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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Enhancing student success prediction in higher education with swarm optimized enhanced efficientNet attention mechanism. by Meshari Alazmi, Nasir Ayub

“…Predicting student performance is crucial for providing personalized support and enhancing academic performance. Advanced machine-learning approaches are being used to understand student performance variables as educational data grows. …”
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XI2S-IDS: An Explainable Intelligent 2-Stage Intrusion Detection System by Maiada M. Mahmoud, Yasser Omar Youssef, Ayman A. Abdel-Hamid

“…The challenge is further compounded by the fact that most IDS rely on black-box machine learning (ML) and deep learning (DL) models, making it difficult for security teams to interpret their decisions. …”
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Application of artificial intelligence techniques for the profiling of visitors to tourist destinations by Juan Schrader, Lloy Pinedo, Franz Vargas, Karla Martell, José Seijas-Díaz, Roger Rengifo-Amasifen, Rosa Cueto-Orbe, Cinthya Torres-Silva

“…The aim of this study was to characterize tourists using machine learning techniques in order to identify distinct segments that can inform planning and promotional strategies for the Alto Amazonas destination. …”
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Prediction of long-term recurrence-free and overall survival in early-onset colorectal cancer: the ENCORE multi-centre study by Alessandro Mannucci, Goretti Hernández, Hiroyuki Uetake, Yasuhide Yamada, Francesc Balaguer, Hideo Baba, Tianhui Chen, Jinfei Chen, C. Richard Boland, Giulia Martina Cavestro, Enrique Quintero, Ajay Goel

“…We then trained and independently validated a machine learning model (XGBoost) to predict 5-year recurrence-free and overall survival (RFS and OS) of patients with stage I-III EOCRC. …”
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Neutrosophic Fuzzy Power Management (NFPM): Tackling Uncertainty in Energy Harvesting for Sensor Networks by Musallam M. AlZubi, Mohamed A. Mohamed, Hanan M. Amer, A. A. Salama

“…Future research directions include exploring the integration of NFPM with machine learning algorithms for predictive energy management, assessing its scalability in larger networks, and examining its applicability in other domains requiring energy management under uncertainty.…”
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CoMSeC: A Comparative Analysis of Various Service Classification Techniques by Malabika Das, Ansh Sarkar, Sujata Swain

“…The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has significantly impacted Web Service Classification, a critical task for service discovery, composition, and selection in various applications. …”
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Influences of Temperature Variations Around Subway Ventilations on Fractures of Continuous Welded Rail Fractures by Jung-Youl Choi, Sang-Yeol Park, Sun-Hee Kim, Jee-Seung Chung

“…Load conditions, including temperature changes, were analyzed using a validated numerical model to determine the axial stress in the CWR, which resulted in a 23% reduction in the axial stress in the weld joint. …”
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Rewiring Sustainability: How Digital Transformation and Fintech Innovation Reshape Environmental Trajectories in the Industry 4.0 Era by Zhuoqi Teng, Han Xia, Yugang He

“…Robustness checks via the system-generalized method of moments confirm the stability of these relationships, while machine learning models—Random Forest and XGBoost—highlight digital variables as top predictors of emissions reduction. …”
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An Advanced Generative AI-Based Anomaly Detection in IEC61850-Based Communication Messages in Smart Grids by Aydin Zaboli, Yong-Hwa Kim, Junho Hong

“…Moreover, the system shows a substantial improvement in advanced evaluation metrics, including a Matthews Correlation Coefficient (MCC) of 0.95, highlighting its robust capability to accurately differentiate between normal and anomalous events. The ToD model adapts effectively to new attack scenarios without extensive retraining, unlike traditional machine learning (ML) models or HITL, which require frequent updates. …”
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GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…Extensive comparison across 50 diverse single cell datasets against 18 existing methods demonstrates that GNODEVAE consistently outperforms three major categories of benchmark methods: 8 machine learning dimensionality reduction techniques, 7 deep generative VAE variants, and 3 graph-based and contrastive learning deep predictive models. …”
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Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection by Md. Najmul Mowla, Davood Asadi, Shamsul Masum, Khaled Rabie

“…On the Fire Luminosity Airborne-based Machine Learning Evaluation (FLAME) dataset, the model attained accuracy rates of 99.83%, 99.10%, and 99.32%, with corresponding cKappa values of 99.66%, 98.20%, and 98.65%. …”
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An explainable AI-based framework for predicting and optimizing blast-induced ground vibrations in surface mining by Charan Kumar Ala, Zefree Lazarus Mayaluri, Aman Kaushik, Nikhat Parveen, Surabhi Saxena, Abu Taha Zamani, Debendra Muduli

“…This study proposes a novel hybrid artificial intelligence (AI) framework that integrates physics informed neural networks (PINNs) with conventional machine learning (ML) algorithms for the accurate prediction and optimization of BIGV. …”
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Smart Agile Prioritization and Clustering: An AI-Driven Approach for Requirements Prioritization by Aya M. Radwan, Manal A. Abdel-Fattah, Wael Mohamed

“…The effectiveness of SAPC is evaluated using functional requirements extracted from Software Requirement Specifications (SRS), product backlogs, and customer requests, along with a benchmark dataset for validation. Various machine learning algorithms are tested, with KNN and Random Forest demonstrating the highest accuracy and lowest Mean Squared Error (MSE), outperforming traditional prioritization techniques. …”
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Optimal design of a hybrid system composed of coagulation process and multi-stage filtration unit for on-site treatment of greywater in rural area by Zahra Akbari, Fereshteh Nourmohammadi Dehbalaei, Zahra Mohammad Hosseini, Seyed Taghi Omid Naeeni

“…Multilayer nonlinear machine learning model was utilized to evaluate the performance of this hybrid system and adaptive heuristic search algorithm was used to achieve the optimal configuration of multilayer sand filter using optimization technique. …”
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Unveiling the carbon neutrality pathways of compact cities: a simulation-based scenario analysis from China by Tianhui Fan, Yujie Ren, Andrew Chapman

“…By employing machine learning models, we analyze how key features of compact cities—such as population density, land-use mix, and public transportation development—contribute to the achievement of carbon neutrality. …”
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Sustainable Geothermal Energy: A Review of Challenges and Opportunities in Deep Wells and Shallow Heat Pumps for Transitioning Professionals by Tawfik Elshehabi, Mohammad Alfehaid

“…Sustainable practices, including reinjection, machine learning-driven fracture modeling, and the use of corrosion-resistant alloys, enhance well integrity and long-term performance. …”
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Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer by Ayan Chatterjee, Michael A. Riegler, K. Ganesh, Pål Halvorsen

“…Different machine learning (ML) algorithms, including traditional and ensemble methods, are employed for analyzing both imbalanced and balanced HRV datasets. …”
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Utilisation of Deep Neural Networks for Estimation of Cajal Cells in the Anal Canal Wall of Patients with Advanced Haemorrhoidal Disease Treated by LigaSure Surgery by Inese Fišere, Edgars Edelmers, Šimons Svirskis, Valērija Groma

“…Haemorrhoidectomy specimens were collected from patients undergoing surgery with the LigaSure device. A YOLOv11-based machine learning model, trained on 376 immunohistochemical images, automated ICC detection using the CD117 marker, achieving a mean average precision (mAP50) of 92%, with a recall of 86% and precision of 88%. …”
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Chronological age estimation from human microbiomes with transformer-based Robust Principal Component Analysis by Tyler Myers, Se Jin Song, Yang Chen, Britta De Pessemier, Lora Khatib, Daniel McDonald, Shi Huang, Richard Gallo, Chris Callewaert, Aki S. Havulinna, Leo Lahti, Guus Roeselers, Manolo Laiola, Sudarshan A. Shetty, Scott T. Kelley, Rob Knight, Andrew Bartko

“…To investigate benefits of TRPCA over conventional machine learning models, we benchmarked performance on age prediction from three body sites(skin, oral, gut), with 16S rRNA gene amplicon(16S) and whole-genome sequencing(WGS) data. …”
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