NID-DETR: A novel model for accurate target detection in dark environments by Qingyuan Pan, Qiang Liu, Wei Huang

“…Initially, we enhance the dataset during model training to optimize machine vision perception. …”
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Deep Learning-Augmented Evolutionary Strategies for Intelligent Global Optimization by Absalom El-Shamir Ezugwu, Olaide Nathaniel Oyelade, Jeffrey Ovre Agushaka, Apu Kumar Saha

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Integration of Hybrid Machine Learning and Multi-Objective Optimization for Enhanced Turning Parameters of EN-GJL-250 Cast Iron by Yacine Karmi, Haithem Boumediri, Omar Reffas, Yazid Chetbani, Sabbah Ataya, Rashid Khan, Mohamed Athmane Yallese, Aissa Laouissi

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Research on SPAD Inversion of Rice Leaves at a Field Scale Based on Machine Vision and Leaf Segmentation Techniques by Bailin Yue, Yong Jin, Shangrong Wu, Jieyang Tan, Youxing Chen, Hu Zhong, Guipeng Chen, Yingbin Deng

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NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat by Kamrunnahar Khan Bristy, Dip Ghosh, Md. Abul Hashem

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A neural network-based method for input parameter optimization of edge transport modeling utilizing experimental diagnostics by Y. Luo, S. Xu, Y. Liang, E. Wang, J. Cai, Y. Feng, D. Reiter, A. Knieps, S. Brezinsek, D. Harting, M. Krychowiak, D. Gradic, P. Ren, D. Zhang, Y. Gao, G. Fuchert, A. Pandey, M. Jakubowski, the W7-X Team

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Maximal productivity rate of threading machine operations by Ryspek Usubamatov, Darina Kurganova, Sarken Kapayeva

“…This work considers the analytical optimization of the cutting speed for the threading machine tool by the criterion of its maximal productivity rate.…”
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CAMP-GNN: A Constraint-Aware Message-Passing Model for Optimal Resource Allocation in Software Projects by Muhammad Asif, Fahd M. Aldosari, Donia Y. Badawood, Abdu Salam

“…CAMP-GNN also introduces domain-specific message aggregation rules to unify urgency, skill alignment, and budget slack, advancing GNNs beyond basic relational modeling. A novel preprocessing pipeline is proposed to convert raw project timelines, skill dependencies, and budget thresholds into machine-learnable features, ensuring effective integration with computational models. …”
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Application of machine learning to growth model in fisheries by Semra Benzer, Recep Benzer, Ali Gül

“… Traditional growth models, such as length-weight relationships (LWRs) and the von Bertalanffy (VB) growth function, have been widely used in fishery science. …”
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Optimizing economics of machining for LM25Al/VC composite material using analytical modeling, deep neural network and GRA coupled with RSM by Mesay Alemu Tolcha, Hirpa Gelgele Lemu, Yosef Wakjira Adugna

“…Additionally, new analytical models and DNN programming were developed in this work to predict machining costs, power consumption, material removal rate, and surface finish quality. …”
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Digital Twin-Enabled Building Information Modeling–Internet of Things (BIM-IoT) Framework for Optimizing Indoor Thermal Comfort Using Machine Learning by Fahad Iqbal, Shayan Mirzabeigi

“…Furthermore, it also uses a hybrid machine learning model that combines Facebook Prophet and Long Short-Term Memory (LSTM) to predict the future indoor environmental parameters. …”
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Optimizing the Architecture of a Quantum–Classical Hybrid Machine Learning Model for Forecasting Ozone Concentrations: Air Quality Management Tool for Houston, Texas by Victor Oliveira Santos, Paulo Alexandre Costa Rocha, Jesse Van Griensven Thé, Bahram Gharabaghi

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Hybrid optimization of thermally-enhanced Zn-Fe LDH catalysts for fenton-like reactions: Integrating design of experiments with machine learning models for optimisation by Ramadhan Muhammad Naufal, Nawwal Hikmah, Dessy Ariyanti

“…This study presents a novel hybrid modeling framework that combines Response Surface Methodology (RSM) with machine learning (ML) algorithms– Support Vector Regression (SVR) and Gradient Boosting Regression (GBR)– to contribute to the predictive modeling and optimization of thermally-activated ZnFe-LDH based Fenton catalysis. …”
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Short-term prediction of regional energy consumption by metaheuristic optimized deep learning models by Ngoc-Quang Nguyen, Phuong-Thao-Nguyen Nguyen, Quynh-Chau Truong

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Influences of Sampling Design and Model Selection on Predictions of Chemical Compounds in Petroferric Formations in the Brazilian Amazon by Niriele Bruno Rodrigues, Theresa Rocco Barbosa, Helena Saraiva Koenow Pinheiro, Marcelo Mancini, Quentin D. Read, Joshua Blackstock, Edwin H. Winzeler, David Miller, Phillip R. Owens, Zamir Libohova

“…The optimal covariates for modeling were selected using Recursive Feature Elimination (RFE), processing 10 runs for each chemical element. …”
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Enhancing Engineering and Architectural Design Through Virtual Reality and Machine Learning Integration by Ali Shehadeh, Odey Alshboul

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Price Forecast of Treasury Bond Market Yield: Optimize Method Based on Deep Learning Model by Weiying Ping, Yuwen Hu, Liangqing Luo

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Hybrid Metaheuristic Optimized Random Forest Models for Predicting Compressive Strength of Alkali Activated Concrete by Mana Alyami, Muhammad Faisal Javed, Irfan Ullah, Hisham Alabduljabbar, Furqan Ahmad

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Prediction of buckling damage of steel equal angle structural members using hybrid machine learning techniques by Nang Xuan Ho, Tien-Thinh Le, The-Hung Dinh, Van-Hai Nguyen

“…Abstract This article deals with prediction of buckling damage of steel equal angle structural members using a surrogate model combining machine learning and metaheuristic optimization technique. …”
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An interpretable machine learning model to predict hospitalizations by Hagar Elbatanouny, Hissam Tawfik, Tarek Khater, Anatoliy Gorbenko

“…This study underscores the potential of interpretable machine learning in aiding hospital managers to optimize resource allocation, hospitalization cases, and make data-driven decisions during pandemics.…”
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