Snake-Efficient Feature Selection-Based Framework for Precise Early Detection of Chronic Kidney Disease.

Chronic kidney disease (CKD) refers to impairment of the kidneys that may worsen over time. Early detection of CKD is crucial for saving millions of lives. As a result, several studies are currently focused on developing computer-aided systems to detect CKD in its early stages.

AUTO-HAR: An adaptive human activity recognition framework using an automated CNN architecture design.

Convolutional neural networks (CNNs) have demonstrated exceptional results in the analysis of time- series data when used for Human Activity Recognition (HAR). The manual design of such neural architectures is an error-prone and time-consuming process. The search for optimal CNN architectures is considered a revolution in the design of neural networks.

Correction to: A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19.

[This corrects the article DOI: 10.1007/s11227-021-04222-4.].

Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care.

The understanding of various health-oriented vital sign data generated from body sensor networks (BSNs) and discovery of the associations between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where occupants' health status is continuously monitored remotely, it is essential to provide the required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach for mining the periodic patterns obtained from BSN data.