Results 41 to 50 of about 465,688 (129)

Correlation Between Depression-Associated Genes and Cancer Types: Predicting Cancer Based on Mutation Frequencies

IEEE Access
This study examines the potential interconnection between genetic mutations frequently observed in various types of cancer and depression, aiming to elucidate shared genetic factors that may influence the etiology and treatment of both conditions ...
Fernando Patricio Carranco-Avila, Clayanela Zambrano-Caicedo, Jonathan Javier Loor-Duque, Ariana Deyaneira Jimenez-Narvaez, Ivan Galo Reyes-Chacon, Paulina Vizcaino, Isidro Rafael Amaro Martin, Manuel Eugenio Morocho-Cayamcela   +7 more
doaj   +1 more source

Predictive Modeling for Fetal Health: A Comparative Study of PCA, LDA, and KPCA for Dimensionality Reduction

IEEE Access
Pregnancy complications significantly impact maternal and fetal health, requiring accurate and timely diagnostic methods for life-saving interventions.
Ariana Deyaneira Jimenez-Narvaez, Victor David Casa Vaca, Jonathan Javier Loor-Duque, Isidro Rafael Amaro Martin, Ivan Galo Reyes-Chacon, Paulina Vizcaino, Manuel Eugenio Morocho-Cayamcela   +6 more
doaj   +1 more source

Vehicle-in-the-Loop in Global Coordinates for Advanced Driver Assistance System

Applied Sciences, 2020
Most vehicle controllers are developed and verified with V-model. There are several traditional methods in the automotive industry called “X-in-the-Loop (XIL)”.
Changwoo Park, Seunghwan Chung, Hyeongcheol Lee   +2 more
doaj   +1 more source

Synthetic Data Enhancement and Network Compression Technology of Monocular Depth Estimation for Real-Time Autonomous Driving System

Sensors
Accurate 3D image recognition, critical for autonomous driving safety, is shifting from the LIDAR-based point cloud to camera-based depth estimation technologies driven by cost considerations and the point cloud’s limitations in detecting distant small ...
Woomin Jun, Jisang Yoo, Sungjin Lee
doaj   +1 more source

Driving policies of V2X autonomous vehicles based on reinforcement learning methods

IET Intelligent Transport Systems, 2020
Autonomous driving has been achieving great progress since last several years. However, the autonomous vehicles always ignore the important traffic information on the road because of the uncertainties of driving environment and the limitations of onboard
Zhenyu Wu, Kai Qiu, Hongbo Gao
doaj   +1 more source

Reinforcement-Learning-Based Trajectory Learning in Frenet Frame for Autonomous Driving

Applied Sciences
Autonomous driving is a complex problem that requires intelligent decision making, and it has recently garnered significant interest due to its potential advantages in convenience and safety.
Sangho Yoon, Youngjoon Kwon, Jaesung Ryu, Sungkwan Kim, Sungwoo Choi, Kyungjae Lee   +5 more
doaj   +1 more source

Semantic Label Reduction Techniques for Autonomous Driving

, 2019
Semantic segmentation maps can be used as input to models for maneuvering the controls of a car. However, not all labels may be necessary for making the control decision.
Khan, Qadeer, Schön, Torsten, Wenzel, Patrick   +2 more
core  

Legal AI for All: Reducing Perplexity and Boosting Accuracy in Normative Texts With Fine-Tuned LLMs and RAG

IEEE Access
This study investigates the potential of open-source Large Language Models (LLMs) to enhance the accessibility and understanding of complex legal information.
Patricio Santiago Garcia-Montero, Paulina Vizcaino, Ivan Galo Reyes-Chacon, Manuel Eugenio Morocho-Cayamcela   +3 more
doaj   +1 more source

Study on Following Car Model with Different Driving Styles Based on Proximal PolicyOptimization Algorithm [PDF]

Jisuanji kexue
Autonomous driving plays a crucial role in reducing traffic congestion and improving driving comfort.It remains of significant research importance to enhance public acceptance of autonomous driving technology.Customizing different driving styles for ...
YAN Xin, HUANG Zhiqiu, SHI Fan, XU Heng
doaj   +1 more source

Developing a Risk Recognition System Based on a Large Language Model for Autonomous Driving

Engineering Proceedings
Autonomous driving systems have the potential to reduce traffic accidents dramatically; however, conventional modules often struggle to accurately detect risks in complex environments.
Donggyu Min, Dong-Kyu Kim
doaj   +1 more source