Design and Development of Efficient Energy Systems

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There is not a single industry which will not be transformed by machine learning and Internet of Things (IoT). IoT and machine learning have altogether changed the technological scenario by letting the user monitor and control things based on the prediction made by machine learning algorithms. There has been substantial progress in the usage of platforms, technologies and applications that are based on these technologies. These breakthrough technologies affect not just the software perspective of the industry, but they cut across areas like smart cities, smart healthcare, smart retail, smart monitoring, control, and others. Because of these “game changers,” governments, along with top companies around the world, are investing heavily in its research and development. Keeping pace with the latest trends, endless research, and new developments is paramount to innovate systems that are not only user-friendly but also speak to the growing needs and demands of society.
This volume is focused on saving energy at different levels of design and automation including the concept of machine learning automation and prediction modeling. It also deals with the design and analysis for IoT-enabled systems including energy saving aspects at different level of operation.
The editors and contributors also cover the fundamental concepts of IoT and machine learning, including the latest research, technological developments, and practical applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in the area of IoT and machine technology, this is a must-have for any library.

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A versatile application likewise might be known as an application, web application, online application, iPhone application or cell phone application.

3.4 Experimental Results

The trial investigation of both article recognition and following as far as subjective and quantitative is given below.

Despite the ban on manual scavenging in India, over 300 deaths due to manual scavenging were reported across the country in 2017 alone, according to a reply given by the Ministry of Social Justice and Empowerment to the Lok Sabha in December last year.

Compared with other existing methods, the proposed work is suitable for real-time applications as shown in Figure 3.6and Figure 3.7for complete experimental design. The proposed system observes the data second by second and immediately gives the alert when the condition is abnormal. Additionally, this framework gives an answer for the progressively changing sewer condition. This occurs since the stream of sewage water differs greatly over time also, relies upon various components, similar to water siphon condition, gas maintenance and harm to office. This framework takes account of these elements as minute-by-minute examination is open from remote areas, on account of the web checking. This empowers exact comprehension of CH4, CO and other sewer gases and their emanation from sewers and helps in measuring and powerfully changing city arranging, a component that was absent in past recommendations.

Figure 36 Experimental design Figure 37 Experimental design 35 - фото 45

Figure 3.6 Experimental design.

Figure 37 Experimental design 35 Conclusion Many cities across the world - фото 46

Figure 3.7 Experimental design.

3.5 Conclusion

Many cities across the world are facing drainage system problems. Implementing this proposed system can put an end to manual scavenging and the deaths of many manual scavengers. Such a system can enhance cleanliness and reduce the number of people who get sick from sewage exposure.

The proposed system is used to detect gas leakage and cracks with the help of IOT sensors such as Arduino Uno sensor, Raspberry Pi3. Sewer gas is detected with the help of a gas sensor. An alert is given to the control room when the normal threshold level is exceeded. The proposed Tristate pattern can be used to train the sensor datas and find the risk level of the gas. A vibration sensor attached to Raspberry Pi3 can be used to detect crack and take immediate remedies.

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* Corresponding author : bharathi.durai@gmail.com

4

Machine Learning for Smart Healthcare Energy-Efficient System

S. Porkodi1*, Dr. D. Kesavaraja1† and Dr. Sivanthi Aditanar2

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