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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics
Jeevitha Sakkarai
Kalasalingam Academy of Research and Education
Economics, Econometrics & Finance Electrical & Electronics Engineering

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A comprehensive analysis on IoT based smart farming solutions using machine learning algorithms Ahamed Ali Samsu Aliar; Justindhas Yesudhasan; Manjunathan Alagarsamy; Karthikram Anbalagan; Jeevitha Sakkarai; Kannadhasan Suriyan
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3310

Abstract

Agriculture and farming are the most important and basic industries that are very important to humanity and generate a considerable portion of any nation's GDP. For good agricultural and farming management, technological advancements and support are required. Smart agriculture (or) farming is a set of approaches that uses a variety of current information and communication technology to improve the production and quality of agricultural products with minimum human involvement and at a lower cost. Smart farming is mostly based on IoT technology, since there is a need to continually monitor numerous aspects in the agricultural field, such as water level, light, soil characteristics, plant development, and so on. Machine learning algorithms are used in smart farming to increase production and reduce the risk of crop damage. Data analytics has been shown through extensive study to improve the accuracy and predictability of smart agricultural systems. Data analytics is utilised in agricultural fields to make decisions and recommend acceptable crops for production. This study provides a comprehensive overview of the different methods and structures utilised in smart farming. It also provides a thorough analysis of different designs and recommends appropriate answers to today's smart farming problems.
IoT based E-vehicle monitoring system using sensors and imaging processing algorithm Manjunathan Alagarsamy; Prabakaran Kasinathan; Geethalakshmi Manickam; Prabu Ragavendiran Duruvarajan; Jeevitha Sakkarai; Kannadhasan Suriyan
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 11, No 2: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v11.i2.pp196-204

Abstract

Human evolution has included the development of transportation systems. People are currently driving a significant number of fuel-powered automobiles. This resulted in an increase in the number of accidents as well as pollution in the environment. To address the disadvantages of gasolinebased vehicles, this study presents an IoT-based E-vehicle monitoring system (E-VMS) for early accident detection and to make the environment cleaner and greener by using alternative energy. E-VMS employs internet of things (IoT) technology to continuously monitor the vehicle as well as to access and control it remotely. The IoT devices installed in vehicles are built using an Arduino microcontroller and sensors to detect accidents quickly. When an accident occurs, the E-VMS recognizes it quickly and determines the severity of the incident. The machine will then promptly alert the authorities. The E-VMS is also familiar with the GPS system. This will allow the E-VMS to maintain track of the cars' location in real time. This information will be used to locate the car in the event of an accident or theft. The E-VMS system's results were promising in terms of accurately identifying accidents, determining the severity of the accident, and determining the position of the vehicle.
Classification of covid patient image dataset using modified deep convolutional neural network system Manjunathan Alagarsamy; Karthikram Anbalagan; Yuvaraja Thangavel; Jeevitha Sakkarai; Jenopaul Pauliah; Kannadhasan Suriyan
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3290

Abstract

The number of people infected with the corona virus is steadily rising. Even after being treated and returned to normality, many who were impacted are still suffering from a variety of health problems. We suggest a new, more effective approach to dealing with this issue, as well as putting in place preventative measures to prevent the spread of disease. The modified convolutional neural networks (M-CNN) architecture is modified deepCNN architecture. Using existingcorona virus disease 2019(COVID-19) computerizedtomographyscan (CT scan) images, this suggested approach intends to develop a deep model for screening and forecasting the risk of disease propagation. The suggested model was trained using 1000 scan pictures from various sources, yielding a prediction accuracy of 93 percent, which is much greater than previous methods.
Co-Authors Ahamed Ali Samsu Aliar Geethalakshmi Manickam Jenopaul Pauliah Justindhas Yesudhasan Kannadhasan Suriyan Karthikram Anbalagan Karthikram Anbalagan Manjunathan Alagarsamy Manjunathan Alagarsamy Prabakaran Kasinathan Prabu Ragavendiran Duruvarajan Yuvaraja Thangavel