A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Wenya Linda Bi, Ahmed Hosny, Matthew B. Schabath, et al.
Review of AI applications in cancer imaging across lung, brain, breast, and prostate tumors, highlighting clinical challenges and the need for validation.
Davy Tsz Kit Ng, Jac Ka Lok Leung, Samuel Kai Wah Chu, et al.
This exploratory review conceptualizes AI literacy by proposing four aspects—know and understand, use and apply, evaluate and create, and ethical issues—based on 30 peer-reviewed articles.
Yuyi Mao, Changsheng You, Jun Zhang, et al.
A comprehensive survey of mobile edge computing from the communication perspective, focusing on joint radio-and-computational resource management, open challenges, and standardization efforts.
Xiangbin Liu, Liping Song, Shuai Liu, et al.
A comprehensive review of deep-learning-based medical image segmentation methods, summarizing three main approaches, their limitations, and future directions to address accuracy and data challenges.
Christoph Strecha, Alex M. Bronstein, Michael M. Bronstein, et al.
LDAHash maps high-dimensional SIFT-like descriptors into compact binary strings via learned linear projections, enabling efficient Hamming-space matching with reduced storage.
Zheng Lin, Guanqiao Qu, Qiyuan Chen, et al.
This article explores deploying large language models at the 6G edge through end-edge cooperation, discussing architecture, training, inference, and open challenges.
Syed Junaid Nawaz, Shree Krishna Sharma, Shurjeel Wyne, et al.
This paper reviews machine learning, quantum computing, and quantum machine learning for 6G networks and proposes a QC-assisted QML framework for beyond-5G communication systems.
Guillermo Gallego, Tobi Delbrück, Garrick Orchard, et al.
A comprehensive survey of event-based vision, covering event camera principles, available sensors, processing algorithms, applications, and future challenges.
Baoguang Shi, Xiang Bai, Cong Yao
A novel end-to-end trainable neural network integrates feature extraction, sequence modeling, and transcription for scene text recognition without character segmentation.
Zhengxia Zou, Keyan Chen, Zhenwei Shi, et al.
A comprehensive survey tracing 25 years of object detection evolution, from 1990s hand-crafted methods to modern deep learning detectors, covering milestones, datasets, metrics, and architectures.
Junda He, Christoph Treude, David Lo
A systematic literature review of LLM-based multi-agent systems across the software development lifecycle, with case studies, research gaps, and a forward-looking agenda.
Juan R. Terven, Diana Margarita Córdova Esparza, Julio-Alejandro Romero-González, et al.
A comprehensive survey of loss functions and metrics in deep learning, offering a unified framework, multi-loss insights, and guidance for modern tasks like RAG.