Nowadays health care units play a vital role of the human existence after the pandemic periods. It is very essential to monitor the potential signals of the human body for survival on regular basis. In this paper extracting the values of different biopotential signals produced in human body, monitoring and analysing them using various machine learning algorithms. Monitoring involves observing and checking the progress or quality of data over a period of time and keeping it under system review. The beauty of effective computing is to make machine more emphatic to the user. Machine with the capability of human electrical signal recognition can look inside the user’s body. This paper generalises the view of training of the bio potentials signals data in the MATLAB software as well in python software. Analysis with different machine learning algorithms like K-Nearest Neighbours (KNN), Decision tree (DT), Logistic Regression (LR), Support Vector Machine(SVM) are used in the training ,testing and validation of the data. Better performance is achieved with these algorithms.

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Ad hoc mobile networks contain remote nodes linking through electronic media, without any set backend facilities. Disturbance happens in any type of intermediate nodes in these networks when data packages travel from resource to destination, leading to high package loss and also lengthy delay, triggering network efficiency destruction. This paper presents EDAODV congestion and also command directing protocol for mobile ad-hoc networks. Via determining line position and also identifying congestion degrees, EDAODV senses node-level blockage. Based on blockage rates, EDAODV makes use of the uncongested precursor and successor nodes of an overloaded node as well as starts bi-directional processes to determine alternative, uncongested courses in between them for data transmission. The algorithm discovers a lot more non-congested remedies, picking the very best solitary course for data transmission.