The Impact of a Deep Learning Self-Adaptive Colour Restoration Pipeline for Deep Underwater Images in 3D Reconstruction by M. Vlachos, D. Skarlatos, S. Demesticha

“…The findings are intended to inform future development of hybrid approaches that combine physical modelling with deep learning, aiming to optimize both visual clarity and geometric fidelity in underwater mapping and documentation. …”
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Navigating the landscape: Unveiling the reflection of AI in the sharing economy through bibliometric-based approach by Veljko Uskoković, Milica Maričić, Aleksandra Dacić-Pilčević, Veljko Jeremić

“…Furthermore, AI optimizes resource allocation and improves matching algorithms on online labour platforms, thereby boosting platform efficiency. …”
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Advancing cardiovascular care through actionable AI innovation by Giuseppe Biondi-Zoccai, Arjun Mahajan, Dylan Powell, Mariangela Peruzzi, Roberto Carnevale, Giacomo Frati

“…Indeed, offline RL refers to a class of ML algorithms that learn optimal decision-making policies from a fixed dataset of previously collected experiences—such as electronic health records or registries—without the need for active, real-time interaction with the clinical environment. …”
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Aspect-Based Sentiment Analysis for Afaan Oromoo Movie Reviews Using Machine Learning Techniques by Obsa Gelchu Horsa, Kula Kekeba Tune

“…The implementation result shows that the optimal values of models’ performance evaluation parameters were generated using different hyperparameter tuning settings.…”
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An FPGA-accelerated multi-level AI-integrated simulation framework for multi-time domain power systems with high penetration of power converters by Chen Liu, Peng Su, Hao Bai, Xizheng Guo, Alber Filbà Martínez, Jose Luis Dominguez Garcia

“…The framework structures systems hierarchically using energy transmission functions and unified energy information flow-based surrogate models with defined ports, ensuring compatibility with artificial intelligence algorithms. …”
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Hole Cleaning during Drilling Oil and Gas Wells: A Review for Hole-Cleaning Chemistry and Engineering Parameters by Mohammed Al-Rubaii, Mohammed Al-Shargabi, Dhafer Al-Shehri

“…The development of hole-cleaning models is important for use as hole-cleaning indicators to ensure optimized drilling efficiency. …”
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A joint three-plane physics-constrained deep learning based polynomial fitting approach for MR electrical properties tomography by Kyu-Jin Jung, Thierry G. Meerbothe, Chuanjiang Cui, Mina Park, Cornelis A.T. van den Berg, Stefano Mandija, Dong-Hyun Kim

“…For the prediction of optimal fitting coefficients, three neural networks were separately trained on simulated heterogeneous brain models to predict optimal polynomial weighting parameters in three orthogonal planes. …”
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A review of recent artificial intelligence for traditional medicine by Chengbin Hou, Yanzhuo Gao, Xinyu Lin, Jinchao Wu, Ning Li, Hairong Lv, William Cheng-Chung Chu

“…By leveraging advanced algorithms and models, AI can improve decision-making efficiency, optimize diagnosis accuracy, enhance patient experience, and reduce costs. …”
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From Code to Life: The AI‐Driven Revolution in Genome Editing by Zhidong Li, Wasi Ullah Khan, Genxiang Bai, Chao Dong, Jungang Wang, Youpeng Zhang, Chong Wang, Hongbin Zhang, Wenyi Wang, Ming Luo, Fei Chen

“…Recent advancements in artificial intelligence (AI) have significantly enhanced genome editing by improving target selection, minimizing off‐target effects, and optimizing CRISPR‐associated systems. …”
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Electricity nowcasting and forecasting efficiency in blockchain-integrated energy trading platforms by Ameni Boumaiza

“…Our methodology integrates real-time nowcasting algorithms and machine learning-based forecasting models to optimize electricity distribution, battery storage utilization, and cost efficiency within the marketplace. …”
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Machine Learning-Based Detection of Archeological Sites Using Satellite and Meteorological Data: A Case Study of Funnel Beaker Culture Tombs in Poland by Krystian Kozioł, Natalia Borowiec, Urszula Marmol, Mateusz Rzeszutek, Celso Augusto Guimarães Santos, Jerzy Czerniec

“…The machine learning models, including logistic regression and decision tree-based algorithms, demonstrated strong potential for predicting site visibility. …”
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Artificial Intelligence and Smart Technologies in Safety Management: A Comprehensive Analysis Across Multiple Industries by Jiyoung Park, Dongheon Kang

“…AI-driven solutions, such as predictive analytics, machine learning algorithms, IoT sensor integration, and digital twin models, are shown to proactively identify and mitigate potential hazards, optimize energy consumption, and enhance operational efficiency. …”
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A Novel Active RFID and TinyML based system for livestock Localization in Pakistan by Syed Atir Raza Shirazi, Maham Fatima, Abdul Wahab, Sadaf Ali

“…To realize the full potential of this unique Active RFID and TinyML-based livestock localization system in Pakistan, further research should focus on optimizing localization algorithms, enhancing TinyML models, and exploring interaction with upcoming technologies …”
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Applications, Challenges, and Future Perspectives of Artificial Intelligence in Psychopharmacology, Psychological Disorders and Physiological Psychology: A Comprehensive Review by Mohammad Hossein Salemi, Elham Foroozandeh, Molouk Khademi Ashkzari

“…However, challenges persist, including data privacy, algorithmic bias, ethical dilemmas, and regulatory hurdles, emphasizing the need for robust governance. …”
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Benefits and Challenges of AI-Based Digital Twin Integration in the Saudi Arabian Construction Industry: A Correspondence Analysis (CA) Approach by Aljawharah A. Alnaser, Haytham Elmousalami

“…However, several challenges hinder the realization of these benefits, including a lack of standardization of integrated DT and AI in construction projects, a lack of understanding of AI’s capabilities, a lack of logistics and the limited availability of IT infrastructure, and the complexity of AI algorithms. These findings underscore the transformative potential of integrating AI-driven DTs to optimize construction performance, improve decision-making, and address real-world complexities. …”
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Radiology AI and sustainability paradox: environmental, economic, and social dimensions by Burak Kocak, Andrea Ponsiglione, Valeria Romeo, Lorenzo Ugga, Merel Huisman, Renato Cuocolo

“…Socially, AI risks perpetuating healthcare disparities through biases in algorithms and unequal access to technology. On the other hand, AI has the potential to improve sustainability in healthcare by reducing low-value imaging, optimizing resource allocation, and improving energy efficiency in radiology departments. …”
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Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion by Yice Cao, Chenchen Liu, Zhenhua Wu, Lei Zhang, Lixia Yang

“…Although segmentation algorithms based on convolutional neural networks (CNNs) or Transformers have achieved significant performance improvements, the trade-off between segmentation precision and computational complexity remains a key limitation for practical applications. …”
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Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence by David B. Olawade, Kusal Weerasinghe, Mathugamage Don Dasun Eranga Mathugamage, Aderonke Odetayo, Nicholas Aderinto, Jennifer Teke, Stergios Boussios

“…AI algorithms, particularly those utilizing machine learning (ML) and deep learning (DL), have demonstrated remarkable success in diagnosing conditions such as diabetic retinopathy (DR), age-related macular degeneration, and glaucoma with precision comparable to, or exceeding, human experts. …”
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Advancing Grid-Forming Inverter Technology: Comprehensive PQ Capability and Performance Analysis by Md Nurunnabi, Shuhui Li, Himadry Shekhar Das

“…Recognizing that IBR operational constraints are distinct from those of synchronous generators, this study develops advanced PQ capability models and algorithmic frameworks that accurately characterize GFM inverter operational constraints across various coupling filter configurations (L, LC, and LCL). …”
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Literature Review of Prognostic Factors in Secondary Generalized Peritonitis by Valerii Luțenco, Adrian Beznea, Raul Mihailov, George Țocu, Verginia Luțenco, Oana Mariana Mihailov, Mihaela Patriciu, Grigore Pascaru, Liliana Baroiu

“…Emerging evidence suggests that machine learning algorithms may improve early risk stratification and individualized outcome prediction when integrated with conventional scoring systems. …”
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