Advancing Credit Rating Prediction: The Role of Machine Learning in Corporate Credit Rating Assessment by Nazário Augusto de Oliveira, Leonardo Fernando Cruz Basso

“…The study evaluated multiple ML models, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, Gradient Boosting (GB), and Neural Networks, using rigorous data pre-processing, feature selection, and validation techniques. …”
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Millets on the Global Stage: Exploring Export Opportunities for Asia by Harsh Saharan, Aditi Mathur, Anubhav Beniwal, I. P. Singh, Yashwant Singh Rathore

“…Challenges include processing inefficiencies and trade barriers. Findings highlight India’s role in boosting global millet trade and offer insights for other Asian countries to capitalize on rising demand. …”
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Intelligent Classification Method for Rail Defects in Magnetic Flux Leakage Testing Based on Feature Selection and Parameter Optimization by Kailun Ji, Ping Wang, Yinliang Jia

“…For real-world limited samples (100 sets), adaptive optimization achieved 80% accuracy while boosting minority class (“spalling”) F1-score by 0.25 with 50% false alarm reduction. …”
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Comprehensive Performance Assessment of Multi-Neural Ensemble Model for Mortality Prediction in ICU by M. Fathima Begum, Subhashini Narayan

“…In comparison to prior deep learning-driven mortality classification research, we designed a comprehensive structure that encompasses a novel feature pre-processing methods and stacking ensemble algorithm for classification.…”
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Optimizing Xylanase Production: Bridging Statistical Design and Machine Learning for Improved Protein Production by Merve Aslı Ergün, Başak Esin Köktürk-Güzel, Tuğba Keskin-Gündoğdu

“…Proteins are crucial for medicine, pharmaceuticals, food, and environmental applications since they are used in various fields such as synthesis of drugs, industrial enzyme production, biodegradable plastics, bioremediation processes, etc. Xylanase is an important and versatile enzyme with applications across various industries, including pulp and paper, biofuel production, food processing, textiles, laundry detergents, and animal feed. …”
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Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation by Nasser Alnazari, Omar Ibrahim Alanazi, Muath Owaidh Alosaimi, Ziyad Mohamed Alanazi, Ziyad Mohammed Alhajeri, Khaled Mohammed Alhussaini, Abdulkarim Mekhlif Alanazi, Ahmed Y. Azzam

“…Results The gradient boosting model demonstrated strong performance (AUC 0.837, 95% CI: 0.802–0.872) with accuracy of 0.796 ± 0.050 and sensitivity of 0.388 ± 0.129. …”
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Exploring cement Production's role in GDP using explainable AI and sustainability analysis in Nepal by Ramhari Poudyal, Biplov Paneru, Bishwash Paneru, Tilak Giri, Bibek Paneru, Tim Reynolds, Khem Narayan Poudyal, Mohan B. Dangi

“…Quarrying, raw material processing, and calcination are steps in cement production. …”
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Large Language Model Synergy for Ensemble Learning in Medical Question Answering: Design and Evaluation Study by Han Yang, Mingchen Li, Huixue Zhou, Yongkang Xiao, Qian Fang, Shuang Zhou, Rui Zhang

“… Abstract BackgroundLarge language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, including medical question-answering (QA). …”
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From Concept to Market: Ensemble Predictive Model for Research Project Crowdfunding Readiness by Andreea Cristina Ionica, Stanislav Cseminschi, Monica Leba

“…This study introduces an ensemble model that integrates random forest, gradient boosting, and logistic regression to predict the success of crowdfunding campaigns. …”
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A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes by Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada

“…Bayesian Search had the best computational efficiency, consistently requiring less processing time than the Grid and Random Search methods. …”
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Machine learning-driven predictive modeling of mechanical properties in diverse steels by Movaffaq Kateb, Sahar Safarian

“…Moreover, other alternative approaches, such as support vector machines, extreme gradient boosting machines, and artificial neural networks, were also evaluated to ensure that the predictions made by the RF model are as accurate as possible. …”
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Predicting diabetic peripheral neuropathy through advanced plantar pressure analysis: a machine learning approach by Mehewish Musheer Sheikh, Mamatha Balachandra, Narendra V. G., Arun G. Maiya

“…An automated image processing algorithm segmented plantar pressure images into forefoot and hindfoot regions for precise pressure distribution measurement. …”
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Towards safer steel operations with a multi model framework for accident prediction and risk assessment simulation by Shatrudhan Pandey, Abhishek Kumar Singh, Shreyanshu Parhi, Sanjay Kumar Jha

“…Abstract This research concentrates on an introduction of a multi-model approach integrating Bayesian Networks (BN), Machine Learning (ML) models, Natural Language Processing (NLP) with Sentiment Analysis, Agent-Based Modeling (ABM), and Survival Analysis to improve predictive modelling of accident causation in high-risk steel industries. …”
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Design of the Urban Lighting Control System Based on Optical Multisensor Technology and the GM Model by Weili Wu, Xiang Tang

“…Programmable logic controller (PLC) serves as the system’s central processing unit, with light intensity sensors and color sensor-detecting devices placed strategically throughout each city and linked directly to the controller. …”
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Separate Analysis of Informational Signs in Multi-Parametric Combined Patterns Recognition Systems by Zakhozhay O.I., Menyaylenko A.S., Lyfar V.A.

“…Well known solutions to this problem: data processing algorithms complication, boosting algorithms using and install more productive computing systems. …”
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Machine learning-based classification of geological structures from magnetic anomaly data: Case study of Northern Nigeria basement complex by Ema Abraham, Ayatu Usman, Ifunanya Amano

“…Through the integration of analytic signal processing with machine learning classifiers (Random Forest (RF) and Gradient Boosting (GB)), we analyze magnetic anomalies to predict subsurface geological features with a classification accuracy of 95.5%. …”
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Comparison of Two Machine Learning Models for Predicting Volumetric Errors From On-The-Fly R-Test Type Device Data and Virtual End Point Constraints by Min Zeng, J. R. R. Mayer, Miao Feng, Elie Bitar-Nehme, Xuan Truong Duong

“…Two ML models are trained and compared, Neural Network (NN) and eXtreme Gradient Boosting (XGBoost), to find the most suitable model and the required amount of training data to predict volumetric errors of a five-axis machine tool with wCBXfZY(S)t topology based on axis commands. …”
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Hybrid feature selection for real-time road surface classification on low-end hardware: A machine learning approach by Cong Ngo Van, Duc-Nghia Tran, Ton That Long, Nguyen Gia Minh Thao, Duc-Tan Tran

“…We evaluate the proposed method in three machine learning models, Random Forest, Gradient Boosting, and XGBoost, to classify road surface type into three classes: asphalt, dirt, and cobblestone. …”
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Advances in Optical Contrast Agents for Medical Imaging: Fluorescent Probes and Molecular Imaging by Divya Tripathi, Mayurakshi Hardaniya, Suchita Pande, Dipak Maity

“…Additionally, enhancing biocompatibility, boosting fluorescent probe signal-to-noise ratios, and utilizing cutting-edge imaging technologies like machine learning for better image processing should be the main goals of future research. …”
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Leveraging machine learning and open accessed remote sensing data for precise rainfall forecasting by Bambang Kun Cahyono, Muhammad Hidayatul Ummah, Ruli Andaru, Neil Andika, Adjie Pamungkas, Hepi Hapsari Handayani, Paramita Atmodiwirjo, Rory Nathan

“…Machine learning methods, including Support Vector Regression, Gradient Boosting Regression, Random Forest, and Deep Neural Networks, were applied. …”
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