Secure Data Aggregation Based on End-to-End Homomorphic Encryption in IoT-Based Wireless Sensor Networks.

By definition, the aggregating methodology ensures that transmitted data remain visible in clear text in the aggregated units or nodes. Data transmission without encryption is vulnerable to security issues such as data confidentiality, integrity, authentication and attacks by adversaries. On the other hand, encryption at each hop requires extra computation for decrypting, aggregating, and then re-encrypting the data, which results in increased complexity, not only in terms of computation but also due to the required sharing of keys.

A CAD System for Alzheimer's Disease Classification Using Neuroimaging MRI 2D Slices.

Developments in medical care have inspired wide interest in the current decade, especially to their services to individuals living prolonged and healthier lives. Alzheimer's disease (AD) is the most chronic neurodegeneration and dementia-causing disorder. Economic expense of treating AD patients is expected to grow.

Diet, Fluid Intake, Urine Output and Urinary Sodium/Potassium Ratios in Children With Urolithiasis.

We performed a cross-sectional study on 25 children (17 boys) with urolithiasis with normal glomerular functions at a tertiary care teaching hospital between March, 2018 to March, 2019. Dietary assessment showed that caloric intake was below recommended dietary allowance (RDA) in 68% patients while the median protein intake was 34.3% more.

An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network.

Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD.

Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network.

Alzheimer's disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty.

Nutritional assessment in children with chronic kidney disease.

Growth failure is a major problem in pediatric patients with chronic kidney disease (CKD), and the onset of the condition in infancy is more likely to have an adverse impact on growth than its development in later childhood. This study was aimed to assess nutritional intake and anthropometry of children presenting with CKD in a developing country. In this cross-sectional observational study, children (1-18 years) with CKD visiting the outpatient services were enrolled.