DengueFog: A Fog Computing-Enabled Weighted Random Forest-Based Smart Health Monitoring System for Automatic Dengue Prediction.

Dengue is a distinctive and fatal infectious disease that spreads through female mosquitoes called Aedes aegypti. It is a notable concern for developing countries due to its low diagnosis rate. Dengue has the most astounding mortality level as compared to other diseases due to tremendous platelet depletion.

Feature Importance Score-Based Functional Link Artificial Neural Networks for Breast Cancer Classification.

Growth of malignant tumors in the breast results in breast cancer. It is a cause of death of many women across the world. As a part of treatment, a woman might have to go through painful surgery and chemotherapy that may further lead to severe side effects.

: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning.

Pulmonary fibrosis is a severe chronic lung disease that causes irreversible scarring in the tissues of the lungs, which results in the loss of lung capacity. The Forced Vital Capacity () of the patient is an interesting measure to investigate this disease to have the prognosis of the disease. This paper proposes a deep learning-based architecture to predict the progression of the disease from the patient's computed tomography (CT) scan and the patient's metadata.

Analysis of the Effect of Incentive Nursing Intervention in Children with Severe Viral Encephalitis and Myocarditis during Rehabilitation Based on Diffusion Weighted MRI.

Background: Severe viral encephalitis in children causes a viral infection that damages their central nervous system. This situation arises the mental abnormalities, sudden rise in body temperature, disturbance of consciousness, and so forth in children, which can be life-threatening.

Objective: This work aimed at exploring the effect of diffusion weighted MRI on children with severe viral encephalitis and myocarditis.