E. Anna Devi

Sathyabama Institute of Science and Technology

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

L. Magthelin Therase

Sathyabama Institute of Science and Technology

J. Manickam

St. Joseph’s College of Engineering

R.M. Joany

Sathyabama Institute of Science and Technology

P. Josephin Shermila

Arunai Engineering College

S. Yogalakshmi

Karpagam Academy of Higher Education

Sajeev Ram Arumugam

Manonmaniam Sundaranar University

E. Ahila Devi

St. Joseph's Institute of Technology

P. Sujatha Therese

Noorul Islam University

M. Eugine Prince

Presidency University

V. Swetha

Velammal Medical College Hospital and Research Institute

Cooperative Institutions

Anna University, Chennai

Sathyabama Institute of Science and Technology

SRM Institute of Science and Technology

St. Joseph’s College of Engineering

Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

St. Joseph's Institute of Technology

Vellore Institute of Technology University

Karpagam Academy of Higher Education

KPR Institute of Engineering and Technology

Dr. M.G.R. Educational and Research Institute

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Reconnection of Wireless Sensor Network Partitions on Multi-Agent Platform

International Journal of Business Analytics (2018)

E. Anna Devi J. Manickam

In wireless sensor networks, repairing partitions is of high priority. Various methods have been proposed for detecting partitions in the networks. One approach for reconnecting a partitioned network is to repair partitions using mobile nodes. For reconnecting the partitions approaches like transmission range adjustment and message ferry methods have been proposed but these are based on the degree of connectivity with neighbors. In the proposed method, we consider a partition detection system where the base station knows the position of the sensor nodes and the base station communicates with the nodes at regular intervals. The failure of the base station to communicate with a group of sensor nodes located together is the proof that some partitions have occurred. There could be two or more partitions occurring at a time and so multiple mobile nodes are to be employed. The aim of the algorithm is to coordinate among the mobile nodes and the partitioned network and to reconnect the partition. Here the safety of nodes, security of the network and scalability are considered.

Network partition

10.4018/ijban.2019010103

Cite

Citations (3)

Survey on Light-Fidelity

2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) (2018)

V. Swetha E. Anna Devi

In the present era, Wi-Fi is the most useful wireless communication. It works on the basis of the radio wave spectrum. Radio waves are very harmful. To overcome these and to increase bandwidth, efficiency, and speed, a new technology LI-FI is developing. LI-FI stands for light fidelity. It is also a wireless communication but instead of radio waves it sends data through LED light bulbs it uses light as a medium, whose intensity varies is faster than human eye focus. It transmits data at a high speed i.e 1000 times faster than WIFI. Since the LED light bulbs are low-cost it has many applications to explore, as public internet access through street light and auto-piloted cars that communicate through their headlights. In this paper, it focuses on to develop LIFI technology. In the future the smartphones, laptops, tablets can transmit data through the light in a room.

Electromagnetic spectrum

10.1109/icssit.2018.8748272

Cite

Citations (8)

Wireless Power Transmission

IOP Conference Series Materials Science and Engineering (2019)

V. Bhavya Keerthi V. Swetha E. Anna Devi

Abstract In recent days, Wireless Power Transfer (WPT) and harvesting energy received lots of attentions due to the necessity of having a flexible, suitable and secure source of energy. With the explosion of wireless technologies including wireless sensor network (WSN) like internet of things, such intelligent systems had highlighted this need. Wireless power transfer is a key technology to enable any device operation without replacing their batteries, often challenging due to cost of replacement and the difficulty to reach inaccessible areas. Anyhow wireless power transfer is being considered as eco-friendly. This also removes the need to dispose dead batteries. Inductive coupling can be used to charge the low power devices quickly and effectively without the help of wires. This method is done by using charging a resonant coil from AC and then transmitting subsequent power to the resistive load. In this case, oscillating circuit is used in the conversion of DC to AC in order to transmit the magnetic field by passing frequency and inducing the receiver coil. The system is designed and constructed as two parts. They are Transmitter part and Receiver part. To calculate the inductive coupling between the transmitter oil and receiver coil, Ampere’s Law, Biot-Savart’s Law and Faraday’s Law are used as per the requirement.

Wireless Power Transfer

Faraday cage

Power transmission

Inductive charging

Inductive coupling

10.1088/1757-899x/590/1/012031

Cite

Citations (2)

Design of Self Tuning Fuzzy PI Controller for MIMO process using an analytical approach for selecting the scaling factors

2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) (2022)

Sujatha Therese P Eugine Prince M Josephin Shermila P E. Anna Devi

Control of the Multi-Input Multi-Output process is a complicated problem due to its non-stationary behavior, coupling, and severe disturbances. The technique of self-tuning the fuzzy PI controller and an analytical technique to determine the scaling factors of Self Tuning Fuzzy Proportional Integral Controller is proposed in this paper. The Multi-Input Multi-Output process considered here is the transfer function of the Heater Mixer Experimental setup. The process is simulated under normal as well as under variations in gain and time constant. The performances of Self Tuning Fuzzy Proportional Integral Controller are compared with the proposed Analytically tuned Self Tuning Fuzzy Proportional Integral Controller for the heater mixer experimental set up in terms of the performance measures Integral Absolute Error (IAE), Integral Square Error (ISE) and Integral Time Absolute Error (ITAE). Simulation results show that the Analytically tuned Self Tuning Fuzzy Proportional Integral Controller gives improved performance compared to Self-Tuning Fuzzy Proportional Integral Controller.

Self-tuning

10.1109/icicict54557.2022.9917626

Cite

Citations (0)

Comparison of machine learning and deep learning algorithms for prediction of coronary heart disease

Institution of Engineering and Technology eBooks (2022)

Sajeev Ram Arumugam E. Anna Devi T. Abimala S. Oswalt Manoj

The primary aim of this work was to compare the performance of the ML and DL algorithms for the CHD dataset. The dataset holds 4,240 with 16 features. We have almost done the entire process step by step, starting from visualizing the dataset, removing the missing values, followed by selecting the best performing features using the Bourta algorithm, which identified AGE, BP and BMI as the best. Since the number of features was too low we took the top eight features. The next step was to balance the class in the dataset as the class was identified to be imbalanced and hence applied SMOTE method to balance the class, and finally, standard scalar was applied to normalize the dataset. Once all these data preparation and pre-processing methods were done, ML and DL methods, namely, logistic regression, decision tree, k-nearest neighbours, random forest, naïve Bayes, support vector machine and feed-forward neural network was deployed, to evaluate the performance of the models. Several metrics were calculated and compared, from which it was identified that the random forest and neural network methods gave the same results. Although the working methodology and the assumptions made by the algorithms also differ, with these results it could not be concluded that a particular classification method is the best. One of the major drawbacks is that the dataset available does not hold all the features which are mandatory to confirm the disease. The dataset was not approved by any doctors in that field and hence the model developed could not be used for real-time implementation.

10.1049/pbhe043e_ch7

Cite

Citations (0)

Plant Disease Classification using CNN-LSTM Techniques

2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) (2023)

E. Anna Devi S. Gopi U. Padmavathi Sajeev Ram Arumugam S.P. Premnath

Crops have implications for global food security and the agrarian society. Their rapid recognition and prevention improve the probability of implementing effective interventions, and this is why our society is interested in finding automated systems that can do so. The CNN-LSTM classifier is proposed in this study to detect plant diseases. This study has deep feature extraction from several fully linked layers in transfer learning. The collected features are provided as input into the LSTM layer to build a hybrid model for plant disease identification. The LSTM layers' output predictions chose the input images' class labels. The trials are run with data from the PlantVillage dataset, and the accuracy is computed to assess performance. The experiment results reveal that the proposed method produces outcomes enhanced than pre-trained deep architectures with an accuracy of 99.17%.

Transfer of learning

10.1109/icssit55814.2023.10061003

Cite

Citations (4)

A Deep Learning-based study of Crop Diseases Recognition and Classification

2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS) (2022)

Roshni Polly E. Anna Devi

Agriculture feeds both human and cattle populations around the world. To increase agricultural productivity effectively, fast and precise disease diagnosis is essential. Farmers face a drag like water shortage, fertility problems, pests, disease attacks, low yield, and sort of monetary problems. Farmers use the normal method for locating these problems. But those techniques aren't much effective. Detection of the disease in its initial phase will help in preventing the loss of crops, improve the productivity of plants, and therefore the quality of the products. To solve agricultural issues image processing techniques can be used. Deep Learning includes a cutting-edge image processing approach. This involves putting present work in the area of agricultural leaf disease identification and diagnosis utilizing Deep Learning algorithms in its proper context. This study looks at the latest developments and issues in using Deep Learning and advanced picture processing technologies to identify crop leaf disease. The survey looks at the various agricultural problems being researched, the models utilized, the sources of information used, and the total precision attained using the researchers' performance criteria. By enhancing system reliability and efficiency, the creation of this review enables longer-term study to identify deeper capabilities of Deep Learning when detecting and categorizing crop diseases.

10.1109/icais53314.2022.9742950

Cite

Citations (3)

Fire Alarm Based Obstacle Avoiding And Bluetooth Controlled Robot

E. Anna Devi L. Magthelin Therase R.M. Joany Akshaya Sri Saravanan

This project aims to develop a fire alarm-based obstacle-avoiding and Bluetooth-controlled robot. The robot is designed to detect fires, navigate its environment while avoiding obstacles, and be controlled remotely via Bluetooth. By combining these features, the robot can autonomously detect and respond to fires while providing a convenient means of control. The hardware components of the robot include a chassis, wheels or tracks for mobility, motors, a microcontroller or single-board computer, fire sensors, obstacle detection sensors (such as ultrasonic or infrared sensors), and a Bluetooth module for wireless communication. The fire detection system is responsible for monitoring the environment and triggering an alarm or alert when a fire is detected. The obstacle avoidance system utilizes sensors to detect obstacles in the robot's path and employs appropriate maneuvers to avoid them. By combining these features, the robot can autonomously detect and respond to fires while providing a convenient means of control. In autonomous mode, the robot operates independently, detecting fires and navigating obstacles. In manual mode, the user can direct the robot's movements and control its actions using a Bluetooth-enabled device.

10.1109/iccsp60870.2024.10543899

Cite

Citations (0)

New Hybrid Cuk-Landsman High Gain Dc-Dc Converter Modelling and Analysis

International Journal of Electrical and Electronics Engineering (2022)

P Deepa S Rajakumar Josephin Shermila P E. Anna Devi Eugine Prince M

Due to the rising adoption of energy sources, the requirement for DC-DC converters has increased. As solar source's low voltage, high gain boost converters are necessary for distributed photovoltaic production systems. The converter is created thus for the drawbacks of being larger and less efficient. This research proposes a novel hybrid Cuk and Landsman converter (HCLC) designed to obtain a higher voltage conversion ratio, better efficiency, and reduced driver circuits. The proposed converter is considering the use of a soft switching technique of the switches to reduce the current and voltage. Due to the obvious constant source current, the developed converter achieves low input current stress. MATLAB-Simulink is used to model the proposed work. To assess the effectiveness of the suggested structure, extensive comparisons and replication data are offered.

10.14445/23488379/ijeee-v9i8p102

Cite

Citations (4)

A Diagnostic Study on Prediction of Covid-19 by Symptoms Using Machine Learning

2022 International Conference on Electronics and Renewable Systems (ICEARS) (2022)

E. Anna Devi V. Athappan Rahul R. Rajendran E. Ahila Devi G. Emayavaramban

Effective screening helps for quick and accurate detection of COVID-19 and it also decreases the burden on the healthcare system. Prediction models with numerous criteria have been developed to estimate the probability of infection. These are designed to assist medical workers across the world in triaging victi ms, especially in places with limited medical resources. For predicting the COVID-19 using symptoms, the dataset is taken from the website of the Israeli Ministry of Health. The dataset contains 9 attributes and 2,78,848 samples. The raw dataset is cleaned using pre-processing techniques. The Machine learning algorithms like Random Forest, K Nearest Neighbor, Decision Tree, and hybrid Random Forest, K Nearest Neighbor, and Decision Tree are applied on the 1,95,194 samples to identify the model. The predicted model is tested on 83,654 samples to ensure the quality of the designed model. The performance metrics like ROC [Receiver Operating Characteristic] curve, True Positive and Negative Rate, False Positive and Negative Rate, Positive and Negative Predictive Value, and Accuracy are applied to check the model. From the evaluation result, the proposed hybrid model gives high accuracy of 98.97% . The proposed technique might be utilized to priorities COVID-19 screening when testing capabilities are constrained., among several other things.

Christian ministry

False positive rate

Tree (set theory)

10.1109/icears53579.2022.9752301

Cite

Citations (6)