Article
Computer Science, Artificial Intelligence
Ali Degirmenci, Omer Karal
Summary: This study proposes a new incremental multi-class outlier detection model (iMCOD) that combines an incremental support vector machine (iSVM) with an incremental local outlier factor (iLOF) in a unified framework to detect outliers from multi-class data streams.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Civil
Philipp Zissner, Paulo H. L. Rettore, Bruno P. Santos, Johannes F. Loevenich, Roberto Rigolin F. Lopes
Summary: This paper introduces DataFITS, an open-source framework that collects and fuses traffic-related data from various sources, creating a comprehensive dataset. The hypothesis that a heterogeneous data fusion framework can enhance information coverage and quality for traffic models was verified through two applications utilizing traffic estimation and incident classification models. DataFITS significantly increased road coverage by 137% and improved information quality for up to 40% of all roads through data fusion. Traffic estimation achieved an R-2 score of 0.91 using a polynomial regression model, while incident classification achieved 90% accuracy on binary tasks and around 80% on classifying three different types of incidents.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Mozamel M. Saeed
Summary: This study aims to improve the performance of classifiers in identifying signatures of unknown attacks and establishes a hybrid classifier model based on the evaluation of commonly used classifiers. A quantitative methodology was adopted to collect and interpret data, and the evaluation was conducted in virtual networked environments with traffic workloads. The study reveals that certain features make significant contributions to anomaly detection.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
S. Ramraj, G. Usha
Summary: The purpose of traffic classification is to allocate bandwidth to different types of data on a network. Application-level traffic classification is important for identifying high-demand applications on the network. This research focuses on evaluating the performance of the Support Vector Machine (SVM) in classifying network packets by application type and type of data communicated within an application. Using a simple feed-forward Deep Neural Network (DNN) can improve the performance of the SVM algorithm, and various feature learning frameworks based on deep learning are compared. The study concludes that DNN can significantly improve the F1 score of the SVM classifier and using a hybrid framework of DNN with SVM can address class imbalance problems.
CONNECTION SCIENCE
(2023)
Article
Environmental Sciences
Anita Sabat-Tomala, Edwin Raczko, Bogdan Zagajewski
Summary: Recent developments in computer hardware have enabled the assessment of permutation-based approaches in image classification, which involve sampling a reference dataset multiple times to train machine learning models and evaluate accuracy. The study applied support vector machine algorithm to classify invasive plant species with high accuracy, ranging from F1-scores of 0.87 to 0.99 for different species.
Article
Neurosciences
Jiaxiu He, Li Yang, Ding Liu, Zhi Song
Summary: This study compares the classification effects of two classifiers on epileptic EEG and finds that GBDT achieves better classification accuracy and F1 score.
Article
Computer Science, Artificial Intelligence
Anand Kumar Srivastava, Yugal Kumar, Pradeep Kumar Singh
Summary: In the medical field, researchers have proposed the K-Mean(++) data imputation technique and ABC-based outlier detection technique to address missing values and outliers in medical datasets. The performance of the proposed hybrid diabetes prediction framework using LS-SVM classifiers has been evaluated with high accuracy, sensitivity, specificity, kappa, and AUC rates, outperforming 34 state-of-the-art techniques.
Article
Computer Science, Information Systems
Chao Liu, Xiao Gao, Xiaokang Wang
Summary: This study proposes a novel functional outlier detection algorithm using real-time occupancy data from Paris, which helps BSS operators identify abnormal patterns and improve system efficiency.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Theory & Methods
Huijie He, Yingxu Lai, Yipeng Wang, Siqi Le, Zijian Zhao
Summary: With the continuous development of network technology, the rise in volume of encrypted traffic from unknown applications poses a significant challenge to conventional traffic classification methods. This paper proposes a novel data skew-based classification method for Transport Layer Security (TLS) application unknown traffic (DSCU), achieving consistent classification with outstanding performance on TLS flow classification.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Construction & Building Technology
Sen Zheng, Chenfei Shao, Chongshi Gu, Yanxin Xu
Summary: An automatic process line identification method for detecting outliers in dam safety monitoring data is proposed in this paper. The method utilizes scatter plots, image processing, and adjustment techniques to effectively detect and identify outliers.
STRUCTURAL CONTROL & HEALTH MONITORING
(2022)
Article
Computer Science, Hardware & Architecture
Menghan Zhang, Xianliang Jiang, Guang Jin, Penghui Li, Haiming Chen
Summary: With the popularity of 4G/5G cellular networks in the mobile Internet, the accuracy of predicting available bandwidth is becoming increasingly important. However, the dynamic nature of these networks makes bandwidth prediction challenging. To solve this problem, we propose a real-time bandwidth prediction method called CapRadar, which classifies bandwidth into scenarios and matches the optimal prediction model for each scenario. Experimental results show that CapRadar can significantly improve the accuracy of bandwidth prediction.
Article
Biology
Yonghui Ni, Jianghua He, Prabhakar Chalise
Summary: Differential expression (DE) analysis and differential network (DN) analysis are usually conducted independently. However, this article proposes an integrative analysis method called DNrank, which considers both DE and DN, to identify disease-associated molecular features. The proposed method has been demonstrated to be effective in several experiments.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Engineering, Marine
Xinqiang Chen, Chenxin Wei, Guiliang Zhou, Huafeng Wu, Zhongyu Wang, Salvatore Antonio Biancardo
Summary: Automatic Identification System (AIS) data-supported ship trajectory analysis plays a crucial role in helping maritime regulations and practitioners make informed traffic controlling and management decisions. This study proposes a novel ship trajectory exploitation and prediction framework using the bidirectional long short-term memory (Bi-LSTM) model, which shows satisfactory prediction performance according to evaluation metrics.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Physiology
Jia Li, Jiangwei Li, Chenxu Wang, Fons J. Verbeek, Tanja Schultz, Hui Liu
Summary: This article proposes an adaptive mini-minimum spanning tree-based method, which utilizes a novel distance measure by scaling the Euclidean distance, to detect outliers without prior knowledge of outlier percentages. The results demonstrate the effectiveness of the proposed method compared to state-of-the-art methods.
FRONTIERS IN PHYSIOLOGY
(2023)
Article
Biochemical Research Methods
K. Syama, J. Angel Arul Jothi, Namita Khanna
Summary: This study proposes a novel deep learning framework based on boosted GraphSAGE for automatic disease prediction from metagenomic data. The framework includes a metagenomic disease graph construction module and a disease prediction network module. The effectiveness of the proposed method has been demonstrated on real and synthetic datasets, achieving high performance in terms of classification accuracy, AUC, F1-score, and AUPRC.
BMC BIOINFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Henry Y. T. Ngan, Nelson H. C. Yung, Anthony G. O. Yeh
IET INTELLIGENT TRANSPORT SYSTEMS
(2015)
Article
Optics
Henry Y. T. Ngan, Grantham K. H. Pang
OPTICAL ENGINEERING
(2015)
Article
Computer Science, Artificial Intelligence
Colin S. C. Tsang, Henry Y. T. Ngan, Grantham K. H. Pang
PATTERN RECOGNITION
(2016)
Article
Computer Science, Artificial Intelligence
Michael K. Ng, Henry Y. T. Ngan, Xiaoming Yuan, Wenxing Zhang
SIAM JOURNAL ON IMAGING SCIENCES
(2017)
Article
Automation & Control Systems
Henry Y. T. Ngan, Grantham K. H. Pang
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2009)
Article
Automation & Control Systems
Henry Y. T. Ngan, Grantham K. H. Pang, Nelson H. C. Yung
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2010)
Article
Automation & Control Systems
Michael K. Ng, Henry Y. T. Ngan, Xiaoming Yuan, Wenxing Zhang
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2014)
Review
Computer Science, Artificial Intelligence
Henry Y. T. Ngan, Grantham K. H. Pang, Nelson H. C. Yung
IMAGE AND VISION COMPUTING
(2011)
Article
Computer Science, Artificial Intelligence
Henry Y. T. Ngan, Grantham K. H. Pang, Nelson H. C. Yung
PATTERN RECOGNITION
(2010)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Xiang Lan, Wei Liu, Henry Y. T. Ngan
2017 22ND INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP)
(2017)
Proceedings Paper
Computer Science, Theory & Methods
Li-Li Wang, Henry Y. T. Ngan, Wei Liu, Nelson H. C. Yung
2016 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA)
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
Henry Y. T. Ngan, Nelson H. C. Yung, Anthony G. O. Yeh
IMAGE PROCESSING: MACHINE VISION APPLICATIONS VIII
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Walter Y. H. Lam, Henry Y. T. Ngan, Peter Y. P. Wat, Henry W. K. Luk, Tazuko K. Goto, Edmond H. N. Pow
IMAGE PROCESSING: MACHINE VISION APPLICATIONS VIII
(2015)
Proceedings Paper
Engineering, Electrical & Electronic
Taurus T. Dang, Henry E. T. Ngan, Wei Liu
2015 IEEE INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP)
(2015)
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)