Many individuals throughout the globe need to be constantly monitored for health reasons, including diabetes patients and individuals with other chronic diseases, the elderly, and the disabled. At any time, these individuals may be at a higher risk of suffering life-threatening falls or experiencing fainting. HAR (Human Activity Recognition) model using machine learning techniques play an important role in observing the activities of the people. The existing methods of activity monitoring lacks accuracy. Hence, the proposed method focuses to improve the prediction accuracy using accelerometer and gyroscope data. The research work analysis accelerometer and gyroscope data using various decomposition techniques such as EMD(Empirical Mode Decomposition), DWT (Discrete Wavelet Transform), FFT (Fast Fourier Transform) to process non-linear data and to split series of signal data into set of IMF(Intrinsic Mode Function), PCA(Principal Component Analysis) was performed for selecting optimal features. Then human activities are recognized by using multi-class classification techniques. The proposed EMD method achieves better performance with 98.4% accuracy, 100% Precision, 100% Recall and 100% F-measure.
Background: Alzheimer's disease (AD) is a neurological illness that causes short-term memory loss. There are currently no viable therapeutic therapies for this condition that can totally cure it. The source of Alzheimer's disease is unknown, although genetic factors are thought to have a role in the illness's development, with about 70% of the disease's risk attributed to the vast number of genes associated. Despite the discovery of a number of potential AD susceptibility genes through genetic association studies, there is greater challenge to identify unidentified AD-associated genes and drug targets in order to gain a good insight of the disease-causing mechanisms of Alzheimer's disease and develop effective AD therapeutics.The proposed CG-DC model brings an accuracy of 96% for ANN model, 87.3% for KNN classifier, 86% for SVM classifier, 85.3% than Decision Tree. It is clearly visible; the proposed network topology model performs good for ANN classifier than other existing models. Similarly, the model also brings a sensitivity measure of 97% for ANN model, 84% for KNN classifier, 84.2% for SVM classifier and 84% for Decision tree classifier.Results: In the proposed research work, a novel network topology measure (DC-GC) and intelligent based machine learning models are used for identifying candidate genes from protein-protein interaction and sequence features of genes.Conclusions:The proposed approach DC-GC based network topology measure provides remarkable improvement than existing centrality measures. This helps in the identification of disease associated candidate genes, disease treatment and suitable drug development.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.