The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …

Skin cancer is a deadly disease, and its early diagnosis enhances the chances of survival.
Deep learning algorithms for skin cancer detection have become popular in recent years. A …

Single Image Super-resolution (SISR) produces high-resolution images with fine spatial
resolutions from a remotely sensed image with low spatial resolution. Recently, deep …

Skin cancer, particularly melanoma, has remained a severe issue for many years due to its
increasing incidences. The rising mortality rate associated with melanoma demands …

Software-based methods can improve CT spatial resolution without changing the hardware
of the scanner or increasing the radiation dose to the object. In this work, we aim to develop …

Image dehazing is a fast-growing research area in image processing and computer vision.
Due to the extreme fog, haze, and air dispersion within an environment, the hazy image …

Multimodal medical images are widely used by clinicians and physicians to analyze and
retrieve complementary information from high-resolution images in a non-invasive manner …

Acoustic tomography (AT) is one of a few non-contact measurement techniques that can
present information about the temperature distribution. Its successful application greatly …

Bulk sensitivity is an important parameter to validate the efficiency of the photonic
waveguide sensor. Due to recent advancements in point-of-care silicon photonic …

Hierarchical Temporal Memory (HTM) is an unsupervised algorithm in machine learning. It
models several fundamental neocortical computational principles. Spatial Pooler (SP) is one …