Evaluation of different spectral indices for wheat lodging assessment using machine learning algorithms by Shikha Sharda, Sumit Kumar, Raj Setia, Prince Dhiman, N. R. Patel, Brijendra Pateriya, Ali Salem, Ahmed Elbeltagi

“…These indices were used as inputs to RF, DT, and SVM models. The optimal set of features were identified using random forest feature importance selection approach. …”
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Numerical Investigation on Vibration Performance of an Improved Switched Reluctance Machine with Double Auxiliary Slots by Zhengyuan Gao, Shanming Wang, Zhiguo An, Pengfei Sun

“…Considerable vibration and acoustic noise limit the further application of Switched Reluctance Machine (SRM) due to its structural characteristics and working principle. …”
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Optimized window functions for spectral analysis based on digital filters by R.V. Petrosian

“…The article addresses a relevant issue of improving the accuracy of spectral analysis in computerized systems by optimizing window functions used in the Discrete Fourier Transform (DFT). …”
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Machine Learning Approach to Aerodynamic Analysis of NACA0005 Airfoil: ANN and CFD Integration by Taiba Kouser, Dilek Funda Kurtulus, Srikanth Goli, Abdulrahman Aliyu, Imil Hamda Imran, Luai M. Alhems, Azhar M. Memon

“…The ANN has a two-layer architecture, 9 fixed neurons in the first hidden layer and a varying number of neurons in the second layer to achieve optimal performance. The model yielded coefficients of determination (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) of 0.994 (Coefficient of lift (<inline-formula> <tex-math notation="LaTeX">$C_{l}$ </tex-math></inline-formula>)) and 0.9615 (Coefficient of drag (<inline-formula> <tex-math notation="LaTeX">$C_{d}$ </tex-math></inline-formula>)) for training, and 0.9563 (<inline-formula> <tex-math notation="LaTeX">$C_{l}$ </tex-math></inline-formula>) and 0.9085 (<inline-formula> <tex-math notation="LaTeX">$C_{d}$ </tex-math></inline-formula>) for testing. …”
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Predictive Torque Control of Three Phase Axial Flux Permanent Magnet Synchronous Machines by Mohsen Siami, S. Asghar Gholamian

“…In predictive torque control presented in this paper, responses of torque and flux are computed for all possible switching states of the inverter at every sample time according to the discrete time model of the machine and then the switching state that optimizes ripples of torque and flux will be applied in next discrete-time interval. …”
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Design and research of English material archives management system based on machine learning technology by Yi Qin

“…This study aims to explore the optimal design of an English data archives management system based on machine learning technology to improve the accuracy and efficiency of data retrieval. …”
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A Guide to the Implementation and Design of Ex Vivo Perfusion Machines for Vascularized Composite Allotransplantation by Tessa E. Muss, MD, Amanda H. Loftin, DVM, Zachary H. Zamore, BA, Eleni M. Drivas, MD, Yi-Nan Guo, MD, Yichuan Zhang, MS, John Brassil, MS, Byoung Chol Oh, DVM, PhD, Gerald Brandacher, MD, FAST

“…Background:. Ex vivo machine perfusion (EVMP) is a versatile platform utilized in vascularized composite allotransplantation (VCA) to prolong preservation, salvage tissue, and evaluate graft viability. …”
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Enhancing Visual Perception in Sports Environments: A Virtual Reality and Machine Learning Approach by Taiyang Wang, Peng Luo, Sihan Xia

“…Furthermore, this study identifies the best-performing machine learning model for predicting sports perception, which is subsequently integrated with a genetic algorithm to optimize environmental design thresholds. …”
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Automated Seedling Contour Determination and Segmentation Using Support Vector Machine and Image Features by Samsuzzaman, Md Nasim Reza, Sumaiya Islam, Kyu-Ho Lee, Md Asrakul Haque, Md Razob Ali, Yeon Jin Cho, Dong Hee Noh, Sun-Ok Chung

“…The segmented boundary contour files were converted into annotation files to train a YOLOv8 model, which achieved a precision ranging from 96% to 98.5% and a recall ranging from 96% to 98%. …”
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Merging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics by N. D. Beres, N. D. Beres, J. Burkart, J. Burkart, E. Graf, Y. Zeder, L. A. Dailey, B. Weinzierl

“…The last model, using both the holographic images and fluorescence information for each particle, was the most optimal model used, providing the highest classification accuracy compared to employing models using only the holography or fluorescence response separately. …”
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Generative Diffusion Models for Compressed Sensing of Satellite LiDAR Data: Evaluating Image Quality Metrics in Forest Landscape Reconstruction by Andres Ramirez-Jaime, Gonzalo R. Arce, Nestor Porras-Diaz, Oleg Ieremeiev, Andrii Rubel, Vladimir Lukin, Mateusz Kopytek, Piotr Lech, Jarosław Fastowicz, Krzysztof Okarma

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AI4EF: Artificial Intelligence for Energy Efficiency in the building sector by Alexandros Menelaos Tzortzis, Georgios Kormpakis, Sotiris Pelekis, Ariadni Michalitsi-Psarrou, Evangelos Karakolis, Christos Ntanos, Dimitris Askounis

“…AI4EF (Artificial Intelligence for Energy Efficiency) is an advanced, user-centric tool designed to support decision-making in building energy retrofitting and efficiency optimization. Leveraging machine learning (ML) and data-driven insights, AI4EF (Artificial Intelligence for Energy Efficiency) enables stakeholders such as public sector representatives, energy consultants, and building owners—to model, analyze, and predict energy consumption, retrofit costs, and environmental impacts of building upgrades. …”
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Machine Learning Applications in Road Pavement Management: A Review, Challenges and Future Directions by Tiago Tamagusko, Matheus Gomes Correia, Adelino Ferreira

“…This review examines the integration of Machine Learning (ML) into Pavement Management Systems (PMS), presenting an analysis of state-of-the-art ML techniques, algorithms, and challenges for application in the field. …”
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What factors enhance students' achievement? A machine learning and interpretable methods approach. by Hui Mao, Ribesh Khanal, ChengZhang Qu, HuaFeng Kong, TingYao Jiang

“…This study addresses these limitations by employing an ensemble of five machine learning algorithms (SVM, DT, ANN, RF, and XGBoost) to model multivariate relationships between four behavioral and six instructional predictors, using final exam performance as our outcome variable. …”
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Aspect Based Sentiment Analysis on Shopee Application Reviews Using Support Vector Machine by Dyah Ayu Wulandari, Fitra A. Bachtiar, Indriati Indriati

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Machine Learning Applications in Gray, Blue, and Green Hydrogen Production: A Comprehensive Review by Xuejia Du, Shihui Gao, Gang Yang

“…Hydrogen is increasingly recognized as a key contributor to a low-carbon energy future, and machine learning (ML) is emerging as a valuable tool to optimize hydrogen production processes. …”
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Enhancing Invoice Processing Automation Through the Integration of DevOps Methodologies and Machine Learning by Oana-Alexandra Dragomirescu, Pavel-Cristian Crăciun, Ana Ramona Bologa

“…In today’s rapidly evolving digital landscape, organizations are increasingly seeking systemic approaches to optimize their financial operations, particularly in invoice processing. …”
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Dynamic scheduling for flexible job shop based on MachineRank algorithm and reinforcement learning by Fujie Ren, Haibin Liu

“…To improve the quality of the model solutions, a MachineRank algorithm (MR) is proposed, and based on the MR algorithm, seven composite scheduling rules are introduced. …”
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Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques by Behzad Vaferi, Mohsen Dehbashi, Reza Yousefzadeh, Ali Hosin Alibak

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Detecting cognitive impairment in cerebrovascular disease using gait, dual tasks, and machine learning by Vânia Guimarães, Inês Sousa, Miguel Velhote Correia

“…Nested cross-validation was used for model training, hyperparameter tuning, and evaluation. …”
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