Advancements and gaps in natural language processing and machine learning applications in healthcare: a comprehensive review of electronic medical records and medical imaging by Priyanka Khalate, Shilpa Gite, Shilpa Gite, Biswajeet Pradhan, Chang-Wook Lee

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Intelligent System for Student Performance Prediction Using Machine Learning by Mustafa S. Ibrahim Alsumaidaie, Ahmed Adil Nafea, Abdulrahman Abbas Mukhlif, Ruqaiya D. Jalal, Mohammed M AL-Ani

“… Accurately predicting student performance remains a significant challenge in the educational sector. Identifying students who need additional support early can significantly impact their academic outcomes. …”
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Development of Machine Learning Models for Sandface Pressure Prediction in Oil Well by Lorraine P. Oliveira, Raul M. Foronda, Alexandre V. Grillo, Brunno F. dos Santos

“…4, demonstrating that RTA data effectively supports BHP prediction and that DT models are well-suited for this application. …”
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Management of Business Development for Machine-Building Companies in the Frame of integrated Corporate Structures by T. N. Topoleva

“…The topicality of the study is determined by the problems of technical and process design renewal of the Russian industry, the lack of investment support of modernization programs in the production sector, and the development of new forms of public demand typical for the innovative economy. …”
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Perbandingan Metode Supervised Machine Learning untuk Prediksi Prevalensi Stunting di Provinsi Jawa Timur by M Syauqi Haris, Ahsanun Naseh Khudori, Wahyu Teja Kusuma

“…In addition, several methods in supervised machine learning are also compared, namely, linear regression, support vector regression, and random forest regression. …”
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A comparative analysis of variants of machine learning and time series models in predicting women’s participation in the labor force by Rasha Elstohy, Nevein Aneis, Eman Mounir Ali

“…Various machine learning (ML) algorithms, such as support vector machine (SVM), neural network, K-nearest neighbor (KNN), linear regression, random forest, and AdaBoost, in addition to popular time series algorithms, including autoregressive integrated moving average (ARIMA) and vector autoregressive (VAR) models, have been applied to an actual dataset from the public sector. …”
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A Two-Stage Feature Selection Approach for Fruit Recognition Using Camera Images With Various Machine Learning Classifiers by Tri Tran Minh Huynh, Tuan Minh Le, Long Ton That, Ly Van Tran, Son Vu Truong Dao

“…The final subset feature has been used for recognizing fruits using several machine learning classifiers, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Multilayer Perceptron (MLP). …”
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Image dataset of Taro Leaf Blight disease collected from the West African Sub-RegionMendeley Data by Chidiebere Nwaneto, Chika Yinka-Banjo, Ogban-Asuquo Ugot, Obiageli Umeugochukwu, Thompson Annor

“…By enabling the application of advanced diagnostics through technologies such as smartphone apps and AI-based analysis tools, this dataset not only aims to enhance the technological capabilities within agricultural sectors but also serves as a vital educational resource. …”
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Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools by Fu Limei, Xu Feng

“…Rubberized cementitious composites have emerged as a sustainable alternative in the construction sector by promoting circular economy principles. However, their reduced compressive strength (CS) due to the inclusion of rubber remains a significant barrier to widespread adoption. …”
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Scenario projections of future irrigation water demand for field crops in Germany considering farmers’ adaptive land use by Jasmin Heilemann, Mansi Nagpal, Simon Werner, Bernd Klauer, Erik Gawel, Christian Klassert

“…This study underscores the importance of integrating multi-agent, process-based, and machine learning models to enhance irrigation demand projections and support proactive water resource management under climate and socioeconomic change.…”
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A comprehensive review of AI-based brain-computer interface with prefrontal cortex and sensory-motor rhythms systemization for rehabilitation by Anna Latha M, Ramesh R

“…Key findings: The results show that the random forest classifier is more suitable for eye state classification, achieving an accuracy up to 99.80 %, and support vector machine classification provides a higher accuracy of 100 % for MI conditions. …”
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State Planning in Russia: Challenges of Aligning Strategic Priorities with Budgetary Constraints and Pathways to Solutions by T. I. Vinogradova

“…To address these imbalances, the author proposes improving indicator development through multi-level KPIs supported by machine learning and blockchain; establishing an interdepartmental scenario modeling platform based on artificial intelligence; implementing adaptive budgeting; and unifying the regulatory framework through a State Planning Code. …”
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Ural Region’s Contribution to Reconstruction of Belarus’s Economy in 1944–1950s by V. V. Zapariy, E. V. Zaytseva

“…The restoration of industry, transportation, agriculture, and other sectors is highlighted. The study emphasizes that substantial support was provided by the republics of the Soviet Union in this endeavor. …”
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Federated Learning Based on an Internet of Medical Things Framework for a Secure Brain Tumor Diagnostic System: A Capsule Networks Application by Roman Rodriguez-Aguilar, Jose-Antonio Marmolejo-Saucedo, Utku Köse

“…Artificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. …”
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Quality failures in Energy- saving renovation projects in Northern China by Yuting Qi

“… The building sector contributes to about one-third of the total energy consumption worldwide (Liu, Li, et al. 2020). …”
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Hybrid pre trained model based feature extraction for enhanced indoor scene classification in federated learning environments by Monica Dutta, Deepali Gupta, Vikas Khullar, Sapna Juneja, Roobaea Alroobaea, Pooja Sapra

“…Primitive classification methods like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), provide a compromised performance with complex indoor environments due to light variations, intra-class similarities, and occlusions. …”
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