Ultrasound combined with serological markers for predicting neonatal necrotizing enterocolitis: a machine learning approach by Yi Yang, Shoulan Zhou, Xiaomin Liu, Yanhong Zhang, Liping Lin, Chenhan Zheng, Xiaohong Zhong

“…Current diagnostic methods, relying on clinical signs and radiography, often lack sensitivity for early detection. This study aimed to develop and validate a machine learning (ML) model integrating ultrasound and serological markers to improve NEC prediction in neonates.MethodsThis retrospective, case-control study included 191 neonates (cases with Bell's stage ≥ II NEC and matched controls) admitted to a tertiary NICU. …”
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Comparative Analysis of Deep Learning Methods for Classification of Ablated Regions in Hyperspectral Images of Atrial Tissue by Yeva Gabrielyan, Arpi Hunanyan, Sona Bezirganyan, Lusine Davtyan, Ani Avetisyan, Narine Sarvazyan, Aram Butavyan, Varduhi Yeghiazaryan

“…The best eight algorithms are further compared in a subjective experiment. …”
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Representation Learning of Multi-Spectral Earth Observation Time Series and Evaluation for Crop Type Classification by Andrea González-Ramírez, Clement Atzberger, Deni Torres-Roman, Josué López

“…To address these issues, efforts have been made to implement frameworks that generate meaningful representations from the irregularly sampled data streams and alleviate the deficiencies of the data sources and supervised algorithms. …”
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A Robust Behavioral Biometrics Framework for Smartphone Authentication via Hybrid Machine Learning and TOPSIS by Moceheb Lazam Shuwandy, Qutaiba Alasad, Maytham M. Hammood, Ayad A. Yass, Salwa Khalid Abdulateef, Rawan A. Alsharida, Sahar Lazim Qaddoori, Saadi Hamad Thalij, Maath Frman, Abdulsalam Hamid Kutaibani, Noor S. Abd

“…The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methodology has also been incorporated to obtain the most affected and valuable features, which are then fed as input to three different Machine Learning (ML) algorithms: Random Forest (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (KNN). …”
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Integrating transcriptomics and hybrid machine learning enables high-accuracy diagnostic modeling for nasopharyngeal carcinoma by Hehe Wang, Junge Zhang, Peng Cheng, Lujie Yu, Chunlin Li, Yaowen Wang

“…This study aimed to develop robust machine learning (ML)-driven diagnostic models and identify key biomarkers through integrated analysis of multi-cohort transcriptomic data. …”
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Developing a comprehensive BACnet attack dataset: A step towards improved cybersecurity in building automation systemsKaggle by Seyed Amirhossein Moosavi, Mojtaba Asgari, Seyed Reza Kamel

“…One of the popular and evolving protocols used for communication between devices in smart buildings, especially HVAC systems, is the BACnet protocol. Machine learning algorithms and neural networks require datasets of normal traffic and real attacks to develop intrusion detection (IDS) and prevention (IPS) systems that can detect anomalies and prevent attacks. …”
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Machine Learning-Assisted NIR Spectroscopy for Dynamic Monitoring of Leaf Potassium in Korla Fragrant Pear by Mingyang Yu, Weifan Fan, Junkai Zeng, Yang Li, Lanfei Wang, Hao Wang, Feng Han, Jianping Bao

“…By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. “Near-infrared spectroscopy coupled with machine learning can enable accurate, non-destructive monitoring of potassium dynamics in Korla pear leaves, with prediction accuracy (R²) exceeding 0.86 under field conditions.” …”
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Hierarchical genetic structure in an evolving species complex: Insights from genome wide ddRAD data in Sebastes mentella. by Atal Saha, Matthew Kent, Lorenz Hauser, Daniel P Drinan, Einar E Nielsen, Jon-Ivar Westgaard, Sigbjørn Lien, Torild Johansen

“…We identified a SNP panel with only 21 loci to discriminate populations in mixed samples based on a machine-learning algorithm. This first SNP based investigation clarifies the population structure of S. mentella, and provides novel and high-resolution genomic tools for future investigations. …”
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AlgAlert: A two-level approach for algae bloom prediction using deep learning by Areej Alsini, Amina Saeed, Dawood Amin

“…With the increasing availability of real-time water quality, meteorological and tidal sensor data, there is growing potential to harness this information through data-driven approaches such as machine learning to support aquatic systems management. …”
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A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization by Md. Saddam Hossain, Md. Parvez Khandocar, Farzana Akter Riti, Md. Yeakub Ali, Prithbey Raj Dey, S M Jahurul Haque, Amira Metouekel, Atrsaw Asrat Mengistie, Mohammed Bourhia, Farid Khallouki, Khalid S. Almaary

“…Abstract With human guidance, computers now use machine learning (ML) in artificial intelligence (AI) to learn from data, detect trends, and make predictions. …”
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Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia by Ewunate Assaye Kassaw, Ewunate Assaye Kassaw, Ashenafi Kibret Sendekie, Ashenafi Kibret Sendekie, Bekele Mulat Enyew, Biruk Beletew Abate, Biruk Beletew Abate

“…Despite its significance, there is limited evidence regarding the use of machine learning (ML) algorithms to predict medication adherence within the Ethiopian population. …”
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Transforming Manufacturing Quality Management with Cognitive Twins: A Data-Driven, Predictive Approach to Real-Time Optimization of Quality by Asif Ullah, Muhammad Younas, Mohd Shahneel Saharudin

“…By utilizing the power of machine learning algorithms, the cognitive twin constantly monitors and then analyzes real-time data. …”
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The application of suitable sports games for junior high school students based on deep learning and artificial intelligence by Xueyan Ji, Shamsulariffin Bin Samsudin, Muhammad Zarif Bin Hassan, Noor Hamzani Farizan, Yubin Yuan, Wang Chen

“…They are obviously lower than those of other algorithms. ST-GCN action detection algorithm based on deep learning and artificial intelligence technology can significantly improve the accuracy of action recognition in junior middle school students’ sports activities, and provide an immediate and accurate feedback mechanism for physical education teaching. …”
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Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak

“…Third, XGBoost and Random Forest machine learning algorithms were employed to examine and validate the importance of the spatial lag component. …”
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Pengembangan Deep Learning untuk Sistem Deteksi Dini Komplikasi Kaki Diabetik Menggunakan Citra Termogram by Medycha Emhandyksa, Indah Soesanti, Rina Susilowati

“…The research aims to develop a deep convolutional neural network algorithm to produce an early detection system for diabetic foot complications with the lowest computational cost (least number of parameters) and maintain high detection capability (highest average value of evaluation parameters). …”
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Exploring Machine Learning Methods for Aflatoxin M1 Prediction in Jordanian Breast Milk Samples by Abdullah Aref, Eman Omar, Eman Alseidi, Nour Elhuda A. Alqudah, Sharaf Omar

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Detecting Temporal Trends in Straw Incorporation Using Sentinel-2 Imagery: A Mann-Kendall Test Approach in Household Mode by Jian Li, Weijian Zhang, Jia Du, Kaishan Song, Weilin Yu, Jie Qin, Zhengwei Liang, Kewen Shao, Kaizeng Zhuo, Yu Han, Cangming Zhang

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Methodology for Estimating the Cost of Construction Equipment Based on the Analysis of Important Characteristics Using Machine Learning Methods by Nataliya Boyko, Oleksii Lukash

“…This paper describes in detail the preliminary data processing. The main stages of preprocessing are presented in detail: detection and processing of missing data, removing anomalous data, coding of categorical data, and scaling. …”
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An online 11 kv distribution system insulator defect detection approach with modified YOLOv11 and mobileNetV3 by Arnav Bhagwat, Soham Dutta, Debdeep Saha, Maddikara Jaya Bharata Reddy

“…The intricate background, limited image dataset and small-scale object makes the problem of detection more complex. Owing to the exponential advancement in deep learning, deep learning-based insulator defect detection is gradually attaining a foothold in the research domain. …”
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Diagnosing prostate cancer in the PSA gray zone through machine learning and transrectal ultrasound video by Qin Wu, Chengyi Wu, Maoliang Zhang, Jie Yang, Junxiang Zhang, Yun Jin, Yanhong Du, Xingbo Sun, Liyuan Jin1, Kai Wang, Zhengbiao Hu, Xiaoyang Qi1, Jincao Yao, Zhengping Wang, Dong Xu

“…The selected features were employed to construct radiomics models based on four machine learning algorithms support vector machine (SVM), random forest (RF), adaptive boosting (ADB) and gradient boosting machine (GBM). …”
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