Smart power analysis and consumption management for GreenComputing models

Smart power analysis and consumption management for Green Computing models

In the current digitally operated world, most of the smart systems rely on batteries. The efficient utilization of power towards the connected devices in the cloud is important to monitor. Few sources are collected from solar panel-based operations. The solar energy is collected from sunlight and utilized as the primary source. To connect, monitor and evaluate the solar power usage is one of the important task. Green computing models are virtual systems designed with in-built Artificial Intelligence (AI) operated models which reads the power consumption data through the log files, learn the pattern of power usage and optimize the usage through alerts and auto power shut down if required. The programmable power management system often enables the data cleaning mechanism during over load of the cache memory. the logged data is opted to data cleaning process, to preprocess, prepare the data for analysis through feature extraction and applying the data into the classification and analysis model. further, the data is utilized for making wise decisions on power consumption analysis. The monitoring is continous and keep updated through reinforced learning process.

To Design such system , all it required is the customized front end hardware to collect the data from real world power sourcing and optimize the data through machine learning algorithm through cloud. The algorithm performance is analyzed through accuracy, precision, recall and F1Score.

 

The Designed hardware

Simulation and Analysis Screenshots

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Detection of Bradycardia and Tachycardia through optimized neural computing model

Detection of Bradcardia and Tachycardia through optimized neural computing model

So what is Bradcardia  and Tachycardia , as the name represents something about the Cardia yes, it denotes the pulse operations. As a normal human holds 70 to 100 beats per minute is considered as the primary pulse rate, where the Bradcardia and tachycardia is the detection of slow heart rate and fast heart rate.

Bradcardia

Bradcardia is the term represents the slower heart rate that beats less than that of 60 bpm. the reason for bradcardia is due to certain medications, aging, electrolyte imbalance etc.

Tachycardia

Tachycardia represents the faster heart rate more than 100 Beats per minute. The reason for tachycardia is due to stress, anxiety, heart disease and unhealthy life style habits.

An optimized detection of Bradcardia and Tachycardia using Neural computing model requires the stacked data of heart rate and analyze the model using the complete training data. Neural computing architecture is utilized here in order to detect the dynamic changes occuring in the models. 

Some of the sample results shows like this.