Results 91 to 100 of about 9,033 (258)

A Large Language Model‐Based Approach for Fault Detection and Its Application

Safety Science and Technology, EarlyView.
This work proposes an interpretable fault detection framework utilizing pre‐trained large language models to overcome small sample sizes and label scarcity in industrial datasets. A stepwise tuple‐based validation mitigates hallucinations, ensuring reliable detection.
Yihua Ye, Yin Zhu, Liming Che, Hua Zhou
wiley   +1 more source

Reinforcement Learning With Timed Constraints for Robotics Motion Planning

Artificial Intelligence for Engineering, EarlyView.
This work presents a unified automata‐based reinforcement learning framework that enforces MITL time‐bounded task specifications in both MDPs and POMDPs. Results from grid‐world and office scenarios show robust policy learning under stochastic dynamics and partial observability.
Zhaoan Wang, Junchao Li, Mohammad Mahdi Dehghan Pir, Shaoping Xiao   +3 more
wiley   +1 more source

Assessment of Data Reliability of Wireless Sensor Network for Bioinformatics [PDF]

International Journal Bioautomation, 2017
As a focal point of biotechnology, bioinformatics integrates knowledge from biology, mathematics, physics, chemistry, computer science and information science. It generally deals with genome informatics, protein structure and drug design.
Ting Dong
doaj  

Graph neural network‐based attack prediction for communication‐based train control systems

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract The Advanced Persistent Threats (APTs) have emerged as one of the key security challenges to industrial control systems. APTs are complex multi‐step attacks, and they are naturally diverse and complex. Therefore, it is important to comprehend the behaviour of APT attackers and anticipate the upcoming attack actions.
Junyi Zhao, Tao Tang, Bing Bu, Qichang Li   +3 more
wiley   +1 more source

A secured cloud‐medical data sharing with A‐BRSA and Salp ‐Ant Lion Optimisation Algorithm

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Sharing medical data among healthcare providers, researchers, and patients is crucial for efficient healthcare services. Cloud‐assisted smart healthcare (s‐healthcare) systems have made it easier to store EHRs effectively. However, the traditional encryption algorithms used to secure this data can be vulnerable to attacks if the encryption key
Adel Binbusayyis, Abed Alanazi, Shtwai Alsubai, Areej Alasiry, Mehrez Marzougui, Abdullah Alqahtani, Mohemmed Sha, Muhammad Aslam   +7 more
wiley   +1 more source

Enhancing breast cancer treatment selection through 2TLIVq-ROFS-based multi-attribute group decision making

Frontiers in Artificial Intelligence
IntroductionBreast cancer is an extremely common and potentially fatal illness that impacts millions of women worldwide. Multiple criteria and inclinations must be taken into account when selecting the optimal treatment option for each patient.MethodsThe
Muhammad Waheed Rasheed, Abid Mahboob, Anfal Nabeel Mustafa, Israa Badi, Zainab Abdulkhaleq Ahmed Ali, Zainb H. Feza   +5 more
doaj   +1 more source

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source

AGT: Efficient Offline Reinforcement Learning With Advantage‐Guided Transformer

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Offline reinforcement learning (RL) is a paradigm that seeks to train policies directly based on fixed datasets derived from previous interactions with the environment. However, offline RL faces critical challenges in environments characterised by sparse rewards and datasets dominated by suboptimal trajectories.
Jiaye Wei, Xin Xu, Yixing Lan, Tenglong Liu, Yueying Wang   +4 more
wiley   +1 more source

Credit‐Driven Adaptive Grouping for Refined Cooperative Multi‐Agent Reinforcement Learning

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Policy heterogeneity is crucial for achieving sophisticated coordination in complex collaborative tasks, which has emerged as one of the key challenges in multi‐agent reinforcement learning (MARL) in recent years. Notably, the grouping paradigm has made remarkable progress in addressing policy heterogeneity.
Yirui Liu, Xueqiang Gu, Caiming Zheng, Xin Xu, Zhangling Wang, Wanpeng Zhang, Yixing Lan   +6 more
wiley   +1 more source

A Multi‐Agent Bidding Strategy in Day‐Ahead Joint Energy and Reserve Markets Considering Risk Preferences

IET Smart Energy Systems, EarlyView.
A PER‐MATD3‐based bidding model for generators in day‐ahead joint energy and reserve markets is proposed. An aggressiveness coefficient quantifies risk preference, KAN improves interpretability and simulation results demonstrate enhanced coordinated decision‐making and training stability.
Xinge Xu, Teng Yan, Xiuming Bao, Dong Liu, Guodong Guo, Yingyun Sun   +5 more
wiley   +1 more source