Indoor temperature modeling is a crucial part towards efficient Heating, Ventilation and Air
Conditioning (HVAC) systems. Data-driven black-box approaches have been an attractive …

The 3D convolutional neural network is able to make use of the full nonlinear 3D context
information of lung nodule detection from the DICOM (Digital Imaging and Communications …

Digital Image processing is a subcategory of digital signal processing that lays emphasis on
the study of processing techniques used for enhancement or restoration. De-noising of …

Deep convolutional neural networks (CNNs) are used for the detection of COVID-19 in X-ray
images. The detection performance of deep CNNs may be reduced by noisy X-ray images …

Recently, deep learning techniques are widely used in various computer vision applications
such as pattern recognition, data classification, object detection, image enhancement, etc …

Data in the healthcare industry and machine learning techniques is useful to analyse a huge
amount of data to identify the hidden patterns in the disease, to give personalised treatment …

The noise in sensor data has a substantial impact on the reliability and accuracy of (ML)
algorithms. A comprehensive framework is proposed to analyze the effects of diverse noise …

Lung cancer is an abnormality where the body's cells multiply uncontrollably. The disease
can be deadly if not detected in the initial stage. To address this issue, an automated lung …

Background The convolutional neural network (CNN) is a mainstream deep learning (DL)
algorithm, and it has gained great fame in solving problems from clinical examination and …

Noise is an unwanted element that degrades the quality of digital images. Salt and pepper
noise is a type of noise that is introduced in one or more steps during image acquisition …