The Use of BIM Models and Drone Flyover Data in Building Energy Efficiency Analysis by Agata Muchla, Małgorzata Kurcjusz, Maja Sutkowska, Raquel Burgos-Bayo, Eugeniusz Koda, Anna Stefańska

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Parameter Tuning for Local-Search-Based Matheuristic Methods by Guillermo Cabrera-Guerrero, Carolina Lagos, Carolina Castañeda, Franklin Johnson, Fernando Paredes, Enrique Cabrera

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From Baseline to Best Practice: An Advanced Feature Selection, Feature Resampling and Grid Search Techniques to Improve Injury Severity Prediction by Soukaina EL Ferouali, Zouhair Elamrani Abou Elassad, Sara Qassimi, Abdelmounaîm Abdali

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Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique by Daniyal Alghazzawi, Hayat Ullah, Naila Tabassum, Sahar K. Badri, Muhammad Zubair Asghar

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Predicting patient risk of leaving without being seen using machine learning: a retrospective study in a single overcrowded emergency department by Arianna Scala, Teresa Angela Trunfio, Massimo Majolo, Michelangelo Chiacchio, Giuseppe Russo, Paolo Montuori, Giovanni Improta

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Impact of missing electronic fetal monitoring signals on perinatal asphyxia: a multicohort analysis by Debjyoti Karmakar, Lochana Mendis, Emerson Keenan, Marimuthu Palaniswami, Roxanne Hastie, Enes Makalic, Fiona Brownfoot

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Enhancing Medical Image Classification with Unified Model Agnostic Computation and Explainable AI by Elie Neghawi, Yan Liu

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Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability by Amir Reza Nikzad, Amr Adel Mohamed, Bala Venkatesh, John Penaranda

“…Our proposal comprises: 1) ovel deep learning-based architecture with a few convolutional neural network and long short-term memory (CNN-LSTM) modules to represent feeder connected aggregate models of DERs and loads and associated training algorithms; 2) method for estimating aggregate capacities of connected renewables and loads; and 3) method for short-term (hourly) high-resolution forecasting. …”
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Potential Health Risks of Chloroacetanilide Herbicides: An In Silico Analysis by Nihan Akıncı Kenanoğlu, Ahmet Ali Berber, Şefika Nur Demir

“…Vega Hub uses QSAR models, Toxtree uses a decision tree approach, Lazar uses data mining algorithms, and TEST uses QSAR methods to estimate toxicity. …”
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Digital pathology-based artificial intelligence model to predict microsatellite instability in gastroesophageal junction adenocarcinomas by Zhenqian Li, JingQi Chen, Miaomiao Sun, Daoming Li, Kuisheng Chen

“…A whole-slide image (WSI)-level AI model was constructed by integrating deep learning- generated pathological features with six machine learning algorithms.ResultsThe MLP model showed demonstrated the highest performance in predicting MSI-H in the test cohort, achieving an AUC of 93.3%, a sensitivity of 0.841, and a specificity of 0.952. …”
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Intelligent Manufacturing in Wine Barrel Production: Deep Learning-Based Wood Stave Classification by Frank A. Ricardo, Martxel Eizaguirre, Desmond K. Moru, Diego Borro

“…Several techniques using classical image processing and deep learning have been developed to detect tree-ring boundaries, but they often struggle with woods exhibiting heterogeneity and texture irregularities. (2) Methods: This study proposes a hybrid approach combining classical computer vision techniques for preprocessing with deep learning algorithms for classification, designed for continuous automated processing. …”
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Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras by Alejandro Dionis-Ros, Joan Vila-Francés, Rafael Magdalena-Benedito, Fernando Mateo, Antonio J. Serrano-López

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Mitigating the Risk of Cascading Blackouts: A Data Inference Based Maintenance Method by Feng Liu, Jinpeng Guo, Xuemin Zhang, Yunhe Hou, Shengwei Mei

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Hybrid Automata-Based Control Framework for Real-Time Optimization in Space-Based Solar Power Transmission by Patil Ankita, Ranjan Mritunjay, Deore Kalyani, Sonje Pranjal, Patil Kiran, Patil Rutuja

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Artificial Intelligence in IVF Laboratories: Elevating Outcomes Through Precision and Efficiency by Yaling Hew, Duygu Kutuk, Tuba Duzcu, Yagmur Ergun, Murat Basar

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DS4NN: Direct training of deep spiking neural networks with single spike-based temporal coding by Maryam Mirsadeghi, Majid Shalchian, Saeed Reza Kheradpisheh

“…Backpropagation is the foremost prevalent and common algorithm for training conventional neural networks with deep construction. …”
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Baseline high-resolution maps of soil nutrients in Morocco to support sustainable agriculture by Yassine Bouslihim, Abdelkrim Bouasria, Ahmed Jelloul, Lotfi Khiari, Sara Dahhani, Rachid Mrabet, Rachid Moussadek

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Blind Recognition of Convolutional Codes Based on the ConvLSTM Temporal Feature Network by Lu Xu, Yixin Ma, Rui Shi, Juanjuan Li, Yijia Zhang

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Privacy-Preserving Federated Learning for Space–Air–Ground Integrated Networks: A Bi-Level Reinforcement Learning and Adaptive Transfer Learning Optimization Framework by Ling Li, Lidong Zhu, Weibang Li

“…Specifically, (1) an adaptive knowledge-sharing mechanism based on transfer learning is designed to balance device heterogeneity and data distribution divergence through dynamic weighting factors; (2) a bi-level reinforcement learning device selection strategy is proposed, combining meta-learning and hierarchical attention mechanisms to optimize global–local decision-making and enhance model convergence efficiency; (3) dynamic privacy budget allocation and robust aggregation algorithms are introduced to reduce communication overhead while ensuring privacy. …”
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Leveraging artificial intelligence in disaster management: A comprehensive bibliometric review by Arief Wibowo, Ikhwan Amri, Asep Surahmat, Rusdah Rusdah

“…Six research clusters were identified through keyword network mapping: (1) disaster monitoring and prediction using IoT networks, (2) AI-based geospatial technology for risk management, (3) decision support systems for disaster emergency management, (4) social media analysis for emergency response, (5) machine learning algorithms for disaster risk reduction, and (6) big data and deep learning for disaster management. …”
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