The adequate aging hypothesis seeks to help people live longer, healthy lives. Diabetic patients who stay remotely need an infrastructure to monitor them continuously and provide timely treatment. Ambient assisted living (AAL) encourages the establishment of solutions that may help optimize older people’s assistive environment while also reducing their impairments. The blood glucose levels of diabetic patients are continuously monitored by gold oxide sensors placed over the human body. The signals associated with the glucose levels in the human body are plotted over a spectrogram image using the short-time Fourier transform, which is further classified using the deep learning model based on finetuned AlexNet, which has employed random oversampling and batch normalization for better precision in the results. The model classifies the spectrogram images as low and high glucose levels and normal glucose levels. Thereby alarming the caretakers for effective treatment of the individuals. Body area networks (BANs) gather information from biosensors and send it to a domain controller to assist caretakers and physicians in recommending the physical exercises for their clients. Evaluation criteria such as sensitivity and specificity, precision, and Mathew’s correlation coefficient are used to assess the effectiveness of the proposed model in this current diabetes study. The cross-validation of the model at multiple folds is being evaluated to analyze the performance. It is evident from the obtained results that the proposed model has exhibited an acceptable performance in precisely sensing the individuals with abnormal glucose levels.
The brain tumor is a dangerous disease and its characterization is a difficult undertaking for radiologists in light of the heterogeneous idea of the tumor cells. Computer-Aided tasks and its implementation of the current models with their frameworks would suffice the design metrics to recognize and relate the different tumors including brain though the process with scanning of the brain would emphasize on MRI. The models with support vector machine-nearest neighbor, naïve bayes analysis are obtained aren’t enough to produce the different scenario at each set of layers and its correlation values of the performance observed. We propose a design model with fast Adaboost binary classifier, ensemble approach with fast boosting algorithm with the pre-trained model’s dataset to analyze the different problem which proposes a strategy for multiple application scenarios of feature extraction to provide a classification model for different tumors in the brain to improve different performance parametric values for each trained and test sets with the prediction algorithm with each design formulation.
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