok Lursinsap
INFORMATION SCIENCES
(2017)
Article
Computer Science, Artificial Intelligence
Vera Sa-ing, Pongpat Vorasayan, Nijasri C. Suwanwela, Supatana Auethavekiat, Chedsada Chinrungrueng
IET IMAGE PROCESSING
(2018)
Article
Surgery
Chumpon Wilasrusmee, Jackrit Suthakorn, Claire Guerineau, Yuttana Itsarachaiyot, Vera Sa-Ing, Napaphat Proprom, Panuwat Lertsithichai, Sopon Jirasisrithum, Dilip Kittur
ASIAN JOURNAL OF SURGERY
(2010)
Article
Computer Science, Information Systems
Anita Manassakorn, Supatana Auethavekiat, Vera Sa-Ing, Sunee Chansangpetch, Kitiya Ratanawongphaibul, Nopphawan Uramphorn, Visanee Tantisevi
Summary: This study introduces a glaucoma diagnosis network named GlauNet, which utilizes convolutional neural network for feature extraction and classification, improving the accuracy of glaucoma diagnosis and demonstrating robustness against artifacts.
Proceedings Paper
Engineering, Electrical & Electronic
Autcharaporn Sukperm, Polapat Rojnuckarin, Benjapom Akkawat, Vera Sa-ing
Summary: Venous thromboembolism (VTE) is a significant disease in Thailand due to lack of awareness, leading to an increase in patients. Developing an effective VTE risk assessment model is crucial, and this research uses machine learning to predict important risk factors with an accuracy of 96.6%.
2021 IEEE INTERNATIONAL IOT, ELECTRONICS AND MECHATRONICS CONFERENCE (IEMTRONICS)
(2021)
Article
Engineering, Electrical & Electronic
V. Sa-Ing, P. Vorasayan, N. C. Suwanwela, S. Auethavekiat, C. Chinrungrueng
ELECTRONICS LETTERS
(2017)
Proceedings Paper
Engineering, Biomedical
Vera Sa-ing, Kongyot Wangkaoom, Saowapak S. Thongvigitmanee
2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2013)
Proceedings Paper
Engineering, Biomedical
Apivan Tuntakurn, Saowapak S. Thongvigitmanee, Vera Sa-Ing, Shoichi Hasegawa, Stanislav S. Makhanov
6TH BIOMEDICAL ENGINEERING INTERNATIONAL CONFERENCE (BMEICON 2013)
(2013)
Proceedings Paper
Engineering, Biomedical
Apivan Tuntakurn, Saowapak S. Thongvigitmanee, Vera Sa-Ing, Stanislav S. Makhanov, Shoichi Hasegawa
5TH BIOMEDICAL ENGINEERING INTERNATIONAL CONFERENCE (BMEICON 2012)
(2012)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
