|
|
|
|
|
Noor Ul Ain Tahir, Zuping Zhang, Muhammad Asim, Junhong Chen and Mohammed ELAffendi
Enhancing the environmental perception of autonomous vehicles (AVs) in intelligent transportation systems requires computer vision technology to be effective in detecting objects and obstacles, particularly in adverse weather conditions. Adverse weather ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jinghua Groppe, Sven Groppe, Daniel Senf and Ralf Möller
Given a set of software programs, each being labeled either as vulnerable or benign, deep learning technology can be used to automatically build a software vulnerability detector. A challenge in this context is that there are countless equivalent ways to...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jiacun Wang, Guipeng Xi, Xiwang Guo, Shujin Qin and Henry Han
The scheduling of disassembly lines is of great importance to achieve optimized productivity. In this paper, we address the Hybrid Disassembly Line Balancing Problem that combines linear disassembly lines and U-shaped disassembly lines, considering multi...
ver más
|
|
|
|
|
|
|
|
|
|
|
Max Schrötter, Andreas Niemann and Bettina Schnor
Over the last few years, a plethora of papers presenting machine-learning-based approaches for intrusion detection have been published. However, the majority of those papers do not compare their results with a proper baseline of a signature-based intrusi...
ver más
|
|
|
|
|
|
|
|
|
|
|
Moiz Hassan, Kandasamy Illanko and Xavier N. Fernando
Single Image Super Resolution (SSIR) is an intriguing research topic in computer vision where the goal is to create high-resolution images from low-resolution ones using innovative techniques. SSIR has numerous applications in fields such as medical/sate...
ver más
|
|
|
|
|
|
|
|
|
|
|
Suryakant Tyagi and Sándor Szénási
Machine learning and speech emotion recognition are rapidly evolving fields, significantly impacting human-centered computing. Machine learning enables computers to learn from data and make predictions, while speech emotion recognition allows computers t...
ver más
|
|
|
|
|
|
|
|
|
|
|
Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Francisco J. Ribadas-Pena and Néstor Bolaños
In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are expe...
ver más
|
|
|
|
|
|
|
|
|
|
|
Tamim Mahmud Al-Hasan, Aya Nabil Sayed, Faycal Bensaali, Yassine Himeur, Iraklis Varlamis and George Dimitrakopoulos
Recommender systems are a key technology for many applications, such as e-commerce, streaming media, and social media. Traditional recommender systems rely on collaborative filtering or content-based filtering to make recommendations. However, these appr...
ver más
|
|
|
|
|
|
|
|
|
|
|
Chen Zhang, Celimuge Wu, Min Lin, Yangfei Lin and William Liu
In the advanced 5G and beyond networks, multi-access edge computing (MEC) is increasingly recognized as a promising technology, offering the dual advantages of reducing energy utilization in cloud data centers while catering to the demands for reliabilit...
ver más
|
|
|
|
|
|
|
|
|
|
|
Liang Liu, Tianbin Li and Chunchi Ma
Three-dimensional (3D) models provide the most intuitive representation of geological conditions. Traditional modeling methods heavily depend on technicians? expertise and lack ease of updating. In this study, we introduce a deep learning-based method fo...
ver más
|
|
|
|
|
|