Artificial intelligence-assisted nursing interventions in psychiatry for oral cancer patients: A concise narrative review by Abdulmalik Fareeq Saber, Sirwan Khalid Ahmed, Safin Hussein, Karzan Qurbani

“…Challenges and ethical considerations in integrating AI into healthcare include data privacy, algorithm validation, and the need for professional training.…”
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Hybrid nanofluid flow around a circular cylinder: A coupled ANN and numerical study of MHD, bioconvection, and thermal radiation by Muhammad Fawad Malik

“…This study provides significant information for researchers and engineers seeking to understand the characteristics of flow, its dynamics, and its predictive modeling.…”
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Intelligent Fault Warning Method for Wind Turbine Gear Transmission System Driven by Digital Twin and Multi-Source Data Fusion by Tiantian Xu, Xuedong Zhang, Wenlei Sun

“…A digital twin system architecture is developed, comprising a high-precision geometric model and a dynamic mechanism model, enabling real-time interaction and data fusion between the physical transmission system and its virtual model. At the algorithmic level, a CNN-LSTM-Attention fault prediction model is proposed, which innovatively integrates the spatial feature extraction capabilities of a convolutional neural network (CNN), the temporal modeling advantages of long short-term memory (LSTM), and the key information-focusing characteristics of an attention mechanism. …”
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Robust Load Frequency Control in Cyber-Vulnerable Smart Grids with Renewable Integration by Rambaboo Singh, Ramesh Kumar, Utkarsh Raj, Ravi Shankar

“…In light of these concerns, this study proposes a cyber-physical LFC framework incorporating a fuzzy linear active disturbance rejection controller (F-LADRC), wherein the controller parameters are systematically optimized using the quasi-opposition-based reptile search algorithm (QORSA). Furthermore, the proposed approach integrates a comprehensive cyberattack detection and prevention scheme, employing Haar wavelet transforms for anomaly detection and long short-term memory (LSTM) networks for predictive mitigation. …”
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Optimisation of railway tamping scheduling by Mahdi Khosravi, Alireza Ahmadi, Ahmad Kasraei, Arne Nissen

“…To achieve this, we formulated the track geometry tamping scheduling problem as a mixed integer linear programming model and employed a genetic algorithm for its resolution. Key track quality indicators, including the standard deviation of the longitudinal level and single defects, were considered.We developed predictive models for the evolution of standard deviation and single defects over time, which were utilised to schedule preventive tamping activities and anticipate potential corrective actions. …”
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SorpVision: A Comprehensive Dataset for Cementitious Sorptivity Analysis Powered by Computer Vision by Hossein Kabir, Jordan Wu, Sunav Dahal, Tony Joo, Nishant Garg

“…SorpVision, combined with a cost-effective USB camera setup and a robust vision algorithm, facilitates real-time water level detection in cementitious systems. …”
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Hybrid Paddy disease classification using optimized statistical feature based transformation technique with explainable AI by M Amudha, Brindha K

“…This research proposes a machine learning strategy based on Explainable AI that can assist farmers quickly diagnose and manage rice leaf diseases. To enhance predictive performance by transforming features, an improved Owl Search Optimization (IOSO) algorithm is used. …”
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Intelligent fault diagnosis and operation condition monitoring of transformer based on multi-source data fusion and mining by Jingping Cui, Wei Kuang, Kai Geng, Pihua Jiao

“…Furthermore, Apriori correlation analysis is performed on the transformer load rate and upper oil layer, winding temperature, and fusion indices of gas components by support and confidence levels to achieve a predictive assessment of the transformer state. Finally, the validity of the algorithm is verified by applying actual data from a power system monitoring platform. …”
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A Cell Component-Related Prognostic Signature for Head and Neck Squamous Cell Carcinoma Based on the Tumor Microenvironment by Siyu Li, Yajun Gu, Junguo Wang, Dengbin Ma, Xiaoyun Qian, Xia Gao

“…The tumor microenvironment (TME) is composed of numerous noncancerous cells that contribute to tumorigenesis and prediction of therapeutic effects. In this study, we aimed to develop a cell component-related prognostic model based on TME. …”
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Molecular function validation and prognostic value analysis of the cuproptosis-related gene ferredoxin 1 in papillary thyroid carcinoma by Shiyue He, Wenzhong Peng, Xinyue Hu, Yong Chen

“…Based on six selected cuproptosis-related genes a predictive prognosis model was established and CRRS displayed a good prediction accuracy. …”
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Advanced hybrid computational analysis of febuxostat solubility using machine learning in supercritical processing method by Turki Al Hagbani, Rami M. Alzhrani, Majed Ahmed Algarni

“…As an innovative aspect of this research, in addition to the Voting model, the HHO algorithm has been used to tune the hyper-parameters of the models. …”
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The role of artificial intelligence in enhancing disaster preparedness and trauma care by Mohamad Sorani

“…Furthermore, ethical considerations such as data privacy, algorithmic bias, and equitable access are crucial to ensuring that AI-driven solutions are implemented responsibly and inclusively.This review also explores future directions for AI, including the development of sophisticated predictive models, autonomous systems for high-risk tasks, immersive training programs utilizing virtual and augmented reality, and global collaboration aimed at standardizing AI tools and practices. …”
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Variable Selection for Multivariate Failure Time Data via Regularized Sparse-Input Neural Network by Bin Luo, Susan Halabi

“…Both methods are optimized using a composite gradient descent algorithm combining standard gradient steps with proximal updates. …”
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An Optical Sensor for Measuring In-Plane Linear and Rotational Displacement by Suhana Jamil Ahamed, Michael Aaron McGeehan, Keat Ghee Ong

“…Variations in reflected R, G, B and C light intensities, caused by displacements, were used to predict linear and rotational motion via a polynomial regression algorithm. …”
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Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis by Ioannis D. Apostolopoulos, Nikolaos I. Papandrianos, Dimitrios J. Apostolopoulos, Elpiniki Papageorgiou

“…These results highlight a potential synergistic relationship between human expertise and advanced algorithmic predictions, suggesting a hybrid approach as a promising direction for enhancing CAD diagnostics.…”
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Development of a battery free, solar powered, and energy aware fixed wing unmanned aerial vehicle by Jackson Liller, Rishabh Goel, Abdul Aziz, Josiah Hester, Phuc Nguyen

“…We also derive the Greedy Energy-Aware Control (GEAC) and Predictive Energy-Aware Control (PEAC) solar control algorithm that overcomes power system brownouts and total-loss-of-thrust events, enabling the prototype to perform maneuvers without a battery. …”
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Combination treatment optimization using a pan-cancer pathway model. by Robin Schmucker, Gabriele Farina, James Faeder, Fabian Fröhlich, Ali Sinan Saglam, Tuomas Sandholm

“…In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. …”
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Location allocation approach based on influence of points of dispensing by Nusaybah Alghanmi, Reem Alotaibi, Sultanah Alshammari, Arif Mahmood

“…It also obtained the closest results to the case study in terms of number, location and predictive performance.…”
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Privacy, ethics, transparency, and accountability in AI systems for wearable devices by Petar Radanliev, Petar Radanliev

“…The integration of artificial intelligence (AI) and machine learning (ML) into wearable sensor technologies has substantially advanced health data science, enabling continuous monitoring, personalised interventions, and predictive analytics. However, the fast advancement of these technologies has raised critical ethical and regulatory concerns, particularly around data privacy, algorithmic bias, informed consent, and the opacity of automated decision-making. …”
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Enhancing Power Allocation in DAS: A Hybrid Machine Learning and Reinforcement Learning Model by S. Gnanasekar, K. C. Sriharipriya

“…The proposed method combines Machine Learning (ML) for predictive modeling with Multi-Agent Reinforcement Learning (MARL) for real-time coordination among RAUs. …”
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