Article
Computer Science, Artificial Intelligence
Amarjeet Prajapati
Summary: In this study, the performance of nine large-scale multi-objective optimization optimizers was evaluated and compared over five large-scale many-objective software clustering problems. The results showed that S3-CMA-ES and LMOSCO performed better in most cases, while H-RVEA was the worst performer.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
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
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
Green & Sustainable Science & Technology
Remy Rigo-Mariani
Summary: The paper proposes a strategy for reducing time horizon in power and energy studies. The proposed method displays smaller errors, is more scalable, and has less impact on system operation compared to conventional approaches.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Computer Science, Information Systems
Jiajun Zhou, Shijie Rao, Liang Gao, Chao Lu, Jun Zheng, Felix T. S. Chan
Summary: This paper proposes a novel bi-partition assisted environmental selection strategy for addressing the complexities of the Pareto front in many-objective optimization problems. The strategy uses exemplar selection and an adaptive scalarizing function to handle diversity and convergence.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Maoqing Zhang, Lei Wang, Wuzhao Li, Bo Hu, Dongyang Li, Qidi Wu
Summary: This paper proposes a Many-Objective Evolutionary Algorithm with Adaptive Reference Vector (MaOEA-ARV) that can ensure both the spread and convergence of candidate solutions by dynamically adjusting reference vectors and adaptively partitioning candidate solutions into clusters. Experimental results demonstrate the effectiveness of MaOEA-ARV on test suites with up to 12 objectives.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Jiajun Zhou, Liang Gao, Xinyu Li, Chunjiang Zhang, Chengyu Hu
Summary: Counterpoising the trade-off between convergence and diversity in many-objective optimization is a challenging task. The proposed HPEA algorithm effectively balances convergence and diversity through a three-stage search process, achieving competitive performance on various types of many-objective problems.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Songbai Liu, Junhao Zheng, Qiuzhen Lin, Kay Chen Tan
Summary: This paper proposes an MOEA using clustering with a flexible similarity metric to tackle multi and many-objective optimization problems with irregular Pareto fronts. By predicting the concavity or convexity of the problem and setting a flexible reference point, the algorithm can properly measure the similarity between solutions and classify them effectively in environmental selection, showing significant advantages in experimental results.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Dinesh Kumar Kotary, Satyasai Jagannath Nanda, Rachana Gupta
Summary: The paper proposes a many-objective whale optimization algorithm to handle robust distributed clustering in WSNs, which shows better convergence and diversity. By incorporating a leader selection method and weight-based approach, the proposed algorithm outperforms existing methods in terms of clustering performance. The simulations results demonstrate significant improvements in clustering effectiveness compared to other algorithms such as DMaOPSO and DK-Means.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Hardware & Architecture
Rui Ding, Hong-bin Dong, Gui-sheng Yin, Jing Sun, Xiao-dong Yu, Xian-bin Feng
Summary: The proposed objective reduction algorithm based on adaptive propagating tree clustering for MaOPs shows adaptiveness and accuracy. It can improve the friendliness of human-computer interaction visualization of Pareto Front and performs well in dealing with problems with irregular shapes.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
Article
Automation & Control Systems
Songbai Liu, Qiuzhen Lin, Kay Chen Tan, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes a fuzzy decomposition-based MOEA that estimates the population's shape using fuzzy prediction and selects weight vectors to fit the Pareto front shapes of different multi-objective optimization problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Vikas Palakonda, Jae-Mo Kang, Heechul Jung
Summary: An ensemble approach combining mating and environmental selection operators of different MOEAs using AdaBoost and K-means clustering algorithms is proposed to enhance the performance of MOEAs on MaOPs.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Xujian Wang, Fenggan Zhang, Minli Yao
Summary: Research has shown that considering the convexity-concavity of Pareto fronts can improve the performance of multiobjective evolutionary algorithms (MOEAs). Based on this advantage, we propose a many-objective evolutionary algorithm, MaOEA-3C, which estimates the convexity-concavity of the Pareto fronts and incorporates clustering. The algorithm updates an elitist archive using non-dominated sorting and a niche-based method to estimate the convexity-concavity of the Pareto fronts and guides the evolving directions of the current population using clustering. The performance of MaOEA-3C is compared with seven state-of-the-art algorithms and demonstrates its effectiveness and competitiveness in many-objective optimization problems.
INFORMATION SCIENCES
(2023)
Article
Green & Sustainable Science & Technology
Da Huang, Christian Doh Dinga, Yuan Tao, Zongguo Wen, Yihan Wang
Summary: This study reduces the complexity of energy conservation and emission reduction optimization in the iron and steel industry by building a constrained multi-objective optimization model and introducing dimensionality reduction technique. It points out that ECER policies should focus on cost, energy, CO2, and PM intensity control as the key objectives, and provides optimization suggestions.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Computer Science, Artificial Intelligence
Hongyuan Zhang, Yanan Zhu, Xuelong Li
Summary: This study proposes a novel projected clustering framework to capture the essence of deep clustering by summarizing the core properties of powerful models, especially deep models. The framework introduces an aggregated mapping, consisting of projection learning and neighbor estimation, to obtain clustering-friendly representation. The study also addresses the problem of severe degeneration in simple clustering-friendly representation learning, and develops a self-evolution mechanism to alleviate the risk of over-fitting.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Multidisciplinary Sciences
Chi-Tung Cheng, Yirui Wang, Huan-Wu Chen, Po-Meng Hsiao, Chun-Nan Yeh, Chi-Hsun Hsieh, Shun Miao, Jing Xiao, Chien-Hung Liao, Le Lu
Summary: Pelvic radiographs are crucial for detecting proximal femur and pelvis injuries in trauma patients, yet current algorithms are limited in accurately detecting all trauma-related radiographic findings. This study introduces PelviXNet, a deep learning algorithm trained with weakly supervised point annotation, which demonstrates high accuracy and sensitivity in a clinical population test set.
NATURE COMMUNICATIONS
(2021)
Article
Multidisciplinary Sciences
Chen- Hsieh, Kang Zheng, Chihung Lin, Ling Mei, Le Lu, Weijian Li, Fang-Ping Chen, Yirui Wang, Xiaoyun Zhou, Fakai Wang, Guotong Xie, Jing Xiao, Shun Miao, Chang-Fu Kuo
Summary: The automated tool shows excellent performance in predicting hip and lumbar spine bone density and can effectively identify high-risk fracture patients.
NATURE COMMUNICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Yuhang Lu, Kang Zheng, Weijian Li, Yirui Wang, Adam P. Harrison, Chihung Lin, Song Wang, Jing Xiao, Le Lu, Chang-Fu Kuo, Shun Miao
Summary: Accurate segmentation of anatomical structures is crucial for medical image analysis. This study introduces a one-shot anatomy segmentation method, CTN, which outperforms non-learning methods and competes with state-of-the-art fully supervised deep learning methods on segmentation tasks of four different anatomies. With minimal human-in-the-loop editing feedback, segmentation performance can be further improved.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Youbao Tang, Ning Zhang, Yirui Wang, Shenghua He, Mei Han, Jing Xiao, Ruei-Sung Lin
Summary: This paper proposes a Transformer-based network, MeaFormer, for lesion RECIST diameter prediction and segmentation. It enhances high-resolution features by capturing long-range dependencies and introduces consistency losses to optimize the relationships among tasks. Experimental results demonstrate that MeaFormer achieves state-of-the-art performance on a large-scale dataset and produces promising results on clinic-relevant tasks.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT IV
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Xiao-Yun Zhou, Bolin Lai, Weijian Li, Yirui Wang, Kang Zheng, Fakai Wang, Chihung Lin, Le Lu, Lingyun Huang, Mei Han, Guotong Xie, Jing Xiao, Kuo Chang-Fu, Adam Harrison, Shun Miao
Summary: A semi-supervised graph-based method called few-shot DAG is proposed in this paper, achieving significant improvements in medical image analysis through consistent experiments.
DEEP GENERATIVE MODELS, AND DATA AUGMENTATION, LABELLING, AND IMPERFECTIONS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Kang Zheng, Yirui Wang, Xiao-Yun Zhou, Fakai Wang, Le Lu, Chihung Lin, Lingyun Huang, Guotong Xie, Jing Xiao, Chang-Fu Kuo, Shun Miao
Summary: Bone mineral density (BMD) is a crucial indicator of osteoporosis, usually measured by dual-energy X-ray absorptiometry (DEXA). Limited access to DEXA machines and examinations leads to under-diagnosis and under-treatment of osteoporosis. By using a self-training algorithm and pseudo BMD derivation from unlabeled images, BMD regression estimation from plain X-ray images can be achieved, offering a cost-effective and accessible screening method for osteoporosis.
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT V
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Xinyu Zhang, Yirui Wang, Chi-Tung Cheng, Le Lu, Adam P. Harrison, Jing Xiao, Chien-Hung Liao, Shun Miao
Summary: A new bone fracture detection method for X-ray images is proposed in this work, utilizing point-based annotations and converting them into pixel-wise supervision with lower and upper bounds. The method outperforms previous state-of-the-art image classification and object detection baselines, achieving an AUROC of 0.983 and FROC score of 89.6% on 4410 pelvic X-ray images of unique patients.
THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Yirui Wang, Le Lu, Chi-Tung Cheng, Dakai Jin, Adam P. Harrison, Jing Xiao, Chien-Hung Liao, Shun Miao
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2019, PT VI
(2019)
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)
