In-Field Forage Biomass and Quality Prediction Using Image and VIS-NIR Proximal Sensing with Machine Learning and Covariance-Based Strategies for Livestock Management in Silvopasto... by Claudia M. Serpa-Imbett, Erika L. Gómez-Palencia, Diego A. Medina-Herrera, Jorge A. Mejía-Luquez, Remberto R. Martínez, William O. Burgos-Paz, Lorena A. Aguayo-Ulloa

“…This study investigates the in-field dynamics of Mombasa grass (<i>Megathyrsus maximus</i>) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies. …”
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Interdependencies among SDGs: evidence-based insights for sustainable development indicators and policy by Ruttachai Seelajaroen, Boonlert Jitmaneeroj

“…These results underscore the importance of integrated, cross-sectoral policy indicators that align socio-economic goals with ecological sustainability. By combining machine learning with statistical modeling of interdependencies, this study contributes a scalable methodology for monitoring SDG interdependencies and developing actionable indicators for sustainable environmental governance.…”
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Forecasting monthly residential natural gas demand in two cities of Turkey using just-in-time-learning modeling. by Burak Alakent, Erkan Isikli, Cigdem Kadaifci, Tonguc S Taspinar

“…In the current study, the historical monthly NG consumption data between 2014 and 2024 provided by SOCAR, the local residential NG distribution company for two cities in Turkey, Bursa and Kayseri, was used to determine out-of-sample monthly NGD forecasts for a period of one year and nine months using various time series models, including SARIMA and ETS models, and a novel proposed machine learning method. …”
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Efficient Explainable Models for Alzheimer’s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning by Yogita Dubey, Aditya Bhongade, Prachi Palsodkar, Punit Fulzele

“…Also, challenges such as data imbalance and high-dimensional feature sets often hinder model performance. <b>Objective:</b> This paper aims to propose a computationally efficient, reliable, and transparent machine learning-based framework for the classification of Alzheimer’s disease patients. …”
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A tied-weight autoencoder for the linear dimensionality reduction of sample data by Sunhee Kim, Sang-Ho Chu, Yong-Jin Park, Chang-Yong Lee

“…Abstract Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. …”
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Noise Reduction Methods in the Vehicle Industry: Using Vibroacoustic Simulation for Sustainability by Krisztián Horváth, Ambrus Zelei

“…Capturing the current momentum of the industry, machine learning capabilities in vibroacoustic models can help engineers identify sources and eliminate or mitigate noise in the early design phase. …”
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Deep learning-based recognition model of football player’s technical action behavior using PCA–LBP algorithm by Hongtao Chen, Zhengbai Lin, Quan Xu

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Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals by Ahmed Shaheen, Liang Ye, Chrishni Karunaratne, Tapio Seppänen

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A deep operator network for Bayesian parameter identification of self-oscillators by Tobias Sugandi, Bayu Dharmaputra, Nicolas Noiray

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Classification of Nitrogen-Efficient Wheat Varieties Based on UAV Hyperspectral Remote Sensing by Yumeng Li, Chunying Wang, Junke Zhu, Qinglong Wang, Ping Liu

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ML-Driven Energy Savings for Cellular Baseband Units via Traffic Prediction by Aneta Kolackova, Viet Anh Phan, Jan Jerabek, Sergey Andreev, Jiri Hosek

“…PESBiU 2.0 uses granular interval datasets and machine learning (ML) models to predict traffic loads and optimize power states. …”
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Assessing the impact of multi-source environmental variables on soil organic carbon in different land use types of China using an interpretable high-precision machine learning meth... by Feng Wang, Ruilin Liang, Shuyue Li, Meiyan Xiang, Weihao Yang, Miao Lu, Yingqiang Song

“…To explore the impact of environmental factors on soil organic carbon (SOC) with machine learning (ML) model is of great significance for mitigating climate change and soil carbon sequestration and emission reduction. …”
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Decoding dynamic landslide hazard processes for a massive refugee camp in Bangladesh by Dewan Mohammad Enamul Haque, Ritu Roy, Sumya Tasnim, Shamima Ferdousi Sifa, Suniti Karunatillake, A.S.M. Maksud Kamal, Juan M. Lorenzo

“…Our GAM approach performs better than standard machine learning (ML) techniques (e.g., Random Forest, Support Vector Machine, Neural Networks), achieving an overall ROC-AUC of 0.84 and a mean cross-validated AUC of 0.81, compared to AUC (0.64-0.74) for ML models. …”
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Mapping football tactical behavior and collective dynamics with artificial intelligence: a systematic review by José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, José E. Teixeira, Eduardo Maio, Eduardo Maio, Eduardo Maio, Pedro Afonso, Pedro Afonso, Samuel Encarnação, Samuel Encarnação, Samuel Encarnação, Guilherme F. Machado, Guilherme F. Machado, Ryland Morgans, Tiago M. Barbosa, Tiago M. Barbosa, António M. Monteiro, António M. Monteiro, Pedro Forte, Pedro Forte, Pedro Forte, Ricardo Ferraz, Ricardo Ferraz, Luís Branquinho, Luís Branquinho, Luís Branquinho, Luís Branquinho

“…By synthesizing findings from 32 studies, this review elucidates the available AI-based techniques to analyze tactical behavior and identify the collective dynamic based on artificial neural networks, deep learning, machine learning, and time-series techniques. …”
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Land Cover Transformations in Mining-Influenced Areas Using PlanetScope Imagery, Spectral Indices, and Machine Learning: A Case Study in the Hinterlands de Pernambuco, Brazil by Admilson da Penha Pacheco, João Alexandre Silva do Nascimento, Antonio Miguel Ruiz-Armenteros, Ubiratan Joaquim da Silva Junior, Juarez Antonio da Silva Junior, Leidjane Maria Maciel de Oliveira, Sylvana Melo dos Santos, Fernando Dacal Reis Filho, Carlos Alberto Pessoa Mello Galdino

“…This approach proves the potential of remote sensing and machine learning techniques to effectively monitor environmental changes, reinforcing strategies for sustainable management in mining areas.…”
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Design of reinforcement learning based robust μ-synthesis controller for single phase grid-connected VSI by P. Shambhu Prasad, Alivelu M. Parimi

“…The order of the controller has been reduced with a balanced model reduction approach. A novel methodology of tuning the weighting functions of the controller with advanced machine learning-based reinforcement learning has been adapted and performance specifications of the controller have been studied with tuned weighting functions. …”
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Effective multimodal hate speech detection on Facebook hate memes dataset using incremental PCA, SMOTE, and adversarial learning by Emmanuel Ludivin Tchuindjang Tchokote, Elie Fute Tagne

“…To effectively address class imbalance and improve classification accuracy, our hybrid model combines ResNet for image processing with RoBERTa for text analysis, leveraging Synthetic Minority Over-sampling Technique (SMOTE) and Incremental Principal Component Analysis (PCA) combined with adversarial machine learning techniques. …”
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A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. by Ilaria Amodeo, Giorgio De Nunzio, Genny Raffaeli, Irene Borzani, Alice Griggio, Luana Conte, Francesco Macchini, Valentina Condò, Nicola Persico, Isabella Fabietti, Stefano Ghirardello, Maria Pierro, Benedetta Tafuri, Giuseppe Como, Donato Cascio, Mariarosa Colnaghi, Fabio Mosca, Giacomo Cavallaro

“…<h4>Introduction</h4>Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical data, to provide neonatal PH as the first outcome and, possibly: favorable response to fetal endoscopic tracheal occlusion (FETO), need for Extracorporeal Membrane Oxygenation (ECMO), survival to ECMO, and death. …”
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Player Tracking Data and Psychophysiological Features Associated with Mental Fatigue in U15, U17, and U19 Male Football Players: A Machine Learning Approach by José E. Teixeira, Pedro Afonso, André Schneider, Luís Branquinho, Eduardo Maio, Ricardo Ferraz, Rafael Nascimento, Ryland Morgans, Tiago M. Barbosa, António M. Monteiro, Pedro Forte

“…Optimizing recovery is crucial for maintaining performance and reducing fatigue and injury risk in youth football players. This study applied machine learning (ML) models to classify mental fatigue in U15, U17, and U19 male players using wearable signals, tracking data, and psychophysiological features. …”
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FLEM-XAI: Federated learning based real time ensemble model with explainable AI framework for an efficient diagnosis of lung diseases by Sivan Durga, Esther Daniel, Surleese Seetha, Vijaya Kumar Reshma, Vasily Sachnev

“…The computer-aided diagnosis helps medical professionals detect and classify lung diseases from chest X-rays by leveraging medical image processing and central server-based machine learning models. These technologies provide real-time assistance to analyze the input and help efficiently detect the abnormalities at the earliest. …”
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