Supporting the editing of dot maps using the spectral clustering algorithm by Dziuba Natalia, Szombara Stanisław

“…These data were entered into the spectral clustering algorithm based on a predefined dot value. The output clusters were then visualized in ArcGIS Pro, where manual adjustments, such as the definition of the dot size and the dispersion of overlapping markers, completed the map editing process. …”
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FI-SCAPE: A Divergence Theorem Based Emission Quantification Model for Air/Spaceborne Imaging Spectrometer Derived X_CH4 Observations by Yiyang Huang, Ge Han, Tianqi Shi, Siwei Li, Huiqin Mao, Yihuang Nie, Wei Gong

“…In this article, we propose a divergence-theorem-based emission quantification model, named flux integration method based on sinusoidal cosine optimization algorithm to inverse the methane point source emissions, which utilizes XCH4 observations derived from airborne imaging spectrometers to achieve rapid and accurate estimation of methane point source emission rates. …”
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Application of PID algorithm by using filter in temperature control system by Yao Liping, Liu Weizhang, Wu Wenming, Jiang Yangyang, Lei Peng, Tan Zhongwei, Wang Kangning, Li Guixiang, Xu Fei, Tang Yuanliang, Wu Xinshe, Huang Dequn, Chen Jun, Gu Heng

“…The experimental results show that the PID algorithm with filter outperforms PID combined with Bang Bang, differential first PID, step PID and variable integral PID, which improves the ability of anti-interference and has the practical value and application value in engineering temperature control.…”
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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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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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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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AI-Driven Innovation in Skin Kinetics for Transdermal Drug Delivery: Overcoming Barriers and Enhancing Precision by Nubul Albayati, Sesha Rajeswari Talluri, Nirali Dholaria, Bozena Michniak-Kohn

“…Machine learning models trained on extensive molecular datasets predict skin permeability and accelerate the selection of suitable drug candidates. AI-driven algorithms optimize formulations, including penetration enhancers and advanced delivery technologies like microneedles and liposomes, while ensuring safety and efficacy. …”
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Navigating the landscape of AI literacy education: insights from a decade of research (2014–2024) by Yuqin Yang, Ying Zhang, Daner Sun, Wenmeng He, Yantao Wei

“…Abstract As artificial intelligence (AI) becomes increasingly integrated into various fields, the need to enhance learners’ AI literacy is more urgent than ever. …”
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An algorithm for cardiac disease detection based on the magnetic resonance imaging by Heng Li, Qingni Yuan, Yi Wang, Pengju Qu, Chunhui Jiang, Hu Kuang

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