Enhancing explainability in pacu fish image segmentation using saliency maps and combined explainable AI methods by Juliana da C. Feitosa, Fabrício M. Batista, Juliana C.F. Catharino, Milena V. Freitas, Diogo T. Hashimoto, João Paulo Papa, José Remo F. Brega

“…Advances in Artificial Intelligence (AI) have sparked concerns regarding the transparency of model outputs, necessitating the development of eXplainable Artificial Intelligence (XAI) techniques. …”
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DeSFAM: An Adaptive eBPF and AI-Driven Framework for Securing Cloud Containers in Real Time by Sehar Zehra, Hassan Jamil Syed, Fahad Samad, Ummay Faseeha

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SpAi: A machine-learning supported experimental workflow for high-throughput spheroid production and analysis by Nedim Hacıosmanoğlu, Murat Alp Güngen, Eylul Gulsen Yilmaz, Emre Ece, Alphan Uzun, Arda Taşcan, Burak M. Görmüş, Ismail Eş, Fatih Inci

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STAA: Spatio-Temporal Attention Attribution for Real-Time Interpreting Transformer-Based AI Video Models by Zerui Wang, Yan Liu

“…The limitation is the lack of explainability of these complex models. Current Explainable AI (XAI) methods focus on feature importance analysis of only one dimension, either spatial or temporal features. …”
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AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes by José E. Valerio, Guillermo de Jesús Aguirre Vera, Maria P. Fernandez Gomez, Jorge Zumaeta, Andrés M. Alvarez-Pinzon

“…These innovations improve trial efficiency, broaden treatment options, and enhance patient outcomes. However, integrating AI into clinical trial frameworks presents challenges such as data standardization, regulatory hurdles, and the need for extensive validation. …”
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RF-Based Sensing and AI Decision Support for Stroke Patient Monitoring: A Digital Twin Approach by Sagheer Khan, Usman Anwar, Aftab Khan, Tughrul Arslan

“…Regular monitoring and timely intervention are critical to minimizing the damage and improving outcomes. This article presents a novel Radio Frequency (RF) sensing and Artificial Intelligence (AI) based Digital Twin (DT) model for effective detection of stroke. …”
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AI-Driven Digital Twins for Enhancing Indoor Environmental Quality and Energy Efficiency in Smart Building Systems by Ibrahim Yitmen, Amjad Almusaed, Muaz Hussein, Asaad Almssad

“…The integration of AI with DT presents a transformative approach to improving IEQ and EE in SBS. …”
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High-Performance and Lightweight AI Model with Integrated Self-Attention Layers for Soybean Pod Number Estimation by Qian Huang

“…<b>Methods:</b> To address these challenges, this study presents a set of lightweight, efficient AI models designed to overcome these limitations. …”
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AI-based approaches for improving autonomous mobile robot localization in indoor environments: A comprehensive review by Shoude Wang, Nur Syazreen Ahmad

“…We systematically categorize and critically analyze existing research on Simultaneous Localization and Mapping (SLAM)-based methods, odometry-based approaches, and multi-sensor fusion techniques, elucidating the principles and implementations of various AI methodologies. Additionally, we discuss real-time performance challenges associated with AI-based approaches and delineate the distinctions between AI-enhanced localization methods and traditional localization techniques, highlighting the necessity and advantages of adopting AI-based solutions. …”
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FraudX AI: An Interpretable Machine Learning Framework for Credit Card Fraud Detection on Imbalanced Datasets by Nazerke Baisholan, J. Eric Dietz, Sergiy Gnatyuk, Mussa Turdalyuly, Eric T. Matson, Karlygash Baisholanova

“…Credit card fraud detection is a critical research area due to the significant financial losses and security risks associated with fraudulent activities. This study presents FraudX AI, an ensemble-based framework addressing the challenges in fraud detection, including imbalanced datasets, interpretability, and scalability. …”
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Decoding student cognitive abilities: a comparative study of explainable AI algorithms in educational data mining by Tianyue Niu, Ting Liu, Yiming Taclis Luo, Patrick Cheong-Iao Pang, Shuaishuai Huang, Ao Xiang

“…This study employs data-driven artificial intelligence (AI) models supported by explainability algorithms and PSM causal inference to investigate the factors influencing students’ cognitive abilities, and it delved into the differences that arise when using various explainability AI algorithms to analyze educational data mining models. …”
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Advanced AI techniques for landslide susceptibility mapping and spatial prediction: A case study in Medellín, Colombia by I.N. Gómez-Miranda, C. Restrepo-Estrada, A. Builes-Jaramillo, João Porto de Albuquerque

“…We applied AI models — Naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Decision Trees, Random Forest, and Support Vector Machine (SVM) — to a case study in Medellín, a mountainous city in northwest Colombia. …”
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Revolutionising Educational Management with AI and Wireless Networks: A Framework for Smart Resource Allocation and Decision-Making by Christos Koukaras, Euripides Hatzikraniotis, Maria Mitsiaki, Paraskevas Koukaras, Christos Tjortjis, Stavros G. Stavrinides

“…To address challenges like these, this work proposes a conceptual framework for smart campus management which integrates Artificial Intelligence (AI) and advanced wireless networks based on 5G. …”
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Adopting green AI for SME sustainability: mediating role of green investment and moderation by green servant leadership by Faizan ul Haq, Norazah Mohd Suki, Made Setini, Asim Masood, Taimur Ahmed Khan

“…Green Artificial Intelligence (Green AI) presents a strategic pathway for small and medium-sized enterprises (SMEs) in emerging economies to enhance sustainability by optimizing energy use, minimizing waste, and fostering eco-innovation. …”
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Transfer deep learning and explainable AI framework for brain tumor and Alzheimer's detection across multiple datasets by Shtwai Alsubai, Stephen Ojo, Thomas I. Nathaniel, Mohamed Ayari, Jamel Baili, Ahmad Almadhor, Abdullah Al Hejaili

“…IntroductionThe pressing need for accurate diagnostic tools in the medical field, particularly for diseases such as brain tumors and Alzheimer's, poses significant challenges to timely and effective treatment.MethodsThis study presents a novel approach to MRI image classification by integrating transfer learning with Explainable AI (XAI) techniques. …”
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AI-enhanced profiling of phage-display-identified anti-TIM3 and anti-TIGIT novel antibodies by Astrid Musnier, Yannick Corde, Adrien Verdier, Mélanie Cortes, Jean-René Pallandre, Christophe Dumet, Adeline Bouard, AbdelRaouf Keskes, Zakaria Omahdi, Vincent Puard, Anne Poupon, Thomas Bourquard

“…However, the initial leads identified through this approach lack sufficient characterization in terms of developability and epitope definition, which are typically performed at late stages. In this study, we present a pipeline that integrates early-stage phage display screening with AI-based characterization, enabling more informed decision-making throughout the selection process. …”
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Domain-specific AI segmentation of IMPDH2 rod/ring structures in mouse embryonic stem cells by Samuel T. M. Ball, Meagan J. Hennessy, Yuhan Tan, Kai F. Hoettges, Neil D. Perkins, David J. Wilkinson, Michael R. H. White, Yalin Zheng, David A. Turner

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AI-Driven Prediction of Symptom Trajectories in Cancer Care: A Deep Learning Approach for Chemotherapy Management by Joseph Finkelstein, Aref Smiley, Christina Echeverria, Kathi Mooney

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A Bibliometric Assessment of AI, IoT, Blockchain, and Big Data in Renewable Energy-Oriented Power Systems by Manuel Jaramillo, Diego Carrión, Jorge Muñoz, Luis Tipán

“…This study presents a systematic bibliometric review of digital innovations in renewable energy-oriented power systems, with a focus on Blockchain, Artificial Intelligence (AI), the Internet of Things (IoT), and Data Analytics. …”
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A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI by Rashiduzzaman Shakil, Sadia Islam, Bonna Akter

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