No abstract

Chronic Kidney disease (CKD) is a lifelong health hazard that can cause the failure of kidneys. Symptoms of this develop slowly and are not obvious. Early detection of Chronic Kidney Disease can lead to significant progress in finding the cure for this disease. Through this study, we aim to employ ML techniques for the prediction and diagnosis of Chronic Kidney Disease. The findings obtained from our predictive analysis combined with the expertise of healthcare professionals can help in making an accurate prognosis. For this, we have used a dataset containing data from 400 individuals acquired from the University of California Irvine (UCI) repository. Various feature selection techniques have been used to optimize the number of features affecting Chronic Kidney Disease. Subsequently, these desirable features are chosen and used in different ML models and their accuracy, sensitivity is compared. Multiple Machine learning algorithms have been explored such as Logistic Regression, Naïve Bayes, KNN, SVM, Decision Trees, Random Forest Classifier, and Extra Trees Classifier. It was concluded that Decision Trees using information gain gave six optimal features and the Extra Trees Classifier model gives the best accuracy of 99.36 % with Extra Trees Classifier having one of the least execution times.

No abstract

Renewable energy will drive the future. The applications of mobile phone is no longer limited to communication between each other but can also be used on an everyday basis starting from grocery shopping to watching series for entertainment. Thus our mobile phones require constant charging of power. It is almost impossible to increase the mAh capacity of the battery indefinitely; therefore the need of a battery re-charging source is inevitable. The objective of this research work is to design a sustainable, portable charger in which power is generated with the help of piezoelectric sensors embedded into the shoes of an individual. The underlying principle is to transduce the pressure applied on the piezoelectric sensors to power. This power can be used to charge mobile phones using a USB cable at any convenient time. The light weight compatibility of the device makes it easily portable. In the proposed system, a grid of piezoelectric sensors of suitable size is incorporated in the shoes and an Android application is developed to monitor the power generated as well as to suggest the optimal walking pace for the user to increase power generation. The key highlight of this prototype is that it is user friendly and is paired with an Android application to facilitate maximum power generation.