Statistical and machine learning models for predicting university dropout and scholarship impact. by Stephan Romero, Xiyue Liao

“…Whether a student's tuition fee is up to date, whether they owe money to a debtor, whether they are scholarship holders, and students' age at enrollment are also found to be important features. The Generalized Additive Model (GAM) performs competitively and offers clear interpretability, revealing how changes in actionable variables influence dropout risk. …”
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No-load and on-load performance analysis of 10-stator-slots five phase flux switching machines with non-overlapped winding configurations by Muhammad Yousuf, Faisal Khan, Ahmed Tameemi, Wasiq Ullah

“…An evolutionary optimization process based on the genetic algorithm (GA) integrated with the JMAG software is executed to optimize the machine models, resulting in significant improvements in the analyzed WFFS machine models. …”
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Machine learning model for random forest acute oral toxicity prediction by A.M. Elsayad, M.M. Zeghid, K.A. Elsayad, A.N. Khan, ِA.K.M. Baareh, A. Sadiq, S.A. Mukhtar, H.F. Ali, S. Abd El-kader

“…A diverse set of two-dimensional molecular descriptors generated via rational discovery kit were used as input features, and model preprocessing involved normalization, validation, and feature selection. …”
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Mine pressure prediction based on empirical mode decomposition linear model by Yuwei ZHU, Pengfei WANG, Huixian WANG, Qiangqiang NIU, Liang XIN

“…Unlike most fixed-length single-feature mine pressure prediction models, this model first introduces the Empirical Mode Decomposition (EMD) method to separate periodic and trend components from the mine pressure signals. …”
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Compositional and Machine Learning Tools to Model Plant Nutrition: Overview and Perspectives by Léon Etienne Parent

“…The additive and orthogonal log ratios of CoDa reduce a <i>D</i>-parts soil composition to <i>D</i>-1 variables, alleviating redundancy in the predictive ML models. Using a Brazilian onion (<i>Allium cepa</i>) database, the combined CoDa and ML methods returned crop response patterns, allowing feature-specific fertilizer recommendations to be made. …”
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Enhancing leaf disease classification using GAT-GCN hybrid model by Shyam Sundhar, Riya Sharma, Priyansh Maheshwari, Suvidha Rupesh Kumar, T. Sunil Kumar

“…To further optimize training, weight initialization techniques are applied. The hybrid model is evaluated against the individual performance of the GCN and GAT models and the hybrid model achieved a precision of 0.9822, recall of 0.9818, and F1-score of 0.9818 in apple leaf disease classification, a precision of 0.9746, recall of 0.9744, and F1-score of 0.9743 in potato leaf disease classification, and a precision of 0.8801, recall of 0.8801, and F1-score of 0.8799 in sugarcane leaf disease classification. …”
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Short-term solar irradiance forecasting using deep learning models by Syed Saad Ahmed, Chang Wei Bin, Nisar Humaira, Riaz Hannan Naseem, Yeap Kim Ho, Zaber Nursaida Mohamad

“…Deep Learning techniques have shown outstanding performance in modeling these complexities. In this paper, short-term solar forecasting models are proposed using deep learning to reliably predict the amount of solar irradiance for optimal power generation. …”
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Data-driven integration of synthetic representative volume elements and machine learning for improved microstructure-property linkage and material performance in ceramics by Mohammad Rezasefat, James Hogan

“…The analysis is conducted using statistical, data-driven, and machine learning techniques, including principal component analysis and k-means clustering, to assess the microstructural features' impact on material performance. The study focuses on the use of RVEs for accurately capturing essential microstructural characteristics and addresses the challenges in developing synthetic models and the limitations of simulation capabilities. …”
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Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models by Baris Balaban, Seyma Selcan Magara, Caglar Yilgor, Altug Yucekul, Ibrahim Obeid, Javier Pizones, Frank Kleinstueck, Francisco Javier Sanchez Perez-Grueso, Ferran Pellise, Ahmet Alanay, Erkay Savas, Cetin Bagci, Osman Ugur Sezerman

“…Therefore, it is vital to address privacy and security concerns of both data and model together with other performance metrics when commercializing machine learning models. …”
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Predicting student next-term performance in degree programs using AI-based approach: a case study from Ghana by John-Bosco Diekuu, M. S. Mekala, Ulric Sena Abonie, John Isaacs, Eyad Elyan

“…Moreover, the results evident that high school grades and semester GPAs are the most powerful discriminant features influencing the models’ performance, emphasizing the importance of consistent in-course performance.…”
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Improved YOLOv8 Model for Multi-type Lung Nodule Detection by BAO Qiangqiang, TANG Siyuan, LI Qingqian, WANG Naiyu, YANG Min, GU Yu, ZHAO Jinliang, GAO Jingbo, WANG Jiaxin, QU Yuhan

“…The improved model has better detection performance than the original model when tested on a specialized dataset of four types (solid, ground glass, mixed, and microminiature) of lung nodules. …”
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Diagnostic performance of DCE-MRI radiomics in predicting axillary lymph node metastasis in breast cancer patients: A meta-analysis. by Fei Dong, Jie Li, Junbo Wang, Xiaohui Yang

“…In conclusion, DCE-MRI radiomic models exhibit good diagnostic performance in predicting ALNM and SLNM in breast cancer. …”
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A comprehensive evaluation of photovoltaic simulation software: A decision-making approach using Analytic Hierarchy Process and performance analysis by Md. Ashraful Islam, M.M. Naushad Ali, Ian B. Benitez, Sidahmed Sidi Habib, Taskin Jamal, Aymen Flah, Vojtech Blazek, Claude Ziad El-Bayeh

“…This study evaluates five widely used PV simulation software packages—SAM, PVsyst, HOMER, PV∗SOL, and RETScreen—by analyzing their features and performance across ten critical criteria, including cost, solar database accessibility, modeling capabilities, and ease of use. …”
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Prognostic model for predicting recurrence in breast cancer patients in Saudi Arabia by Ousman Khan, Jimoh Olawale Ajadi, Fahad Almsned, Hani Almohanna, Amjad Alrasheed, Ridwan A. Sanusi, Nurudeen A. Adegoke

“…We evaluated the performance of the models using both the area under the receiver operating characteristic curve for multivariate logistic regression and C-index for multivariate Cox regression. …”
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A Machine Learning Approach to Wireless Propagation Modeling in Industrial Environment by Mohammad Hossein Zadeh, Marina Barbiroli, Franco Fuschini

“…The proposed model relies on a combination of predictive algorithms, including a linear regression model and a Multi-Layer Perceptron, working collaboratively to model the relationship between the considered propagation markers and input features like frequency and machine size, spacing, and density. …”
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Deep Learning Model for Prediction of Dementia Severity based on EEG Signals by Noor Kamal Al-Qazzaz, Sawal Hamid Bin Mohd Ali, Siti Anom Ahmad

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Elucidating Semiconducting Properties and Photocatalytic Performance of Surface-Decorated BiVO₄ for the Removal of Contaminants of Emerging Concern by Marin Popović, Suresh Kumar Pandey, Josipa Papac Zjačić, Vladimir Dananić, Marijana Kraljić Roković, Marin Kovačić, Hrvoje Kušić, Andraž Šuligoj, Urška Lavrenčič Štangar, Ana Lončarić Božić

“…The structural, morphological, and semiconducting features of the material were examined employing various techniques (XRD, SEM, UV-DRS, PL, and photoelectrochemical tests). …”
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USER ACCEPTANCE OF MOBILE-JKN: INSIGHTS FROM THE TECHNOLOGY ACCEPTANCE MODEL by Ferina Septiani Damanik, Anna Wahyuni Widayanti, Chairun Wiedyaningsih

“…Data were collected through a questionnaire developed based on the TAM model, involving 406 respondents across Indonesia. …”
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Providing a development model for the effectiveness of persuasive advertising in social networks by Hamzeh Hessani Khabr, Payam Paslari, Mehdi Bagheri, Saeed Muradpour

“…Advertising has many economic benefits, including increased product sales, increased firm performance and value, and improved market efficiency, which have received considerable research attention (Bayer et al, 2020).Social NetworksSocial networks are a new generation of sites and bases that operate on the Internet and by using a search engine and adding features such as chat, electronic messaging, image and sound transmission, bring together a group of Internet users with special characteristics and provide the possibility of more communication of users in the form of a network of individual and group relationships and finally, these users can share content (share and express their interests, thoughts and activities) with others users (Jaffali, 2019).Saghafian et al, (2024) investigated the presentation of the model of customer participation with brands in social networks with emphasis on cultural differences. …”
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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

“…Feature selection was performed based on data availability, Spearman’s correlation analysis, and Principal Component Analysis (PCA), leading to the exclusion of the "Oil Volume" variable from model training to improve predictive performance. …”
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