A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein by Bahareh Behkamal, Fatemeh Asgharian Rezae, Amin Mansoori, Rana Kolahi Ahari, Sobhan Mahmoudi Shamsabad, Mohammad Reza Esmaeilian, Gordon Ferns, Mohammad Reza Saberi, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…Additionally, SHAP analysis identified hs-CRP, BIL.D, ALT, and sex as the most influential predictors of MetS. These findings suggest that leveraging liver function biomarkers and hs-CRP within an automated ML pipeline can facilitate early, non-invasive detection of MetS, supporting clinical decision-making and risk stratification efforts in healthcare systems.…”
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Comparison of Deep Learning-Based Auto-Segmentation Results on Daily Kilovoltage, Megavoltage, and Cone Beam CT Images in Image-Guided Radiotherapy by Zhixing Wang PhD, Chengyu Shi PhD, Carson Wong BS, Seyi M Oderinde PhD, William T Watkins PhD, Kun Qing PhD, Bo Liu PhD, Terence M Williams MD, PhD, An Liu PhD, Chunhui Han PhD

“…Deep learning auto-segmentation models using a convolutional neural network algorithm were used to generate organs-at-risk contours. …”
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Land Surface Temperature Super-Resolution With a Scale-Invariance-Free Neural Approach: Application to MODIS by Romuald Ait-Bachir, Carlos Granero-Belinchon, Aurelie Michel, Julien Michel, Xavier Briottet, Lucas Drumetz

“…Due to the tradeoff between the temporal and spatial resolution of thermal spaceborne sensors, super-resolution methods have been developed to provide fine-scale Land Surface Temperature (LST) maps. Most of them are trained at low resolution but applied at fine resolution, and so they require a scale-invariance hypothesis that is not always adapted. …”
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AI in Medical Questionnaires: Innovations, Diagnosis, and Implications by Xuexing Luo, Yiyuan Li, Jing Xu, Zhong Zheng, Fangtian Ying, Guanghui Huang

“…Overall, 24 AI technologies were identified, covering traditional algorithms such as random forest, support vector machine, and k-nearest neighbor, as well as deep learning models such as convolutional neural networks, Bidirectional Encoder Representations From Transformers, and ChatGPT. …”
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POTA: A Pipelined Oblivious Transfer Acceleration Architecture for Secure Multi-Party Computation by Li Xiaolin, Yan Wei, Liu Hongwei, Zhang Yong, Hao Qinfen, Liu Yong, Sun Ninghui

“…In the POTA design, we develop efficient subsystems targeting the two most compute-intensive parts: the construction of puncturable pseudoran- dom function (PPRF), and large matrix-vector multiplications under the learning parity with noise (LPN) assumption within the silent OT protocol. …”
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A neural network approach for line detection in complex atomic emission spectra measured by high-resolution Fourier transform spectroscopy by Milan Ding, Sean Z J Lim, Xiaoran Yu, Christian P Clear, Juliet C Pickering

“…These transitions underpin most spectroscopic plasma diagnostics, yet their fundamental data remain incomplete and are in high demand in astronomy and fusion research. …”
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CD-STMamba: Toward Remote Sensing Image Change Detection With Spatio-Temporal Interaction Mamba Model by Shanwei Liu, Shuaipeng Wang, Wei Zhang, Tao Zhang, Mingming Xu, Muhammad Yasir, Shiqing Wei

“…Change detection (CD) is a critical Earth observation task. Convolutional neural network (CNN) and Transformer have demonstrated their superior performance in CD tasks. …”
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Prediction of Alzheimer’s Disease Based on Multi-Modal Domain Adaptation by Binbin Fu, Changsong Shen, Shuzu Liao, Fangxiang Wu, Bo Liao

“…However, the structure and semantics of different modal data are different, and the distribution between different datasets is prone to the problem of domain shift. Most of the existing methods start from the single-modal data and assume that different datasets meet the same distribution, but they fail to fully consider the complementary information between the multi-modal data and fail to effectively solve the problem of domain distribution difference. …”
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Tree Species Detection and Enhancing Semantic Segmentation Using Machine Learning Models with Integrated Multispectral Channels from PlanetScope and Digital Aerial Photogrammetry i... by Arun Gyawali, Mika Aalto, Tapio Ranta

“…For semantic segmentation, the CatBoost model with 20 bands outperformed other models, achieving 85% accuracy, 80% Kappa, and 81% MCC, with CHM, EVI, NIRPlanet, GreenPlanet, NDGI, GNDVI, and NDVI being the most influential variables. These results indicate that a simple boosting model like CatBoost can outperform more complex CNNs for semantic segmentation in young forests.…”
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Dual attention mechanisms with patch-level significance embedding for ischemic stroke classification in brain CT images by Mahesh Anil Inamdar, Anjan Gudigar, U. Raghavendra, Massimo Salvi, Nithin Raj, J. Pooja, Ajay Hegde, Girish R. Menon, U. Rajendra Acharya

“…Stroke is currently a major contributor to disability and mortality across the globe, with ischemic stroke being the most predominant subtype. Accurate and timely diagnosis is critical for effective treatment. …”
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Automatic Identification of Amharic Text Idiomatic Expressions Using a Deep Learning Approach by Habtamu Hunegnaw Limenih, Abebe Belay Adege, Abrham Yaregal Alene, Habtamu Tariku Demasu, Habtamu Molla Belachew

“…Natural Language Processing (NLP) is a tract of artificial intelligence and linguistics devoted to making computers understand the statements or words written in human languages. Amharic, the most widely spoken language in Ethiopia, uses a lot of idiomatic expressions and proverbs to emphasize the message of the text. …”
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Revolutionizing total hip arthroplasty: The role of artificial intelligence and machine learning by Umile Giuseppe Longo, Sergio De Salvatore, Alice Piccolomini, Nathan Samuel Ullman, Giuseppe Salvatore, Margaux D'Hooghe, Maristella Saccomanno, Kristian Samuelsson, Rocco Papalia, Ayoosh Pareek

“…The aim of this review is to analyze the most updated articles on AI/ML applications in THA as well as present the potential of these tools in optimizing patient care and THA outcomes. …”
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Expression Dynamics and Genetic Compensation of Cell Cycle Paralogues in <i>Saccharomyces cerevisiae</i> by Gabriele Schreiber, Facundo Rueda, Florian Renner, Asya Fatima Polat, Philipp Lorenz, Edda Klipp

“…Due to the duplication of the yeast genome during evolution, most of the cyclins are present as a pair of paralogues, which are considered to have similar functions and periods of expression. …”
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Universal conditional networks (UniCoN) for multi-age embryonic cartilage segmentation with sparsely annotated data by Nishchal Sapkota, Yejia Zhang, Zihao Zhao, Maria Jose Gomez, Yuhan Hsi, Jordan A. Wilson, Kazuhiko Kawasaki, Greg Holmes, Meng Wu, Ethylin Wang Jabs, Joan T. Richtsmeier, Susan M. Motch Perrine, Danny Z. Chen

“…While DL approaches have been proposed to automate cartilage segmentation, most such models have limited accuracy and generalizability, especially across data from different embryonic age groups. …”
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Explainable multi-view transformer framework with mutual learning for precision breast cancer pathology image classification by Haewon Byeon, Mahmood Alsaadi, Richa Vijay, Purshottam J. Assudani, Ashit Kumar Dutta, Monika Bansal, Pavitar Parkash Singh, Mukesh Soni, Mohammed Wasim Bhatt

“…Breast cancer remains the most prevalent cancer among women, where accurate and interpretable analysis of pathology images is vital for early diagnosis and personalized treatment planning. …”
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Deep Learning with Transfer Learning on Digital Breast Tomosynthesis: A Radiomics-Based Model for Predicting Breast Cancer Risk by Francesca Galati, Roberto Maroncelli, Chiara De Nardo, Lucia Testa, Gloria Barcaroli, Veronica Rizzo, Giuliana Moffa, Federica Pediconi

“…<b>Results</b>: The ResNet50 model outperformed DenseNet201 across most metrics. On the internal testing set, ResNet50 achieved a ROC–AUC of 63%, accuracy of 60%, sensitivity of 39%, and specificity of 75%. …”
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A Computer-Aided Approach to Canine Hip Dysplasia Assessment: Measuring Femoral Head–Acetabulum Distance with Deep Learning by Pedro Franco-Gonçalo, Pedro Leite, Sofia Alves-Pimenta, Bruno Colaço, Lio Gonçalves, Vítor Filipe, Fintan McEvoy, Manuel Ferreira, Mário Ginja

“…This study presents an AI-driven system for automated measurement of the femoral head center to dorsal acetabular edge (FHC/DAE) distance, a key metric in CHD evaluation. Unlike most AI models that directly classify CHD severity using convolutional neural networks, this system provides an interpretable, measurement-based output to support a more transparent evaluation. …”
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A deep Reinforcement learning-based robust Intrusion Detection System for securing IoMT Healthcare Networks by Jamshed Ali Shaikh, Chengliang Wang, Muhammad Wajeeh Us Sima, Muhammad Arshad, Muhammad Owais, Dina S. M. Hassan, Reem Alkanhel, Mohammed Saleh Ali Muthanna

“…The methodology begins with Enhanced Mutual Information Feature Selection (MIFS) to preprocess the CICIoMT2024 dataset, selecting the most relevant features while reducing noise and computational complexity. …”
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Deep Learning Methods for Inferring Industrial CO₂ Hotspots from Co-Emitted NO₂ Plumes by Erchang Sun, Shichao Wu, Xianhua Wang, Hanhan Ye, Hailiang Shi, Yuan An, Chao Li

“…The trained model performed well on the test set, with most samples achieving an identification accuracy above 80% and more than half exceeding 94%. …”
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Accurate modeling and simulation of the effect of bacterial growth on the pH of culture media using artificial intelligence approaches by Suleiman Ibrahim Mohammad, Hamza Abu Owida, Asokan Vasudevan, Suhas Ballal, Shaker Al-Hasnaawei, Subhashree Ray, Naveen Chandra Talniya, Aashna Sinha, Vatsal Jain, Ahmad Abumalek

“…Sensitivity analysis using Monte Carlo simulations revealed bacterial cell concentration as the most influential factor, followed by time, culture medium type, initial pH, and bacterial type. …”
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