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

“…This research addresses a significant gap in energy demand forecasting across three building types by comparing six machine learning algorithms: Artificial Neural Networks, Random Forest, XGBoost, Radial Basis Function Network, Autoencoder, and Decision Trees. …”
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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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Machine Learning Unveils the Impacts of Key Elements and Their Interaction on the Ambient-Temperature Tensile Properties of Cast Titanium Aluminides Employing SHAP Analysis by Shiqiu Liu, Li Liang

“…Comparative analysis of three algorithms within the training dataset proved the random forest regression (RFR) as the optimal modeling approach. …”
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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

“…This toolbox provides algorithms for supervised and unsupervised source separation (SS). …”
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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

“…Four machine learning algorithms (Random Forest, Support Vector Classification, Gradient Boosting, and Extremely Randomized Tree Classifier) were applied to create eight classification models based on bacterial abundance at the genus level and KEGG pathways. …”
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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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Exosomal microRNA signatures in youth at clinical high risk for bipolar disorder by Xinyu Meng, Shengmin Zhang, Yingzhen Xu, Zaohui Ma, Shuzhe Zhou, Yantao Ma, Hong Ma, Xin Yu, Lili Guan

“…Using machine-learning algorithms, target miRNAs were selected from differentially expressed candidates. …”
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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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Constitutional-Legal Aspect of Creating Large Language Models: the Problem of Digital Inequality and Linguistic Discrimination by I. G. Ilin

“…While direct forms of discrimination can be detected in predictive algorithms, generative models create more subtle but no less significant cumulative effects. …”
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AgrUNet: A Multi-GPU UNet Based Model for Crops Classification by Andrea Miola, Enrico Calore, Sebastiano Fabio Schifano

“…This approach requires high-performance computing systems since DL algorithms are known to be very computing-heavy, and recent multi-GPU HPC systems can boost by one or two orders of magnitude the processing power of classical computing systems based only on CPUs. …”
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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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Responsible AI in structural engineering: a framework for ethical use by Vagelis Plevris, Haidar Hosamo

“…This study addresses these issues through eight fictional but realistic case studies that illustrate plausible ethical dilemmas, such as algorithmic bias in predictive models and tensions between AI-generated recommendations and human engineering judgment. …”
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Optimization of multi-objective feature regression models for designing performance assessment methods in college and university educational reform by Fengjun Qi, Zhenping Liu, Wenzheng Zhang, Zhenjie Sun

“…Experimental evaluations on three datasets—Education Reform (EDU-REFORM), Programme for International Student Assessment (PISA), and National Assessment of Educational Progress (NAEP)—demonstrate the superior performance of MOFR-LSF, achieving relative root mean square error (RRMSE) values of 0.634, 0.332, and 0.925, respectively, outperforming existing multi-objective regression algorithms. The proposed model not only enhances predictive accuracy but also strengthens the scientific validity and fairness of performance evaluations, offering meaningful contributions to educational reform in colleges and universities. …”
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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

“…Spirometry indices, patient symptoms, and vital statistics were used to calculate the VIEW™ score using machine learning algorithms. Result: The Bland–Altman plots showed that FEV1 reduced slightly up to 21–28 days and comes back to normal around 42 days. …”
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