Article
Chemistry, Analytical
Hongli Ma, Tao Wang, Bolong Li, Weiyang Cao, Min Zeng, Jianhua Yang, Yanjie Su, Nantao Hu, Zhihua Zhou, Zhi Yang
Summary: The novel quantification technique for electronic nose presented in this study, utilizing a double-step strategy combined with hierarchical classifier and partial least squares regression, demonstrates outstanding performance in identifying toxic gases and estimating concentrations. The approach is applicable for E-nose-based odor quantification.
SENSORS AND ACTUATORS B-CHEMICAL
(2022)
Article
Chemistry, Multidisciplinary
Gui Li, Fan Liu, Cheng Wu, Yuan Yao, Guangxin Wu, Zhu Wang, Yanchun Zhang
Summary: This paper proposes a classification framework based on multiple weighted class association rules (C-MWCAR) to improve classification performance. The framework includes a CAR mining algorithm, a CAR selection algorithm, and a weighted CARs-based classifier. Experimental results demonstrate that C-MWCAR outperforms four baseline methods.
APPLIED SCIENCES-BASEL
(2023)
Article
Plant Sciences
Qian Xu, Jianrong Cai, Lixin Ma, Bin Tan, Ziqi Li, Li Sun
Summary: Huanglongbing (HLB) is a highly contagious and devastating citrus disease that causes huge economic losses to the citrus industry. Timely detection of the HLB infection status of plants and removal of diseased trees are effective ways to reduce losses. In this study, a vision system was developed for rapid HLB detection in the field, using reflection-transmission image acquisition and a step-by-step classification model.
Article
Computer Science, Artificial Intelligence
Lingraj Dora, Sanjay Agrawal, Rutuparna Panda, Ram Bilas Pachori
Summary: This paper proposes a multiple kernel-based convolutional neural network (MK-CNN) approach for automated pathological brain classification task. By using multi-scale features and considering both regional specifics and global spatial consistency, the proposed method outperforms state-of-the-art techniques on real patient data and can aid experts in conducting clinical follow-up studies.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Muhammad Irfan, Jiangbin Zheng, Muhammad Iqbal, Zafar Masood, Muhammad Hassan Arif
Summary: This paper proposes a novel continual learning model for real-world image classification. The model can continuously learn by utilizing previously learned knowledge, and can handle both multi-task and single incremental task scenarios. Experimental results demonstrate that the proposed model outperforms other methods in continual learning scenarios.
INFORMATION SCIENCES
(2022)
Article
Remote Sensing
Sajjad Hassany Pazoky, Parham Pahlavani
Summary: Misclassification of features is a major source of uncertainty in OSM. This study aims to predict road classes using MCSs and calculated fourteen parameters to enhance accuracy. Through five-fold cross-validation and different fusion methods, the BKS fusion method with SVM and Random Forest achieved the highest accuracy of 97.19%.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Computer Science, Information Systems
Hajer Walhazi, Ahmed Maalej, Najoua Essoukri Ben Amara
Summary: This paper proposes a framework for automatic classification of fingerprints using deep transfer learning and a majority voting system. The multi-classifier system efficiently classifies six different types of fingerprints. Experimental results show that the majority voting approach using three deep transfer learning models (DenseNet 121, ResNet152V1, and EfficientNetB7) outperforms individual transfer learning structures in terms of fingerprint classification accuracy.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Haipei Dong, Fuli Wang, Dakuo He, Yan Liu
Summary: This study proposed a decision system that utilizes the natural properties of copper ore to output the flotation backbone flowchart. The system includes three decision tasks: product scheme, flotation scheme, and grinding scheme, each of which is a multi-label classification problem. To improve classification effectiveness, extreme gradient boosting (XGBoost) is used as a subclassifier, and a modified classifier chain (MCC) is proposed to selectively utilize relations between sub-labels. The decision system demonstrates high performance through hypothesis testing.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Cheng-Feng Wu, Shian-Chang Huang, Chei-Chang Chiou, Yu-Min Wang
Summary: The study demonstrates that the deep multiple kernel classifier outperforms conventional and ensemble models in credit risk assessment, helping credit card issuers appropriately approve applicants for credit cards, improving risk management, avoiding bad debt, and benefiting banks.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Trung B. Nguyen, Will N. Browne, Mengjie Zhang
Summary: This work introduces a continual-learning system (ConCS) that can utilize solutions from solved problems to solve further problems. By using parallel LCSs, the system can automatically identify sets of patterns linking features to classes that can be reused in related problems. Experimental results demonstrate that by combining knowledge from simple problems, complex problems can be successfully solved at increasing scales.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Environmental Sciences
Achala Shakya, Mantosh Biswas, Mahesh Pal
Summary: This paper proposes a gradient-based data fusion and classification approach for Synthetic Aperture Radar (SAR) and optical image. The fusion of SAR and optical images is performed using a gradient method and color components. The classification accuracy is evaluated using different gradient-based classifiers, and the Extreme Gradient Boosting Classifier shows better performance.
Article
Computer Science, Artificial Intelligence
Preeti Sharma, M. Gangadharappa
Summary: In this paper, a framework is proposed for classifying multiple anomalies present in video frames that occur in a complex background. An attention U-net model is used to create binary segmented anomalous images, and a watershed algorithm is utilized to distinguish each anomaly. The proposed methodology achieves high accuracy on various datasets.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Fatemeh Zamani, Mansour Jamzad, Hamid R. Rabiee
Summary: The paper proposed an MKL-SRC method with non-fixed kernel weights, which can compute an atom-specific multiple kernel dictionary in the training phase for classifying test images. The effectiveness of the proposed approach was demonstrated through experimental results.
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
(2021)
Article
Microscopy
Samuel Bitrus, Harald Fitzek, Eugen Rigger, Johannes Rattenberger, Doris Entner
Summary: This paper investigates the application of single classifiers and multiple classifier systems in correlative microscopy and demonstrates the feasibility and superiority of automated classification in this context.
Article
Computer Science, Artificial Intelligence
Ping Yuan, Biao Wang, Zhizhong Mao
Summary: This study proposed a dynamic outlier ensemble method to relax the assumption of independent errors made by base detectors. Artificial outliers are generated using the concept of multiple classifier behavior to estimate competences, and validation sets are optimized to find more representative objects. Competences of base detectors are estimated using a probabilistic method, and a switching mechanism is proposed for robust detection results.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Computer Science, Information Systems
Min Yang, Wenting Tu, Qiang Qu, Kai Lei, Xiaojun Chen, Jia Zhu, Ying Shen
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2019)
Article
Computer Science, Artificial Intelligence
Min Yang, Qiang Qu, Ying Shen, Kai Lei, Jia Zhu
NEURAL COMPUTING & APPLICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Jia Zhu, Zetao Zheng, Min Yang, Gabriel Pui Cheong Fung, Yong Tang
DATA MINING AND KNOWLEDGE DISCOVERY
(2020)
Article
Computer Science, Artificial Intelligence
Jin Huang, Tinghua Zhang, Weihao Yu, Jia Zhu, Ercong Cai
Summary: Community detection is a challenging and important problem in complex network analysis, and existing methods often overlook the overall characteristics and microscopic structure properties of the community. This paper proposes a novel model MDNMF, which can preserve both the topology information and intuitive structural properties of the community simultaneously, outperforming state-of-the-art approaches on well-known datasets.
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Jin Huang, TingHua Zhang, Jia Zhu, Weihao Yu, Yong Tang, Yang He
Summary: Knowledge graph completion is a significant research problem, with previous approaches focusing on surface information without capturing fine-grained features. This paper introduces a novel model that explores directional information and deep characteristics of the triples, achieving state-of-the-art results in experimental evaluation.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jing Xiao, Haichao Li, Guangzhuo Qu, Hamido Fujita, Yang Cao, Jia Zhu, Changqin Huang
Summary: In this paper, a simple yet effective method called HOPE using Heatmap and Offset for Pose Estimation is proposed to improve the accuracy in human pose estimation. By embedding coordinate offset into the neural network structure, the HOPE method allows self-learning of slight offsets, leading to state-of-the-art performance in terms of accuracy and computational complexity, as shown in experimental results on various datasets.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Article
Computer Science, Information Systems
YangJie Qin, Ming Li, Jia Zhu
Summary: This study examines the issues with existing multimedia course recommendation systems and proposes a federated learning framework to address these concerns. By using an attention-based hierarchical reinforcement learning model, the crucial parts of user data are determined. The experiment demonstrates that the federated system operates effectively in a privacy-preserving mode.
Article
Computer Science, Artificial Intelligence
Xianqing Wang, Zetao Zheng, Jia Zhu, Weihao Yu
Summary: In this paper, the authors propose a method using finite state automaton (FSA) to interpret the fluctuation problem in deep knowledge tracing (DKT). They also introduce two novel attention-based models to address the issue. The experimental results demonstrate that the proposed models achieve state-of-the-art performance in knowledge tracing.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yuling Xing, Jia Zhu, Yu Li, Jin Huang, Jinlong Song
Summary: This study proposes an improved Spatial Temporal Graph Convolutional Network (IST-GCN) model to enhance the robustness of action recognition. The experimental results demonstrate that the model performs outstandingly when handling incomplete and noisy skeleton samples.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jia Zhu, Changqin Huang, Pasquale De Meo
Summary: Entity alignment is critical for integrating multiple knowledge graphs. This paper proposes the DFMKE framework to address entity alignment, providing an ultimate fusion solution by leveraging early and late fusion methods.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Zhiwen Yan, Ying Chen, Jinlong Song, Jia Zhu
Summary: Most existing methods in video captioning task disregard the relationship between objects and correlation between multimodal features, as well as the effect of caption length. This study introduces a novel video captioning framework based on object relation graph and multimodal feature fusion. The proposed framework utilizes the relationships between objects and the fusion of multimodal features to improve the accuracy and richness of the generated captions.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2023)
Review
Computer Science, Artificial Intelligence
Yuling Xing, Jia Zhu
Summary: Action recognition based on 3D skeleton data is a widely studied topic in computer vision, with the advantages of combining skeleton data and deep learning being gradually demonstrated. Previous research has mainly focused on video or RGB data methods, while GCN-based methods are gaining attention.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2021)
Proceedings Paper
Computer Science, Software Engineering
Pengyuan Xie, Jing Xiao, Yang Cao, Jia Zhu, Asad Khan
2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME)
(2019)
Proceedings Paper
Computer Science, Software Engineering
Haoye Dong, Xiaodan Liang, Chenxing Zhou, Hanjiang Lai, Jia Zhu, Jian Yin
2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME)
(2019)
Proceedings Paper
Computer Science, Software Engineering
Yangwo Jian, Jing Xiao, Yang Cao, Asad Khan, Jia Zhu
2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME)
(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)
