Aquatic system assessment of potentially toxic elements in El Manzala Lake, Egypt: A statistical and machine learning approach by Asmaa Nour Aly Al-Falal, Salah Elsayed, Ezzat A. El Fadaly, Aissam Gaagai, Hani Amir Aouissi, Mohamed S. Abd El-baki, Mohamed Hamdy Eid, Abdallah Elshawadfy Elwakeel, Zaher Mundher Yaseen, Osama Elsherbiny, E.I. Eltahir, Mohamed Gad

“…These indices were refined using multivariate techniques, such as Principal Component Analysis (PCA) and Cluster Analysis (CA). Additionally, six machine learning models, including Multiple Linear Regression (MLR), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting Regression (AdaBoost), and Multilayer Perceptron (MLP), were developed to predict water quality parameters. …”
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Reaching machine learning leverage to advance performance of electrocatalytic CO2 conversion in non-aqueous deep eutectic electrolytes by Ahmed Halilu, Mohamed Kamel Hadj-Kali, Hanee Farzana Hizaddin, Mohd Ali Hashim, Emad M. Ali, Suresh Bhargava

“…Our findings demonstrate that ensemble and k-Nearest Neighbours algorithms learn the CO2RR dataset, achieving a prediction accuracy of over 99%. The models were verified visually and quantitatively by overlaying predicted and experimental dataset. …”
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Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study by Rishishankar E. Suresh, M S Zobaer, Matthew J. Triano, Brian F. Saway, Parneet Grewal, Nathan C. Rowland

“…Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying movement phases in hemiparetic individuals undergoing simultaneous NIBS and EEG recordings. …”
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Optimizing MRI Scheduling in High-Complexity Hospitals: A Digital Twin and Reinforcement Learning Approach by Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan, Paula Sáez

“…The digital twin simulates realistic hospital dynamics using parameters extracted from a MRI publicly available dataset, modeling patient arrivals, examination durations, MRI machine reliability, and clinical priority stratifications. …”
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AI, Optimization, and Human Values: Mapping the Intellectual Landscape of Industry 4.0 to 5.0 by Albérico Travassos Rosário, Ricardo Jorge Gomes Raimundo

“…This review highlights the emerging role of enabling technologies that reconcile technical performance with social and environmental values, promoting a more inclusive and sustainable model for industrial development.…”
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Detection of Bipolar Disorder and Schizophrenia Employing Bayesian-Optimized Grad-CAM-Driven Deep Learning by Osman Tayfun Bişkin, Cemre Candemir, Mustafa Alper Selver

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An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining by Charan Kumar Ala, Zefree Lazarus Mayaluri, Aman Kaushik, Nikhat Parveen, Surabhi Saxena, Abu Taha Zamani, Debendra Muduli

“…This study proposes a novel hybrid artificial intelligence (AI) framework that integrates physics informed neural networks (PINNs) with conventional machine learning (ML) algorithms for the accurate prediction and optimization of BIGV. …”
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Integrated GBR–NSGA-II Optimization Framework for Sustainable Utilization of Steel Slag in Road Base Layers by Merve Akbas

“…This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. …”
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Optimizing Milling Parameters and Halloysite Nanotube Concentration to Enhance Surface Quality and Reduce Energy Consumption by Laura Peña-Parás, Martha Rodríguez-Villalobos, Demófilo Maldonado-Cortés, Jaime A. González-García, Mónica Herrera-Maldonado, Gabriela Trousselle-Strozzi, Oscar E. Montemayor, Ángel G. Romero-Cantú, Daniel I. Quintanilla-Correa

“…A Response Surface Methodology (RSM) model was developed to optimize the input variables, which indicated that the optimal HNT concentration ranged from 0.11 to 0.17 wt.%. …”
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APD-BayNet: Jakarta Air Quality Index Prediction Using Bayesian Optimized Tabnet by Raey Faldo, Satria Mandala, Rina Pudji Astuti, Ary Setijadi Prihatmanto, Mohd Soperi Mohd Zahid

“…Our methodology consists of four key stages: data preprocessing, model development, hyperparameter tuning using BO, and performance evaluation through 5-fold cross-validation, applied consistently across all models. …”
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W-band frequency selective digital metasurface using active learning-based binary optimization by Kim Young-Bin, Park Jaehyeon, Kim Jun-Young, Seo Seok-Beom, Kim Sun-Kyung, Lee Eungkyu

“…The digital metasurface is composed of a periodic array of sub-wavelength unit cells, each containing hundreds of metal or dielectric pixels that act as binary states. By utilizing a machine learning model, we apply active learning-aided binary optimization to determine the optimal binary state configurations for a given target FSS profile. …”
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Sample Denoising and Optimization Technique Based on Noise Filtering and Evolutionary Algorithms for Imbalanced Data Classification by Fhira Nhita, Asniar, Isman Kurniawan, Adiwijaya

“…Then, the selected train set is used to develop classification model using five classifier, i.e., decision tree, logistic regression, support vector machine, k-nearest neighbors, and naive bayes. …”
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Harnessing feature pruning with optimal deep learning based DDoS cyberattack detection on IoT environment by Eunmok Yang, Sooyong Jeong, Changho Seo

“…Meanwhile, DDoS attacks are recognized using a sparse denoising autoencoder (SDAE) model. Furthermore, the parameter tuning of the SDAE classifier is accomplished by utilizing the Fish Migration Optimizer (FMO) technique. …”
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Adaptive Learning Algorithms for Low Dose Optimization in Coronary Arteries Angiography: A Comprehensive Review by Komal Tariq, Muhammad Adnan Munir, Hafiza Tooba Aftab, Amir Naveed, Ayesha Yousaf, Sajjad Ul Hassan

“…Results: The extracted data shows a comprehensive data on various techniques that are used for low dose CAA, advancements in image segmentation, noise reduction, and operator dose reduction highlight the potential of machine learning techniques. Innovative methods such as Model-Based Deep Learning (MBDL) and Self-Attention Generative Adversarial Networks (SAGAN) demonstrate efficient reconstruction capabilities. …”
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Optimized design of metal rubber lightweight electrode with beryllium copper/stainless steel wire mixture by FU Hailong, LI Hong, AI Shigang, WANG Yue

“…Thus, material preparation schemes need optimization for multifunctional lightweight electrode designs. …”
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A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study by Maria Habib, Victor Vicente-Palacios, Pablo García-Sánchez

“…The optimization problem has been formulated as a wrapper-based algorithm for feature selection and multi-objective optimization relying on four machine learning algorithms: K-Nearest Neighbour algorithm (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM). …”
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Optimizing Cloud Computing Performance With an Enhanced Dynamic Load Balancing Algorithm for Superior Task Allocation by Raiymbek Zhanuzak, Mohammed Alaa Ala'Anzy, Mohamed Othman, Abdulmohsen Algarni

“…Cloud computing, particularly within the Infrastructure as a Service (IaaS) model, faces significant challenges in workload distribution due to limited resource availability and virtual machines (VMs). …”
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A data driven framework for optimizing droplet microfluidics with residual block and Fourier enhanced networks by Alireza Samari, Kamal Jannati, Azadeh Jafari

“…In this study, we present a data-driven framework that employs machine learning to predict droplet size and generation frequency, while simultaneously optimizing device geometry. …”
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Harnessing optimization with deep learning approach on intelligent transportation system for anomaly detection in pedestrian walkways by Sultan Refa Alotaibi, Fatma S. Alrayes, Mashael Maashi, Mohammed Maray, Mohammed A. Alliheedi, Donia Badawood, Moneerah Alotaibi

“…The empirical findings reported better outcomes for the HODLAITS-ADPW technique when compared to other models.…”
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Performance evaluation, prediction analysis and optimization of experimental ORC using artificial neural networks (ANN) by Diki Ismail Permana, Federico Fagioli, Maurizio De Lucia, Dani Rusirawan, Istvan Farkas

“…While many experimental studies on ORC have been conducted, significant gaps remain in accurately predicting unknown or unmeasured data and identifying optimal operating conditions. This research addresses these challenges using machine learning, specifically an artificial neural network (ANN), a self-learning and nonlinear method capable of approximating complex functions, making it ideal for ORC prediction models. …”
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