Volume IX, Issue I
Title– LEAF DISEASES DETECTION AND MEDICATION
Author– Mandava Siva Sai, Vighnesh, MD Shakir Alam and Vinitha. S
Abstract
India is fast developing country and agriculture is the back bone for the countries development in the early stages. Now a day’s technology plays vital role in all the fields but till today we are using some old methodologies in agriculture. Identifying plant disease wrongly leads to huge loss of yield, time, money and quality of product. Identification of plant disease is very difficult in agriculture field. Leaf disease detection requires huge amount of work, knowledge in the plant diseases, and also require the more processing time. The objective of this research is to make use of significant features and prediction is done using computer vision technique. This method mainly download the image from the server then it converts the image into a gray-scale by calculating its pixels and it shows out only the defected parts of the leaf.This approach can significantly support an accurate detection of leaf disease. We can extend this approach by using image processing technique. It displays the output in graphical view that is X and Y coordinates. The user can also view the output in mobile application by retrieving the result from the server.
Index Term– Natural Language Processing, Gray scale image, Maximum Likelihood Estimation, Machine Learning.
DOI- 10.30696/IJEEA.IX.I.2021.01-07.
Reference to this paper should be made as follows: Mandava Siva Sai Vighnesh, MD Shakir Alam and Vinitha.S, (2021), “Leaf Diseases Detection and Medication” Int. J. of Electronics Engineering and Applications, Vol. 9, No. 1, pp. 01-07.
Title– VOICE AND GESTURE BASED HOME AUTOMATION SYSTEM
Author– Pradeep M, Ragul K and Varalakshmi K
Abstract
It is now common to control home appliances and electronic gadgets through an Infrared remote control. These tasks can also be done more easily. The primary motive of proposing a new system of hand gesture control is to eliminate the need for the elderly/disabled people to use a physical remote but rather use simple gestures. Gesture means a movement of part of body. Gesture Recognition is the technology that is used to identify physical actions. It recognizes hand, arms, head or any part of the body. So the goal is to provide a human interface to the computer. The devices can be controlled not only by using gestures but also by using voice commands as well. Smart assistant such as Google assistant can be used for this purpose.
Index Term– IoT, Arduino, Flex, Google Assistant, Gesture and Voice commands.
DOI- 10.30696/IJEEA.IX.I.2021.08-18.
Reference to this paper should be made as follows: Pradeep M, Ragul K and Varalakshmi K, “Voice and Gesture Based Home Automation System” Int. J. Electronics Engineering and Applications, Vol. 9, No. 1, pp. 08-18.
Title– ENGROSSMENT OF STREAMING DATA WITH AGGLOMERATION OF DATA IN ANT COLONY
Author– Jagan K, Parthiban E and Manikandan B
Abstract
A data stream is an ongoing process to appear a series of data and clustering data streams essential supplementary analysis to standard clustering. A stream is possible unlimited, data points appear online and each data point can be surveyed only once. This inflicts constraints on accessible memory and processing time. Moreover, streams can be strident and the number of clusters in the data and their statistical estate can change over time. This operation presents an online approach to clustering energetic data streams. A stochastic method is employed to find these rough clusters; this is shown to crucially speeding up the data with only a minor cost to performance, as compared to a deterministic approach. The rough clusters are then filtered using a method inspired by the discover sorting behavior of ants. Ants pick-up and drop items based on the correlation with the surrounding items. Artificial ant sort clusters by probabilistically picking and dropping micro clusters based on local density and local similarity in these operations.
Index Term– Data Stream, Local Density, Ants, Clusters and Data Points.
DOI- 10.30696/IJEEA.IX.I.2021.19-27.
Reference to this paper should be made as follows: Jagan K, Parthiban E Manikandan B,(2021), “Engrossment of Streaming Data with Agglomeration of Data in
Ant Colony” Int. J. of Electronics Engineering and Applications, Vol. 9, No. 1, pp. 19-27.
Title– IOT INTEGRATED FOREST FIRE DETECTION AND PREDICTION USING NODE MCU
Author– M. Khadar, V. Ranjith, K. Varalakshmi
Abstract
Forest fires in Riau Province Indonesia are issues that regularly happen with affected the length and breadth of Indonesia. The effects forest fire and of haze on human health as reported in that particular year were about 20 million people have suffered from respiratory problems and serious deterioration in overall health in Riau community. This research proposes development of Wireless Sensor Network (WSN) for detection of forest fire in Riau province for the region that high risk forest fire in dry session, with WSN technology data be able to collect from the sensor deploy in the forest area. The deployment of sensors will be located at several locations that has badly impacted in previous case and forecast location with potential fire happen. Mathematical analysis is used in this case for modelling number of sensor required to deploy and the size of forest area represented overall of Riau Province. An early indication of forest fires is needed for quick prevention before they become uncontrollable and overwhelming. The design and development of WSN sensors give high feasibility to overcome current issues in Riau Province because of forest fire. The development of this system used WSN highly applicable for early warning and alert to representative institution for action taken.
Index Term– WSNs, Forest Fire, Sensors, Detection.
DOI- 10.30696/IJEEA.IX.I.2021.28-35.
Reference to this paper should be made as follows: M. Khadar, V. Ranjith, K Varalakshmi, “Iot Integrated Forest Fire Detection and Prediction using NodeMCU” Int. J. of Electronics Engineering and Applications, Vol. 9, No. 1, pp. 28—35.
Title– PREDICTION OF BREAST CANCER STAGES USING MACHINE LEARNING
Author– Gayathri. M, Poorviga. A and Vasantha Raja S.S
Abstract
Disease is the normal issue for all individuals on the planet with various kinds. Especially, Breast Cancer is the most regular ailment as a disease type for ladies. Along these lines, any advancemen for analysis and expectation of malignant growth illness is capital significant for a solid life. AI methods can cause a colossal to contribute on the cycle of early analysis and expectation of disease. In this paper, two of the most mainstream AI methods have been utilized for characterization of Wisconsin Breast Cancer (Original) dataset and the arrangement execution of these procedures have been contrasted and each other utilizing the estimations of exactness, accuracy, review and ROC Area. The best exhibition has been acquired by Support Vector Machine strategy with the most elevated exactness.
Index Term– machine learning; breast cancer; classification; early diagnosis.
DOI- 10.30696/IJEEA.IX.I.2021.36-42.
Reference to this paper should be made as follows: Gayathri.M, Poorviga.A and Mr.VasanthaRaja S.S, (2021), “Prediction Of Breast Cancer Stages Using Machine Learning” Int. J. of Electronics Engineering and Applications, Vol. 9, No. 1, pp. 36-42.
Title– NOVEL METHOD OF REAL TIME FIRE DETECTION AND VIDEO ALERTING SYSTEM USING OPEN-CV TECHNIQUES
Author– M. Karthikeyen, N. Ramya, M. Sai Priya and C. Yuvalakshmi
Abstract
Fire detectors play a very important role. It helps in detecting fire at an early stage. Commercial fire detecting systems usually have an alarm signaling, with the help of a buzzer. In this paper, computer vision-based fire detection is used. In the proposed model a webcam is used as an alternative of surveillance camera for monitoring the interiors of buildings. The video is processed using open CV techniques using fire detection (Hue, Saturation, Value (HSV)) algorithms and if a fire is detected, a short duration of the live video is sent to the security or the higher officials followed by an alert message. Thus the number of peoples stuck in the fire blazing area can be rescued. In the existing system, MATLAB tool is used for processing. While in the proposed system, Open CV techniques are used for processing. Open CV has more functions for computer vision and its processing time is less. Using this project, fire can be detected at early stage without any false alarming strategies and peoples can be rescued thereby.
Index Term– Open CV, Capturing the video, Fire detection, Alert to the user.
DOI- 10.30696/IJEEA.IX.I.2021.43-50.
Reference to this paper should be made as follows: M. Karthikeyen, N. Ramya, M. Sai Priya and C. Yuvalakshmi, (2021), “Novel Method Of Real Time Fire Detection And Video Alerting System Using Open-CV Techniques” Int. J. of Electronics Engineering and Applications, Vol. 9, No. 1, pp. 43-50.
Title– SECURE ONLINE TRANSACTION WITH USER AUTHENTICATION
Author– L. Prinslin, M. A. Srenivasan and R. Naveen
Abstract
Online transaction process is secure with one-time password (OTP). Generating OTP has many factors that can make OTP unique for every time it is generated. In this paper we implement user Identification using Face Recognition to verify the user. In case of emergency situation, the login can be done using OTP and also the person image is captured and Mail to the Account Holder. Thus, our system has improved security compared to existing System.
Index Term– Face Recognition, Internet of Things, OTP, ID Cards, M-Banking.
DOI- 10.30696/IJEEA.IX.I.2021.51-57.
Reference to this paper should be made as follows: L.Prinslin, M.A.Srenivasan and R.Naveen (2021), “Secure Online Transaction With User Authentication” Int. J. of Electronics Engineering and Applications, Vol. 9, No. 1, pp. 51-57.
Title– EDGE BASED ECOSYSTEM FOR INTERNET OF THINGS (EBEFIOT)
Author– S Lokehwar , A Hemaranjanee , V. Narayanee
Abstract
This paper describes about an entirely new ecosystem which is designed for the future to provide resources to IOT devices. This is a hybrid ecosystem which combines the edge computing capabilities along with a new functionality that aims to provide the end users to benefit more accurate and faster experience compared to existing solutions. Apart from proving processing power and storage (like edge computing), the key advantage of this ecosystem is to provide more locality specific information to the end devices. It also utilizes the 5G infrastructure. This paper provides an insight of various aspects of this ecosystem like its functionalities, use cases, implementation, etc.
Index Term– EBEIOT- IOT- cloud-edge- MDC- MEC- plugin- latency- bandwidth-server- ULI- AI-deployment- base station
DOI- 10.30696/IJEEA.IX.I.2021.58-67.
Reference to this paper should be made as follows: S Lokewar, A Hemaranjanee and V. Narayanee (2021), “Edge Based Ecosystem For Internet Of Things (EBEFIOT)” Int. J. of Electronics Engineering and Applications, Vol. 9, No. 1, pp. 58-67.