OncoTrace‐TOO: Interpretable Machine Learning Framework for Cancer Tissue‐of‐Origin Identification Using Transcriptomic Signatures by Yang Hao, Haochun Huang, Daiyun Huang, Jianwen Ruan, Xin Liu, Jianquan Zhang

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Single-cell multi-omics-based immune temporal network resolution in sepsis: unravelling molecular mechanisms and precise therapeutic targets by Han Liu, Qun Liang

“…Dynamical simulations identified two intervention windows—0–18 h (selective MyD88–NF-κB blockade) and 36–48 h (PD-1/TIM-3 dual inhibition)—forecasting 2.1-fold and 1.6-fold survival gains, respectively, in pre-clinical models.ConclusionIn this study, an “immune clock” model of sepsis was constructed based on single-cell multi-omics data, which accurately depicted three key decision nodes, namely, monocyte-macrophage differentiation, initiation of T-cell depletion and irreversible immune suppression, and identified the corresponding molecular targets (e.g., IRF8, TOX). …”
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The use of pretrained neural networks for solving the problem of reverse searching of X-ray images of prohibited items and substances by A. K. Volkov, L. V. Mironova, S. E. Potapova

“…In order to apply this model to extract image feature vectors, the last classification layer was preliminarily removed. …”
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A Unified Deep Learning Ensemble Framework for Voice-Based Parkinson’s Disease Detection and Motor Severity Prediction by Madjda Khedimi, Tao Zhang, Chaima Dehmani, Xin Zhao, Yanzhang Geng

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Enhancing Security in Industrial IoT Networks: Machine Learning Solutions for Feature Selection and Reduction by Ahmad Houkan, Ashwin Kumar Sahoo, Sarada Prasad Gochhayat, Prabodh Kumar Sahoo, Haipeng Liu, Syed Ghufran Khalid, Prince Jain

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Nomogram for predicting the efficacy of high-intensity focused ultrasound ablation for uterine fibroids based on oxytocin experimentation and ultrasonographic features: a retrospec... by Sheng Chen, Danling Zhang, Guisheng Ding, Mengqi Chen, Songsong Wu, Jianzhong Zou

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Integrating artificial intelligence for sustainable waste management: Insights from machine learning and deep learning by Son V.T. Dao, Tuan M. Le, Hieu M. Tran, Hung V. Pham, Minh T. Vu, Tuan Chu

“…Furthermore, the MobileNetV3 DL model is employed for feature extraction. …”
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Schema Understandability: A Comprehensive Empirical Study of Requirements Metrics by Tanu Singh, Vinod Patidar, Manu Singh, Álvaro Rocha

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Object prediction and detection of ground-based weapon with an improved YOLO11 approach by Hanyul Ryu, Mingyu Park, Dae-Yeol Kim

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Additive value of computed tomography severity scores to predict lengths of stay in hospital and ICU for COVID-19 patients: a machine learning study by Seyed Salman Zakariaee, Mikaeil Molazadeh, Hossein Salmanipour, Negar Naderi

“…Four well-known ML classification models including kNN, MLP, SVM, and C4.5 decision tree algorithms were used to predict hospital and ICU LOSs of COVID-19 patients. …”
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Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning by Muhammad Asim Saleem, Ashir Javeed, Watit Benjapolakul, Wattanasak Srisiri, Surachai Chaitusaney, Pasu Kaewplung

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Advancing Ovarian Cancer Diagnosis Through Deep Learning and eXplainable AI: A Multiclassification Approach by Meera Radhakrishnan, Niranjana Sampathila, H. Muralikrishna, K. S. Swathi

“…In the work, we have used and explored various DL models such as MobileNetV2, VGG19, ResNet18, ResNeXt, Xception, EfficientNet, and InceptionV3 to perform the classification task. …”
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Study of methods for extracting contours of objects on raster images of amber samples by Yu.O. Podchashynskyi, A.V. Ryzhuk

“…The use of these operators in TVS provides the most complete and reliable information for building a three-dimensional model, classifying and evaluating the quality of amber samples. …”
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Comprehensive Review and Future Research Directions on ICT Standardisation by Mohammed Najah Mahdi, Ray Walshe, Sharon Farrell, Harshvardhan J. Pandit

“…These three databases presented 216 articles that were divided into five categories: standard-related review and survey studies, information management across hardware and software standards, energy management standards, machine learning model classification performance, privacy-aware software system standards, and health information and communications technology standards. …”
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Usefulness of Noncontrast MRI-Based Radiomics Combined Clinic Biomarkers in Stratification of Liver Fibrosis by Ru Zhao, Hong Zhao, Ya-Qiong Ge, Fang-Fang Zhou, Long-Sheng Wang, Hong-Zhen Yu, Xi-Jun Gong

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Assessment of Equipment Operation State with Improved Random Forest by Na Yang, She Liu, Jie Liu, Changjie Li

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Blockchain arbitration: roadmap to recognition and enforcement of arbitral award by Danrivanto Budhijanto, Prita Amalia, Naufal Ahmad Shiddiq

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Advancing fishery dependent and independent habitat assessments using automated image analysis: A fisheries management agency case study. by Scott N Evans, Bronson Philippa, Carlo Mattone, Nick Konzewitsch, Renae K Hovey, Marcus Sheaves, Gary A Kendrick, Lynda M Bellchambers

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An FPGA Prototype for Parkinson’s Disease Detection Using Machine Learning on Voice Signal by Mujeev Khan, Abdul Moiz, Gani Nawaz Khan, Mohd Wajid, Mohammed Usman, Jabir Ali

“…To enhance classification performance and reduce computational complexity, we evaluate three feature selection algorithms &#x2014; Chi-squared (<inline-formula> <tex-math notation="LaTeX">$\chi ^{2}$ </tex-math></inline-formula>), Minimum Redundancy Maximum Relevance (mRMR), and Analysis of Variance (ANOVA) &#x2014; and adopt an incremental feature selection approach, where each feature set increment is assessed across five classifiers: K-Nearest Neighbors (KNN), Decision Tree (DT), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM). …”
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Attention-enhanced hybrid CNN–LSTM network with self-adaptive CBAM for COVID-19 diagnosis by Fatin Nabilah Shaari, Aimi Salihah Abdul Nasir, Wan Azani Mustafa, Wan Aireene Wan Ahmed, Abdul Syafiq Abdull Sukor

“…Our comprehensive evaluation across multiple baseline models for three-class classification (normal, pneumonia, COVID-19) demonstrates that Dual-Attention CNN-LSTM surpasses state-of-the-art performance, achieving a remarkable weighted accuracy of 99.97 %, with precision, recall, specificity, F1-score, and MCC all exceeding 99.95 %. …”
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