Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence by Muhammad Sajid Farooq, Muhammad Hassan Ghulam Muhammad, Oualid Ali, Zahid Zeeshan, Muhammad Saleem, Munir Ahmad, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal

“…The proposed model utilizes machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, K-Neighbors Classifier, and Gradient Boosting Classifier, enhanced with Explainable AI (XAI) techniques like SHAP and LIME. …”
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Artificial intelligence in vaccine research and development: an umbrella review by Rabie Adel El Arab, May Alkhunaizi, May Alkhunaizi, Yousef N. Alhashem, Alissar Al Khatib, Munirah Bubsheet, Salwa Hassanein, Salwa Hassanein

“…Realizing these benefits requires not only investments in infrastructure and stakeholder engagement but also transparent model documentation, interdisciplinary ethics oversight, and routine algorithmic bias audits. Moreover, bridging the gap from in silico promise to real‑world impact demands large‑scale validation studies and methods that can accommodate heterogeneous evidence, ensuring AI‑driven innovations deliver equitable global health outcomes and reinforce pandemic preparedness.…”
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Artificial Intelligence in Wind Turbine Fault Detection and Diagnosis: Advances and Perspectives by Nejad Alagha, Anis Salwa Mohd Khairuddin, Zineddine N. Haitaamar, Obada Al-Khatib, Jeevan Kanesan

“…Focusing on high-impact faults in gearboxes, blades, and generators, these components are responsible for over 70% of turbine failures, the review maps prevalent ML and DL algorithms, such as CNNs, LSTMs, and SVMs, to specific fault types, revealing hybrid AI models and real-world data integration as key drivers of diagnostic accuracy. …”
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Emerging technologies and applications in multimodality imaging for ischemic heart disease: current state and future of artificial intelligence by Praveen Kumar Chandra Sekar, Ramakrishnan Veerabathiran

“…AI algorithms can optimize image acquisition, processing, and interpretation of complex imaging data. …”
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Forecast uncertainties real-time data-driven compensation scheme for optimal storage control by Arbel Yaniv, Yuval Beck

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Artificial Intelligence for Fault Detection of Automotive Electric Motors by Federico Soresini, Dario Barri, Ivan Cazzaniga, Federico Maria Ballo, Gianpiero Mastinu, Massimiliano Gobbi

“…The 1D CNN AE outperformed the other networks in fault detection, showing high accuracy, stability and computational efficiency. The model is integrated into an algorithm for semi-real-time fault monitoring. …”
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Method of evaluation of the extent of skeletal forms of malocclusion by I. N. Zhmyrko, N. S. Drobysheva

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Artificial Intelligence in the Organization of Nursing Care: A Scoping Review by João Ventura-Silva, Maria Manuela Martins, Letícia de Lima Trindade, Ana da Conceição Alves Faria, Soraia Pereira, Samuel Spiegelberg Zuge, Olga Maria Pimenta Lopes Ribeiro

“…Background: The integration of artificial intelligence (AI) in the organization of nursing care has continually evolved, driven by the need for innovative solutions to ensure quality of care. …”
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Machine Learning Applications in Road Pavement Management: A Review, Challenges and Future Directions by Tiago Tamagusko, Matheus Gomes Correia, Adelino Ferreira

“…This review examines the integration of Machine Learning (ML) into Pavement Management Systems (PMS), presenting an analysis of state-of-the-art ML techniques, algorithms, and challenges for application in the field. …”
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Reviewing Breakthroughs and Limitations of Implantable and External Medical Device Treatments for Spinal Cord Injury by Tooba Wallana, Konstantinos Banitsas, Wamadeva Balachandran

“…Future advancements should work on implementing wireless data logging with AI algorithms to increase SCI device adaptability, as well as maintaining regulatory and health system integration.…”
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HybridBranchNetV2: Towards reliable artificial intelligence in image classification using reinforcement learning. by Ebrahim Parcham, Mansoor Fateh, Vahid Abolghasemi

“…Many artificial intelligence (AI) algorithms struggle to adapt effectively in dynamic real-world scenarios, such as complex classification tasks and object relationship extraction, due to their predictable but non-adaptive behavior. …”
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A Review of Generative Design Using Machine Learning for Additive Manufacturing by Parankush Koul

“…This paper also focuses on how generative design can help accelerate innovation and product creation because it empowers designers to play in a wider space of design and provide solutions that cannot be reached with traditional techniques. AI integration with existing production processes is also vital to real-time manufacturing optimization—further increasing overall operational effectiveness. …”
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Research advances in energy management and harvesting technologies for autonomous profiling floats by Yuxia Yu, Yuxia Yu, Qunhui Yang, Qunhui Yang, Qunhui Yang, Fuwu Ji, Fuwu Ji, Wei Zhou

“…However, the limited energy storage capacity of batteries imposes significant constraints on their operational lifespan, observation frequency, and the integration of advanced sensors, which has emerged as a critical bottleneck hindering long-term autonomous observations. …”
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Exploration of Advanced Applications of Triboelectric Nanogenerator-Based Self-Powered Sensors in the Era of Artificial Intelligence by Yifeng Su, Dezhi Yin, Xinmao Zhao, Tong Hu, Long Liu

“…This review provides a comprehensive overview of the synergy between Deep Learning and sensors, with a particular focus on the applications of triboelectric nanogenerator (TENG)-based self-powered sensors combined with artificial intelligence (AI) algorithms. First, the evolution of Deep Learning is reviewed, highlighting the advantages, limitations, and application domains of several classical models. …”
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Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease by Hsin-Yueh Liang, Hsin-Yueh Liang, Kai-Cheng Hsu, Kai-Cheng Hsu, Kai-Cheng Hsu, Shang-Yu Chien, Chen-Yu Yeh, Ting-Hsuan Sun, Meng-Hsuan Liu, Kee Koon Ng

“…A convolutional recurrent neural network algorithm, integrating electrocardiographic (ECG) signals and features from ExECG reports, was developed to predict the risk of significant CAD. …”
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Congo red fluorescence enhances digital pathology workflow in cardiac amyloidosis by Giorgio Cazzaniga, Monica De Gaspari, Vincenzo L’Imperio, Carlo Beretta, Angela Greco, Stefania Rizzo, Cristina Basso, Fabio Pagni

“…The feasibility of developing AI algorithms applicable to centers lacking a fluorescence scanner was investigated leveraging a computational pipeline that enables fluorescence outcome visualization in brightfield. …”
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Learning Online MEMS Calibration with Time-Varying and Memory-Efficient Gaussian Neural Topologies by Danilo Pietro Pau, Simone Tognocchi, Marco Marcon

“…Both of these were deployed into the Intelligent Sensor Processing Unit (ISPU), integrated into the LSM6DSO16IS Inertial Measurement Unit (IMU), which is a programmable 5–10 MHz DSP on which the programmer can compile and execute AI models. …”
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A Damage Detection Approach in the Era of Industry 4.0 Using the Relationship between Circular Economy, Data Mining, and Artificial Intelligence by Meisam Gordan, Saeed-Reza Sabbagh-Yazdi, Khaled Ghaedi, Zubaidah Ismail

“…In addition, vibration characteristics of a composite bridge deck structure are employed as inputs of AI algorithms. Likewise, an artificial neural network (ANN) integrated with a genetic algorithm (GA) was also developed for detecting the damage. …”
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Global trends in the use of artificial intelligence for urological tumor histopathology: A 20-year bibliometric analysis by Fazhong Dai, Yifeng He, Juan Duan, Kangjian Lin, Qian Lv, Zhongxiang Zhao, Yesong Zou, Jianhong Jiang, Zongtai Zheng, Xiaofu Qiu

“…Future advancements hinge on developing explainable AI frameworks, multimodal systems integrating histopathology, radiomics, and genomics and establishing global collaborative networks to address resource disparities. …”
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Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review by Anastasios Anastasiadis, Antonios Koudonas, Georgios Langas, Stavros Tsiakaras, Dimitrios Memmos, Ioannis Mykoniatis, Evangelos N. Symeonidis, Dimitrios Tsiptsios, Eliophotos Savvides, Ioannis Vakalopoulos, Georgios Dimitriadis, Jean de la Rosette

“…Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. …”
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