Design of a Low-Complexity Deep Learning Model for Diagnosis of Type 2 Diabetes.

Background: Recent research demonstrates that diabetes can lead to heart problems, neurological damage, and other illnesses.

Method: In this paper, we design a low-complexity Deep Learning (DL)-based model for the diagnosis of type 2 diabetes. In our experiments, we use the publicly available PIMA Indian Diabetes Dataset (PIDD).

Hybrid CNN-LSTM for Predicting Diabetes: A Review.

Background: Diabetes is a common and deadly chronic disease caused by high blood glucose levels that can cause heart problems, neurological damage, and other illnesses. Through the early detection of diabetes, patients can live healthier lives. Many machine learning and deep learning techniques have been applied for noninvasive diabetes prediction.

Elbow angle generation during activities of daily living using a submovement prediction model.

The present study aimed to develop a realistic model for the generation of human activities of daily living (ADL) movements. The angular profiles of the elbow joint during functional ADL tasks such as eating and drinking were generated by a submovement-based closed-loop model. First, the ADL movements recorded from three human participants were broken down into logical phases, and each phase was decomposed into submovement components.

The Effects of Upper Limb Motor Recovery on Submovement Characteristics among the Patients with Stroke: A Meta-Analysis.

Objective: To evaluate the evidence related to the effect of upper limb motor recovery on submovement characteristics, including duration, amplitude, overlap, interpeak distance, and the number of submovements in stroke patients using a meta-analysis.

Type Of Study: Meta-analysis.

Literature Survey: The literature search was restricted to articles written in English published from inception to October 2018 in Web of Science, PubMed, Science Direct, IEEE Explore, MEDLINE, CDSR, Scopus, Compendex, Wiley Online Library, Springer Link, and REHABDATA.