A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Shigeru Kondo, Takashi Miura
This review explains the reaction-diffusion model as a framework for biological pattern formation, highlighting its relevance and applications in developmental biology.
Mohsen Soori, Behrooz Arezoo, Roza Dastres
This review surveys how AI, ML, and DL are transforming advanced robotics across autonomous navigation, cobots, manufacturing, aviation, and transportation.
Keyan Cao, Yefan Liu, Gongjie Meng, et al.
This paper reviews edge computing as a new paradigm for local data processing near the network edge, addressing cloud computing limitations in IoE scenarios.
Fabrizio Sebastiani
This survey reviews machine learning approaches to automated text categorization, covering document representation, classifier construction, and evaluation.
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.
Laurianne David, Amol Thakkar, Rocío Mercado, et al.
This review catalogs molecular representations for AI-driven drug discovery, serving as a practical guide for researchers new to chemical syntax.
Yadi Zhou, Fei Wang, Jian Tang, et al.
This review provides guidelines on using AI and network medicine for drug repurposing, exemplified by COVID-19, emphasizing reduced timelines and costs.
Veronica Salmaso, Stefano Moro
This paper reviews the evolution of structure-based drug discovery from static molecular docking to enhanced molecular dynamics simulations for studying ligand-protein recognition.
Gokul Yenduri, M. Ramalingam, G. Chemmalar Selvi, et al.
A comprehensive review of GPT covering architecture, training, enabling technologies, applications, challenges, and future directions.
Xuewen Chen, Xiaotong Lin
This paper reviews deep learning's role in big data analytics, highlighting challenges and future trends.
Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola
A comprehensive review of machine learning methods using positive definite kernels, formulated in reproducing kernel Hilbert spaces.
John Hancock, Taghi M. Khoshgoftaar
This paper provides the first interdisciplinary survey of CatBoost for big data, covering both its strengths and weaknesses in classification and regression tasks.