Application of machine learning algorithms in economic analysis by Mohammad Sadegh SALEM

“…This article explores the application of machine learning algorithms in economic sciences, focusing on data analysis, forecasting, and risk management. …”
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Development of an Intelligent Tablet Press Machine for the In-Line Detection of Defective Tablets Using Machine Learning and Deep Learning Models by Sun Ho Kim, Su Hyeon Han

“…<b>Objectives:</b> This study aims to develop a tablet press machine (TPM) integrated with machine learning (ML) and deep learning (DL) models for in-line detection of defective tablets as a Process Analytical Technology (PAT) tool. …”
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Predictive Analytics Leveraging a Machine Learning Approach to Identify Students’ Reasons for Dropping out of University by Asmaa El Mahmoudi, Nour El Houda Chaoui, Habiba Chaoui

“…This paper proposes a framework that predicts student dropout rates using machine learning techniques, based on data collected from various sources. …”
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Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging by Vijaya Bhaskar Sadu, Sathvik Bagam, Mohd Naved, Siva Krishna Reddy Andluru, Kamalakar Ramineni, Meshal Ghalib Alharbi, Sudhakar Sengan, Rahmaan Khadhar Moideen

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Machine Learning Approaches for Predicting Employee Turnover: A Systematic Review by Hojat Talebi, Amid Khatibi Bardsiri, Vahid Khatibi Bardsiri

“…This paper presents a systematic review of 58 studies focused on applying machine learning (ML) algorithms to predict employee turnover. …”
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Early Detection of Dementia in Populations With Type 2 Diabetes: Predictive Analytics Using Machine Learning Approach by Phan Thanh Phuc, Phung-Anh Nguyen, Nam Nhat Nguyen, Min-Huei Hsu, Nguyen Quoc Khanh Le, Quoc-Viet Tran, Chih-Wei Huang, Hsuan-Chia Yang, Cheng-Yu Chen, Thi Anh Hoa Le, Minh Khoi Le, Hoang Bac Nguyen, Christine Y Lu, Jason C Hsu

“…This study applied 8 machine learning algorithms to develop prediction models, including logistic regression, linear discriminant analysis, gradient boosting machine, light gradient boosting machine, AdaBoost, random forest, extreme gradient boosting, and artificial neural network (ANN). …”
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Data-Driven Insights into Concrete Flow and Strength: Advancing Smart Material Design Using Machine Learning Strategies by Muwaffaq Alqurashi

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Support Vector and Linear Regression Machine Learning Model on Amperometric Signals to Predict Glucose Concentration and Hematocrit Volume by Kirti Sharma, Pawan K. Tiwari, Sanjay Kumar Sinha

“…The emergence of advanced machine learning tools and techniques, subsequent to a substantial evolution in data mining strategies, has bestowed the capacity to glean insights and discern concealed patterns from vast datasets, thus enabling extensive analytical pursuits. …”
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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. …”
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Defect Detection and Correction in OpenMP: A Static Analysis and Machine Learning-Based Solution by Norah A. Al-Johany, Fathy E. Eassa, Sanaa A. Sharaf, Eynas H. Balkhair, Sara M. Assiri

“…A rule-based correction engine is employed to repair detected defects through minimally invasive code transformations, such as inserting critical sections, correcting directive placement, and adjusting data-sharing clauses. To enhance predictive accuracy, the tool incorporates machine learning classifiers&#x2014;Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), and Linear Support Vector Machine (LSVM)&#x2014;trained on various feature combinations, including Abstract Features (AF), Halstead Features (HF), and Semantic Features (SF). …”
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Forecasting Cancer Incidence in Canada by Age, Sex, and Region Until 2026 Using Machine Learning Techniques by Ehsan Kaviani, Kalpdrum Passi

Subjects: “…machine learning…”
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Comparative Analysis of Machine Learning and Deep Learning Models for Individual Tree Structure Segmentation Using Terrestrial LiDAR Point Cloud Data by Sangjin Lee, Woodam Sim, Yongkyu Lee, Jeongmook Park, Jintaek Kang, Jungsoo Lee

“…This study aims to segment individual tree structures (stem, crown, and ground) from terrestrial LiDAR-derived point cloud data (PCD) and to compare the segmentation accuracy between two models: XGBoost (machine learning) and PointNet++ (deep learning). …”
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Machine Learning untuk Prediksi Kegagalan Mesin dalam Predictive Maintenance System by Nisa'ul Hafidhoh, Ardian Prima Atmaja, Gus Nanang Syaifuddiin, Ikhwan Baidlowi Sumafta, Salva Mahardhika Pratama, Hafsah Nur Khasanah

Subjects: “…crisp-dm, exploratory data analysis, kegagalan mesin, machine learning, predictive maintenance system…”
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A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analytics by Nitin Rai, Harsh Pathak, Maria Villamil Mahecha, Dennis R. Buckmaster, Yanbo Huang, Paul Overby, Xin Sun

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Unbiased machine learning-assisted approach for conditional discretization of human performances by Thepparit Banditwattanawong, Masawee Masdisornchote

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A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques by Cong Peng, Cheng Gong, Xiaoya Zhang, Duxian Liu

“…This study sought to create a robust, interpretable machine learning-based model that predicts 1-, 3-, and 5-year survival in patients with extremely aggressive prostate cancer. …”
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Enhanced Forecasting of Equity Fund Returns Using Machine Learning by Fabiano Fernandes Bargos, Estaner Claro Romão

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Quantum algorithms and complexity in healthcare applications: a systematic review with machine learning-optimized analysis by Agostino Marengo, Vito Santamato

“…This approach revealed two primary research directions: (1) quantum computing for artificial intelligence in healthcare, and (2) quantum computing for healthcare data security. We highlight the theoretical advances underlying these domains, from novel quantum machine learning algorithms for biomedical data to quantum cryptographic protocols for securing medical information. …”
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Acoustic response discrimination of phulae pineapple maturity and defects using factor analysis of mixed data and machine learning algorithms by Sujitra Arwatchananukul, Saowapa Chaiwong, Nattapol Aunsri, Sila Kittiwachana, Kietsuda Luengwilai, Tatiya Trongsatitkul, Pramod Mahajan, Jose Blasco, Rattapon Saengrayap

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Neuroimaging and machine learning in eating disorders: a systematic review by Francesco Monaco, Annarita Vignapiano, Benedetta Di Gruttola, Stefania Landi, Ernesta Panarello, Raffaele Malvone, Stefania Palermo, Alessandra Marenna, Enrico Collantoni, Giovanna Celia, Valeria Di Stefano, Paolo Meneguzzo, Martina D’Angelo, Giulio Corrivetti, Luca Steardo

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