Nature-inspired MPPT algorithms for solar PV and fault classification using deep learning techniques by S. Senthilkumar, V. Mohan, S. P. Mangaiyarkarasi, R. Gandhi Raj, K. Kalaivani, N. Kopperundevi, M. Chinnadurai, M. Nuthal Srinivasan, L. Ramachandran

“…To select the best optimization model for MPPT under PSC, the nature-inspired dragonfly algorithm (DA), moth flame optimization algorithm (MFOA), grasshopper optimization algorithm (GOA), and salp swarm optimization algorithm (SSOA) are used in this work to evaluate the tracking efficiency (TE) of the solar PV systems. …”
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A multi-modal graph-based framework for Alzheimer’s disease detection by Najmeh Mashhadi, Razvan Marinescu

“…Each directed path in the graph functions as a DL predictor, supporting both forward propagation for transforming data representations, as well as backpropagation for model finetuning, saliency map computation, and input data optimization. …”
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Enhancing water saturation predictions from conventional well logs in a carbonate gas reservoir with a hybrid CNN-LSTM model by Ali Gohari Nezhad, Mohammad Emami Niri

“…The methodology includes data pre-processing to de-noise and transform data into a proper format, feature engineering followed by feature selection based on domain knowledge, models’ training, testing, and optimizing. XGBoost hyperparameters were tuned using grid search, and each neural network model was optimized using a genetic algorithm. …”
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Green Video Transcoding in Cloud Environments Using Kubernetes: A Framework With Dynamic Renewable Energy Allocation and Priority Scheduling by B. M. Beena, Prashanth Cheluvasai Ranga, A. Vinitha Chowdary, Rohan Gamidi, M. Hemasri, Tejaswi Muppala

“…The research addresses these challenges by developing a green, energy-aware video transcoding system that predicts energy availability from renewable sources (solar and wind) using machine learning techniques and optimizes tasks allocation. …”
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Evidential deep learning for uncertainty quantification and out-of-distribution detection in jet identification using deep neural networks by Ayush Khot, Xiwei Wang, Avik Roy, Volodymyr Kindratenko, Mark S Neubauer

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A Survey on Statistical and ML-Based Demand Forecasting Methods for Spare Parts in Aviation by Chelsea M. Zuvieta, Joffrey L. Leevy, Taghi M. Khoshgoftaar

“…While classical methods remain popular due to their ease of use, explainability, and cost-effectiveness, ML models demonstrate potential for higher accuracy under optimal conditions. …”
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Development of an AI-Empowered Novel Digital Monitoring System for Inhalation Flow Profiles by Ziyi Fan, Yuqing Ye, Jiale Chen, Ying Ma, Jesse Zhu

“…Four optimal machine learning models were selected for subsequent inhalation parameter prediction, given their superior generalization ability. …”
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An AI-Based Framework for Characterizing the Atmospheric Fate of Air Pollutants Within Diverse Environmental Settings by Nataša Radić, Mirjana Perišić, Gordana Jovanović, Timea Bezdan, Svetlana Stanišić, Nenad Stanić, Andreja Stojić

“…This study introduces a novel artificial intelligence (AI) modeling framework that combines machine learning algorithms optimized through metaheuristics with explainable AI to capture complex interactions among pollutant concentrations, meteorological data, and socio-economic indicators. …”
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A Structured and Methodological Review on Multi-View Human Activity Recognition for Ambient Assisted Living by Fahmid Al Farid, Ahsanul Bari, Abu Saleh Musa Miah, Sarina Mansor, Jia Uddin, S. Prabha Kumaresan

“…We examine how activity recognition systems have transitioned to multi-view architectures using advanced deep learning models optimized for Ambient Assisted Living, thereby improving accuracy and robustness. …”
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AI-Powered Forecasting of Environmental Impacts and Construction Costs to Enhance Project Management in Highway Projects by Joon-Soo Kim

“…The optimal ANN yielded average error rates of 29.8% for EL and 21.0% for CC at the design stage. …”
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Design and control algorithm of a motion sensing-based fruit harvesting robot by Ziwen CHEN, Yuhang CHEN, Hui LI, Pei WANG

“…The D-H method is employed for both forward and inverse kinematic modeling of the robotic arm. A four-step inverse kinematic optimal solution selection method, including mechanical interference, correctness, rationality, and smoothness of motion, is proposed. …”
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Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models by Wang Yuxin

“…This study examines the effectiveness of four machine learning models—Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM)—in forecasting traffic patterns using both web-based and real-world datasets. …”
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Efficient Management of Safety Documents Using Text-Based Analytics to Extract Safety Attributes From Construction Accident Reports by Vedat Togan, Fatemeh Mostofi, Onur Behzat Tokdemir, Fethi Kadioglu

“…Future work should focus on API creation, secure machine learning pipelines, and optimized deployment of LLMs, particularly in complex contexts.…”
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Dissecting the link between PD-1/PD-L1-based immunotherapy and cancer pain: mechanisms, research implications, and artificial intelligence perspectives by Marco Cascella, Brigida Miranda, Carmen Gagliardi, Lucia Santaniello, Milena Mottola, Alida Mancusi, Laura Ferrara, Federica Monaco, Francesca Gargano, Francesco Perri, Alessandro Ottaiano, Maurizio Capuozzo, Ornella Piazza, Stefano Pepe, Anna Crispo, Agostino Guida, Giovanni Salzano, Giustino Varrassi, Luigi Liguori, Francesco Sabbatino, The TRIAL Group

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Artificial Intelligence Models for Predicting Stock Returns Using Fundamental, Technical, and Entropy-Based Strategies: A Semantic-Augmented Hybrid Approach by Gil Cohen, Avishay Aiche, Ron Eichel

“…This study examines the effectiveness of combining semantic intelligence drawn from large language models (LLMs) such as ChatGPT-4o with traditional machine-learning (ML) algorithms to develop predictive portfolio strategies for NASDAQ-100 stocks over the 2020–2025 period. …”
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Leveraging Large Language Models for Discrepancy Value Prediction in Custody Transfer Systems: A Comparative Analysis of Probabilistic and Point Forecasting Approaches by Fiki Hidayat, Arbi Haza Nasution, Fajril Ambia, Dike Fitriansyah Putra, Mulyandri

“…This study evaluates the effectiveness of Large Language Models (LLMs), specifically the Chronos-FineTuning Amazon Chronos T5 Small model, alongside statistical, machine learning, and deep learning models, in both probabilistic and point forecasting tasks. …”
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Learning-Based Energy Management System for Scheduling of Appliances inside Smart Homes by Nastaran Gholizadeh, Mehrdad Abedi, Hamed Nafisi, Mousa Marzband

“…The proposed structure is formulated as a mixed-integer linear model with its optimization performed in the General Algebraic Modeling System environment. …”
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Optimisation Study of Investment Decision-Making in Distribution Networks of New Power Systems—Based on a Three-Level Decision-Making Model by Wanru Zhao, Ziteng Liu, Rui Zhang, Mai Lu, Wenhui Zhao

“…Next, the Pearson correlation coefficient is employed to screen key influencing factors, and in conjunction with the grey MG(1,1) model and the support vector machine algorithm, precise forecasting of the investment scale is achieved. …”
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Study on debris flow vulnerability of ensemble learning model based on spy technology A case study of upper Minjiang river basin by Yutao Chen, Ning Li, Fucheng Xing, Han Xiang, Zilong Chen

“…In this paper, a debris flow susceptibility assessment model is constructed based on RF (Random Forest) and XGBoost (Extreme Gradient Boosting) models with Stacking ensmble learning method, and SPY technique is introduced to optimize the negative sample selection. …”
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Combating electricity fraud: Employing hybrid learning and computer vision for sustainable energy management by Jui-Sheng Chou, Nader Anwar Charaf, Dani Nugraha Limantono, Hoang-Minh Nguyen

“…The performance of these models is rigorously evaluated using various performance metrics, with results showing that the machine learning model with hyperparameters optimized by a metaheuristic optimization algorithm achieved the highest prediction accuracy. …”
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