A multi-tiered feature selection model for android malware detection based on Feature discrimination and Information Gain by Parnika Bhat, Kamlesh Dutta

“…This work presents the Optimal Static Feature Set (OSFS) and, Most Important Features (MIFs) discovered with each machine learning approach. …”
Get full text

Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study by Jiani Liu, Xin Zhang, Wei Li, Francis Manyori Bigambo, Dandan Wang, Xu Wang, Beibei Teng

“…Additionally, we evaluated the performance of the nine machine learning algorithms to determine the optimal model. …”
Get full text

Retrieval of crop traits using PROSAIL-based hybrid radiative transfer model and EnMAP hyperspectral data by Prachi Singh, Prashant K. Srivastava, Prakash Kumar Jha, Jochem Verrelst, Pashupati Nath Singh, Rajendra Prasad

“…The proposed methodology involves the integration and detailed analysis of Radiative Transfer Modelling (RTM) with an integrated approach of machine learning (ML) and Active Learning (AL) algorithms for the retrieval of the Leaf Chlorophyll Content (LCC), Carotenoids (Car) and Leaf Area index (LAI) of wheat cropland from the continuous three years of the dataset. …”
Get full text

Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost Algorithms by Liu　Yuxiao, Dong　Yanwu, Jiang　Zhouhua, Chen　Xi

Get full text

A hybrid machine learning algorithm approach to predictive maintenance tasks: A comparison with machine learning algorithms by Jorge Paredes, Danilo Chávez, Ramiro Isa-Jara, Diego Vargas

“…This data can provide valuable insights into the behavior of a specific machine, enabling optimization or the prediction of potential malfunctions. …”
Get full text

Flexible Job Shop Dynamic Scheduling and Fault Maintenance Personnel Cooperative Scheduling Optimization Based on the ACODDQN Algorithm by Jiansha Lu, Jiarui Zhang, Jun Cao, Xuesong Xu, Yiping Shao, Zhenbo Cheng

“…In order to address the impact of equipment fault diagnosis and repair delays on production schedule execution in the dynamic scheduling of flexible job shops, this paper proposes a multi-resource, multi-objective dynamic scheduling optimization model, which aims to minimize delay time and completion time. …”
Get full text

Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data by Yasuyuki Kawai, Koji Yamamoto, Keisuke Tsuruta, Keita Miyazaki, Hideki Asai, Hidetada Fukushima

“…Therefore, we developed a machine-learning model to accurately predict the actual values of vital signs at hospital arrival using limited patient characteristic data and prehospital vital signs. …”
Get full text

Development of Explainable Machine Learning Models to Identify Patients at Risk for 1-Year Mortality and New Distant Metastases Postendoprosthetic Reconstruction for Lower Extremit... by Jiawen Deng, BHSc, Myron Moskalyk, BHSc, MSc, Madhur Nayan, MD, PhD, Ahmed Aoude, MEng, MD, FRCSC, Michelle Ghert, MD, FRCSC, Sahir Bhatnagar, PhD, Anthony Bozzo, MSc, MD, FRCSC

“…This study aims to develop and internally validate explainable machine learning (ML) models to predict the 1-year risk of new distant metastases and mortality in these patients. …”
Get full text

Electrical and seismic refraction methods: Fundamental concepts, current trends, and emerging machine learning prospects by Adedibu Sunny Akingboye

“…These challenges highlight the need for  multidisciplinary strategies, including methodological innovations and integrative data frameworks. Recently, machine learning (ML) techniques have been increasingly applied to these geophysical methods, particularly joint ERT and SRT analyses, optimizing nonlinear inversion processes and improving the interpretation of complex subsurface conditions. …”
Get full text

A machine learning-based screening model for the early detection of prostate cancer developed using serum microRNA data from a mixed cohort of 8,741 participants by Cong Lai, Zhensheng Hu, Zhuohang Li, Zhikai Wu, Kuiqing Li, Lin Li, Hongze Liu, Juanyi Shi, Yi Zhou, Kewei Xu, Cheng Liu

“…The performance of models was assessed using the testing dataset, and the model with the optimal predictive power was chosen for further analysis. …”
Get full text

A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions by Raman Kumar, Raman Kumar, Sarvesh Garg, Rupinder Kaur, M. G. M. Johar, Sehijpal Singh, Sehijpal Singh, Soumya V. Menon, Pulkit Kumar, Pulkit Kumar, Ali Mohammed Hadi, Shams Abbass Hasson, Jasmina Lozanović

“…To systematically investigate this field, the literature is organized into five thematic categories such as “Heart Disease Detection and Diagnostics,” “Machine Learning Models and Algorithms for Healthcare,” “Feature Engineering and Optimization Techniques,” “Emerging Technologies in Healthcare,” and “Applications of AI Across Diseases and Conditions.” …”
Get full text

A Classification-Based Blood–Brain Barrier Model: A Comparative Approach by Ralph Saber, Sandy Rihana

“…<b>Results</b>: The results indicate that the GA method outperformed SFS, leading to a higher prediction accuracy (96.23%) when combined with a support vector machine (SVM) classifier. Furthermore, the GA approach, utilizing a fitness function based on classifier performance, consistently improved prediction accuracy across all tested models, whereas SFS showed lower effectiveness. …”
Get full text

The impact of soil physical properties on combine harvester efficiency: a composite model from correlation trends by Mohd. Saifur Rahman, Nafis Shahid Fahim, Bodruzzaman Khan, Md. Fahad Jubayer, Tariqul Islam, Md Altaf Hossain

Get full text

Evaluating Translation Quality: A Qualitative and Quantitative Assessment of Machine and LLM-Driven Arabic–English Translations by Tawffeek A. S. Mohammed

“…This study investigates translation quality between Arabic and English, comparing traditional rule-based machine translation systems, modern neural machine translation tools such as Google Translate, and large language models like ChatGPT. …”
Get full text

Immune-evasive beta cells in type 1 diabetes: innovations in genetic engineering, biomaterials, and computational modeling by Ismail Can Karaoglu, Doğukan Duymaz, Mudassir M. Rashid, Seda Kizilel

“…We mention that recent advances in machine learning and computational modeling also play a crucial role in optimizing therapeutic outcomes, predicting clinical responses, and facilitating personalized treatment approaches. …”
Get full text

Analyzing and Predicting the Agronomic Effectiveness of Fertilizers Derived from Food Waste Using Data-Driven Models by Ksawery Kuligowski, Quoc Ba Tran, Chinh Chien Nguyen, Piotr Kaczyński, Izabela Konkol, Lesław Świerczek, Adam Cenian, Xuan Cuong Nguyen

“…These findings highlight machine learning’s ability to analyze complex datasets, improve agricultural decision-making, and optimize food waste utilization.…”
Get full text

Opportunities of machine learning algorithms for education by Olga Ovtšarenko

“…This study explores the potential of machine learning algorithms to build and train models using log data from the "3D Modeling" e-course on the Moodle platform at TTK University of Applied Sciences, Tallinn, Estonia. …”
Get full text

Advancements and future outlook of Artificial Intelligence in energy and climate change modeling by Mobolaji Shobanke, Mehul Bhatt, Ekundayo Shittu

“…The process of conducting a systematic review of the contemporary literature highlights significant advances in optimization and predictive analytics within energy and climate change modeling systems driven by artificial intelligence and machine learning. …”
Get full text

Dashboard‑Driven Machine Learning Analytics and Conceptual LLM Simulations for IIoT Education in Smart Steel Manufacturing by Mehdi Imani, Ali Imanifard, Babak Majidi, Abdolah Shamisa

“…Through advanced analytical models such as machine learning (ML) and, conceptually, Large Language Models (LLMs), this study explores how Industrial Internet of Things (IIoT) applications can transform educational experiences in the context of smart steel production. …”
Get full text

A holistic research based on RSM and ANN for improving drilling outcomes in Al–Si–Cu–Mg (C355) alloy by Şenol Bayraktar, Cem Alparslan, Nurten Salihoğlu, Murat Sarıkaya

Get full text