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A data representation method using distance correlation

Frontiers of computer science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO Keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Recent metaheuristic algorithms for medical object localization using MSER detector in computer-aided diagnosis system

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Multimedia Tools and Applications 2025年第15期84卷 14433-14487页

作者： Ait Mehdi, Mohamed Belattar, Khadidja Souami, Feryel Faculty of Computer Science Data Science and Artificial Intelligence Department Laboratory for Research in Artificial Intelligence University of Science and Technology Houari Boumediene BP 32 El-Alia Algiers Bab-Ezzouar16111 Algeria Department of Computer Science University of Algiers Benyoucef Benkhedda Algiers16000 Algeria

Object localization is a critical task in image analysis, often facilitated by artificial intelligence techniques. While the Maximally Stable Extremal Regions (MSER) detection algorithm is a popular choice for local detection, its exhaustive approaches can be algorithmically complex and prone to suboptimal results with improper parameter selection. Various metaheuristic algorithms have been proposed for medical object localization to address this. In this context, four contributions are presented. Firstly, recent metaheuristics, including the Slime Mould Algorithm (SMA), Marine Predators Algorithm (MPA), Heap-based Optimizer (HBO), and Gradient-based Optimizer (GBO), are adapted to tackle the MSER localization problem. These algorithms are rigorously evaluated across diverse medical image datasets using multiple metrics, with their performance statistically validated through the Friedman mean rank test. The second contribution introduces a novel objective function to improve the localization process. The third contribution involves a comparative analysis of the recent algorithms against seven standard metaheuristics specifically designed for MSER localization. Lastly, we present an improved computer-aided diagnosis system that integrates an SVM-based model with Local Binary Patterns (LBP) descriptors extracted from each MSER alongside DenseNet-121 features. Experimental results demonstrate that the HBO and SMA optimizers outperform conventional algorithms, showing a higher diversity, improved exploitation and exploration capabilities, a well-balanced exploration-exploitation trade-off, elevated fitness values, faster convergence speeds, and enhanced computational efficiency. Additionally, we integrate these findings into an improved computer-aided diagnosis system, yielding classification results surpassing state-of-the-art models. These findings highlight the potential advantages of this approach, especially in object detection applications. © The Author(s), under

关键词： Object detection

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Fine-Tuning a Biased Model for Improving Fairness

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IEEE Transactions on Big data 2025年第3期11卷 1397-1410页

作者： Chen, Huiqiang Zhu, Tianqing Liu, Bo Zhou, Wanlei Yu, Philip S. University of Technology Sydney Center for Cyber Security and Privacy School of Computer Science SydneyNSW2007 Australia City University of Macau Faculty of Data Science 999078 China University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

Fairness has emerged as a crucial concern in machine learning since biased models would generate dissimilar predictions for different groups, perpetuating social inequalities. Although numerous techniques have been proposed to address the fairness issue in machine learning, most rely on incorporating fairness constraints during the training phase, rendering them ineffective once the model is deployed. This paper explores the potential of fine-tuning biased models to enhance fairness, particularly suitable for scenarios where retraining the model is not feasible. Our approach is rooted in an empirical analysis of the distribution of bias within a biased model, and we fine-tune the model parameter in a limited scope so that the performance of the original model can be maintained. We first observe that fine-tuning a biased model leads to deviations from its initial state, with deep layers undergoing the most significant changes. We then design and apply a bias-discovery algorithm, revealing that bias predominantly resides in the model’s deep layers. Based on these observations, we propose a straightforward yet highly effective method for debiasing the model: fine-tuning the classification head. We conduct a thorough theoretical analysis to justify the proposed method and provide guidance for fine-tuning. Furthermore, we experimentally validate our method on tabular and image datasets using four networks (CNN, AlexNet, VGG-11, and ResNet-18). © 2015 IEEE.

关键词： Adversarial machine learning

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Immersive video interaction system: a survey

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Visual Intelligence 2025年第1期3卷 1-14页

作者： Fang, Xiaohan Chen, Peilin Wang, Meng Wang, Shiqi Department of Computer Science City University of Hong Kong Hong Kong School of Data Science Lingnan University Hong Kong

As communication technologies undergo rapid evolution, human interaction technologies have become increasingly efficient and accessible. Videoconferencing, for example, facilitates real-time, face-to-face communication worldwide, providing unprecedented convenience. Nevertheless, traditional two-dimensional (2D) videoconferencing systems frequently fall short of delivering a fully immersive experience, as they lack the ability to convey physical presence, depth perception, and natural interaction—key elements that are of particular significance in replicating the subtleties inherent in in-person communication. This disconnection leaves users yearning for the rich, multi-sensory experiences that real-life interactions can offer. In response, significant research efforts have been dedicated to the development of immersive video interaction systems (IVISs). This paper introduces the concept of IVISs and reviews a spectrum of systems advanced by researchers and industry innovators, each of whom contributes unique insights into the evolution of this field. A comparative analysis of the characteristics of current IVISs is provided, offering insights across multiple dimensions. This survey concludes with a discussion of potential future research directions and the challenges that remain in enhancing the IVIS technology. Through a comprehensive analysis, this work aims to foster further exploration and innovation in immersive communication technologies. © The Author(s) 2025.

关键词： 3D vision Immersive video interaction systems Survey Video communication

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CBIR: a novel identification approach for college students in need based on consumer behavior psychology theory

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Neural Computing and Applications 2025年第6期37卷 4663-4677页

作者： Liu, Xinze Liu, Shixi Hu, Xiaojing Zhang, Yudong Fang, Xianwen School of Computer and Information Engineering Chuzhou University Chuzhou239000 China School of Computer Science and Engineering Southeast University Nanjing210096 China School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

The accurate identification of students in need is crucial for governments and colleges to allocate resources more effectively and enhance social equity and educational fairness. Existing approaches to identifying students in need rely on manual operations that include manually extracting consumption behavior information, statistical consumption characteristics and principal component analysis. However, this issue may lead to low prediction accuracy and inefficiency in identifying students in need. We design a three-stage framework to accurately identify college students in need from the perspective of consumer behavior psychology. The consumption behavior information is first obtained from the student consumption records using the consumption behavior clustering approach. The consumption behavior matrix is then built by extracting consumption and spatiotemporal information in different periods. Finally, a novel consumption behavior identification ResNeSt (CBIR) model is proposed to identify college students in need accurately. The experimental results on real datasets show that the CBIR model has higher prediction accuracy than the baseline models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Social psychology

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Lung Cancer Detection: A Comparative Approach Using Regressor Methods 4

Lung Cancer Detection: A Comparative Approach Using Regresso...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Puri, Chetan Sharma, Mansi Reddy, K.T.V. Department of Computer Science and Design Wardha India Department of Artificial Intelligence and Data Science Wardha India

ISBN: (纸本)9798331523923

Lung cancer detection is the detection of tumors or cancerous cells in lung tissue. It is done using several medical imaging modalities, such as nuclear and genetic tests, magnetic resonance imaging (MRI), computed tomography (CT) scans, and X-rays. Detection of lung cancer at an early stage is very important as it increases the likelihood of successful treatment. For better diagnostic accuracy and patient outcomes, sophisticated detection methods now utilize regression models and machine learning algorithms. As one of the most common reasons for cancer fatalities globally, lung cancer highlights the urgent need for early and accurate diagnostic techniques. This research considers the use of regression-based strategies in lung cancer detection, suggesting their ability to improve diagnostic sensitivity and patient results. We created a strong predictive model that could effectively differentiate malignant nodules through sophisticated machine learning methods, such as support vector machines (SVM), decision trees, and linear regression. Regression analysis was used to assess how well benign and malignant lung lesions could be differentiated using a large clinical and medical imaging dastaset. Findings from research show that regression methods provide a sound method of enhancing early lung cancer detection, allowing for timely intervention and increased survival rates. The significance of machine learning in medical diagnosis is also illustrated through discussions on clinical implications and future research directions. The models that were tested, Random Forest had the best accuracy (94.6%), according to Stratified K-Fold cross-validation. The other models, including Gradient Boosting, Support Vector Classifier (SVC), and XGBoost, also showed high levels of accuracy, while the Multinomial Naïve Bayes model had the worst accuracy (75.7%). By reviewing clinical and imaging information and subjecting it to machine learning algorithms to identify patterns and associat

关键词： Lung cancer

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Towards Adaptive Masked Structural Learning for Graph-Level Clustering

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IEEE Transactions on Network science and Engineering 2025年第3期12卷 2021-2032页

作者： Yang, Jinbin Cai, Jinyu Zhang, Yunhe Huang, Sujia Wang, Shiping Fuzhou University College of Computer and Data Science Fuzhou350116 China Bank of Quanzhou Company Ltd. Quanzhou362046 China National University of Singapore Institute of Data Science 117602 Singapore Shenzhen Research Institute of Big Data Shenzhen518172 China Chinese University of Hong Kong Guangdong Provincial Key Laboratory of Big Data Computing Shenzhen518172 China

Graph data presents a vast landscape for real-world applications. Current graph-level clustering approaches predominantly utilize graph neural networks to capture the intricate structural information for graph data. However, a significant challenge arises in effectively integrate structural and feature information under the prevalent noise in the real-world scenario. The advent of masking strategies has marked significant strides in boosting model robustness, accommodating incomplete data, and enhancing generalization capabilities. Yet, research attention on leveraging mask strategy for facilitating graph-level clustering is still limited. In this paper, we introduce a novel graph-level clustering method, towards adaptive masked structural learning for graph-level clustering. The method performs adaptive masking through reconstruction loss, and jointly adaptive mask representation learning and clustering in an end-to-end unsupervised framework. The mutual information between maximized the entire graph and substructure representations is also utilized to learn to generate cluster-oriented graph-level representations. Extensive experiments on eight real graph-level benchmark datasets demonstrate the effectiveness and superiority of the proposed method. © 2013 IEEE.

关键词： Graph neural networks

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Steel Surface Defect Detection Using Learnable Memory Vision Transformer

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computers, Materials & Continua 2025年第1期82卷 499-520页

作者： Syed Tasnimul Karim Ayon Farhan Md.Siraj Jia Uddin Department of Computer Science and Engineering BRAC UniversityDhaka1212Bangladesh Department of AI and Big Data Endicott CollegeWoosong UniversityDaejeon34606Republic of Korea

This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel *** awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and *** techniques mitigated overfitting,stabilized training,and improved generalization *** LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,*** findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature *** additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial *** instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often *** study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are *** research m

关键词： Learnable Memory Vision Transformer(LMViT) Convolutional Neural Networks(CNN) metal surface defect detection deep learning,computer vision image classification learnable memory gradient clipping label smoothing t-SNE visualization

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Palmprint Recognition Using Bifurcation Line Direction Coding

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IEEE Access 2025年 13卷 70366-70377页

作者： Wang, Hongxia Cao, Xulei Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei230088 China University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230026 China

Palmprint recognition is a challenging task due to the variability in image quality, scale, and angle. Traditional methods often rely on single line features, which may not effectively capture the local bifurcation information critical for accurate recognition. To address these challenges, this paper proposes the Bifurcation Line Direction Coding (BLDC) method, which better captures the local bifurcation characteristics of palmprint images. Our approach leverages an improved Gabor filter for image preprocessing, where the main direction guides the branch direction. By considering both the maximum and minimum direction subscripts of the main direction, we generate robust feature codes that accurately represent the palmprint’s unique structure. Extensive experiments across five challenging palmprint datasets demonstrate that BLDC outperforms existing methods in recognition rate and feature extraction speed, providing a more effective solution for palmprint recognition in practical applications. © 2013 IEEE.

关键词： Feature extraction Gabor filters Palmprint recognition Encoding Bifurcation Training data mining Information filters Matched filters Landline

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Adapt Anything: Tailor Any Image Classifier Across Domains and Categories Using Text-to-Image Diffusion Models

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IEEE Transactions on Big data 2025年第3期11卷 1013-1026页

作者： Chen, Weijie Wang, Haoyu Yang, Shicai Zhang, Lei Wei, Wei Zhang, Yanning Lin, Luojun Xie, Di Zhuang, Yueting Zhejiang University College of Computer Science and Technology Hangzhou310027 China Hikvision Research Institute Hangzhou310052 China Northwestern Polytechnical University Xi’an710072 China Fuzhou University College of Computer and Data Science Fuzhou350025 China

We study a novel problem in this paper, that is, if a modern text-to-image diffusion model can tailor any image classifier across domains and categories. Existing domain adaption works exploit both source and target data for domain alignment so as to transfer the knowledge from the labeled source data to the unlabeled target data. However, as the development of text-to-image diffusion models, we wonder if the high-fidelity synthetic data can serve as a surrogate of the source data in real world. In this way, we do not need to collect and annotate the source data for each image classification task in a one-for-one manner. Instead, we utilize only one off-the-shelf text-to-image model to synthesize images with labels derived from text prompts, and then leverage them as a bridge to dig out the knowledge from the task-agnostic text-to-image generator to the task-oriented image classifier via domain adaptation. Such a one-for-all adaptation paradigm allows us to adapt anything in the world using only one text-to-image generator as well as any unlabeled target data. Extensive experiments validate the feasibility of this idea, which even surprisingly surpasses the state-of-the-art domain adaptation works using the source data collected and annotated in real world. © 2015 IEEE.

关键词： Spatio-temporal data

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