Comprehensive Survey
Comprehensive surveys in various scientific fields systematically review existing research, aiming to synthesize key findings, identify gaps, and guide future directions. Current research focuses on evaluating and improving the trustworthiness, efficiency, and bias mitigation of models across diverse domains, including large language models, image generation, and autonomous systems. These surveys are crucial for advancing understanding within specific subfields and facilitating the development of more robust and reliable technologies with broader practical applications.
Papers
Quantum-Inspired Evolutionary Algorithms for Feature Subset Selection: A Comprehensive Survey
Yelleti Vivek, Vadlamani Ravi, P. Radha Krishna
Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey
Shahab Saquib Sohail, Yassine Himeur, Hamza Kheddar, Abbes Amira, Fodil Fadli, Shadi Atalla, Abigail Copiaco, Wathiq Mansoor
Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI
Yang Liu, Weixing Chen, Yongjie Bai, Guanbin Li, Wen Gao, Liang Lin
Mobile Edge Intelligence for Large Language Models: A Contemporary Survey
Guanqiao Qu, Qiyuan Chen, Wei Wei, Zheng Lin, Xianhao Chen, Kaibin Huang
Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges
Yanli Li, Zhongliang Guo, Nan Yang, Huaming Chen, Dong Yuan, Weiping Ding
AI-Driven Approaches for Optimizing Power Consumption: A Comprehensive Survey
Parag Biswas, Abdur Rashid, Angona Biswas, Md Abdullah Al Nasim, Kishor Datta Gupta, Roy George
Present and Future of AI in Renewable Energy Domain : A Comprehensive Survey
Abdur Rashid, Parag Biswas, Angona Biswas, MD Abdullah Al Nasim, Kishor Datta Gupta, Roy George