Automated Container Terminal Production Operation and Optimization via an AdaBoost-Based Digital Twin Framework by Yu Li, Daofang Chang, Yinping Gao, Ying Zou, Chunteng Bao

“…Digital twin (DT), machine learning, and industrial Internet of things (IIoT) provide great potential for the transformation of the container terminal from automation to intelligence. …”
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Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence by Yi-Ming Chen, Tzu-Hung Hsiao, Ching-Heng Lin, Yang C. Fann

“…Through the synergistic approach of integrating AI across diverse data sets, clinicians gain a holistic view of patient health and potential risks. Machine learning models excel at identifying high-risk patients, predicting disease activity, and optimizing therapeutic strategies based on clinical, genomic, and immunological profiles. …”
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Radial Basis Function Coupling with Metaheuristic Algorithms for Estimating the Compressive Strength and Slump of High-Performance Concrete by Amir Reza Taghavi Khangah, Erfan Khajavi, Hasti Azizi, Amir Reza Alizade Novin

“…The results highlight hybrid machine learning models as the potential to solve complex challenges in civil engineering and provide new approaches toward sustainable and efficient infrastructure development.…”
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Controlling Embedded Systems Remotely via Internet-of-Things Based on Emotional Recognition by Mohammad J. M. Zedan, Ali I. Abduljabbar, Fahad Layth Malallah, Mustafa Ghanem Saeed

“…The methodology is achieved by combining machine learning (for smiling recognition) and embedded systems (for remote control IoT) fields. …”
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<italic>C&#x2099;</italic>&#x00B2; Modeling for Free-Space Optical Communications: A Review by Florian Quatresooz, Claude Oestges

“…Boundary layer <inline-formula> <tex-math notation="LaTeX">$C_{n}^{2}$ </tex-math></inline-formula> models are also addressed, and recent machine learning approaches for <inline-formula> <tex-math notation="LaTeX">$C_{n}^{2}$ </tex-math></inline-formula> modeling are discussed. …”
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Adaptive Learning Algorithms for Low Dose Optimization in Coronary Arteries Angiography: A Comprehensive Review by Komal Tariq, Muhammad Adnan Munir, Hafiza Tooba Aftab, Amir Naveed, Ayesha Yousaf, Sajjad Ul Hassan

“…Results: The extracted data shows a comprehensive data on various techniques that are used for low dose CAA, advancements in image segmentation, noise reduction, and operator dose reduction highlight the potential of machine learning techniques. Innovative methods such as Model-Based Deep Learning (MBDL) and Self-Attention Generative Adversarial Networks (SAGAN) demonstrate efficient reconstruction capabilities. …”
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Detecting travel modes from smartphone-based travel surveys with continuous hidden Markov models by Guangnian Xiao, Qin Cheng, Chunqin Zhang

“…This set of features is expected to help achieve high classification accuracy with few features. Third, as a machine learning approach incorporating high resistance to noise in features, a continuous hidden Markov model is used to classify segments in dataset 1 that comprises Global Positioning System data alone. …”
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Regional-specific trends of PM2.5 and O3 temperature sensitivity in the United States by Lifei Yin, Bin Bai, Bingqing Zhang, Qiao Zhu, Qian Di, Weeberb J. Requia, Joel D. Schwartz, Liuhua Shi, Pengfei Liu

“…Here, we examined the sensitivity of surface-level fine particulate matter (PM2.5) and ozone (O3) to summer temperature anomalies in the contiguous US as well as their decadal changes using high-resolution datasets generated by machine learning. Our findings demonstrate that in the eastern US, stringent emission control strategies have significantly reduced the positive responses of PM2.5 and O3 to summer temperature, thereby lowering the population exposure associated with warming-induced air quality deterioration. …”
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Intelligent Prediction of Flood Disaster Risk Levels Based on Knowledge Graph and Graph Neural Networks by Peisheng Yang, Xiaohua Xu, Meilan Shao, Yewei Liu

“…Compared with the three classical models in traditional machine learning, such as RF, SVM and ANN, the performance of this model is improved, and it is better than the traditional model. …”
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Statistical Learning for Semantic Parsing: A Survey by Qile Zhu, Xiyao Ma, Xiaolin Li

“…In this paper, we review recent algorithms for semantic parsing including both conventional machine learning approaches and deep learning approaches. …”
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A Review of CNN Applications in Smart Agriculture Using Multimodal Data by Mohammad El Sakka, Mihai Ivanovici, Lotfi Chaari, Josiane Mothe

“…A comparative analysis shows how CNNs perform with respect to other techniques that involve traditional machine learning and recent deep learning models in image processing, particularly when applied to high-dimensional or temporal data. …”
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Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier by Ranjana Agrawal, Sucheta Kulkarni, Madan Deshpande, Anita Gaikwad, Rahee Walambe, Ketan V. Kotecha

“…Stages 1–3 were classified using machine Learning (ML) models. • This study aims to improve accuracy of Stages 1–3 classification and identify Pre-plus/ Plus disease using MultiCNN_LSTM networks. …”
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A review of electric vehicle technology: Architectures, battery technology and its management system, relevant standards, application of artificial intelligence, cyber security, an... by Vincent M. Macharia, Vijay K. Garg, Dinesh Kumar

“…Furthermore, this article gives a comprehensive overview of various emerging technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI) & Machine Learning (ML), to improve EVs' performance through digitalisation. …”
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Perceptions and attitudes towards AI among trainee and qualified radiologists at selected South African training hospitals by Ayanda I. Nciki, Linda T. Hlabangana

“…Participants agreed that AI could reliably detect pathological conditions (89%), reach reliable diagnosis (89%), improve daily work (78%), and 89% favoured AI practice; 89% believed that in the future, machine learning will not be independent of the radiologist. …”
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The data-intensive research paradigm: challenges and responses in clinical professional graduate education by Chunhong Yang, Yijing Chen, Changshun Qian, Fangmin Shi, Fangmin Shi, You Guo, You Guo, You Guo, You Guo, You Guo

“…With the widespread application of big data, artificial intelligence, and machine learning technologies in the medical field, a new paradigm of data-intensive clinical research is emerging as a key force driving medical advancement. …”
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Cybersecurity Solutions for Industrial Internet of Things–Edge Computing Integration: Challenges, Threats, and Future Directions by Tamara Zhukabayeva, Lazzat Zholshiyeva, Nurdaulet Karabayev, Shafiullah Khan, Noha Alnazzawi

“…The review emphasizes the integration of advanced security technologies, including machine learning (ML), federated learning (FL), blockchain, blockchain–ML, deep learning (DL), encryption, cryptography, IT/OT convergence, and digital twins, as essential for enhancing the security and real-time data protection of CPS in IIoT–edge computing. …”
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Anxiety classification in virtual reality using biosensors: A mini scoping review. by Deniz Mevlevioğlu, Sabin Tabirca, David Murphy

“…Our inclusion criteria were peer-reviewed studies which take place in a Virtual Reality environment and assess the user's anxiety using machine learning classification models and biosensors.<h4>Results</h4>1749 records were identified and out of these, 11 (n = 237) studies were selected. …”
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Ankrd1 as a potential biomarker for the transition from acute kidney injury to chronic kidney disease by Hailin Li, Lemei Hu, Changqing Zheng, Ying Kong, Ming Liang, Quhuan Li

“…Analysis of intercellular crosstalk, trajectory and machine learning algorithms revealed hub cell clusters and genes. …”
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Transfer learning for non-image data in clinical research: A scoping review. by Andreas Ebbehoj, Mette Østergaard Thunbo, Ole Emil Andersen, Michala Vilstrup Glindtvad, Adam Hulman

“…<h4>Background</h4>Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. …”
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A Multi-Faceted Approach to Trending Topic Attack Detection Using Semantic Similarity and Large-Scale Datasets by Insaf Kraidia, Afifa Ghenai, Samir Brahim Belhaouari

“…We evaluated our approach using a comprehensive framework that assessed textual, image, and overall similarity. Five machine learning models&#x2014;Random Forest, Decision Tree, K-Neighbors, Gradient Boosting, and XGBoost&#x2014;were tested, with results benchmarked against nine baseline methods across different linguistic datasets and learning scenarios. …”
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