Currently, the traffic speed prediction model based on deep learning has become a research hotspot in the field of transportation. With the rapid development of deep learning and the improvement of ...

Recently, some computer scientists have started exploring the possibility of modeling feature learning in DNNs using frameworks and approaches rooted in physics.

Single-molecule detection of post-translational modifications (PTMs) such as phosphorylation plays a crucial role in early diagnosis of diseases and therapeutics development. Although single-molecule ...

Electric Vehicle (EV) cost prediction involves analyzing complex, high-dimensional data that often contains noise, multicollinearity, and irrelevant features. Traditional regression models struggle to ...

Traditional intrusion detection systems (IDS) often face difficulties in identifying these new and evolving threats since they rely on pre-established attack patterns or signatures. This study aims to ...

REAL-TIME-MALWARE-DETECTION-REMOVAL-SYSTEM-USING-MACHINE-LEARNING Detect, quarantine, and eradicate malware as it appears — all in pure Python. Lightweight desktop app with a live inference engine ...

These studies used an alternative second-generation approach with a non-viral lipid nanoparticle delivery system. This exploratory research represents the Company's forward-thinking work within its ...

As a popular structure of the deep neural network, the autoencoder has been widely used in different fields, including shape representation (11) and image segmentation (12). The autoencoder was also ...