K.Gayathri Devi
@drngpit.ac.in
Professor and Electronics and Communication Engineering
Dr.N.G.P institute of Technology
• Completed Ph.D under the guidance of , Principal on the thesis titled “Certain Investigations on Automatic Segmentation of Colon using Clustering and Neural Network Approaches”- Highly Commended .
• Received Ph.D. Guide ship Recognition - Anna University Chennai, Ref. No. 2940076 in Information and Communication Engineering in Jan 2016. Appointed as Doctoral Committee member for guiding scholars.
• Published 2 Patent and 15 papers in reputed Journals,3 Book Chapters, 1 Book and 18 Conference publications.
• Published a book “Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches” CRC Press, Taylor and Francis Group, ISBN No- 978036741727,2020
• Completed 15 online certification courses from NPTEL, Coursera, Mathworks and Great Learning. Topper in the “Digital Image Processing of Remote Sensing Images” and “Outcome based pedagogic principles for effective teaching “ conducted by NPTEL.
• Recipient of Proficiency award
EDUCATION
Ph.D - Anna University, Chennai,2016
ME- College of Engineering and Technology, Pollachi. Anna University-Chennai,2007
BE- Coimbatore Institute of Technology, Coimbatore,Bharathiar University,1998
RESEARCH INTERESTS
Image Processing, AI, Machine learning, Deep Learning
Scopus Publications
Scopus Publications
Lina Dinesh and K. Gayathri Devi
Springer Science and Business Media LLC
AbstractIn big data, analysis data is collected from different sources in various formats, transforming into the aspect of cleansing the data, customization, and loading it into a Data Warehouse. Extracting data in other formats and transforming it to the required format requires transformation algorithms. This transformation stage has redundancy issues and is stored across any location in the data warehouse, which increases computation costs. The main issues in big data ETL are handling high-dimensional data and maintaining similar data for effective data warehouse usage. Therefore, Extract, Transform, Load (ETL) plays a vital role in extracting meaningful information from the data warehouse and trying to retain the users. This paper proposes hybrid optimization of Swarm Intelligence with a tabu search algorithm for handling big data in a cloud-based architecture-based ETL process. This proposed work overcomes many issues related to complex data storage and retrieval in the data warehouse. Swarm Intelligence algorithms can overcome problems like high dimensional data, dynamical change of huge data and cost optimization in the transformation stage. In this work for the swarm intelligence algorithm, a Grey-Wolf Optimizer (GWO) is implemented to reduce the high dimensionality of data. Tabu Search (TS) is used for clustering the relevant data as a group. Clustering means the segregation of relevant data accurately from the data warehouse. The cluster size in the ETL process can be optimized by the proposed work of (GWO-TS). Therefore, the huge data in the warehouse can be processed within an expected latency.
Gayathri Devi K, Kishore Balasubramanian, and Senthilkumar C
Springer Science and Business Media LLC
Kishore Balasubramanian, K. Gayathri Devi, and K. Ramya
Wiley
Gayathri Devi Krishnamoorthy, Kishore Balasubramanian, Shanthi Govindaraj, Parimala Gandhi Ayyavu, and Deepak Anna Durai Geetha
AIP Publishing
Gayathri Devi Krishnamoorthy, Kishore Balasubramanian, and Senthilkumar Chinnusamy
AIP Publishing
Kishore Balasubramanian, K. Ramya, and K. Gayathri Devi
Springer Science and Business Media LLC
Kishore Balasubramanian, K. Ramya, and K. Gayathri Devi
Springer Science and Business Media LLC
Gayathri Devi K. and Kishore Balasubramanian
Bentham Science Publishers Ltd.
Abstract: A tumour is an uncontrolled growth of tissues in any part of the body. Tumours are of different types and characteristics and have different treatments. Detection of a tumour in the earlier stages makes the treatment easier. Scientists and researchers have been working towards developing sophisticated techniques and methods for identifying the form and stage of tumours. This paper provides a systematic literature survey of techniques for brain tumour segmentation and classification of abnormality and normality from MRI images based on different methods including deep learning techniques. This survey covers publicly available datasets, enhancement techniques, segmentation, feature extraction, and the classification of three different types of brain tumours that include gliomas, meningioma, and pituitary and deep learning algorithms implemented for brain tumour analysis. Finally, this survey provides all the important literature on the detection of brain tumours with their developments.
K. Gayathri Devi, Kishore Balasubramanian, C. Senthilkumar, and K. Ramya
Springer Science and Business Media LLC
Kishore Balasubramanian, K. Ramya, and K. Gayathri Devi
Elsevier BV
Kishore Balasubramanian, Ramya Kishore, and Gayathri Devi Krishnamoorthy
Wiley
Mona A. S. Ali, Kishore Balasubramanian, Gayathri Devi Krishnamoorthy, Suresh Muthusamy, Santhiya Pandiyan, Hitesh Panchal, Suman Mann, Kokilavani Thangaraj, Noha E. El-Attar, Laith Abualigah,et al.
MDPI AG
This study proposes a novel glaucoma identification system from fundus images through the deep belief network (DBN) optimized by the elephant-herding optimization (EHO) algorithm. Initially, the input image undergoes the preprocessing steps of noise removal and enhancement processes, followed by optical disc (OD) and optical cup (OC) segmentation and extraction of structural, intensity, and textural features. Most discriminative features are then selected using the ReliefF algorithm and passed to the DBN for classification into glaucomatous or normal. To enhance the classification rate of the DBN, the DBN parameters are fine-tuned by the EHO algorithm. The model has experimented on public and private datasets with 7280 images, which attained a maximum classification rate of 99.4%, 100% specificity, and 99.89% sensitivity. The 10-fold cross validation reduced the misclassification and attained 98.5% accuracy. Investigations proved the efficacy of the proposed method in avoiding bias, dataset variability, and reducing false positives compared to similar works of glaucoma classification. The proposed system can be tested on diverse datasets, aiding in the improved glaucoma diagnosis.
K. Gayathri Devi, K. Yasoda, and Maria Nithin Roy
Springer Nature Singapore
K. Gayathri Devi, K. Yasoda, B. Rajesh, R. Sowmiya, and S. S. Vishalidevi
Springer Nature Singapore
J. Chitra, K. Muthulakshmi, K. Gayathri Devi, Kishore Balasubramanian, and L. Chitra
Elsevier BV
K GAYATHRİ DEVİ, Senthil Kumar C, and Kishore BALASUBRAMANİAN
El-Cezeri: Journal of Science and Engineering
K. G. Devi, N.Sowmiya, K.Yasoda, K.Muthulakshmi and B.Kishore
SynthesisHub Advance Scientific Research
Agricultural drones are one of the important innovations for increasing productivity of the crops in Indian agriculture field. The monitoring of the crops and the need for spraying pesticides and fertilizers at the correct moment and at the exact location of plants is an important parameter to increase the productivity of the crops. Unmanned Aerial Vehicle (UAV) can be used in agricultural sectors which will reduce the time and the hazardous effects that can cause due to the spraying of pesticides and fertilizers. This paper reviews briefly the implementation of UAVs for crop monitoring and pesticide spraying.
M. Dhivya, K. Gayathri Devi, and S. Kanimozhi Suguna
Springer International Publishing
The Controller Area Network is a simple, well designed, highly efficient and reliable in-vehicle bus standard widely used since its development in 1983. Controller Area Network is a serial communication protocol that supports distributed real-time control in Automotive Electronics. CAN is a thrust area for the past two decades in multi-disciplinary research, encompassing various tools and concepts for solving real-time problems. In this paper steering based headlight control system using CAN bus is implemented. The model consists of a steering control unit which determines the direction of the headlight according to the changes in the steering position. The Headlight control unit is varied from −16° to +16°. The proposed model is efficient in comparison with the conventional gaze controlled headlight system in terms of reduced transmission time, the speed of transmission and provides safety to the drivers during night time.
K. Gayathri Devi, M. Dhivya, and S. Preethi
Springer International Publishing
Gayathri Devi K, Radhakrishnan R, and Kumar Rajamani
Springer Science and Business Media LLC
Colorectal cancer (CRC) is the third most common type of cancer. The use of techniques such as flexible sigmoidoscopy and capsule endoscopy for the screening of colorectal cancer causes physical pain and hardship to the patients. Hence, to overcome the above disadvantages, computed tomography (CT) can be employed for the identification of polyps or growth, while screening for CRC. This proposed approach was implemented to improve the accuracy and to reduce the computation time of the accurate segmentation of the colon segments from the abdominal CT images which contain anatomical organs such as lungs, small bowels, large bowels (Colon), ribs, opacified fluid and bones. The segmentation is performed in two major steps. The first step segments the air-filled colon portions by placing suitable seed points using modified 3D seeded region growing which identify and match the similar voxels by 6-neighborhood connectivity technique. The segmentation of the opacified fluid portions is done using fuzzy connectedness approach enhanced with interval thresholding. The membership classes are defined and the voxels are categorized based on the class value. Interval thresholding is performed so that the bones and opacified fluid parts may be extracted. The bones are removed by the placement of seed points as the existence of the continuity of the bone region is more in the axial slices. The resultant image containing bones is subtracted from the threshold output to segment the opacified fluid segments in all the axial slices of a dataset. Finally, concatenation of the opacified fluid with the segmented colon is performed for the 3D rendering of the segmented colon. This method was implemented in 15 datasets downloaded from TCIA and in real-time dataset in both supine and prone position and the accuracy achieved was 98.73%.
S. Dieckmann, G. Krishnamoorthy, M. Aboumadi, Y. Pandian, J. Dersch, D. Krüger, A. S. Al-Rasheed, J. Krüger, and U. Ottenburger
Author(s)
The water supply of many countries in the Middle East relies mainly on water desalination. In Qatar, the water network is completely fed with water from desalination plants. One of these power and desalination plants is located in Ras Abu Fontas, 20 km south of the capital Doha. The heat required for thermal desalination is provided by steam which is generated in waste heat recovery boilers (HRB) connected to gas turbines. Additionally, gas fired boilers or auxiliary firing in the HRBs are used in order to decouple the water generation from the electricity generation. In Ras Abu Fontas some auxiliary boilers run 24/7 because the HRB capacity does not match the demand of the desalination units. This paper contains the techno-economic analysis of two large-scale commercial solar field options, which could reduce the fuel consumption significantly. Both options employ parabolic trough technology with a nominal saturated steam output of 350 t/h at 15 bar (198°C, 240 MW). The first option uses direct steam generation without storage while the second relies on common thermal oil in combination with a molten salt thermal storage with 6 hours full-load capacity. The economic benefit of the integration of solar power depends mainly on the cost of the fossil alternative, and thus the price (respectively opportunity costs) of natural gas. At a natural gas price of 8 US-$/MMBtu the internal rate of return on equity (IRR) is expected at about 5%.
K. Gayathri Devi and R. Radhakrishnan
Hindawi Limited
Purpose. Colon segmentation is an essential step in the development of computer-aided diagnosis systems based on computed tomography (CT) images. The requirement for the detection of the polyps which lie on the walls of the colon is much needed in the field of medical imaging for diagnosis of colorectal cancer.Methods. The proposed work is focused on designing an efficient automatic colon segmentation algorithm from abdominal slices consisting of colons, partial volume effect, bowels, and lungs. The challenge lies in determining the exact colon enhanced with partial volume effect of the slice. In this work, adaptive thresholding technique is proposed for the segmentation of air packets, machine learning based cascade feed forward neural network enhanced with boundary detection algorithms are used which differentiate the segments of the lung and the fluids which are sediment at the side wall of colon and by rejecting bowels based on the slice difference removal method. The proposed neural network method is trained with Bayesian regulation algorithm to determine the partial volume effect.Results. Experiment was conducted on CT database images which results in 98% accuracy and minimal error rate.Conclusions. The main contribution of this work is the exploitation of neural network algorithm for removal of opacified fluid to attain desired colon segmentation result.