计算机科学最新文献

{"title":"Characterization of the Skin Bacteriome and Histology Changes in Diabetic Pigs.","authors":"Meirong Li, Jifang Yuan, Qian Hou, Yali Zhao, Lingzhi Zhong, Xin Dai, Hua Chen, Xiaobing Fu","doi":"10.1177/15347346221100887","DOIUrl":"10.1177/15347346221100887","url":null,"abstract":"<p><p>Chronic wound is one of the most common complications that are associated with diabetes. The cutaneous microbiome is known to play essential roles in the regulation of barrier function and protecting against potential assault. Thus, it is necessary to gain a better understanding of the relationship between microbial community and skin structures in unwounded diabetic skin to explore possible preventive strategies. To achieve the same, a pig diabetic model was built in the present study. Further,16S rDNA sequencing was used to characterize the skin bacteriome. It was observed that the pigs showed skin bacteriome similar to humans in the non-diabetes group, while it varied in the case of diabetes. Further, the β-diversity analysis showed that the bacterial community was significantly different under the diabetes group. More species differences were identified between the two groups at genus level. The predictive function analysis also showed the involvement of significantly different pathways of microbial gene function in diabetes. In agreement with this, skin histology analysis also showed signs of reduced epidermal thickness and rete ridges in diabetic skin. Less proliferation of keratinocytes and impaired TJ barrier was also detected. This evidence suggested that pigs might serve as the best surrogate for cutaneous microbiome studies. Altogether, the present study reported that the skin bacteriome and histology changed significantly in unwounded diabetic skin, which provided a theoretical basis for the regulation of disordered skin bacteriome. The findings of the study would assist in the improvement of the skin environment and prevention of skin infection and chronic wounds.</p>","PeriodicalId":9814,"journal":{"name":"China Communications","volume":"15 1","pages":"426-443"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89780525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TabMixer: advancing tabular data analysis with an enhanced MLP-mixer approach.","authors":"Ali Eslamian, Qiang Cheng","doi":"10.1007/s10044-025-01423-y","DOIUrl":"https://doi.org/10.1007/s10044-025-01423-y","url":null,"abstract":"<p><p>Tabular data, prevalent in relational databases and spreadsheets, is fundamental across fields like healthcare, engineering, and finance. Despite significant advances in tabular data learning, critical challenges remain: handling missing values, addressing class imbalance, enabling transfer learning, and facilitating feature incremental learning beyond traditional supervised paradigms. We introduce TabMixer, an innovative model that enhances the multilayer perceptron (MLP) mixer architecture to address these challenges. TabMixer incorporates a self-attention mechanism, making it versatile across various learning scenarios including supervised learning, transfer learning, and feature incremental learning. Extensive experiments on eight public datasets demonstrate TabMixer's superior performance over existing state-of-the-art methods. Notably, TabMixer achieved substantial improvements in ANOVA AUC across all scenarios: a 4% increase in supervised learning (0.840 to 0.881), 8% in transfer learning (0.803 to 0.872), and 4% in feature incremental learning (0.806 to 0.843). TabMixer demonstrates high computational efficiency and scalability through reduced floating-point operations and learnable parameters. Moreover, it exhibits strong resilience to missing values and class imbalances through both its architectural design and optional preprocessing enhancements. These results establish TabMixer as a promising model for tabular data analysis and a valuable tool for diverse applications.</p>","PeriodicalId":54639,"journal":{"name":"Pattern Analysis and Applications","volume":"28 2","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12053537/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Based Classification and Combined Transform Based Feature Extraction Approach for Mental Stress Prediction of Human Beings Using EEG","authors":"Shashibala Agarwal,&nbsp;Maria Jamal,&nbsp;Parmod Kumar","doi":"10.1002/ett.70155","DOIUrl":"https://doi.org/10.1002/ett.70155","url":null,"abstract":"<div>\u0000 \u0000 <p>Stress is a psychological condition in which a person feels overwhelmed with pressure. Early identification of psychological stress is critical for preventing illness progression and saving lives. Electroencephalography (EEG) is often used to collect psychological information such as brain rhythms in the form of electric waves. Traditional deep learning techniques face limitations like temporal dynamics and feature extraction issues. To address these shortcomings, a deep learning-based classification model was created, combining advanced transform-based feature extraction techniques to more effectively predict mental stress by using EEG signals. The process begins by utilizing physiological parameters extracted from the EEG Psychiatric Disorders Dataset. The raw EEG signals undergo pre-processing to enhance their quality, which includes smoothing, alignment, and addressing non-uniform sampling issues. The signals are then decomposed and their components extracted using the Adaptive Flexible Analytic Wavelet Transform (AFAWT). Short-Term Fourier Transform-Randon Transform (STFT-RT) approach is used to extract the key features of signals. Feature selection is optimized using the Young's Double Slit Experiment Optimizer (YDSE) to ensure only the most relevant features are chosen for classification. Finally, these selected features are fed into the Parallel Neural Networks with Extreme Efficiency (ParNeXt v1-DB) model, which utilizes a drop block mechanism to enhance model generalization and prevent overfitting, ensuring highly effective mental stress prediction. According to simulated research, the proposed approach demonstrated significant improvements over existing algorithms. In Dataset 1, the method achieved an accuracy of 97.8%, selectivity of 95.4%, while Dataset 2 recorded an accuracy of 96.3%, PPV of 93.8%. Thus, the proposed method is the most effective method for predicting human mental stress using EEG.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-delay assisted mechanism and adaptive LSTM hybrid train braking model of heavy haul trains","authors":"Qiang Liu ,&nbsp;Kexuan Xu ,&nbsp;Yating Fu ,&nbsp;Jiang Liu ,&nbsp;Ling Liu","doi":"10.1016/j.conengprac.2025.106392","DOIUrl":"10.1016/j.conengprac.2025.106392","url":null,"abstract":"<div><div>The train braking model (TBM) that describes the dynamic relations of operation speed, mileage, and control force is essential for achieving stable operation and precise stopping of heavy haul trains (HHTs). However, it is difficult to establish the TBM of HHTs due to complex characteristics: (i) the long body and air braking process of the HHTs may lead to unexpected time-delays of control force; and (ii) there are significant unmodeled dynamics caused by rough tracks and external poor environment. Traditional TBM does not take into account the unmodeled dynamics and time-delays caused by air transmission during braking. To address these issues, this study proposes a data mechanism hybrid modeling strategy, which incorporates a braking time-delay assisted mechanism model and an adaptive long and short-term memory (LSTM) model. A new Bayesian optimization based time-delay estimation method is first proposed to determine unknown time-delays of each carriage and the estimated time-delays are incorporated to generate the multi-point-mass kinetic mechanism model. Moreover, the error of the mechanism-driven model is adaptively compensated by a sliding window LSTM model to conduct the unmodeled dynamics. The effectiveness of the proposed method is demonstrated using the field data.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"163 ","pages":"Article 106392"},"PeriodicalIF":5.4,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multidimensional chaotic signals generation using deep learning and its application in image encryption","authors":"Shuang Zhou ,&nbsp;Zhiji Tao ,&nbsp;Uğur Erkan ,&nbsp;Abdurrahim Toktas ,&nbsp;Herbert Ho-Ching Iu ,&nbsp;Yingqian Zhang ,&nbsp;Hao Zhang","doi":"10.1016/j.engappai.2025.111017","DOIUrl":"10.1016/j.engappai.2025.111017","url":null,"abstract":"<div><div>In this paper, we propose a novel artificial intelligence implemented approach to generate multi-dimensional chaotic signals using the Long- and Short-Term Time-Series Network (LSTNet) for a newly contrived Two-Stage pixel/bit level Scrambling and Dynamic Diffusion (TSSDD) color image encryption. Initially, we employ the hyperchaotic Lorenz and Chen chaotic systems to produce chaotic signals. Subsequently, the LSTNet model is trained to predict these produced multi-dimensional chaotic sequences and then it generates new multi-dimensional chaotic signals. Through analysis involving phase diagrams, largest Lyapunov exponent (LE), 0–1 test, Permutation Entropy (PE), Sample Entropy (SE), Correlation Dimension (CD) and National Institute of Standards and Technology (NIST), we observe that these applied artificial intelligence signals exhibit high chaotic states and randomness. Finally, we apply these signals to demonstrate the proposed TSSDD color image encryption wherein simulation experiments indicate competitive performance against common attacks.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"156 ","pages":"Article 111017"},"PeriodicalIF":7.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Point-line feature-based vSLAM systems: A survey","authors":"Hangzhou Qu ,&nbsp;Zhuhua Hu ,&nbsp;Yaochi Zhao ,&nbsp;Junlin Lu ,&nbsp;Kunkun Ding ,&nbsp;Guangfeng Liu ,&nbsp;Yongqing Chen ,&nbsp;Chunyan Shao","doi":"10.1016/j.eswa.2025.127574","DOIUrl":"10.1016/j.eswa.2025.127574","url":null,"abstract":"<div><div>The point-line feature-based vSLAM technology significantly enhances the accuracy and robustness of localization and mapping in complex environments by comprehensively utilizing both point and line geometric information. This paper provides a comprehensive survey of methods and applications for point-line feature-based Simultaneous Localization and Mapping (SLAM) systems. Firstly, it focuses on the core components of the visual frontend in SLAM systems, with a detailed analysis of line feature detection methods and their descriptors, covering both traditional algorithms and learning-based approaches, as well as further improvements to these methods. The paper also discusses several common line feature parameterization methods and different line feature matching strategies. In addition, the paper delves into the backend optimization and loop closure detection mechanisms of SLAM systems, which are critical factors in enhancing the system’s accuracy and robustness. By reviewing these methods and applications, this paper aims to provide a comprehensive understanding of integrated point-line SLAM systems, analyzing the strengths and weaknesses of different technologies, and exploring potential directions for future research. This work offers theoretical foundations and practical guidance from a global perspective for the subsequent design and optimization of SLAM systems.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"289 ","pages":"Article 127574"},"PeriodicalIF":7.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GSCCANet: Dual Decoder Network Fusing Edge Focus and Global Channel Attention for Precise Segmentation of Colonic Polyps","authors":"Jun Su,&nbsp;Xinyi Chen,&nbsp;Orest Kochan,&nbsp;Mariana Levkiv,&nbsp;Krzysztof Przystupa","doi":"10.1002/ima.70129","DOIUrl":"https://doi.org/10.1002/ima.70129","url":null,"abstract":"<div>\u0000 \u0000 <p>Colorectal cancer is the second most common cancer globally. Its high mortality necessitates early polyp detection to mitigate the risk of the disease. However, conventional segmentation methods are susceptible to noise interference and have a limited accuracy in complex environments. To address these challenges, we propose GSCCANet with an encoder-dual decoder co-design. The encoder employs hybrid Transformer (MiT) for efficient multi-scale global feature extraction. Dual decoders collaborate via SAFM and REF-RA modules to enhance segmentation precision through global semantics and boundary refinement. In particular, SAFM enhances lesion coherence via channel-space attention fusion, while REF-RA strengthens low-contrast edge response using high-frequency gradients and reverse attention, optimized through progressive fusion. Additionally, combined Focal Loss and Weighted IoU Loss mitigate the problem of undetected small polyps. Experiments on five datasets show GSCCANet surpasses baselines. It achieves 94.7% mDice and 90.1% mIoU on CVC-ClinicDB (regular) and 80.1% mDice and 72.5% mIoU on ETIS-LaribPolypDB (challenging). Cross-domain tests (CVC-ClinicDB <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>→</mo>\u0000 </mrow>\u0000 <annotation>$$ to $$</annotation>\u0000 </semantics></math> Kvasir) confirm strong adaptability with 0.2% mDice fluctuation. These results prove that GSCCANet offers high-precision and generalizable solutions through global–local synergy, edge enhancement, and efficient computation.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Archive assisted fully informed evolutionary algorithm for expensive many-objective optimization","authors":"Jie Lin ,&nbsp;Sheng Xin Zhang ,&nbsp;Shao Yong Zheng ,&nbsp;Kwai Man Luk","doi":"10.1016/j.swevo.2025.101988","DOIUrl":"10.1016/j.swevo.2025.101988","url":null,"abstract":"<div><div>In many real-world engineering and scientific optimization scenarios, practitioners often face expensive many-objective optimization problems where evaluating candidate solutions incurs prohibitive computational costs. The inherent scarcity of truly calculated data often leads to the construction of models with high uncertainty using limited datasets. This uncertainty can adversely affect the Surrogate-assisted Many-Objective Evolutionary Algorithms (SAMaOEAs). To address this issue and enhance performance, this paper introduces an Archive-assisted Fully Informed Evolutionary Algorithm (AFIEA). In AFIEA, two kinds of models are constructed from archive data to simultaneously predict objective values and uncertainty trends (whether the predictions are overestimated or underestimated). With this foundation, both the optimizer and infill criterion processes are fully guided by the predicted objective values and uncertainty trends. In the optimization phase, a novel Uncertainty Trend Classification (UTC)-based Upper Confidence Bound is employed as the acquisition function. During the infill criterion phase, UTC is used to preprocess the population, enhancing the selection probability of under-estimated solutions, while an archive-based metric selects more precise solutions, guided by the archive in terms of convergence and diversity. The performance of AFIEA is compared with six state-of-the-art SAMaOEAs on artificial benchmark problems and one real-world expensive optimization problem within a limited budget. In the benchmark tests, AFIEA outperforms the six advanced SAMaOEAs across most of the test functions, demonstrating that the proposed mechanism offers strong generality and enhanced search performance. Additionally, in the optimization of electromagnetic devices, AFIEA achieves superior population quality in a shorter time with a limited number of simulations.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"96 ","pages":"Article 101988"},"PeriodicalIF":8.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Entropy-Aware VM Selection and Placement in Cloud Data Centers","authors":"Somayeh Rahmani,&nbsp;Vahid Khajehvand,&nbsp;Mohsen Torabian","doi":"10.1002/cpe.70117","DOIUrl":"https://doi.org/10.1002/cpe.70117","url":null,"abstract":"<div>\u0000 \u0000 <p>The increase in popularity and demand for cloud services has caused a huge growth of cloud data centers, and this has caused the challenge of energy management in data centers. Virtual Machine (VM) consolidation is a critical process aimed at optimizing resource utilization and minimizing energy usage. VM consolidation with the turnoff of underloaded hosts and reducing the load of overloaded hosts establishes a balance between energy consumption and SLA violations. In fact, the consolidation process includes three sub-problems: determining overloaded and underloaded hosts, VM selection in overloaded hosts, and finding a new destination for VMs that will be migrated (VM placement). This paper introduces an entropy-based approach to VM selection and placement to improve efficiency in cloud data centers. Entropy is a quantifiable characteristic often linked to disorder, randomness, or unpredictability. By leveraging entropy as a measure of workload distribution and uncertainty, the proposed method effectively predicts future resource demands, enabling informed decisions that enhance energy efficiency and reduce SLA violations. A key advantage of this approach is the significant reduction in the number of VM migrations, which decreases overhead and minimizes potential service disruptions. Experimental results demonstrate that our entropy-based method outperforms the VM consolidation process in terms of energy consumption, SLA compliance, and system stability. The findings suggest that this approach offers a more sustainable and cost-effective solution for managing cloud resources, contributing to the development of efficient and reliable cloud computing environments.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 15-17","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-Weighted Multiview Clustering Based on Anchor","authors":"Yan Zhang,&nbsp;Yali Peng,&nbsp;Shengnan Wu,&nbsp;Shigang Liu","doi":"10.1002/cpe.70134","DOIUrl":"https://doi.org/10.1002/cpe.70134","url":null,"abstract":"<div>\u0000 \u0000 <p>In the field of multiview clustering, how to make full use of information from multiple data sources to improve the clustering performance has become a hot research topic. However, the rapid growth of high-dimensional multiview data brings great challenges to the research of multiview clustering algorithms, especially the time and space complexity of the algorithms. As an effective solution, anchor-based technique has gained wide attention in large-scale multiview clustering tasks. Nevertheless, the current anchor-based methods fail to fully take into account the importance of different views and the difference and diversity of anchors at the same time, which limits the clustering performance to some extent. To address these problems, we propose a dual-weighted multiview clustering based on anchor (DwMVCA). First, we effectively distinguish the different impacts of high-quality and low-quality views on clustering by adaptively learning the weights of different views. Second, by introducing the adaptive weighting matrix of anchors and self-correlation matrix regularization term, the difference and diversity of anchors are fully considered to effectively reduce the effect of redundant information on clustering. Furthermore, we design a three-step alternating optimization algorithm to solve the resultant optimization problem and prove its convergence. Extensive experimental results show that the proposed DwMVCA has obvious advantages in clustering performance on large-scale datasets, especially on datasets with more than 100,000 samples that still maintain linear time complexity.</p>\u0000 </div>","PeriodicalId":55214,"journal":{"name":"Concurrency and Computation-Practice & Experience","volume":"37 15-17","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}