Revealing the Driving Factors of Household Energy Consumption in High-Density Residential Areas of Beijing Based on Explainable Machine Learning by Zizhuo Qi, Lu Zhang, Xin Yang, Yanxia Zhao

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A synergistic approach using digital twins and statistical machine learning for intelligent residential energy modelling by Ahmad Almadhor, Shtwai Alsubai, Natalia Kryvinska, Nejib Ghazouani, Belgacem Bouallegue, Abdullah Al Hejaili, Gabriel Avelino Sampedro

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Finite Element Simulation and Optimization of Reaming Process by YUE Caixu, HU Desheng, ZHANG Anshan, LIU Xianli, LIU Zhibo, CHEN Zhitao

“…A test method combining fast parametric modeling, finite element simulation and multi-objective optimization is proposed to obtain the optimal blade geometric parameters. …”
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Development and Optimization of a Novel Deep Learning Model for Diagnosis of Quince Leaf Diseases by A. Naderi Beni, H. Bagherpour, J. Amiri Parian

“…DCNNs improve detection or classification accuracy by developing machine-learning models with many hidden layers to extract optimal features. …”
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Maritime decarbonization through machine learning: A critical systematic review of fuel and power prediction models by Son Nguyen, Matthieu Gadel, Ke Wang, Jing Li, Xiaocai Zhang, Siang-Ching Kong, Xiuju Fu, Zheng Qin

“…This study systematically and critically reviews the application of machine learning (ML) in fuel and power estimation and prediction (FEP) in the last decade (2013–2024) regarding the two cores of ML models, including aspects of data and the applied learning algorithms. …”
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A stacked ensemble machine learning model for the prediction of pentavalent 3 vaccination dropout in East Africa by Meron Asmamaw Alemayehu, Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem

“…This study aims to predict Penta 3 vaccination dropout using a stacking ensemble machine learning model with Demographic and Health Survey (DHS) data. …”
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A designed predictive modelling strategy based on data decomposition and machine learning to forecast solar radiation by Mumtaz Ali, Ramendra Prasad, Salman Alamery, Aitazaz Ahsan Farooque

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Optimizing credit card fraud detection with random forests and SMOTE by P. Sundaravadivel, R. Augustian Isaac, D. Elangovan, D. KrishnaRaj, V. V. Lokesh Rahul, R. Raja

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Machine learning applications in placenta accreta spectrum disorders by Mahsa Danaei, Maryam Yeganegi, Sepideh Azizi, Fatemeh Jayervand, Seyedeh Elham Shams, Mohammad Hossein Sharifi, Reza Bahrami, Ali Masoudi, Amirhossein Shahbazi, Amirmasoud Shiri, Heewa Rashnavadi, Kazem Aghili, Hossein Neamatzadeh

“…Future research should focus on larger datasets and validation of biomarkers to refine predictive models in obstetric care.…”
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Artificial Flora Algorithm-Based Feature Selection With Support Vector Machine for Cardiovascular Disease Classification by M. M. Asha, G. Ramya

“…This approach utilizes the Cleveland dataset by combining the Artificial Flora Optimization algorithm with the Support Vector Machine. …”
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Optimal Feature Selection and Classification for Parkinson’s Disease Using Deep Learning and Dynamic Bag of Features Optimization by Aarti, Swathi Gowroju, Mst Ismat Ara Begum, A. S. M. Sanwar Hosen

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Improved Bathymetry Estimation Using Satellite Altimetry-Derived Gravity Anomalies and Machine Learning in the East Sea by Kwang Bae Kim, Jisung Kim, Hong Sik Yun

“…This study aims to improve the accuracy of bathymetry predicted by gravity-geologic method (GGM) using the optimal machine learning model selected from machine learning techniques. …”
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Machine vision-based detection of key traits in shiitake mushroom caps by Jiuxiao Zhao, Jiuxiao Zhao, Wengang Zheng, Wengang Zheng, Yibo Wei, Yibo Wei, Qian Zhao, Qian Zhao, Jing Dong, Jing Dong, Xin Zhang, Xin Zhang, Mingfei Wang, Mingfei Wang

“…Finally,M3 group using GWO_SVM algorithm achieved optimal performance among six mainstream machine learning models tested with an R²value of 0.97 and RMSE only at 0.038 when comparing predicted values with true values. …”
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Design and Performance Characterization of the E-Core Outer-Rotor Hybrid-Excitation Flux Switching Machine by Zhiyuan Xu, Ming Cheng

“…This paper proposes an E-core outer-rotor hybrid-excitation flux switching (OR-HEFS) machine for in-wheel direct driving application. According to the general air gap field modulation theory, the magneto-motive force (MMF) permeance model was established to investigate the air gap flux density, and then the torque generation, the flux regulation principle, and the excitation-winding-induced voltage of the E-core OR-HEFS machine were analyzed. …”
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3D Part Machining Time Prediction With Parameter Extraction, Deep Learning, and 3D Data Augmentation by Umut Nazmi Aktan, Mehmet Dikmen

“…To that end, a regression model that can predict machining times using 3D models of detail parts used in the aerospace industry is proposed. …”
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Federated and ensemble learning framework with optimized feature selection for heart disease detection by Olfa Hrizi, Karim Gasmi, Abdulrahman Alyami, Adel Alkhalil, Ibrahim Alrashdi, Ali Alqazzaz, Lassaad Ben Ammar, Manel Mrabet, Alameen E.M. Abdalrahman, Samia Yahyaoui

“…We used particle swarm optimization (PSO) for feature selection, which optimized the most relevant features in conjunction with voting and stacking approaches to further increase the model's performance. …”
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Field scale wheat yield prediction using ensemble machine learning techniques by Sandeep Gawdiya, Dinesh Kumar, Bulbul Ahmed, Ramandeep Kumar Sharma, Pankaj Das, Manoj Choudhary, Mohamed A. Mattar

“…Big data framework was used to develop and refine several ensemble machine learning models based on field trial datasets. …”
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A deep learning model with machine vision system for recognizing type of the food during the food consumption by Pouya Bohlol, Soleiman Hosseinpour, Mahmoud Soltani Firouz

“…Ultimately, the EfficientNetB7 model with the Lion optimizer was chosen for the dataset. …”
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Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete by Miljan Kovačević, Marijana Hadzima-Nyarko, Predrag Petronijević, Tatijana Vasiljević, Miroslav Radomirović

“…An analysis of this optimal model highlighted the most influential variables affecting the bond strength. …”
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Early Prediction of Massive Transfusion for Patients With Traumatic Hemorrhage: Development of a Multivariable Machine Learning Model by Andrew J. Benjamin, MD, Andrew J. Young, MD, John B. Holcomb, MD, Erin E. Fox, PhD, Charles E. Wade, PhD, Chris Meador, MBA, Jeremy W. Cannon, MD, SM

“…Objective:. Develop a novel machine learning (ML) model to rapidly identify trauma patients with severe hemorrhage at risk of early mortality. …”
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