Compressive sensing (CS) is an effective strategy for data collection and maintaining energy
consumption balance in wireless sensor networks (WSN). CS usually exploits the space …

Deep learning is a category of machine learning algorithms and has sparked a great deal of
interest in its applicability to radiography challenges due to its tremendous improvement. It is …

In recent times, compressive sensing (CS) provides data robustness by projecting the raw
signal into a different domain and transmitting the predicted signal rather than the source …

In recent times, generative modeling (GM) has gained much popularity in machine
intelligence methods, based on its similar likeness to human intelligence. They have …

Realistic Scene understanding is a challenging task by recognizing instances along with the
semantic scene. This work, efficient panoptic image segmentation through a deep UNet …

Recent development in wearable sensor technology helps to collect biological signals at a
low cost. Collecting and analyzing different biomarkers are anticipated to improve the …

The main cause of mortality globally is cancer, with lung cancer being the most common
among all cancer types. Radiologists use computer tomography (CT) scans to detect and …

This paper introduces a novel data collection model called ConciseNet that leverages a
cutting-edge data reconstruction method to significantly enhance signal reconstruction …