Comprehensive Review
Comprehensive reviews across diverse scientific fields synthesize current research, identifying key trends and challenges. Current research focuses on leveraging deep learning architectures, including convolutional and recurrent neural networks, transformers, and generative models, to improve performance in areas like video summarization, medical prognostics, and autonomous driving. These reviews highlight the increasing importance of data-driven approaches and the need for robust, interpretable models, ultimately advancing both scientific understanding and practical applications. The impact spans improved efficiency in various sectors and a deeper understanding of complex systems.
Papers
A Comprehensive Review of Emerging Approaches in Machine Learning for De Novo PROTAC Design
Yossra Gharbi, Rocío Mercado
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing
Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Peng Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Ranran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Jiayang Cheng, Zhaowei Wang, Ying Su, Raj Sanjay Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip S. Yu, Wenpeng Yin
Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A Comprehensive Review
Meng Cui, Xubo Liu, Haohe Liu, Jinzheng Zhao, Daoliang Li, Wenwu Wang
Recent Advances in Traffic Accident Analysis and Prediction: A Comprehensive Review of Machine Learning Techniques
Noushin Behboudi, Sobhan Moosavi, Rajiv Ramnath