An efficacious implementation of Internet of Things (IoTs) based omnipresent sensing and monitoring system for domestic as well as non-domestic environments. The structure of the sensing-monitoring system is established on the combination of ubiquitous sensing units, controlling system for data acquisition, manipulation and aggregation and Internet based platform for setting an efficient monitoring. The proposed model consists of sensing units which perceives the environmental value (such as Humidity, temperature, heat index, gas, etc), voltage and current parameters of the various household appliances for monitoring the amount of power consumed. Which is further calibrated by the controlling system to yields the aggregated data and finally collected on the Internet based platform. The framework of connecting the smart sensor to the Internet is achieved by an IoT platform called Xively, which provides channel utility to deploy the prototype into an integrated product.

Alzheimer’s disease (AD) is a major public health priority. Hippocampus is one of the most affected areas of the brain and is easily accessible as a biomarker using MRI images in machine learning for diagnosing AD. In machine learning, using entire MRI image slices showed lower accuracy for AD classification. We present the select slices method by landmarks on the hippocampus region in MRI images. This study aims to see which views of MRI images have higher accuracy for AD classification. Then, to get the value of three views and categories, we used multiclass classification with the publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset using Resnet50 and LeNet. The models were used in a total dataset of 4,500 MRI slices in three views and categories. Our study demonstrated that the selecting slices performed better than using entire slices in MRI images for AD classification. Our method improves the accuracy of machine learning, and the coronal view showed higher accuracy. This method played a significant role in improving the accuracy of machine learning performance. The results for the coronal view were similar to the medical experts usually used to diagnose AD. We also found that LeNet models became the potential model for AD classification.

In recent years, information about a person can be extracted just from the person's voice signals using certain computational systems. Additionally, it can then be determined whether a person is affected or not by a severe medical condition like Parkinson's Disease (PD). This shows that there is a certain distinct connection between PD and speech impairment. Moreover, it is known that early detection of the disease can result in a better medical diagnosis and treatment. In this research paper, a versatile range of classification based machine learning and deep learning algorithms, employed with different dimensionality reduction (DR) techniques are used. A comparative study of their accuracies in differentiating a healthy human from one who is afflicted by the disease is performed. The time complexities of the algorithms have also been observed to understand the effect of DR techniques. These algorithms have been trained on the Parkinson's speech dataset obtained from UCI machine learning repository.

Urban air pollution rate has grown to alarming state across the India. Most of the cities are facing issue of poor air quality which fails to meet standards of air for good health. It is indeed necessary to develop an air pollution measurement and prediction system for a smart city. This proposed work acquires carbon dioxide and carbon monoxide level in the air along with Global Positioning System (GPS) location by using pollution detection sensor and uploads into Azure cloud services. Low cost embedded Beagle bone board along with gas sensors are used for data acquisition. Microsoft's Azure Machine learning service is used to predict the pollution metrics with the help of previous data. Processed data is fetched and represented by Power BI tool. Calibrated gas sensor data is fetched from sensors and successfully uploaded into cloud. Data stored in cloud is utilized by different cloud services to make the data meaningful. Proposed system is implemented and useful to monitor and reduce the pollution in a smart city by avoiding the pollution causes.