The Publication Of A Research Paper/Case Study / Short Communication In A Peer-Reviewed Journal Is An Important Building Entity In The Development Of A Knowledge Repository. In An Age Of Information Abundance, It Is Very Important And Vital To Help Readers And Research Scholars To Segregate Quality Information. Our Diamond Scholars Working Day And Night And Published Their Work In The Reputed Journals Like SCI, SCOPUS, IEEE Under The Mentor-ship Of Scientists.

THE RESEARCH WORLD
Abstract: In this research paper the system which is proposed that can be used for safe walking for blinds. This system consist of wireless sensor within the stick which provide the information of the obstacle between the way. The main advantage of this system is the safe for the blind people
walking on the road, and make them independent while walking. When obstacle is detected an alert will be given to user with the help of buzzer an vibration . The unique feature of the system is to detect the temperature of a person who passes within the range of 6 feet of which
helps in maintaining the social distancing in COVID situation. The system
contains a wireless sensor that integrates temporary networks that can be made within the navigation stick, which can provide group communication between them, where roaming information and networks can be provided. With the help of IOT the location and alert message shared with family members in case of emergency. The system proposed in this research study is 60% more efficient then conventional system. The information is included in table1 to validate the result.
THE RESEARCH WORLD
Abstract: As the population increases, the need for resources increases too which ultimately degrades the quality of atmosphere that is needed for the survival of human beings. As a result, the carbon footprint increases continuously. This literature explains a novel method for reducing carbon footprint using IoT enabled green technology. The test here uses sensors, thermostat, and arduino along with a web portal to detect the output as a part of the IoT method. In this study, it is found that carbon footprint is reduced by more than 22% for an IoT enabled building when compared to a normal building. The results are solely based on electrical and LPG consumption for a specific time period. The mathematical modelling and practical observations are included to validate the result.

Date Added to IEEE Xplore:

31 March 2021

INSPEC Accession Number: 

20607723
THE RESEARCH WORLD
Abstract: As the population increases, the need for resources increases too which ultimately degrades the quality of atmosphere that is needed for the survival of human beings. As a result, the carbon footprint increases continuously. This literature explains a novel method for reducing carbon footprint using IoT enabled green technology. The test here uses sensors, thermostat, and arduino along with a web portal to detect the output as a part of the IoT method. In this study, it is found that carbon footprint is reduced by more than 22% for an IoT enabled building when compared to a normal building. The results are solely based on electrical and LPG consumption for a specific time period. The mathematical modelling and practical observations are included to validate the result.

Date Added to IEEE Xplore:

31 March 2021

INSPEC Accession Number: 

20607723

Abstract

India loses thousands of metric tons of tomato crop every year due to pests and diseases. Tomato leaf disease is a major issue that causes significant losses to farmers and possess a threat to the agriculture sector. Understanding how does an algorithm learn to classify different types of tomato leaf disease will help scientist and engineers built accurate models for tomato leaf disease detection. Convolutional neural networks with backpropagation algorithms have achieved great success in diagnosing various plant diseases. However, human benchmarks in diagnosing plant disease have still not been displayed by any computer vision method. Under different conditions, the accuracy of the plant identification system is much lower than expected by algorithms. This study performs analysis on features learned by the backpropagation algorithm and studies the state-of-the-art results achieved by image-based classification methods. The analysis is shown through gradient-based visualization methods. In our analysis, the most descriptive approach to generated attention maps is Grad-CAM. Moreover, it is also shown that using a different learning algorithm than backpropagation is also possible to achieve comparable accuracy to that of deep learning models.