Spatiotemporal inhomogeneity of accuracy degradation in AI weather forecast foundation models: A GNSS perspective by Junsheng Ding, Wu Chen, Junping Chen, Jungang Wang, Yize Zhang, Lei Bai, Yuyan Wang, Xiaolong Mi, Tong Liu, Duojie Weng

“…This temporal and spatial inhomogeneity of accuracy and accuracy degradation are related to AI algorithms and attributes of training data, etc., but these characteristics have not been thoroughly explored and analyzed. …”
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Indoor Air Wellness: A Predictive Model for Pollution Control Using Advanced AI Techniques by Kalyan Chatterjee, Muntha Raju, Bhoomeshwar Bala, U. Nikitha, Gampala Prabhas, Naim Ahmad, Ayman Qahmash, Wade Ghribi, Bernardo Lemos, Saurav Mallik

“…This study introduces “Indoor Air Wellness”, a predictive model that utilizes advanced AI techniques, specifically integrating Kalman filtering and artificial neural networks, to enhance indoor air pollution prediction and management. …”
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AI-driven generative and reinforcement learning for mechanical optimization of 2D patterned hollow structures by Yicheng Shan, Leitao Cao, Yu Wang, Jing Ren, Chen Huang, Wenli Gao, Shengjie Ling

“…We generated a comprehensive dataet comprising 1000 samples across five distinct density classes using a custom grid pattern generation algorithm, ensuring a wide range of structural variations. …”
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The research on enhancing LA estimation accuracy across domains for small sample data based on data augmentation and data transfer integration optimization system by Ai-Dong Wang, Rui-Jie Li, Xiang-Qian Feng, Zi-Qiu Li, Wei-Yuan Hong, Hua-Xing Wu, Dan-Ying Wang, Song Chen

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Developing Contextual Ontology for Chronic Diseases: AI-Enhanced Extension and Prediction in an Asthma Case Study by Batoul Msheik, Mehdi Adda, Hamid Mcheick, Youmna Nasser, Mohamed Dbouk

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Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques by Abbas Jafar, Myungho Lee

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An AI-Based Approach for Developing a Recommendation System for Underground Mining Methods Pre-Selection by Elsa Pansilvania Andre Manjate, Natsuo Okada, Yoko Ohtomo, Tsuyoshi Adachi, Bernardo Miguel Bene, Takahiko Arima, Youhei Kawamura

“…The study integrates and evaluates the capability of two approaches for mining methods selection (MMS): the memory-based collaborative filtering (CF) approach aided by the UBC-MMS system to predict the top-3 relevant mining methods and supervised machine learning (ML) classification algorithms to enhance the effectiveness and novelty of the AI-MMRS, addressing the limitations of the CF approach. …”
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AI-Powered Real-time Accessibility Enhancement: A Solution for Web Content Accessibility Issues by Samir Dash

“…The system uses machine learning algorithms to identify and rectify accessibility issues in real-time, integrating with existing development workflows. …”
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An explainable AI-based hybrid machine learning model for interpretability and enhanced crop yield prediction by Anuradha Yenkikar, Ved Prakash Mishra, Manish Bali, Tabassum Ara

“…The method involves: • Implementing a hybrid AI model to improve accuracy in yield predictions. • Integrating XAI methods to enhance model transparency and interpret nuanced feature interactions • Delivering actionable insights via the developed ‘E-Kisan’ web interface.…”
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Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak

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Leveraging Digital Twins and AI for Enhanced Gearbox Condition Monitoring in Wind Turbines: A Review by Houssem Habbouche, Yassine Amirat, Mohamed Benbouzid

“…This includes a comprehensive review of methodologies involving model-based approaches and data-driven techniques using signal processing (SP) and artificial intelligence (AI) algorithms. We address the challenges of “learning with minimal knowledge” and propose a framework for the effective application of DT technology. …”
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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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AI-driven video summarization for optimizing content retrieval and management through deep learning techniques by Deepali Vora, Payal Kadam, Dadaso D Mohite, Nilesh Kumar, Nimit Kumar, Pratheeik Radhakrishnan, Shalmali Bhagwat

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Enactivism, Health, AI, and Non-Neurotypical Individuals: Toward Contextualized, Personalized, and Ethically Grounded Interventions by Jordi Vallverdú

“…Despite their promise, practical adoption of AI technologies in real-world clinical practice remains limited, and persistent challenges such as algorithmic bias, privacy concerns, and the tendency to overlook subjective dimensions raise cautionary notes. …”
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Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI by Leandro Antonio Pazmiño Ortiz, Ivonne Fernanda Maldonado Soliz, Vanessa Katherine Guevara Balarezo

“…By exploring architectural co-design strategies, algorithmic innovations, and pressing research gaps, this paper offers a roadmap for future investigations and industrial applications of AI in resource-constrained devices.…”
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BREATHE: A GeoAI-Powered Air Quality Monitoring and Forecasting System for Urban Sustainability by S. D. Adduri, T. Ali

“…The research proposes BREATHE—a system of integrated real-time monitoring and machine learning-based forecasting to address air pollution challenges in urban environments by using GeoAI. …”
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Monitoring oral health remotely: ethical considerations when using AI among vulnerable populations by Colman McGrath, Chun Wang Reinhard Chau, Gustavo Fabián Molina

“…The accelerated adoption of these digital tools, particularly in the wake of the COVID-19 pandemic, has enhanced accessibility to care while simultaneously raising significant concerns regarding patient consent, data privacy, and algorithmic biases. We review current applications ranging from AI-assisted detection of dental pathologies to blockchain-enabled data transfer within EHR systems, highlighting the potential for improved diagnostic accuracy and the risks associated with over-reliance on remote assessments. …”
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Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices by Muhammad Shoaib Farooq, Rizwan Pervez Mir, Atif Alvi, Kilian Tutusaus, Eduardo Garcia Villena, Fadwa Alrowais, Hanen Karamti, Imran Ashraf

“…This study proposes a novel AI-driven framework that integrates forward chaining and backward chaining algorithms to analyze telemetry data from IoT devices. …”
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An AI-Based Framework for Characterizing the Atmospheric Fate of Air Pollutants Within Diverse Environmental Settings by Nataša Radić, Mirjana Perišić, Gordana Jovanović, Timea Bezdan, Svetlana Stanišić, Nenad Stanić, Andreja Stojić

“…This study introduces a novel artificial intelligence (AI) modeling framework that combines machine learning algorithms optimized through metaheuristics with explainable AI to capture complex interactions among pollutant concentrations, meteorological data, and socio-economic indicators. …”
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AI-driven assessment of over-scanning in chest CT: A systematic review and meta-analysis by Mo’men Bani-Ahmad, Andrew England, Laura McLaughlin, Marwan Alshipli, Kholoud Alzyoud, Yasser H. Hadi, Mark McEntee

“…Results: Five studies employed AI algorithms to assess manual over-scanning in chest CT using either 2D topograms or 3D axial images at low and standard doses. …”
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