Smart Irrigation System with IoT, Machine Learning, and Solar Power for Efficient Plant Care by Justin M. A. Capcha-Ochoa, Jefferson A. Chahua-Benito, Miguel A. Serafin-Cayllahua, Sebastian E. Mamani-Martinez, Jesus G. Mendivil-Imbertis, Jordan I. Mendoza-Fernandez, Roberto J. M. Casas-Miranda, Maritza Cabana-Cáceres, Cristian Castro-Vargas

“…This study aims to develop an intelligent irrigation system based on the Internet of Things (IoT) and machine learning to optimize water use, improve plant monitoring, and enhance security. …”
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Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts: an ALS case study by Shailesh Appukuttan, Shailesh Appukuttan, Aude-Marie Grapperon, Aude-Marie Grapperon, Mounir Mohamed El Mendili, Hugo Dary, Maxime Guye, Annie Verschueren, Jean-Philippe Ranjeva, Shahram Attarian, Wafaa Zaaraoui, Matthieu Gilson

“…In this study, we systematically evaluated the impact of various machine learning pipeline configurations, including scaling methods, feature selection, dimensionality reduction, and hyperparameter optimization. …”
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An innovative efficiency of incubator to enhance organization supportive business using machine learning approach. by Xin Li, Qian Zhang, Hanjie Gu, Salwa Othmen, Somia Asklany, Chahira Lhioui, Ali Elrashidi, Paolo Mercorelli

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Valuation of Urban Public Bus Electrification with Open Data and Physics-Informed Machine Learning by Upadhi Vijay, Soomin Woo, Scott J. Moura, Akshat Jain, David Rodriguez, Sergio Gambacorta, Giuseppe Ferrara, Luigi Lanuzza, Christian Zulberti, Erika Mellekas, Carlo Papa

“…We develop physics-informed machine learning models to evaluate energy consumption, carbon emissions, health impacts, and the total cost of ownership for each transit route. …”
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Mechanical properties and machine learning analysis of concrete incorporating waste glass as coarse aggregate by Bhukya Govardhan Naik, G. Nakkeeran, Dipankar Roy, G. Uday Kiran, Kalyani Gurram, Gade Venkata Ramanjaneyulu, George Uwadiegwu Alaneme, Mutiu Shola Bakare

“…This study explores the use of Waste Glass Coarse Aggregate (WGCA) as a partial replacement for natural coarse aggregates in concrete, assessing its effects on mechanical performance and utilizing machine learning models for predictive analysis. The assessment involved concrete mixtures with replacement levels of 0%, 5%, 10%, and 15% WGCA, focusing on their compressive, tensile, and flexural strengths at both 7 and 28 days of curing. …”
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Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques. by Shayla Naznin, Md Jamal Uddin, Ishmam Ahmad, Ahmad Kabir

“…Additionally, k-fold cross-validation was conducted to ensure robust model evaluation.<h4>Results</h4>This study confirms a significant decline in under-5 mortality in Bangladesh over the study period, with machine learning models providing accurate predictions of future trends. …”
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Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues by Gianmarco Lazzini, Mario D’Acunto

“…In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…This study investigates and predicts the likelihood of operational risk occurrence in the banking industry using machine learning algorithms. The primary objective is to analyze operational risk data and evaluate the performance of various machine learning models to develop effective tools for enhancing risk management and minimizing financial losses in banks and financial institutions. …”
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Evaluating the impact of demolished concrete aggregates on workability, density, and strength with predictive modeling by Hyginus Obinna Ozioko, Emmanuel Ebube Eze

“…ANOVA results (F = 12.97, p = 1.84E− 07) confirmed significant strength differences, with post-hoc tests indicating no significant effect at 2–7% replacement but notable reductions at ≥ 10% (p < 0.05). Three machine learning models: linear regression, polynomial regression, and artificial neural networks, were developed to predict compressive strength. …”
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A Novel Approach to Continual Knowledge Transfer in Multilingual Neural Machine Translation Using Autoregressive and Non-Autoregressive Models for Indic Languages by Shailashree K. Sheshadri, Deepa Gupta, Biswajit Paul, J. Siva Bhavani

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Forest cover restoration analysis using remote sensing and machine learning in central Malawi by Jabulani Nyengere, Precious Masuku, Sylvester Chikabvumbwa, Weston Mwase, Msaiwale Kathewera, Allena Laura Njala, Wilson Tchongwe, Isaac Tchuwa, Tiwonge I Mzumara, Chikondi Chisenga, Wilfred Kadewa, Emmanuel Chinkaka, Harineck Tholo

“…This study employs remote sensing and machine learning techniques to evaluate the effectiveness of such interventions in a village forest area in central Malawi. …”
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Consideration of the geomechanical state of a fractured porous reservoir in reservoir simulation modelling by Yurii A. Kashnikov, Denis V. Shustov, Sergei Yu. Yakimov

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The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine by Andre F. R. Guarda, Nuno M. M. Rodrigues, Fernando Pereira

“…Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. …”
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Development and validation of a risk prediction model for acute kidney injury in coronary artery disease by Ming Ye, Chang Liu, Duo Yang, Hai Gao

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Advanced Handover Optimization (AHO) using deep reinforcement learning in 5G Networks by T. Senthil Kumar, Mardeni Roslee, J. Jayapradha, Yasir Ullah, Chilakala Sudhamani, Sufian Mousa Ibrahim Mitani, Anwar Faizd Osman, Fatimah Zaharah Ali

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An Optimized Multi-Stage Framework for Soil Organic Carbon Estimation in Citrus Orchards Based on FTIR Spectroscopy and Hybrid Machine Learning Integration by Yingying Wei, Xiaoxiang Mo, Shengxin Yu, Saisai Wu, He Chen, Yuanyuan Qin, Zhikang Zeng

“…The proposed framework includes (1) FTIR spectral acquisition; (2) a comparative evaluation of nine spectral preprocessing techniques; (3) dimensionality reduction via three representative feature selection algorithms, namely the Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Principal Component Analysis (PCA); (4) regression modeling using six machine learning algorithms, namely the Random Forest (RF), Support Vector Regression (SVR), Gray Wolf Optimized SVR (SVR-GWO), Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and the Back-propagation Neural Network (BPNN); and (5) comprehensive performance assessments and the identification of the optimal modeling pathway. …”
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A hybrid approach for intrusion detection in vehicular networks using feature selection and dimensionality reduction with optimized deep learning. by Fayaz Hassan, Zafi Sherhan Syed, Aftab Ahmed Memon, Saad Said Alqahtany, Nadeem Ahmed, Mana Saleh Al Reshan, Yousef Asiri, Asadullah Shaikh

“…The intended use of CFS and PCA in the machine learning pipeline serves two folds benefit, first is that the resultant feature matrix contains attributes that are most useful for recognizing malicious traffic, and second that after CFS and PCA, the feature matrix has a smaller dimensionality which in turn means that smaller number of weights need to be trained for the dense layers (connections are required for the dense layers) which resulting in smaller model size. …”
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Implementation of a hierarchical cluster model to analyze wind and solar availability in the department of Antioquia, Colombia by Alejandro Restrepo Román, Didier J. Villegas, Carolina Rodriguez, Adrián Cogollo, Iván D. Bedoya, Andres A. Amell Arrieta

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Prediction of KRAS gene mutations in colorectal cancer using a CT-based radiomic model by Wenjing Wang, Qingbiao Zhang, Shimei Fan, Yuyin Wang, Xingyan Le, Min Ai, Chunqi Du, Junbang Feng, Chuanming Li

“…After dimensionality reduction, machine learning methods such as extremely randomized trees (ERT), random forest (RF), XGBoost, Bagging, and CatBoost were used for model construction. …”
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Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction by Emmanuel Onah, Uche Jude Eze, Abdullahi Salahudeen Abdulraheem, Ugochukwu Gabriel Ezigbo, Kosisochi Chinwendu Amorha, Fidele Ntie-Kang

“…This study aimed to enhance predictive performance by refining feature engineering and evaluating a diverse ensemble of machine learning models using the UCI DTC dataset. …”
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