Article
Computer Science, Information Systems
Chaoyu Gong, Zhi-gang Su, Xinyi Zhang, Yang You
Summary: This paper presents an adaptive evidential K-NN classification (AEK-NN) algorithm that addresses several issues in the K-NN algorithm, including the potential for incorrect classification results due to the use of a predetermined K, and the curse of dimensionality in high-dimensional spaces. By incorporating adaptive neighborhood search and feature weighting, the algorithm is able to effectively handle data with imperfect labels.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Antonio Javier Gallego, Juan Ramon Rico-Juan, Jose J. Valero-Mas
Summary: The paper introduces the caKD+ algorithm which combines various techniques to improve the efficiency of kNN search, outperforming 16 state-of-the-art methods on 10 datasets.
PATTERN RECOGNITION
(2022)
Article
Biology
Jiao Hu, Yi Liu, Ali Asghar Heidari, Yasmeen Bano, Alisherjon Ibrohimov, Guoxi Liang, Huiling Chen, Xumin Chen, Atef Zaguia, Hamza Turabieh
Summary: In this study, a machine learning method combining MQGWO and FKNN was used to analyze data from HD patients, revealing hypoalbuminemia as an important risk factor for mortality in HD patients. The proposed MQGWO method showed great potential in detecting serum albumin level trends.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Hardware & Architecture
Martin Aumueller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri
Summary: This paper studies the r-NN problem in similarity search in the context of individual fairness and equal opportunities. The authors propose efficient data structures for the fair NN problem and highlight the inherent unfairness of existing NN data structures through experimental evaluation.
COMMUNICATIONS OF THE ACM
(2022)
Article
Automation & Control Systems
Hongjiao Guan, Long Zhao, Xiangjun Dong, Chuan Chen
Summary: Imbalanced data classification is a challenging problem in many applications. We propose an extended natural neighbor (ENaN) concept without parameter k to improve the quality of generated examples by accurately reflecting the local distribution. ENaN-based SMOTE (ENaNSMOTE) can improve the sample distribution obtained by SMOTE and NaNSMOTE.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
T. Mathi Murugan, E. Baburaj
Summary: Clustering and classification techniques based on KNN and PSO are used to classify datasets effectively. The approach combines initial clustering with evolved optimization to achieve better performance in data analysis and pattern recognition.
PATTERN ANALYSIS AND APPLICATIONS
(2021)
Article
Engineering, Multidisciplinary
Jian Zhu, Jianhua Liu, Yuxiang Chen, Xingsi Xue, Shuihua Sun
Summary: The paper introduces the Binary Restructuring Particle Swarm Optimization (BRPSO) algorithm as an adaptation of the Restructuring Particle Swarm Optimization (RPSO) algorithm for solving discrete optimization problems. Unlike other binary metaheuristic algorithms, BRPSO does not use transfer functions, instead relying on comparison results and a novel perturbation term for the particle updating process. The algorithm requires fewer parameters and exhibits high exploration capability, as demonstrated by experiments on feature selection problems.
Article
Computer Science, Artificial Intelligence
Jianping Gou, Liyuan Sun, Lan Du, Hongxing Ma, Taisong Xiong, Weihua Ou, Yongzhao Zhan
Summary: This article proposes a novel representation coefficient-based k-nearest centroid neighbor method (RCKNCN) aiming to improve the classification performance and reduce the sensitivity to the neighborhood size k. The method captures both the proximity and geometry of k-nearest neighbors and learns to differentiate the contribution of each neighbor to the classification of a testing sample. A weighted majority voting algorithm is also proposed under the RCKNCN framework.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Benqiang Wang, Shunxiang Zhang
Summary: The study proposes a new locally adaptive k-nearest centroid neighbour classification method based on average distance, which improves classification performance by finding nearest centroid neighbours to determine k neighbours and deriving discrimination classes with different k values based on the number and distribution of neighbours, resulting in better performance compared to other state-of-the-art KNN algorithms.
CONNECTION SCIENCE
(2022)
Article
Computer Science, Information Systems
Wan-Lei Zhao, Hui Wang, Peng-Cheng Lin, Chong-Wah Ngo
Summary: This paper addresses the issue of merging k-nearest neighbor (k-NN) graphs in two different scenarios. A symmetric merge algorithm is proposed to combine two approximate k-NN graphs, facilitating large-scale processing. A joint merge algorithm is also proposed to expand an existing k-NN graph with a raw dataset, enabling the incremental construction of a hierarchical approximate k-NN graph.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Computer Science, Information Systems
Yibang Ruan, Yanshan Xiao, Zhifeng Hao, Bo Liu
Summary: The paper introduces a nearest-neighbor search model for distance metric learning (NNS-DML), which constructs metric optimization constraints by searching different optimal nearest-neighbor numbers for each training instance. This model reduces the influence of irrelevant features on similar and dissimilar instance pairs and develops a k-free nearest-neighbor model for classification problems. Extensive experiments show that NNS-DML outperforms state-of-the-art distance metric learning methods.
INFORMATION SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Cuixia Li, Shanshan Yang, Li Shi, Yue Liu, Yinghao Li
Summary: This paper proposes an end-to-end point cloud registration network model called PTRNet, which improves the registration behavior by considering both local and global features. Experimental results show that PTRNet outperforms other methods in terms of average error and registration accuracy.
APPLIED SCIENCES-BASEL
(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, Information Systems
Naz Gul, Muhammad Aamir, Saeed Aldahmani, Zardad Khan
Summary: The paper proposes a $k$ NN ensemble method that identifies nearest observations based on weighted distances using support vectors, showing better classification performance on datasets with noisy features. Through majority voting, the estimated class of a test observation is decided, outperforming other methods in most cases.
Article
Computer Science, Artificial Intelligence
An-Da Li, Bing Xue, Mengjie Zhang
Summary: This paper proposes an improved sticky binary PSO algorithm for feature selection problems, which aims to enhance evolutionary performance through new mechanisms such as an initialization strategy, dynamic bits masking, and genetic operations. Experimental results show that ISBPSO achieves higher accuracy with fewer features and reduces computation time compared to benchmark PSO-based FS methods.
APPLIED SOFT COMPUTING
(2021)
Article
Nutrition & Dietetics
Wen-Chin Lee, Wei-Hung Kuo, Sin-Hua Moi, Barry Chiu, Jin-Bor Chen, Cheng-Hong Yang
Summary: This study aimed to investigate the differences in cholesterol synthesis and absorption between hemodialysis patients and healthy controls. Results showed that markers for cholesterol homeostasis were not significantly associated with macrovascular events during a 1-year follow-up, shedding light on potential novel therapeutic targets in managing cholesterol absorption in hemodialysis patients.
Article
Engineering, Multidisciplinary
Chen Yang, Fangyin Liao, Shulin Lan, Lihui Wang, Weiming Shen, George Q. Huang
Summary: This research focuses on achieving rapid reconfiguration in a cloud manufacturing environment by proposing a new manufacturing model called software-defined cloud manufacturing (SDCM), which transfers control logic from hardware to software. Edge computing is introduced to complement cloud computing with computation and storage capabilities near end devices. The study also addresses the management of network congestion caused by transmitting a large amount of Internet of Things (IoT) data with different quality of service (QoS) values. An approach integrating genetic algorithm, Dijkstra's shortest path algorithm, and queuing algorithm is proposed to solve the optimization problem. Experimental results demonstrate that the proposed method effectively prevents network congestion and reduces communication latency in the SDCM.
Article
Food Science & Technology
Khongdet Phasinam, Tamal Mondal, Dony Novaliendry, Cheng-Hong Yang, Chiranjit Dutta, Mohammad Shabaz
Summary: The history of data stored can help companies predict potential patterns and make competitive decisions. This study focuses on the diagnosis and estimation of heart disease, and previous research has shown the effectiveness of knowledge exploration methods in predicting heart disease. Currently, there are no real-time methods for analyzing and forecasting heart disease in its early stages.
JOURNAL OF FOOD QUALITY
(2022)
Article
Mathematics
Cheng-Hong Yang, Yin-Syuan Chen, Sin-Hua Moi, Jin-Bor Chen, Li-Yeh Chuang
Summary: This study employed a whale optimization algorithm-based feature selection model to interpret the complex association between time-averaged serum albumin (TSA) and clinical factors among hemodialysis patients. By conducting a multifactor analysis, an optimal multifactor TSA-associated model was constructed, which exhibited superior performance.
Article
Biochemical Research Methods
Cheng-Hong Yang, Kuo-Chuan Wu, Li-Yeh Chuang, Hsueh-Wei Chang
Summary: DNA barcodes are short sequence fragments used for species identification. This study proposes a deep learning framework, called deep barcoding, for species classification using DNA barcodes. By utilizing raw sequence data and deep convolutional neural networks, the deep barcoding model achieves high accuracy in species identification. Although there are challenges, the deep barcoding model has the potential to be an effective tool for species classification.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Mathematics
Cheng-Hong Yang, Guan-Cheng Lin, Chih-Hsien Wu, Yen-Hsien Liu, Yi-Chuan Wang, Kuo-Chang Chen
Summary: Accurate vessel track prediction is crucial for maritime traffic control and management to improve navigation efficiency and safety. This study proposed a DLSTM model for vessel prediction, which combines clustering and training techniques. The results demonstrated that the DLSTM model outperformed other models in terms of prediction accuracy.
Article
Biochemical Research Methods
Cheng-Hong Yang, Ming-Feng Hou, Li-Yeh Chuang, Cheng-San Yang, Yu-Da Lin
Summary: This study extended MOMDR to the many-objective version (MaODR) for better identification of SSI between cases and controls. The MaODR-CLN model, with three objective functions - correct classification rate, likelihood ratio, and normalized mutual information, showed higher detection success rates compared to MOMDR and MDR. MaODR-CLN successfully identified significant SSIs associated with coronary artery disease.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biology
Cheng-Hong Yang, Wen -Ching Chen, Jin-Bor Chen, Hsiu-Chen Huang, Li-Yeh Chuang
Summary: This study proposed an advanced analytic approach, called Fuzzy-based RNNCoxPH, for detecting missense variants associated with high-risk of all-cause mortality in rectum adenocarcinoma. The Fuzzy-based RNNCoxPH model exhibits higher efficacy in identifying and classifying the missense variants related to mortality risk in rectum adenocarcinoma compared to other test methods.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Cheng-Hong Yang, Cheng-Feng Lee, Po-Yin Chang
Summary: Forecasting global foreign trade is crucial for governments and multinational corporations, but accurate predictions are challenging due to complex relationships between exports, imports, and economic variables. Traditional models provide less accurate forecasts for trade data. This study proposes an ensemble learning approach that combines trade and deep learning models to improve forecasting performance. The method establishes cointegration relationships between variables and uses them to predict future trade data. Experimental results show that the ensemble learning method outperforms traditional models in terms of forecasting accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Biochemical Research Methods
Cheng-Hong Yang, Ming-Feng Hou, Li-Yeh Chuang, Cheng-San Yang, Yu-Da Lin
Summary: This study extended the multiobjective approach-based multifactor dimensionality reduction (MOMDR) to the many-objective version (MaODR) to improve the identification of single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) between cases and controls. An objective function selection approach was introduced to determine the optimal measure combination in MaODR among 10 well-known measures. The results showed that the MaODR-CLN model exhibited higher detection success rates in identifying SSIs with weak marginal effects.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Pharmacology & Pharmacy
Cheng-Hong Yang, Yin-Syuan Chen, Sin-Hua Moi, Jin-Bor Chen, Lin Wang, Li-Yeh Chuang
Summary: This study aimed to assess the all-cause mortality risk in hemodialysis (HD) patients and compared the performance of different Cox proportional hazards (CoxPH) models. The whale optimization algorithm (WOA)-CoxPH model showed the highest concordance index and provided better risk assessment compared to other models. Patients with seven or more risk characteristics of eight selected parameters were found to have a potentially increased risk of all-cause mortality in the HD population.
THERAPEUTIC ADVANCES IN CHRONIC DISEASE
(2022)
Article
Computer Science, Information Systems
Cheng-Hong Yang, Chih-Hsien Wu, Jen-Chung Shao, Yi-Chuan Wang, Chih-Min Hsieh
Summary: Accurate vessel trajectory prediction is crucial for maritime traffic control and management, aiding in route planning, distance reduction, and increased efficiency. This study proposes a method that combines data denoising and deep learning prediction to improve accuracy. Experimental results demonstrate the effectiveness of the proposed method.
Article
Pharmacology & Pharmacy
Jin-Bor Chen, Huai-Shuo Yang, Sin-Hua Moi, Li-Yeh Chuang, Cheng-Hong Yang
Summary: The improved DeepSurv model achieved greater balanced accuracy compared with the DeepSurv model and identified 610 high-risk variants associated with overall mortality. The results of gene differential expression analysis indicated nine KIRCC mortality-risk-related pathways, suggesting their associations with cancer cell growth, cancer cell differentiation, and immune response inhibition. The findings support the effectiveness of the improved DeepSurv model in identifying mortality-related high-risk variants and candidate genes in the context of KIRCC overall mortality.
THERAPEUTIC ADVANCES IN CHRONIC DISEASE
(2021)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)