Precision Measurement and Feature Selection in Medical Diagnostics using Hybrid Genetic Algorithm and Support Vector Machine by Gowri Subadra K, Sathish Babu P

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An application of natural language processing for hypoglycemic event identification in patients with diabetes mellitus by J.E. Camacho-Cogollo, Cristhian Felipe Patiño Zambrano, Christian Lochmuller, Claudia C. Colmenares-Mejia, Nicolas Rozo, Mario A. Isaza-Ruget, Paul Rodriguez, Andrés García

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Efficient Management of Safety Documents Using Text-Based Analytics to Extract Safety Attributes From Construction Accident Reports by Vedat Togan, Fatemeh Mostofi, Onur Behzat Tokdemir, Fethi Kadioglu

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Advancing drug-drug interactions research: integrating AI-powered prediction, vulnerable populations, and regulatory insights by Wenzhun Huang, Wenzhun Huang, Xiao Wang, Xiao Wang, Yunhao Chen, Yunhao Chen, Changqing Yu, Shanwen Zhang, Shanwen Zhang

“…Innovative techniques like graph neural networks (GNNs), natural language processing, and knowledge graph modeling are being increasingly utilized in clinical decision support systems (CDSS) to improve the detection, interpretation, and prevention of DDIs across various patient demographics. …”
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Computer Vision Meets Generative Models in Agriculture: Technological Advances, Challenges and Opportunities by Xirun Min, Yuwen Ye, Shuming Xiong, Xiao Chen

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Evaluation of large language models in generating pulmonary nodule follow-up recommendations by Junzhe Wen, Wanyue Huang, Huzheng Yan, Jie Sun, Mengshi Dong, Chao Li, Jie Qin

“…Results: On 1009 reports from 996 patients (median age, 50.0 years, IQR, 39.0–60.0 years; 511 male patients), ERNIE-4.0-Turbo-8K and GPT-4o-mini demonstrated comparable performance in both accuracy of follow-up recommendations (94.6 % vs 92.8 %, P = 0.07) and harmfulness rates (2.9 % vs 3.5 %, P = 0.48). In nodules classification, ERNIE-4.0-Turbo-8K and GPT-4o-mini performed similarly with accuracy rates of 99.8 % vs 99.9 % sensitivity of 96.9 % vs 100.0 %, specificity of 99.9 % vs 99.9 %, positive predictive value of 96.9 % vs 96.9 %, negative predictive value of 100.0 % vs 99.9 %, f1-score of 96.9 % vs 98.4 %, respectively. …”
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Static Analysis-based Detection of Android Malware using Machine Learning Algorithms by Omar Emad Saied, Karam Hatim Thanoon

“…The proposed method utilizes three classification algorithms: Support Vector Machine (SVM), Random Forest, and Decision Tree. …”
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Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

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Probability-Based Early Warning for Seasonal Influenza in China: Model Development Study by Jinzhao Cui, Ting Zhang, Yifeng Shen, Xiaoli Wang, Liuyang Yang, Xuefeng Huang, Qiang Huang, Yu Yang, Weizhong Yang, Zhongjie Li

“…Traditional early warning models rely on binary (0/1) classification methods, which issue alerts only when predefined thresholds are crossed. …”
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Time-Series Large Language Models: A Systematic Review of State-of-the-Art by Shamsu Abdullahi, Kamaluddeen Usman Danyaro, Abubakar Zakari, Izzatdin Abdul Aziz, Noor Amila Wan Abdullah Zawawi, Shamsuddeen Adamu

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An interpretable deep learning framework for medical diagnosis using spectrogram analysis by Shagufta Henna, Juan Miguel Lopez Alcaraz, Upaka Rathnayake, Mohamed Amjath

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Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome by Pedro Pons-Suñer, François Signol, Noemi Alvarez, Claudia Sargas, Sara Dorado, Jose Vicente Gil Ortí, Juan A. Delgado Sanchis, Marta Llop, Laura Arnal, Rafael Llobet, Juan-Carlos Perez-Cortes, Rosa Ayala, Eva Barragán

“…Feature selection based on an enhanced version of SEQENS was conducted for each time point, followed by the comparison of four classifiers (XGBoost, Multi-Layer Perceptron, Logistic Regression and Decision Tree) to assess the impact of feature selection on model performance. …”
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Morphological Analysis and Subtype Detection of Acute Myeloid Leukemia in High-Resolution Blood Smears Using ConvNeXT by Mubarak Taiwo Mustapha, Dilber Uzun Ozsahin

“…Various models, including ResNet50 and Vision Transformers, were benchmarked for comparative performance analysis; (3) Results: ConvNeXt outperformed ResNet50, achieving a classification accuracy of 95% compared to 91% for ResNet50 and 81% for transformer-based models (Vision Transformers). …”
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Enhancing Performance of Credit Card Model by Utilizing LSTM Networks and XGBoost Algorithms by Kianeh Kandi, Antonio García-Dopico

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A fusocelular skin dataset with whole slide images for deep learning models by Rocío del Amor, Miguel López-Pérez, Pablo Meseguer, Sandra Morales, Liria Terradez, Jose Aneiros-Fernandez, Javier Mateos, Rafael Molina, Valery Naranjo

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Molecular and immune landscape of melanoma: a risk stratification model for precision oncology by Miao Sun, Junliang Wu

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Lung Cancer Management: Revolutionizing Patient Outcomes Through Machine Learning and Artificial Intelligence by Taghi Riahi, Bahareh Shateri‐Amiri, Amirhossein Hajialiasgary Najafabadi, Sina Garazhian, Hanieh Radkhah, Diar Zooravar, Sahar Mansouri, Roya Aghazadeh, Mohammadreza Bordbar, Shirin Raiszadeh

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Optimizing credit card fraud detection with random forests and SMOTE by P. Sundaravadivel, R. Augustian Isaac, D. Elangovan, D. KrishnaRaj, V. V. Lokesh Rahul, R. Raja

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Development and validation of a nomogram model for prolonged length of stay in spinal fusion patients: a retrospective analysis by Linghong Wu, Xiaozhong Peng, Yao Lu, Cuiping Fu, Liujun She, Guangwei Zhu, Xianglong Zhuo, Wei Hu, Xiangtao Xie

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Dairy DigiD: a keypoint-based deep learning system for classifying dairy cattle by physiological and reproductive status by Shubhangi Mahato, Hanqing Bi, Suresh Neethirajan, Suresh Neethirajan

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