A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, et al.
Comprehensive survey of federated learning applications in IoT, covering services, key applications, challenges, and future directions.
Swen Hoelder, Paul A. Clarke, Paul Workman
This review outlines contemporary approaches to small molecule cancer drug discovery, emphasizing target validation, lead optimization, and biomarker-driven trials to reduce attrition and advance personalized medicine.
Alex Kuo
This paper evaluates the opportunities and challenges of cloud computing in healthcare across management, technology, security, and legal aspects.
Gokul Yenduri, M. Ramalingam, G. Chemmalar Selvi, et al.
A comprehensive review of GPT covering architecture, training, enabling technologies, applications, challenges, and future directions.
Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence
A survey of case studies on deploying machine learning, mapping challenges to workflow stages to set a research agenda.
Xuewen Chen, Xiaotong Lin
This paper reviews deep learning's role in big data analytics, highlighting challenges and future trends.
Michael Rogerson, Glenn Parry
This paper uses qualitative case studies to show blockchain enhances supply chain visibility and trust, but faces challenges in technology trust, human error, governance, and consumer willingness to pay.
Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, et al.
A structured survey of deep learning methods for time series anomaly detection, including a taxonomy, advantages, limitations, and open challenges.
Bozheng Dou, Zailiang Zhu, Ekaterina Merkurjev, et al.
This review summarizes machine learning and deep learning solutions for small data challenges in molecular science, covering both basic and advanced techniques.
Daniel J. Mathew, Deborah Ebem, Anayo Chukwu Ikegwu, et al.
This paper reviews recent XAI techniques to enhance interpretability and human understanding of complex AI models, addressing transparency challenges.
Alberto Fernández, Salvador García, Francisco Herrera, et al.
This paper reviews the 15-year impact of SMOTE, its applications, and challenges for scaling to Big Data.
Alysa Ziying Tan, Han Yu, Lizhen Cui, et al.
A survey of personalized federated learning techniques addressing data heterogeneity through a taxonomy of key challenges and strategies.