Energy Demand Forecasting Scenarios for Buildings Using Six AI Models by Khaled M. Salem, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández

“…The primary aim is to identify the most effective model for predicting energy consumption based on historical data, contributing to the relationship between energy systems and urban well-being. …”
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Improving Sharpness-Aware Minimization Using Label Smoothing and Adaptive Adversarial Cross-Entropy Loss by Tanapat Ratchatorn, Masayuki Tanaka

“…Recent advances in learning algorithms have identified loss surface sharpness as an effective metric for reducing the generalization gap. …”
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AC-YOLO: A lightweight ship detection model for SAR images based on YOLO11. by Rui He, Dezhi Han, Xiang Shen, Bing Han, Zhongdai Wu, Xiaohu Huang

“…However, existing SAR ship detection algorithms encounter two major challenges: limited detection accuracy and high computational cost, primarily due to the wide range of target scales, indistinct contour features, and complex background interference. …”
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soundscape_IR: A source separation toolbox for exploring acoustic diversity in soundscapes by Yi‐Jen Sun, Shih‐Ching Yen, Tzu‐Hao Lin

“…Abstract Soundscapes contain rich acoustic information associated with animal behaviours, environmental characteristics and human activities, providing opportunities for predicting biodiversity changes and associated drivers. …”
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Exploring community pharmacist's psychological intentions to adopt generative artificial intelligence (GenAI) chatbots for patient information, education, and counseling by Hafidz Ihsan Hidayatullah, Muhammad Taufiq Saifullah, Muhammad Thesa Ghozali, Ayesha Aziz

“…Generative AI (GenAI) chatbots, driven by advanced machine learning algorithms, are emerging as transformative tools for enhancing patient education, information dissemination, and counseling (EIC) in healthcare. …”
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AIoT-Driven Human Activity Recognition for Versatile Framework on Multipurpose Applications by Nanik Triwahyuni, Eni Wardihani, Aminuddin Rizal, Samuel Beta, Ricky Sambora, Rindang Oktaviani

“…The framework integrates sensor data from wearable IoT devices, which is then processed by AI algorithms to classify and predict human activities in real-time. …”
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Parametric Optimization of Train Brake Pad Using Reverse Engineering with Digital Photogrammetry 3D Modeling Method by P Paryanto, Muhammad Faizin, R Rusnaldy

“…Additionally, RE and parametric modeling could assist in creating durable, cost-effective alternatives, and predicting appropriate replacements.…”
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Utilizing Artificial Intelligence for Microbiome Decision-Making: Autism Spectrum Disorder in Children from Bosnia and Herzegovina by Džana Bašić-Čičak, Jasminka Hasić Telalović, Lejla Pašić

“…Additionally, we developed machine learning classifiers to differentiate between the two groups using microbial abundance and predicted functional pathways. Methods: A total of 60 gut microbiome samples (16S rRNA sequences) were analyzed, with 44 from children with ASD and 16 from neurotypical children. …”
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COVID-19 REINFECTION: DOES IT MATTER? by E. Füsun Karaşahin, Ömer Karaşahin, Mehtap Hülya Aslan

“…Conclusion: This study demonstrated that the frequency of COVID-19 reinfection is higher than predicted. The complex algorithms recommended by international health institutions make it difficult to detect these cases. …”
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Hydrologic model calibration approaches for highly regulated river basin: A comprehensive assessment by Md Rasel Sheikh, Paulin Coulibaly

“…The study highlights the challenges of using reservoir outflow as an operational control, which leads to poor water level predictions, reinforcing the need for more region-specific operational constraints. …”
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Coupled memory sampled-data control for fractional stochastic wind energy conversion models by Girija Panneerselvam, Prakash Mani

“…Theoretically, this paper introduces a permanent magnet synchronous generator (PMSG)-based WECS, which is crucial in predicting and optimizing system behavior without physical implementation and also useful in analyzing the various operating conditions. …”
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ML-Enabled Millimeter-Wave Software-Defined Radio With Programmable Directionality by Marc Jean, Murat Yuksel, Xun Gong

“…We design and implement Q-learning, Double Q-learning, and Deep Q-learning algorithms that passively inspect the Received Signal Strength (RSS) of the mmWave beam and autonomously determine the predicted AoA. …”
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AgrUNet: A Multi-GPU UNet Based Model for Crops Classification by Andrea Miola, Enrico Calore, Sebastiano Fabio Schifano

“…The deployment of high-resolution satellite missions, like LandSat and Copernicus Sentinel, combined with AI Deep Learning (DL) methodologies has revolutionized Earth Observation science, enabling studies on yield predictions, soil classifications, and crop mappings on large areas, and the analysis and processing of Big Data using innovative approaches. …”
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Evaluation of underground hard rock mine pillar stability using gene expression programming and decision tree‐support vector machine models by Mohammad H. Kadkhodaei, Ebrahim Ghasemi, Jian Zhou, Melika Zahraei

“…This study mainly focuses on developing two practical models to predict pillar stability status. For this purpose, two robust models were developed using a database including 236 case histories from seven underground hard rock mines, based on gene expression programming (GEP) and decision tree‐support vector machine (DT‐SVM) hybrid algorithms. …”
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View Score: An early warning score to detect possible complications among COVID-19 patients by Meenakshi Bhakare, Rajkumar Nikalje, Reshma Patil, Urvi Shukla, Rupesh Bokade, Gajanan Sakhare, Shardul Joshi, Aditi Pais

“…Conclusion: Home-based spirometry acts as an effective tool for COVID-19 patients to predict lung complications and also promote self-monitoring thereby reducing the burden on the health system.…”
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Computational identification of phospho-tyrosine sub-networks related to acanthocyte generation in neuroacanthocytosis. by Lucia De Franceschi, Giovanni Scardoni, Carlo Tomelleri, Adrian Danek, Ruth H Walker, Hans H Jung, Benedikt Bader, Sara Mazzucco, Maria Teresa Dotti, Angela Siciliano, Antonella Pantaleo, Carlo Laudanna

“…Here, we coupled tyrosine-phosphoproteomic analysis to topological network analysis. We aimed to predict signaling sub-networks possibly involved in the generation of acanthocytes in patients affected by the two core NA disorders, namely McLeod syndrome (MLS, XK-related, Xk protein) and chorea-acanthocytosis (ChAc, VPS13A-related, chorein protein). …”
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Early detection of bloodstream infection in critically ill children using artificial intelligence by Hye-Ji Han, Kyunghoon Kim, June Dong Park

“…Conclusions We developed a machine learning model that predicts BSI with acceptable performance. Further research is necessary to validate its effectiveness.…”
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Understanding Acceleration-Based Load Metrics: From Concepts to Implementation by João Freitas, Alexandre Moreira, João Carvalho, Filipe Conceição, Luisa Estriga

“…In time-series comparisons, Jerk Modulus and cAccel’Rate were predictably found to be nearly identical, while Body Load was the most distinct. …”
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Stability indicators in network reconstruction. by Michele Filosi, Roberto Visintainer, Samantha Riccadonna, Giuseppe Jurman, Cesare Furlanello

“…The number of available algorithms to infer a biological network from a dataset of high-throughput measurements is overwhelming and keeps growing. …”
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Edge Artificial Intelligence Device in Real-Time Endoscopy for Classification of Gastric Neoplasms: Development and Validation Study by Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee

“…Objective: We previously developed artificial intelligence (AI) diagnosis algorithms for predicting the six classes of stomach lesions. …”
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