Background: Continuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication systems, it is now possible to provide customized and distant healthcare. Main problem: Healthcare data grows daily, making storage and processing challenging. We provide intelligent healthcare structures for smart e-health apps to solve the aforesaid problem. The 5G network must offer advanced healthcare services to meet important requirements like large bandwidth and excellent energy efficacy. Methodology: This research suggested an intelligent system for diabetic patient tracking based on machine learning (ML). The architectural components comprised smartphones, sensors, and smart devices, to gather body dimensions. Then, the preprocessed data is normalized using the normalization procedure. To extract features, we use linear discriminant analysis (LDA). To establish a diagnosis, the intelligent system conducted data classification utilizing the suggested advanced-spatial-vector-based Random Forest (ASV-RF) in conjunction with particle swarm optimization (PSO). Results: Compared to other techniques, the simulation’s outcomes demonstrate that the suggested approach offers greater accuracy.

Big data brings with it the promise of data-driven decision-making. Complexity, Privacy, Heterogeneity impede the progress of making valuable decisions from this voluminous data. In this paper, we have reviewed the challenges related to big data, the technologies available for the same. Data Analysis and Retrieval, Spurious data, Noise Accumulation are the other challenges. The discussions here are aimed at providing a comprehensive overview to this new age data. We have concluded this survey by throwing light on the open problems and research issues.

An urban heat island (UHI) is an urban area or metropolitan area that is significantly warmer than its surrounding rural areas due to human activities. The project aims to showcase the effect of UHI using temperature as the independent variable with pollution and population as the dependent factor variables. Using the Time Series analysis we obtained the trend in temperature, population and pollution. Using Multiple Linear Regression we have predicted the temperature based on the factors. The accuracy of the predicted values is depicted by comparing the predicted and measured values of the years 2013-2016. Hence proving that the predicted value is accurate and measures must be taken to prevent this or the temperature will increase to an unlivable extent.

Healthcare is an emerging area in big data. Raw data contains lot of noise in it, hence cannot produce good results when processed. There is a need to improve the quality of data. This study shows ...