Management of elevated liver enzymes in geriatric diabetes by yogic practice

Objects: The liver plays a major role in the pathogenesis of type 2 diabetes. Moderately elevated liver enzymes are found in type 2 diabetes.  This study is designed to appraise the role of yoga on liver enzymes in geriatric type 2 diabetes and consequently, the study constantly monitored the improvement related to glycaemic control during the period of observation. Study design: A total number of 143 type 2 diabetes patients in an age group of 60-70 years with a history of diabetes for 5-10 years and having poor glycaemic control (HbA1c > 8 %) residing in Kozhikode district, Kerala, India participated in this study in test and control group together. The subjects were divided into three groups according to their glycaemic control: group I with HbA1c 8.6 – 9.7 %, group II with HbA1c 9.8 – 10.7 % and group III with HbA1c 10.8 – 12.7 %. The yogic practice sessions for the test group lasted for three months for 90 minutes a day, 6 days a week, under the guidance and supervision of experienced trainers.  Each session was systematically divided into structural components with 15 minutes of pranayamas (breath controlling exercises), 10 minutes of warm up exrcises, 50 minutes of asanas (yogic postures) and 15 minutes of supine relaxation in savasana. The control group, mean while, were asked to continue their routine activities like walking and other normal non specific exercises. Glucose, HbA1c, aspartate aminotransferase, alanine aminotransferase and γ- glutamyl transpeptidase were estimated on base line and after 90 days of all the participants. Results: The participants in the test group showed statistically significant (p<0.001) decrease in glucose, HbA1c, and activity of liver enzymes after yogic practice. Conclusions: After 90 days of yogic practices, significant reduction in the liver enzymes was achieved in test group, corresponding to the reduction in blood glucose and HbA1c levels. The findings of this study demonstrate the efficacy of yogic practice, as a therapeutic, preventative and protective agent in geriatric type 2 diabetes mellitus by normalizing the liver function tests along with betterment in their glycaemic condition

Traffic light detection and control with connected vehicle for automated car using ML algorithm

The paper centers to plan a self-driving vehicle model with the assistance of Raspberry Pi as a handling chip. The model will perform five undertaking's that remember self-driving for track, identifying traffic signal, rain identification, fog discovery and will help in impediment recognition and crash evasion. The essential information from this present reality to the vehicle is given by camera. It utilizes a HC-SR04 following sensor module that data to the framework through raspberry pi to our framework which is associated with PC with a similar organization. The gathered information is then prepared and examined and pertinent data and subtleties is made back aware of vehicle for proper moves to be initiated. The gamble of human blunders is in this way kept away from securely and keenly as the vehicle can arrive at the given destination. Path Detection, Obstacle Detection, Traffic Light Detection, Rain and Fog are distinguished. The vehicle is halted when impediment and traffic signal red is noticed. As a section one static is planned with Arduino to make Bluetooth (HC-05) communication between the vehicles. This association between two vehicles will keep away from accidents

Effect of multi filters in glucoma detection using random forest classifier

Glaucoma is an eye disease that damages the optic nerve which connects the eye to the brain. When the fluid pressure inside the eye (intraocular pressure) increases, the optic nerve get impaired and has doubled the chance for diabetic patients resulting in irreversible loss of vision if not detected in early stages. In developing countries, due to the scarcity of ophthalmic experts and lab facilities, the needs for eye disease detecting automation system are increased without saying. The field of artificial intelligence is providing many solution's especially in health care domain. The proposed work generate models for recognizing the presence of glaucoma based on open access public dataset of retinal fundus images using machine learning algorithms with the help of image feature descriptors. It classifies the given retinal fundus image as normal or abnormal in two stages. Firstly it extracts image features using appropriate filters and then it is trained through tree based ensemble classifier to classify the given input image and then the same is tested to get the better accuracy performance. The above two steps are iterated by varying over the three effective filters like edge histogram, fuzzy color and texture histogram and pyramid histogram of gradients. The proposed experiment based on this approach reveals that the use of Edge histogram filter in combination with fuzzy color and texture histogram with Random forest classifier yields maximum accuracy of 80.43% and AUC 0.884. The results obtained by applying multi filters is better than that obtained by applying single filter