Interdisciplinary Methodology for Resource Allocation Problems Using Artificial Neural Networks and Software Robots by Marta Lilia Erana-Diaz, Marco Antonio Cruz-Chavez, Mario Acosta-Flores, Juana Enriquez-Urbano, Nadia Lara Ruiz, Jorge Pablo Oseguera Gamba

“…By considering psychosocial and productivity factors in the optimization model, the study moves beyond traditional methods that often prioritize efficiency at the expense of human considerations. …”
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A Prediction Model for Sight-Threatening Diabetic Retinopathy Based on Plasma Adipokines among Patients with Mild Diabetic Retinopathy by Yaxin An, Bin Cao, Kun Li, Yongsong Xu, Wenying Zhao, Dong Zhao, Jing Ke

“…This study is aimed at investigating the risk factors for sight-threatening DR (STDR) and establishing a prognostic model for predicting STDR among a high-risk population of patients with type 2 diabetes mellitus (T2DM). …”
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A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework by Afaq Khattak, Jianping Zhang, Pak-wai Chan, Feng Chen, Abdulrazak H. Almaliki

“…The effectiveness of the framework is validated through comparative analyses with standalone machine learning models such as XGBoost, RF, and CatBoost. …”
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Dynamic Error Modeling and Predictive Compensation for Direct-Drive Turntables Based on CEEMDAN-TPE-LightGBM-APC Algorithm by Manzhi Yang, Hao Ren, Shijia Liu, Bin Feng, Juan Wei, Hongyu Ge, Bin Zhang

“…Our methodology comprises four key stages: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based decomposition of historical error data, development of component-specific prediction models using Tree-structured Parzen Estimator (TPE)-optimized Light Gradient Boosting Machine (LightGBM) algorithms for each Intrinsic Mode Function (IMF), integration of component predictions to generate initial values, and application of the Adaptive Prediction Correction (APC) module to produce final predictions. …”
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Assessing CNN and Semantic Segmentation Models for Coarse Resolution Satellite Image Classification in Subcontinental Scale Land Cover Mapping by Tesfaye Adugna, Wenbo Xu, Jinlong Fan, Haitao Jia, Xin Luo

“…Labeled datasets were collected as shapefiles and split into three independent datasets: training, validation, and test datasets, and preprocessed to meet each model's input format requirements. We conducted several experiments to optimize models and select the best models. …”
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FEN-MRMGCN: A Frontend-Enhanced Network Based on Multi-Relational Modeling GCN for Bus Arrival Time Prediction by Ting Qiu, Chan-Tong Lam, Bowie Liu, Benjamin K. Ng, Xiaochen Yuan, Sio Kei Im

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Spinach (<i>Spinacia oleracea</i> L.) Growth Model in Indoor Controlled Environment Using Agriculture 4.0 by Cesar Isaza, Angel Mario Aleman-Trejo, Cristian Felipe Ramirez-Gutierrez, Jonny Paul Zavala de Paz, Jose Amilcar Rizzo-Sierra, Karina Anaya

“…It encompasses a methodology combining data science, machine learning, and mathematical modeling. The growth system was built using LED lighting, automated irrigation, temperature control with fans, and sensors to monitor environmental variables. …”
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Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality by Fernando García-García, Dae-Jin Lee, Mónica Nieves-Ermecheo, Olaia Bronte, Pedro Pablo España, José María Quintana, Rosario Menéndez, Antoni Torres, Luis Alberto Ruiz Iturriaga, Isabel Urrutia, COVID-19 & Air Pollution Working Group

“…The optimal cluster model parameters –including k, the number of phenotypes– were chosen automatically, by maximizing the average Silhouette score across the training set. …”
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Enhancing Smart City Logistics Through IoT-Enabled Predictive Analytics: A Digital Twin and Cybernetic Feedback Approach by Hajar Fatorachian, Hadi Kazemi, Kulwant Pawar

“…By incorporating machine learning-driven predictive analytics, the study demonstrates how AI-powered logistics optimization can enhance urban freight mobility. …”
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Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest by Tássia Fraga Belloli, Diniz Carvalho de Arruda, Laurindo Antonio Guasselli, Christhian Santana Cunha, Carina Cristiane Korb

“…The contribution of this study is the finding that in addition to optimizing RF model parameters, optimizing the AGB and Corg dataset collected in the field, i.e., evaluating normalization and plot sizes, is crucial to obtain more accurate estimates with RS- and ML-based models. …”
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A novel Probabilistic Bi-Level Teaching–Learning-Based Optimization (P-BTLBO) algorithm for hybrid feature extraction and multi-class brain tumor classification using ResNet-50 and... by Mahananda Malkauthekar, Avinash Gulve, Ratnadeep Deshmukh

“…The P-BTLBO method combines probabilistic modeling with a bi-level optimization framework to make feature selection better. …”
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Energy consumption forecasting and thermal insulator selection with random forest regression by Mohammed Fellah, Salma Ouhaibi, Naoual Belouaggadia, Khalifa Mansouri

“…The application of this model appears promising for optimizing energy consumption in buildings.…”
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Backdoor Attack Against Dataset Distillation in Natural Language Processing by Yuhao Chen, Weida Xu, Sicong Zhang, Yang Xu

“…Dataset distillation has become an important technique for enhancing the efficiency of data when training machine learning models. It finds extensive applications across various fields, including computer vision (CV) and natural language processing (NLP). …”
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Rolling Bearing Fault Diagnosis Method Based on Fusion of CNN and CSSVM by LI Yunfeng, LAN Xiaosheng, SHEN Hongchang, XU Tongle

“…The results show that the combination of convolutional neural network to extract fault features and parameters to optimize the classification model structure of support vector machine can not only improve the diagnostic accuracy, but also have strong generalization performance.…”
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Reinforcement learning applications in water resource management: a systematic literature review by Linus Kåge, Vlatko Milić, Vlatko Milić, Maria Andersson, Magnus Wallén

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Analysis of influencing factors for primary membranous nephropathy complicated with atherosclerotic cardiovascular disease and establishment of a nomogram prediction model by Mirixiatijiang· Maimaiti, Zhu Guo-qiang, Su Ming-jie, Halinuer· Shadekejiang, Zhang Xue-qin, Lu Chen

“…DCA showed that the use of nomogram prediction model was more beneficial in predicting ASCVD in PMN when the threshold probability of patients was 0.01 to 1.ConclusionThe nomogram prediction model containing four predictor variables （age， hypertension， BUN， eGFR） developed in this study can be used to predict the risk of ASCVD in patients with PMN.…”
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Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks by Riadh Azzaz, Mohammad Jahazi, Samira Ebrahimi Kahou, Elmira Moosavi-Khoonsari

“…This study aims to develop a machine learning model to estimate steel phosphorus content at the end of the process based on input parameters. …”
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Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial Processes by Luis Rojas, Víctor Yepes, José Garcia

“…The literature was categorized into six key areas: (i) heat transfer with magnetized fluids, (ii) nonlinear control, (iii) big-data-driven optimization, (iv) energy transition via SOEC, (v) fault detection in control valves, and (vi) stochastic modeling with semi-Markov switching. …”
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Enhanced Prediction of Immune Checkpoint Blockade Response in Melanoma Using Ensemble Learning and a Hybrid Feature Selection Technique by Basant Badwy, Nagia M. Ghanem, Karim Banawan, Nour Eldin Ismail

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PREDICTIVE MAINTENANACE AND ITS ROLE IN ENERGY EFFICIENCY IN THE HOSPITALITY INDUSTRY by F. B. Adeleke, A. S Odetoye, E. E. Akerele, O. S. Folorunso, A. A. Bashiru

“… Energy efficiency has emerged as a critical priority in the hospitality industry, driven by the need for sustainable operations and long-term cost optimization. Predictive Maintenance (PdM), an innovative strategy that integrates advanced analytics, machine learning, and real-time monitoring, offers a proactive approach to equipment management. …”
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