Tele health utilizes information and communication mechanisms to convey medical information for providing clinical and educational assistances. It makes an effort to get the better of issues of health service delivery involving time factor, space and laborious terrains, validating cost-efficiency and finer ingress in both developed and developing countries. Tele health has been categorized into either real-time electronic communication, or store-andforward communication. In recent years, a third-class has been perceived as remote healthcare monitoring or tele health, presuming data obtained via Internet of Things (IOT). Although, tele health data analytics and machine learning have been researched in great depth, there is a dearth of studies that entirely concentrate on the progress of ML-based techniques for tele health data analytics in the IoT healthcare sector. Motivated by this fact, in this work a method called, Weighted Bayesian and Polynomial Taylor Deep Network (WB-PTDN) is proposed to improve health prediction in a computationally efficient and accurate manner. First, the Independent Component Data Arrangement model is designed with the objective of normalizing the data obtained from the Physionet dataset. Next, with the normalized data as input, Weighted Bayesian Feature Extraction is applied to minimize the dimensionality involved and therefore extracting the relevant features for further health risk analysis. Finally, to obtain reliable predictions concerning tele health data analytics, First Order Polynomial Taylor DNN-based Feature Homogenization is proposed that with the aid of First Order Polynomial Taylor function updates the new results based on the result analysis of old values and therefore provides increased transparency in decision making. The comparison of proposed and existing methods indicates that the WB-PTDN method achieves higher accuracy, true positive rate and lesser response time for IoT based tele health data analytics than the traditional methods.

The operation complexity of the distribution system increases as a large number of distributed generators (DG) and electric vehicles were introduced, resulting in higher demands for fast online reactive power optimization. In a power system, the characteristic selection criteria for power quality disturbance classification are not universal. The classification effect and efficiency needs to be improved, as does the generalization potential. In order to categorize the quality in the power signal disturbance, this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality disturbances. Then, a multi-label classification algorithm based on rating is proposed to understand the relationship between the labels and identify the various power quality disturbances. The two algorithms are combined to construct a multi-label classification model based on a multi-level extreme learning machine, and the optimal network structure of the multi-level extreme learning machine as well as the optimal multi-label classification threshold are developed. The proposed method can be used to classify the single and compound power quality disturbances with improved classification effect, reliability, robustness, and anti-noise performance, according to the experimental results. The hamming loss obtained by the proposed algorithm is about 0.17 whereas ML-RBF, SVM and ML-KNN schemes have 0.28, 0.23 and 0.22 respectively at a noise intensity of 20 dB. The average precision obtained by the proposed algorithm 0.85 whereas the ML-RBF, SVM and ML-KNN schemes indicates 0.7, 0.77 and 0.78 respectively.

In order to research brain problems using MRI, PET, and CT neuroimaging, a correct understanding of brain function is required. This has been considered in earlier times with the support of traditional algorithms. Deep learning process has also been widely considered in these genomics data processing system. In this research, brain disorder illness incliding Alzheimer's disease, Schizophrenia and Parkinson's diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods. Moeover, deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks (DBN). Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm (DBNJZZ) approach. The suggested approach is executed and tested by using the performance metric measure such as accuracy, root mean square error, Mean absolute error and mean absolute percentage error. Proposed DBNJZZ gives better performance than previously available methods.

Simultaneous wireless information and power transfer (SWIPT) has given new opportunities for dealing with the energe shortage problem in wireless networks.Green transmission for 5G cellular networks of mobile cloud access networks based on SWIPT is being examined. Considering SWIPT as a future potential solution for increasing the battery life, this technique improves energy efficiency (EE). One of the technologies is wireless communication to transfter the power used to give sufficient resources to energy-constrained networks that have consequences for 5G and the internet of things (IoT), energy efficiency, co-operative communication and suitable are supported by the SWIPT. To enhance the capacity, data rate improvement, and better performance of quality of services of further networks. In addition to these criteria, it is also our moral responsibility to protect the environment of wireless networks by lowering power usage. As a result, green communication is a critical requirement. We looked at a variety of strategies for power optimization in the impending 5G network in this article. The utilization of relays and microcells to enhance the network’s energy efficiency is the main focus. The many relaying scenarios for next-generation networks have been discussed.