Stratified Multisource Optical Coherence Tomography Integration and Cross-Pathology Validation Framework for Automated Retinal Diagnostics by Michael Sher, Riah Sharma, David Remyes, Daniel Nasef, Demarcus Nasef, Milan Toma

“…The methodology integrates biomimetic data partitioning, deep biomarker extraction via pretrained VGG16 networks, and automated model selection optimized for clinical decision-making. …”
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Precise estimation of solar photovoltaic parameters via brown bear optimization and Differential Evolution by Khalid M. Hosny, Amr A. Abd El-Mageed, Amr A. Abohany, Reda M. Hussein, Mona Gaffar

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Multi-objective artificial-intelligence-based parameter tuning of antennas using variable-fidelity machine learning by Slawomir Koziel, Anna Pietrenko-Dabrowska, Stanislaw Szczepanski

“…Our algorithm is a machine learning (ML) procedure employing artificial neural network models. …”
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Health condition prediction method of the computer numerical control machine tool parts by ensembling digital twins and improved LSTM networks by Chen Guo, Yin Haifang

“…The proposed model is predicated on a convolutional neural network and a long and short-term memory network, the purpose of which is to extract the feature data of CNC machine tool parts. …”
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Optimizing Feature Selection in Sentiment Analysis of Bank Saqu: A Comparative Study of SVM and Random Forest using Information Gain and Chi-Square by Anelta Tirta Putri Subandono, Dhani Ariatmanto

“…In conclusion, selecting the appropriate feature selection method significantly contributes to enhancing the accuracy of text classification models. This research can serve as a reference for optimizing feature selection techniques in the development of more accurate and efficient machine learning-based systems.…”
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Hybrid IoT-CAD system: optimized feature selection based gated recurrent residual deep learning for cyber attack detection in IoT networks by Susheela Vishnoi, Sunil Kumar, Saikat Samanta

“…After that, the Binary Genetic Dung Beetle Optimization (B-GDBO) method is utilized for optimal feature selection and also reduces the risk of overfitting. …”
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An analytics-driven model for identifying autism spectrum disorder using eye tracking by Deblina Mazumder Setu

“…These properties are utilized in machine learning and deep learning model training with hyperparameter adjusting for optimization. …”
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Designing a System Architecture for Dynamic Data Collection as a Foundation for Knowledge Modeling in Industry by Edmund Radlbauer, Thomas Moser, Markus Wagner

“…This study develops and implements a scalable system architecture for dynamic data acquisition and knowledge modeling in industrial contexts. The objective is to efficiently process large datasets to support decision-making and process optimization within Industry 4.0. …”
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Advancing non-target analysis of emerging environmental contaminants with machine learning: Current status and future implications by Alexa Canchola, Lillian N. Tran, Wonsik Woo, Linhui Tian, Ying-Hsuan Lin, Wei-Chun Chou

“…Recent advancements in machine learning (ML) models offer great potential for enhancing NTA applications. …”
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Enhancing solar energy conversion efficiency: Thermophysical property predicting of MXene/Graphene hybrid nanofluids via bayesian-optimized artificial neural networks by Dheyaa J. jasim, Husam Rajab, As'ad Alizadeh, Kamal Sharma, Mohsen Ahmed, Murizah Kassim, S. AbdulAmeer, Adil A. Alwan, Soheil Salahshour, Hamid Maleki

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A novel hybrid model for predicting the bearing capacity of piles by Li Tao, Xinhua Xue

“…The main objective of this study is to propose a hybrid model coupling least squares support vector machine (LSSVM) with an improved particle swarm optimization (IPSO) algorithm for the prediction of bearing capacity of piles. …”
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AI-Driven Innovation in Skin Kinetics for Transdermal Drug Delivery: Overcoming Barriers and Enhancing Precision by Nubul Albayati, Sesha Rajeswari Talluri, Nirali Dholaria, Bozena Michniak-Kohn

“…Advances in artificial intelligence (AI) address these challenges through predictive modeling and personalized medicine approaches. Machine learning models trained on extensive molecular datasets predict skin permeability and accelerate the selection of suitable drug candidates. …”
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Data-driven approaches in incremental forming: Unravelling the path to enhanced manufacturing efficiency using data acquisition by S. Pratheesh Kumar, V. Joseph Stanley, S. Nimesha

“…Key advancements such as robot-assisted forming, computer-controlled toolpath generation from CAD models, and real-time quality monitoring using machine vision are discussed. …”
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Toward Inclusive Smart Cities: Sound-Based Vehicle Diagnostics, Emergency Signal Recognition, and Beyond by Amr Rashed, Yousry Abdulazeem, Tamer Ahmed Farrag, Amna Bamaqa, Malik Almaliki, Mahmoud Badawy, Mostafa A. Elhosseini

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Multi-Stage Data-Driven Framework for Customer Journey Optimization and Operational Resilience by Tzu-Chien Wang, Ruey-Shan Guo, Chialin Chen, Chia-Kai Li

“…Empirical validation using Taiwanese financial institution data shows a 15% improvement in predictive accuracy compared to traditional machine-learning models, significantly enhancing customer lifetime value (CLV) predictions and multi-channel resource allocation. …”
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Sustainable Cooling Strategies in End Milling of AISI H11 Steel Based on ANFIS Model by Arumugam Balasuadhakar, Sundaresan Thirumalai Kumaran, Saood Ali

“…Furthermore, the ANFIS models demonstrated high accuracy, with 97.4% accuracy in predicting cutting temperature and 92.6% accuracy in predicting surface roughness, highlighting their effectiveness in providing precise forecasts for the machining process.…”
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Enhancing Supply Chain Efficiency Resilience Using Predictive Analytics and Computational Intelligence Techniques by Lixing Bo, Jie Xu

“…To overcome these limitations, the study employs a combination of Transformer models for demand forecasting and Particle Swarm Optimization (PSO) for inventory parameter optimization. …”
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Optimizing Access Point Placement in Industrial IoT: A Deep Reinforcement Learning Approach With Q-Learning Verification and Signal Heatmap Visualization by Manash Mahanta, Attaphongse Taparugssanagorn, Kenta Umebayashi

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Enhancing Pollen Prediction in Beijing, a Chinese Megacity: Leveraging Ensemble Learning Models for Greater Accuracy by Wenxi Ruan, Ziming Li, Zhaobin Sun, Xingqin An, Yuxin Zhao, Shuwen Zhang, Yinglin Liang, Yaqin Bu, Jingyi Xin, Xiaoyi Hang

“…Despite its critical importance, pollen forecasting technology is still not sufficiently optimized. This study leverages multi-year daily pollen concentration observations and ECMWF (European Centre for Medium-Range Weather Forecasts) real-time forecast data, applying twelve machine learning models to learn perturbations separated from characteristic quantities. …”
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Study on Subway Station Street Block-Level Land Use Pattern and Plot Ratio Control Based on Machine Learning by Mingyi Kuang, Fei Fu, Fangzhou Tian, Liwei Lin, Can Du, Yuesong Zhang

“…This study takes Panda Avenue Subway Station as a case study to analyze the evolution of land use patterns around subway stations and explore optimization strategies to enhance land development efficiency and spatial utilizationTo fill this research gap, this paper proposes a CNN-AIMatch model based on machine learning algorithm and an enhanced PLUS-Markov prediction model using the increase and decrease of floor area ratio as a control measure, which adopts an increase in plot ratio as a control measure to improve the accuracy of the Kappa coefficient in different plot ratio scenarios and the prediction of 3D urban spatial growth trends. …”
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