计算机科学最新文献

{"title":"Enderscope.py: A library for computational imaging using the EnderScope automated microscope","authors":"Sirine Gharbi ,&nbsp;Emeric Poiraud ,&nbsp;Hugo Le Guenno ,&nbsp;Erwan Grandgirard ,&nbsp;Charly Rousseau ,&nbsp;Niamh Burke ,&nbsp;Jerome Mutterer ,&nbsp;David Rousseau","doi":"10.1016/j.softx.2025.102210","DOIUrl":"10.1016/j.softx.2025.102210","url":null,"abstract":"<div><div>We describe a new software library written in the Python programming language to perform computational imaging on the so-called EnderScope, an automated imaging system based on the 3-axis motion stage of a 3D printer. This library is designed to be easy to use for beginner users and provides high-level functions to access used devices. We provide practical use cases and example codes using the library as an educational tool for testing, experimenting or developing computational imaging and smart microscopy algorithms.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"31 ","pages":"Article 102210"},"PeriodicalIF":2.4,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204655","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":"ET-WOFS Metaheuristic Feature Selection Based Approach for Endometrial Cancer Classification and Detection","authors":"Ramneek Kaur Brar,&nbsp;Manoj Sharma","doi":"10.1002/ima.70126","DOIUrl":"https://doi.org/10.1002/ima.70126","url":null,"abstract":"<div>\u0000 \u0000 <p>Endometrial Cancer (EC), also referred to as <i>endometrial carcinoma</i>, stands as the most common category of carcinoma of the uterus in females, ranking as the sixth most common cancer worldwide among women. This study introduces a Machine Learning-Based Efficient Computer-Aided Diagnosis (ML-CAD) state-of-the-art model aimed at assisting healthcare professionals in investigating, estimating, and accurately classifying endometrial cancer through the meticulous analysis of H&amp;E-stained histopathological images. In the initial phase of image processing, meticulous steps are taken to eliminate noise from histopathological images. Subsequently, the application of the Vahadane stain normalization technique ensures stain normalization across histopathological images. The segmentation of stain-normalized histopathological images is executed with precision using the k-NN clustering approach, thereby enhancing the classification capabilities of the proposed ML-CAD model. Shallow features and deep features are extracted for analysis. The integration of shallow and deep features is achieved through a middle-level fusion strategy, and the SMOTE-Edited Nearest Neighbor (SMOTE-ENN) pre-processing technique is applied to address the sample imbalance issue. The identification of optimal features from a heterogeneous feature dataset is conducted meticulously using the novel Extra Tree-Whale Optimization Feature Selector (ET-WOFS). For the subsequent classification of endometrial cancer, a repertoire of classifiers, including k-NN, Random Forest, and Support Vector Machine (SVM), is harnessed. The classifier that incorporates ET-WOFS features demonstrates exceptional classification outcomes. Compared with existing models, the outcomes demonstrate that a k-NN classifier utilizing ET-WOFS features showcases remarkable outcomes with a classification accuracy of 95.78%, precision of 96.77%, an impressively low false positive rate (FPR) of 1.40%, and also a minimal false negative rate (FNR) of 4.21%. Further validation of the model's prediction and classification performance is evaluated in terms of the AUC-ROC value and other metrices. These presented assessments affirm the model's efficacy in providing accurate and reliable diagnostic support for endometrial cancer.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206683","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":"Multi-modal semantic feature alignment medical cross-modal hashing","authors":"Qinghai Liu ,&nbsp;Qianlin Wu ,&nbsp;Lun Tang ,&nbsp;Liming Xu ,&nbsp;Qianbin Chen","doi":"10.1016/j.engappai.2025.111158","DOIUrl":"10.1016/j.engappai.2025.111158","url":null,"abstract":"<div><div>Medical cross-modal retrieval facilitates semantic similarity searches across various modalities of medical cases. For example, it can quickly identify pertinent ultrasound images using ultrasound reports, or retrieve matching ultrasound reports using ultrasound images. Consequently, medical cross-modal hashing retrieval technology has become a significant and pioneering area of research. However, the field encounters considerable challenges. Firstly, there are significant semantic differences between different categories of medical images, coupled with subtle and often indistinguishable visual variations. Secondly, there is a significant semantic gap between different modalities of medical data. To address these challenges, this paper presents a novel multi-modal semantic feature alignment medical cross-modal hashing (MSACH) framework. The approach begins with a staged training strategy that effectively combines modality feature extraction with hash function learning, refining low-dimensional features enriched with essential semantic information. Subsequently, a feature extraction module is carefully constructed using a transformer encoder. Three pretraining tasks enable the encoder to derive semantic information from diverse modalities. Hash function learning is then conducted, utilising manifold, balance, and linear classification network constraints to enhance the discriminative power of the hash codes, thus significantly improving the retrieval accuracy across various medical data modalities. Comprehensive experimental results from three medical datasets confirm that MSACH surpasses several leading cross-modal hashing methods in terms of precision and effectiveness.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"157 ","pages":"Article 111158"},"PeriodicalIF":7.5,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212805","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":"IEEE Transactions on Cognitive and Developmental Systems Information for Authors","authors":"","doi":"10.1109/TCDS.2025.3553655","DOIUrl":"https://doi.org/10.1109/TCDS.2025.3553655","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 3","pages":"C4-C4"},"PeriodicalIF":5.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11024000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144213513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Reinforcement-Learning-Based Fuzzy Bipartite Consensus for Multiagent Systems: A Novel Scaling Off-Policy Learning Scheme","authors":"Jing Wang, Qing Yang, Jinde Cao, Leszek Rutkowski, Hao Shen","doi":"10.1109/tcyb.2025.3572104","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3572104","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"6 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144218606","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":"MFEA-RCIM: A Multifactorial Evolutionary Algorithm for Determining Robust and Influential Seeds From Competitive Networks Under Structural Failures","authors":"Shuai Wang, Yaochu Jin","doi":"10.1109/tcyb.2025.3571421","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3571421","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"25 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144218656","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":"O-RIS Assisted NOMA-VLC System: Integrated Positioning and Communications","authors":"Leake Z. Kahsay, Shimaa Naser, Hossien B. Eldeeb, Abdullah Alkhatib, Sami Muhaidat, Paschalis C. Sofotasios","doi":"10.1109/lwc.2025.3576717","DOIUrl":"https://doi.org/10.1109/lwc.2025.3576717","url":null,"abstract":"","PeriodicalId":13343,"journal":{"name":"IEEE Wireless Communications Letters","volume":"17 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144218806","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":"Noise-Coded Illumination for Forensic and Photometric Video Analysis","authors":"Peter Michael, Zekun Hao, Serge Belongie, Abe Davis","doi":"10.1145/3742892","DOIUrl":"https://doi.org/10.1145/3742892","url":null,"abstract":"The proliferation of advanced tools for manipulating video has led to an arms race, pitting those who wish to sow disinformation against those who want to detect and expose it. Unfortunately, time favors the ill-intentioned in this race, with fake videos growing increasingly difficult to distinguish from real ones. At the root of this trend is a fundamental advantage held by those manipulating media: equal access to a distribution of what we consider authentic (i.e., “natural”) video. In this paper, we show how coding very subtle, noise-like modulations into the illumination of a scene can help combat this advantage by creating an information asymmetry that favors verification. Our approach effectively adds a temporal watermark to any video recorded under coded illumination. However, rather than encoding a specific message, this watermark encodes an image of the unmanipulated scene as it would appear lit only by the coded illumination. We show that even when an adversary knows that our technique is being used, creating a plausible coded fake video amounts to solving a second, more difficult version of the original adversarial content creation problem at an information disadvantage. This is a promising avenue for protecting high-stakes settings like public events and interviews, where the content on display is a likely target for manipulation, and while the illumination can be controlled, the cameras capturing video cannot.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"456 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219176","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":"Dynamic Autoregressive Tensor Factorization for Pattern Discovery of Spatiotemporal Systems","authors":"Xinyu Chen, Dingyi Zhuang, HanQin Cai, Shenhao Wang, Jinhua Zhao","doi":"10.1109/tpami.2025.3576719","DOIUrl":"https://doi.org/10.1109/tpami.2025.3576719","url":null,"abstract":"","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"9 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219237","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":"Point-KAN: Leveraging Trustworthy AI for Reliable 3D Point Cloud Completion With Kolmogorov Arnold Networks for 6G-IoT Applications","authors":"Arun Kumar Sangaiah, Jayakrishnan Anandakrishnan, Sujith Kumar, Gui-Bin Bian, Salman A. AlQahtani, Dirk Draheim","doi":"10.1109/jiot.2025.3576434","DOIUrl":"https://doi.org/10.1109/jiot.2025.3576434","url":null,"abstract":"","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"36 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144218630","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}