Graph-Based Radiomics Feature Extraction From 2D Retina Images by Ofelio Jorreia, Nuno Goncalves, Rui Cortesao

“…Medical image analysis offers valuable visual support for clinical decision-making, yet the incorporation of quantitative data is essential for deeper diagnostic insight. …”
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A multimodal approach for enhanced disease management in cauliflower crops: integration of spectral sensors, machine learning models and targeted spraying technology by Rohit ANAND, Roaf Ahmad PARRAY, Indra MANI, Tapan Kumar KHURA, Harilal KUSHWAHA, Brij Bihari SHARMA, Susheel SARKAR, Samarth GODARA, Shideh MOJERLOU, Hasan MIRZAKHANINAFCHI

“…For non-destructive disease assessment, a spectral sensor was used to collect spectral information from diseased and healthy cauliflower parts. The spectral data sets were analyzed using decision tree and support vector machine (SVM) algorithms to identify the most accurate model for distinguishing diseased and healthy plants. …”
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Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios by Wacław Kuś, Waldemar Mucha, Iyasu Tafese Jiregna

“…Such applications rely on sensor data to track changes in the structure. Monitoring accuracy relies on proper sensor placement. …”
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Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection by Ruo-Fei Xu, Zhen-Jing Liu, Shunan Ouyang, Qin Dong, Wen-Jing Yan, Dong-Wu Xu

“…Model performance was systematically evaluated through discrimination (ROC analysis), calibration (Brier score), and clinical utility analyses (decision curve analysis), with additional validation using random forest and support vector machine algorithms across independent samples. Results The resulting stratified screening system consists of an initial four-item rapid screening layer (encompassing emotional, cognitive, and interpersonal dimensions) for detecting probable depression (AUC = 0.982, sensitivity = 0.945, specificity = 0.926), followed by an enhanced assessment layer with five additional items. …”
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RF-Based Sensing and AI Decision Support for Stroke Patient Monitoring: A Digital Twin Approach by Sagheer Khan, Usman Anwar, Aftab Khan, Tughrul Arslan

“…The statistical and autonomous (AutoEncoders (AE) and Stacked AutoEncoders (SAE) with structure 32-16-32, 64-32-16-32-64, and 128-64-32-16-32-64-128) feature data is enlarged through Gaussian noise feature data augmentation. …”
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Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (<i>Annona squamosa</i> L.) by Xiangtai Jiang, Lutao Gao, Xingang Xu, Wenbiao Wu, Guijun Yang, Yang Meng, Haikuan Feng, Yafeng Li, Hanyu Xue, Tianen Chen

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Identification of atrial fibrillation using heart rate variability: a meta-analysis by Ziwei Yin, Changxin Liu, Chenggong Xie, Zixing Nie, Jiaming Wei, Wen Zhang, Hao Liang

“…Subgroup analyses revealed that both deep learning algorithms (sensitivity = 0.95, specificity = 0.98, AUC = 0.99) and multi-database studies (sensitivity = 0.96, specificity = 0.97, AUC = 0.99) demonstrated enhanced accuracy in AF identification compared to other approaches.ConclusionMachine learning can effectively identify AF with HRV in ECG, especially in diagnosis and detection, with deep learning algorithms and multiple-databases outperforming other diagnostic methods.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, PROSPERO (CRD42025634406).…”
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Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model. by Britta Trautwein, Meinrad Beer, Manfred Blobner, Bettina Jungwirth, Simone Maria Kagerbauer, Michael Götz

“…During the postoperative course, patients will be examined in a structured manner on postoperative days 1,3 and 7 to detect POPC. The endpoints determined in this way, together with the clinical and imaging data collected, are then used to train a machine learning model based on neural networks and ensemble methods to predict POPC in the early postoperative phase.…”
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Integrating artificial intelligence in healthcare: applications, challenges, and future directions by Peng Lean Chong, Vikneswaran Vaigeshwari, Basir Khan Mohammed Reyasudin, binti Ros Azamin Noor Hidayah, Purnshatman Tatchanaamoorti, Jian Ai Yeow, Feng Yuan Kong

“…AI technologies such as machine learning and deep learning have enhanced diagnostic accuracy, improved data management, and facilitated personalized treatment strategies. …”
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TF-LIME : Interpretation Method for Time-Series Models Based on Time–Frequency Features by Jiazhan Wang, Ruifeng Zhang, Qiang Li

“…With the widespread application of machine learning techniques in time series analysis, the interpretability of models trained on time series data has attracted increasing attention. …”
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Clustering and classification of early knee osteoarthritis using machine-learning analysis of step-up and down test kinematics in recreational table tennis players by Ui-jae Hwang, Kyu Sung Chung, Sung-min Ha

“…While conventional imaging methods often fail to detect early changes and require specialized expertise for interpretation, this study aimed to investigate the use of frontal plane kinematic data during step-up (SU) and step-down (SD) tests to classify and predict early osteoarthritis (EOA) using machine-learning techniques. …”
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A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference by Sadhana Selvakumar, B. Senthilkumar

“…Abstract Medical image analysis using deep learning algorithms has become a basis of modern healthcare, enabling early detection, diagnosis, treatment planning, and disease monitoring. …”
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Balancing public safety and civil rights: Successes and challenges of AI-based video surveillance systems by S.S. Vaddiparti, F. Babaiev

“…The research examined the functional capabilities of intelligent video systems based on machine learning and deep learning. It revealed that although modern AI video analytics systems are highly effective in enhancing security (e.g., threat detection and behavioural analysis), they also generate significant ethical and legal risks – particularly with respect to privacy violations and algorithmic discrimination. …”
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A 5G network based conceptual framework for real-time malaria parasite detection from thick and thin blood smear slides using modified YOLOv5 model by Swati Lipsa, Ranjan Kumar Dash, Korhan Cengiz, Nikola Ivković, Adnan Akhunzada

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Enhancing IoT Security in 5G Networks by Reem Alzhrani, Mohammed Alliheedi

“…We compared the results of these algorithms with three machine learning methods: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Stochastic Gradient Descent (SGD). …”
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Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning by Shuxian Pan, Zibing Wang

“…Predictive models were constructed using three machine learning algorithms to analyze and statistically evaluate clinical characteristics, including immune cell data. …”
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The value of triglyceride–glucose index–related indices in evaluating migraine: perspectives from multi–centre cross–sectional studies and machine learning models by Zixuan Yan, Lincheng Duan, Hong Yin, Muchen Wang, Jingwen Li, Chenghua Li, Xiao Wang, Dingjun Cai, Fanrong Liang, Wenchuan Qi

“…Weighted logistic regression analysis, subgroup analysis, smooth curve fitting and threshold effect analysis were used to ascertain the intricate relationships among triglyceride glucose–body mass index (TyG–BMI), triglyceride glucose–waist circumference (TyG–WC), triglyceride glucose–waist height ratio (TyG–WHtR) and migraine. Boruta’s algorithm and nine machine learning models were applied. …”
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Quantification of greenhouse gas emissions from livestock using remote sensing &amp; artificial intelligence by Evet Naturinda, Fortunate Kemigyisha, Anthony Gidudu, Isa Kabenge, Emmanuel Omia, Jackline Aboth

“…This research developed a remote sensing and Artificial Intelligence (AI) based approach to quantify GHG emissions from cattle in the Kisombwa Ranching Scheme in Mubende District, central Uganda.We trained a deep learning algorithm, You Only Look Once (YOLO) v4, to detect cattle from the Unmanned Aerial Vehicle (UAV) images of the study area and applied the Simple Online Real-time Tracker (SORT) algorithm for automated counting. …”
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Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma by Yi Li, Rui Zeng, Rui Zeng, Yuhua Huang, Yumin Zhuo, Jun Huang

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Classifying Sunn Pest Damaged and Healthy Wheat Grains Across Different Species with YOLOV8 and Vision Transformers by Melike Çolak, Özgü Özkan, Ali Berkol, Nergis Pervan Akman, Murat Ardıç, Okan Sezer, Nazife Gözde Ayter Arpacıoğlu, Zekiye Budak Başçiftçi, Murat Olgun

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