A Hybrid Quantum-Classical Approach for Multi-Class Skin Disease Classification Using a 4-Qubit Model by Aravinda C V, Emerson Raja Joseph, Sultan Alasmari

“…To ensure practical reusability, the trained model, including quantum circuits, optimized parameters, and PCA transformations, is saved for future deployment. …”
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Adoption of Transplanting Machine among Rice Farmers in Maluku, Indonesia by Inta Prista Novembriyanty Damanik, Meilvis Elsworth Tahitu

“…The strategies to increase the adoption of the machine were to improve the quality of social characteristics of farmers, fulfill all the characteristics of the machine, and provide supporting facilities for the optimal use of the machine. …”
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Machine Learning Techniques for Enhanced Intrusion Detection in IoT Security by Hanadi Hakami, Muhammad Faheem, Majid Bashir Ahmad

“…This research introduces a new model that leverages machine learning (ML) and deep learning (DL) to enhance detection effectiveness and ensure reliability. …”
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Comparison of Machine Learning Methods for Predicting Electrical Energy Consumption by Retno Wahyusari, Sunardi Sunardi, Abdul Fadlil

“…The study employs three Machine Learning (ML) models: k-Nearest Neighbors (KNN), Random Forest (RF), and CatBoost. …”
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Video Coding for Machines With Neural-Network-Based Chroma Synthesis by Mateusz Lorkiewicz, Slawomir Rozek, Olgierd Stankiewicz, Tomasz Grajek, Slawomir Mackowiak, Marek Domanski

“…Within the ISO/IEC standardization activities, efforts are underway to develop a new standard optimized for machine vision tasks rather than just for human-oriented video consumption. …”
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Calculation of hydrogen dispersion in cushion gases using machine learning by Ali Akbari, Mehdi Maleki, Yousef Kazemzadeh, Ali Ranjbar

“…This study addresses these challenges by integrating experimental data with advanced machine learning (ML) techniques to model hydrogen dispersion. …”
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Enabling interpretable machine learning for biological data with reliability scores. by K D Ahlquist, Lauren A Sugden, Sohini Ramachandran

“…Alongside the rapid growth of machine learning, there have also been growing pains: some models that appear to perform well have later been revealed to rely on features of the data that are artifactual or biased; this feeds into the general criticism that machine learning models are designed to optimize model performance over the creation of new biological insights. …”
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AI-based algorithms for estimating hydrochar properties in terms of biomass ultimate analysis by Raouf Hassan, Ali sharifzadegan, Alireza Baghban

“…By demonstrating these models' superior accuracy and reliability, this work highlights the feasibility of machine learning-driven predictive frameworks for optimizing biomass conversion into hydrochar.…”
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Non-destructive textural quality assessment of peaches and nectarines using near-infrared spectroscopy integration time by Eva Cristina Correa, Paola Baltazar, Pilar Barreiro, Natalia Hernández-Sánchez, Lourdes Lleó, Ángela Melado-Herreros, Belén Diezma

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Impact of data spatial resolution on barley yield prediction mapping by F. Ksantini, M. Quemada, N. Arencibia-Pérez, A.F. Almeida-Ñauñay, E. Sanz, Ana M. Tarquis

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Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma by Abdul Basit Ahanger, Syed Wajid Aalam, Tariq Ahmad Masoodi, Asma Shah, Meraj Alam Khan, Ajaz A. Bhat, Assif Assad, Muzafar Ahmad Macha, Muzafar Rasool Bhat

“…Dimensionality reduction and feature selection were applied and Data imbalance was addressed with SMOTE. Five ML models were trained to predict signaling pathways, with Grid Search optimizing hyperparameters and 5-fold cross-validation ensuring unbiased performance. …”
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GreenNav: Spatiotemporal Prediction of CO₂ Emissions in Paris Road Traffic Using a Hybrid CNN-LSTM Model by Youssef Mekouar, Imad Saleh, Mohammed Karim

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DTIP-WINDGRU a novel drug-target interaction prediction with wind-enhanced gated recurrent unit by Kavipriya Gananathan, D. Manjula, Vijayan Sugumaran

“…Finally, the Wind Driven Optimization (WDO) algorithm is utilized to optimally choose the hyperparameters involved in the GRU model. …”
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Optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imagery by Youseef Alotaibi, Krishnaraj Nagappan, Tamilvizhi Thanarajan, Surendran Rajendran

“…The VDTC-CEOADL technique employs a YOLO-HR object detector with a residual network as the backbone model to accomplish this. In addition, CEOA based hyperparameter optimizer is designed for the parameter tuning of the ResNet model. …”
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Advanced multiscale machine learning for nerve conduction velocity analysis by Hossein Sadeghi

“…The proposed framework combines: (i) entropy-optimized wavelet analysis for adaptive multiscale signal decomposition, (ii) thermodynamically-regularized neural networks incorporating Arrhenius kinetics, and (iii) stochastic progression models for uncertainty-aware longitudinal tracking. …”
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Black-box and white-box machine learning tools to estimate the frost formation condition during cryogenic CO2 capture from natural gas blends by Farag M.A. Altalbawy, Fadhel F. Sead, Dharmesh Sur, Anupam Yadav, José Gerardo León Chimbolema, Suhas Ballal, Abhayveer Singh, Anita Devi, Kamal Kant Joshi, Nizomiddin Juraev, Hossein Mahabadi Asl

“…This study deals with the development of machine learning models for predicting the FFT of CO2 in natural gas blends. …”
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Beyond <i>xG</i>: A Dual Prediction Model for Analyzing Player Performance Through Expected and Actual Goals in European Soccer Leagues by Davronbek Malikov, Jaeho Kim

“…Moreover, we employ regression machine learning models, particularly ridge regression, which demonstrates strong performance in forecasting <i>xGg</i> and <i>aGg</i>, outperforming other models in our comparative assessment. …”
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Evaluating the impact of field-measured tree height errors correction on aboveground biomass modeling using airborne laser scanning and GEDI datasets in Brazilian Amazonia by Nadeem Fareed, Izaya Numata

“…We optimized Htree – DBH allometric model based on the previously developed pantropical model of the Western Amazon using existing FFIs data. …”
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Surface water mapping from remote sensing in Egypt’s dry season using an improved U-Net model with multi-scale information and attention mechanism by Yong Li, Xiuhui Liu, Vagner Ferreira, Heiko Balzter, Huiyu Zhou, Ying Ge, Meiyun Lai, Simin Chu, Han Ding, Zhenrong Gu

“…The extraction accuracy can be improved by combining convolutional layers for local feature extraction with Vision Transformer using Manhattan self-attention for global context information. Our model attains optimal performance with IoU, F1-score, recall, and precision reaching 94.26%, 97.05%, 98.18%, and 95.94%, respectively, compared to traditional machine learning methods, particularly in challenging areas with small water bodies, complex backgrounds, and eutrophic water boundaries. …”
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Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM by Fuyu Wang, Huiying Xu, Huifen Ye, Yan Li, Yibo Wang

“…In order to address challenges such as the large computational workload, tedious training process, and multiple influencing factors associated with predicting earthquake casualties, this study proposes a Support Vector Machine (SVM) model utilizing Principal Component Analysis (PCA) and Bayesian Optimization (BO). …”
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