Applications of Probabilistic Forecasting in Demand Response by María Carmen Ruiz-Abellón, Luis Alfredo Fernández-Jiménez, Antonio Guillamón, Antonio Gabaldón

“…The approach is illustrated using hourly demand data from a small Spanish city, and two machine learning methods were used to produce a set of probabilistic forecasts and compare results: Linear Quantile Regression and a Quantile Regression Forest. …”
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Optimized deep neural network architectures for energy consumption and PV production forecasting by Eghbal Hosseini, Barzan Saeedpour, Mohsen Banaei, Razgar Ebrahimy

“…The study evaluates the GA-PSO-enhanced Optimized Deep Neural Network (ODNN) against traditional DNNs and state-of-the-art machine learning methods on multiple real-world energy forecasting tasks. …”
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Integrating AI and Multi-objective Optimization for Enhanced Microgrid Energy Management Using Quadratic Programming by Agrawal Priyanka, Thethi H. Pal, Mohammad Q., Gupta Navya, Asha V., Reddy K. Jyothsna

“…The simulated machine learning possesses good generalisation capacity and an excellent learning structure. …”
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Recurrent Neural Network-Based Autoencoder for Problems of Automat-ic Time Series Analysis at Power Facilities by Matrenin P.V., Khalyasmaa A.I., Potachits Y.V.

“…Thus, despite the large amount of data, there is a shortage of labeled data suitable for training, validating and testing the machine learning models. Labeling by an expert takes too much time, so there is an actual task to automatically identify data fragments that are potentially of interest. …”
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ARTIFICIAL INTELLIGENCE AND BIG DATA ANALYSIS IN CRIME PREVENTION AND COMBAT by George-Marius ȚICAL

“…The development of explainable predictive models, the reduction of biases, and the adoption of clear international regulations are essential. …”
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Control of Overfitting with Physics by Sergei V. Kozyrev, Ilya A. Lopatin, Alexander N. Pechen

“…While there are many works on the applications of machine learning, not so many of them are trying to understand the theoretical justifications to explain their efficiency. …”
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Diabetes-focused food recommender system (DFRS) to enabling digital health. by Esmael Ahmed, Mohammed Oumer, Medina Hassan

“…The methodology involves data collection from diverse patient profiles and model development using Graph Neural Networks (GNN) and other machine learning techniques. …”
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Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture by Khosro Rezaee, Safoura Farsi Khavari, Mojtaba Ansari, Fatemeh Zare, Mohammad Hossein Alizadeh Roknabadi

“…Six standard databases were utilized, achieving an average accuracy of 90.23% with our proposed model, showcasing a 3–4% average accuracy improvement and a 10% variance reduction. …”
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Comparison of sample preparation methods for higher heating values in various sugarcane varieties using near-infrared spectroscopy by Kantisa Phoomwarin, Khwantri Saengprachatanarug, Jetsada Posom, Seree Wongpichet, Kittipong Laloon, Arthit Phuphaphud

“…The integration of NIR spectroscopy with machine learning presents a practical and scalable approach for accelerating the evaluation of sugarcane clones, offering significant advantages in terms of time efficiency, cost reduction, and sample throughput.…”
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Application of ai driven system for estimation of orders in the printing industry by Kostaryev D., Tkachenko V., Sizova N.

“…Initially designed for IT project evaluation, the system was enhanced with machine learning and data mining to deliver automated, transparent, and accurate order assessment. …”
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Laser powder bed fusion dataset for relative density prediction of commercial metallic alloys by Germán Omar Barrionuevo, Iván La Fé-Perdomo, Jorge A. Ramos-Grez

“…This dataset offers a valuable resource for researchers to benchmark their results, better understand key factors influencing RD, and validate models or explore new machine-learning approaches tailored to L-PBF.…”
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Innovative AI Solutions for Water-Efficient Smart Irrigation in Agriculture by Saha Laboni, Dubey Ahilya

“…The proposed AI solutions are integrated platform of machine learning algorithms and sensor data, predictive analytics for the purpose to generate a water efficient irrigation framework reacts dynamically to the crops' needs and environmental conditions. …”
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Optimizing Euclidean Distance Computation by Rustam Mussabayev

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Synergy pathway innovation of carbon emission trading, tradable green certificate and green power trading policies: achieving China’s dual carbon goals by Lili Liu, Tiantian Feng, Cheng Zhong, Mingli Cui, Haoran Wang

“…A dual machine learning model is innovatively introduced to test the robustness of the results. …”
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Benchmarking Variants of Recursive Feature Elimination: Insights from Predictive Tasks in Education and Healthcare by Okan Bulut, Bin Tan, Elisabetta Mazzullo, Ali Syed

“…To help researchers better understand and apply RFE more effectively, this study organizes existing variants into four methodological categories: (1) integration with different machine learning models, (2) combinations of multiple feature importance metrics, (3) modifications to the original RFE process, and (4) hybridization with other feature selection or dimensionality reduction techniques. …”
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Mechanisms and management of self-resolving lumbar disc herniation: bridging molecular pathways to non-surgical clinical success by Yan Zhao, Zhiwei Jia, Abudunaibi Aili, Aikeremujiang Muheremu

“…Future research should focus on elucidating the molecular mechanisms of resorption, regulation of inflammatory response, macrophage polarization, matrix degradation, immune privilege and neovascularization, developing advanced imaging techniques to predict resorption potential, and exploring personalized treatment strategies based on machine learning and deep learning prediction models.…”
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Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence by Yi-Ming Chen, Tzu-Hung Hsiao, Ching-Heng Lin, Yang C. Fann

“…Through the synergistic approach of integrating AI across diverse data sets, clinicians gain a holistic view of patient health and potential risks. Machine learning models excel at identifying high-risk patients, predicting disease activity, and optimizing therapeutic strategies based on clinical, genomic, and immunological profiles. …”
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EFLOP: a sparsity-aware metric for evaluating computational cost in spiking and non-spiking neural networks by Simon Narduzzi, Friedemann Zenke, Shih-Chii Liu, L Andrea Dunbar

“…Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been proposed as candidates for these tasks. …”
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Food Waste Detection in Canteen Plates Using YOLOv11 by João Ferreira, Paulino Cerqueira, Jorge Ribeiro

“…This work presents a Computer Vision (CV) platform for Food Waste (FW) detection in canteen plates exploring a research gap in automated FW detection using CV models. A machine learning methodology was followed, starting with the creation of a custom dataset of canteen plates images before and after lunch or dinner, and data augmentation techniques were applied to enhance the model’s robustness. …”
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Artificial Intelligence-Supported Spatial Scanning for Enhanced Real-Time Spectral Analysis of Heterogeneous Media by Bassem Mortada, Samir Abozyd, Bassam Saadany, Yasser M. Sabry, Diaa Khalil, Tarik Bourouina

“…Analysis time and efficiency of processing the raw spectral data are further boosted by the implementation of machine learning models for identification and quantification purposes. …”
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