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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AI-powered approaches for enhancing remote sensing-based water contamination detection in ecological systems by Li Yang, Zhang Ziwen, Xinhao Lin, Junmiao Hei, Yixiao Wang, Ang Zhang

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ML Auditing and Reproducibility: Applying a Core Criteria Catalog to an Early Sepsis Onset Detection System by Markus Schwarz, Ludwig Christian Hinske, Ulrich Mansmann, Fady Albashiti

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GIS Analysis Model Integration and Service Composition Prospects by L. Ding, P. Cai, W. Huang, H. Zhang, F. Ding, W. Zhao, D. Tang, Z. Wang

“…Key algorithms are systematically integrated to optimize outcomes in urban planning, disaster management, and precision agriculture. …”
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Advancing AI in Higher Education: A Comparative Study of Large Language Model-Based Agents for Exam Question Generation, Improvement, and Evaluation by Vlatko Nikolovski, Dimitar Trajanov, Ivan Chorbev

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Data Aggregation and Scheduling to Optimize Information Freshness In Multi-Hop IoT Networks by Xueling Wu, Long Qu, Maurice J. Khabbaz

“…Simulation results demonstrate that the proposed CGA consistently outperforms EASA, the Genetic Algorithm (GA), and the Juventas Algorithm, achieving an average deviation of only 1.84% from the global optimal solution.…”
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WebShield 5.0: Harnessing AI and NLP to combat web threats in Industry 5.0 by Priyanka Verma, Donna O’Shea, Thomas Newe, Ankit Vidyarthi, Deepak Gupta, Jabir Ali, Hamad Aldawsari, John G. Breslin

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Hospitalization prediction from the emergency department using computer vision AI with short patient video clips by Wui Ip, Maria Xenochristou, Elaine Sui, Elyse Ruan, Ryan Ribeira, Debadutta Dash, Malathi Srinivasan, Maja Artandi, Jesutofunmi A. Omiye, Nicholas Scoulios, Hayden L. Hofmann, Ali Mottaghi, Zhenzhen Weng, Abhinav Kumar, Ananya Ganesh, Jason Fries, Serena Yeung-Levy, Lawrence V. Hofmann

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AI-driven dynamic orthodontic treatment management: personalized progress tracking and adjustments—a narrative review by Xuanchi Guo, Yuhan Shao

“…This review will focus on the fusion of AI-driven multimodal data analysis (e.g., cone-beam CT, intraoral scanning, and 3D facial images) and deep learning algorithms (e.g., convolutional neural networks) to elucidate the technological breakthroughs in key aspects such as tooth movement trajectory prediction and early detection of root resorption. …”
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AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view by Mostafa Jalilifar, Mahdi Sadeghi, Alireza Emami-Ardekani, Ahmad Bitarafan-Rajabi, Kouhyar Geravand, Parham Geramifar

“…Abstract This study aims to enhance the dosimetry accuracy in 131I planar imaging by utilizing a single oblique view and Monte Carlo (MC) validated dose point kernels (DPKs) alongside the integration of artificial intelligence (AI) for accurate dose prediction within planar imaging. …”
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AI-Assisted Design of Drain-Extended FinFET With Stepped Field Plate for Multi-Purpose Applications by Xiaoyun Huang, Hongyu Tang, Chenggang Xu, Yuxuan Zhu, Yan Pan, Dawei Gao, Yitao Ma, Kai Xu

“…This study introduces a novel DE-FinFET featuring a stepped field plate to improve overall performance of device. Moreover, within an AI-assisted design framework, predictive modeling and multi-objective optimization of the device are accomplished using Kolmogorov–Arnold Networks (KAN) and the Nondominated Sorting Genetic Algorithm (NSGA-III). …”
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Interpretable artificial intelligence (AI) for cervical cancer risk analysis leveraging stacking ensemble and expert knowledge by Priyanka Roy, Mahmudul Hasan, Md Rashedul Islam, Md Palash Uddin

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Rate of penetration prediction in drilling operations: a comparative study of AI models and meta-heuristic approaches by Fatemeh Mohammadinia, Ali Ranjbar, Fatemeh Ghazi, Seyyed Taha Hosseini

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“Voice is the New Blood”: a discourse analysis of voice AI health-tech start-up websites by Alden Blatter, Hortense Gallois, Emily Evangelista, Yael Bensoussan, Bridge2AI-Voice Consortium, Jean-Christophe Bélisle-Pipon, Yael E. Bensoussan, Olivier Elemento, Jean-Christophe Bélisle-Pipon, David Dorr, Satrajit Ghosh, Alistair Johnson, Phillip Payne, Maria E. Powell, Anaïs Rameau, Vardit Ravitsky, Alexandros Sigaras

“…However, descriptions of voice datasets used to train algorithms vary widely and are often absent, reflecting broader gaps in acoustic and ethical standards for voice data collection and insufficient incentives for start-ups to disclose key data details.DiscussionStart-ups play a crucial role in the research, development, and marketization of voice AI health-tech, prefacing the integration of this new technology into healthcare systems. …”
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Concrete Crack Detection and Segregation: A Feature Fusion, Crack Isolation, and Explainable AI-Based Approach by Reshma Ahmed Swarna, Muhammad Minoar Hossain, Mst. Rokeya Khatun, Mohammad Motiur Rahman, Arslan Munir

“…To isolate and quantify the crack region, this research combines image thresholding, morphological operations, and contour detection with the convex hulls method and forms a novel algorithm. Two explainable AI (XAI) tools, local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping++ (Grad-CAM++) are integrated with the proposed method to enhance result clarity. …”
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Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer Diagnostics by Jihan Wang, Zhengxiang Zhang, Yangyang Wang

“…Feature selection methods—such as filter, wrapper, and embedded techniques—play a critical role in enhancing the precision of cancer diagnostics by identifying relevant biomarkers. The integration of multi-omics data and ML algorithms facilitates a more comprehensive understanding of tumor heterogeneity, advancing both diagnostics and personalized therapies. …”
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AI-Enabled Digital Marketing in Startups: A Systematic Review of Global Practices and Egyptian Implementation Challenges by Wael Bedda, Heba Al-Ashry, Hamed Ead

“…The study also examines Egypt’s entrepreneurial ecosystem, noting government-backed incubators like INTILAC and a youth-driven, tech-savvy population as potential enablers for AI integration. Challenges such as ethical concerns, algorithmic bias, and cultural readiness still persist. …”
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Whose Bias is it, Anyway? The Need for a Four-Eyes Principle in AI-Driven Competion Law Proceedings by Jerome De Cooman

“…The argument is that although the use of algorithms increases procedural fairness by removing noise and cognitive biases from the decision-making process, the overall impact on procedural fairness remains far from clear given that the introduction of AI systems in public administration might perniciously generates new type of biases or harmful behaviours. …”
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The role of explainable AI in enhancing breast cancer diagnosis using machine learning and deep learning models by Zulfikar Ali Ansari, Manish Madhava Tripathi, Rafeeq Ahmed

“…Although artificial intelligence (AI) has showed amazing promise in breast cancer prediction mainly machine learning (ML) algorithms as well as deep learning (DL), practical use of these models is greatly hampered by their lack of interpretability and transparency. …”
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