Chandrakala Kuruba
@vignannirula.org
Assistant Professor
Vignan's Nirula Institute of Technology and Science for Women
RESEARCH INTERESTS
Machine Learning
Deep Learning
Image Processing
BioMedical Images
8
Scopus Publications
178
Scholar Citations
4
Scholar h-index
2
Scholar i10-index
Scopus Publications
- Robust blood vessel detection with image enhancement using relative intensity order transformation and deep learning
Chandrakala Kuruba and N.P. Gopalan
Elsevier BV - Data mining and deep learning-based hybrid health care application
Chandrakala Kuruba, N. Pushpalatha, Gandikota Ramu, I. Suneetha, M. Rudra Kumar, and P. Harish
Springer Science and Business Media LLC - Nuclear reactor application on Jeffrey fluid flow with Falkner-skan factor, Brownian and thermophoresis, non linear thermal radiation impacts past a wedge
G. Dharmaiah, Fateh Mebarek-Oudina, M. Sreenivasa Kumar, and K. Chandra Kala
Elsevier BV - Efficient Transfer Learning Model for Automated Diabetic Retinopathy Grading in Resource-Constrained Environment
K. Chandrakala, Y. Tulasi, D. Shanmukhi, M. RaveenaRai, and D. SriHarshitha
IEEE
The first among the outcomes of diabetes mellitus was Diabetic Retinopathy (DR), can cause severe damage for the retinal arterial blood vessels and vision. Early detection of DR stage may reduce the damage to the retina and vision loss. There are different stages of DR. Many existing methodologies has been presented to predict the stage of DR which uses conventional CNN and succeeded in achieving better performance. But the drawback of these models is training such huge network from scratch takes lot of time and resources. In this paper we proposed a novel hybrid approach to solve the DR problem by minimizing the resources utilization. This approach is a 2-step process. During the first step, the essential features from the retinal fundus images are extracted by using transfer learning technique called EfficientNet. And these feature representations are given as input to second step. In second step an ensemble machine learning boosting algorithm is used to predict the DR stage. Transfer learning techniques enable to use the existing pretrained weights which make the training process fast and minimize the resource consumption. And also, the hybrid approach which uses deep learning and machine learning together to deliver better accuracy. This study has conducted experiments on 2 datasets APTOS and IDRiD datasets by applying 3 different boosting algorithms like AdaBoost, XGBoost and LightGBM. The combination of EffiecientNet with Xtreme Gradient Boost (XGBoost) has given 99.1% & 99.2% accuracy on given 2 datasets. - Paillier Cryptography Based Message Authentication Code for IoMT Security
S. Siamala Devi, Chandrakala Kuruba, Yunyoung Nam, and Mohamed Abouhawwash
Computers, Materials and Continua (Tech Science Press) - Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan
S. Siamala Devi, Chandrakala Kuruba, Yunyoung Nam, and Mohamed Abouhawwash
Computers, Materials and Continua (Tech Science Press) - Hall and ion slip impact on magneto-titanium alloy nanoliquid with diffusion thermo and radiation absorption
G. Dharmaiah, W. Sridhar, K. S. Balamurugan, and K. Chandra Kala
Informa UK Limited - A Real Time Stock tendency prognostication using Quantopian
P Rajesh, Mansoor Alam, Mansour Tahernezhadi, K. Vamshikrishna Reddy, and K Chandrakala
IEEE
Stock trends prediction project is about designing a strategy that helps in predicting the stock trends in real time. To achieve it, we have considered Quantopian, one of the world’s leading financial marketing strategy analysis as our platform. Quantopian follows the 3-Step approach of Alpha coding, Code Optimization & Portfolio. Alpha Code is the main algorithm that harbours, our strategy in stock trend prediction. We consider a single factor that influences our code and gradually come up with two or more factors combination to advance efficiency of optimization strategy. Sentiment factor is used to give an efficiency of 9.3% at the end of back testing. When the combination of sentiment, operation ratio and revenue growth are passed to alpha lens, it is found that the efficiency is raised to 64%, making it much more reliable and stable. The accuracy of multiple alpha factors model attains more than 60%, contrast with earlier prediction algorithms with a single alpha factor have around 9% accuracy increases with 51%.
RECENT SCHOLAR PUBLICATIONS
- Efficient Transfer Learning Model for Automated Diabetic Retinopathy Grading in Resource-Constrained Environment
K Chandrakala, Y Tulasi, D Shanmukhi, M RaveenaRai, D SriHarshitha
2023 2nd International Conference on Automation, Computing and Renewable 2023 - Robust blood vessel detection with image enhancement using relative intensity order transformation and deep learning
C Kuruba, NP Gopalan
Biomedical Signal Processing and Control 86, 105195 2023 - Paillier Cryptography Based Message Authentication Code for IoMT Security.
SS Devi, C Kuruba, Y Nam, M Abouhawwash
Comput. Syst. Sci. Eng. 44 (3), 2209-2223 2023 - Data mining and deep learning-based hybrid health care application
C Kuruba, N Pushpalatha, G Ramu, I Suneetha, MR Kumar, P Harish
Applied Nanoscience 13 (3), 2431-2437 2023 - Nuclear reactor application on Jeffrey fluid flow with Falkner-skan factor, Brownian and thermophoresis, non linear thermal radiation impacts past a wedge
K Dharmaiah, G. , Mebarek-Oudina, F. , Sreenivasa Kumar, M. , Chandra Kala
Journal of the Indian Chemical Society 100 (2) 2023 - Hybrid optimisation with black hole algorithm for improving network lifespan
SS Devi, C Kuruba, Y Nam, M Abouhawwash
Intell. Autom. Soft Comput. 35 (2), 1873-1887 2023 - Hall and ion slip impact on magneto-titanium alloy nanoliquid with diffusion thermo and radiation absorption
G Dharmaiah, W Sridhar, KS Balamurugan, K Chandra Kala
International Journal of Ambient Energy 43 (1), 3507-3517 2022 - A Real Time Stock tendency prognostication using Quantopian
P Rajesh, M Alam, M Tahernezhadi, KV Reddy, K Chandrakala
2020 19th IEEE International Conference on Machine Learning and Applications 2020 - DETECTING TYPE OF BRAIN TUMOR USING DEEP LEARNING TECHNIQUES K
RSRIL K.CHANDRAKALA, A.SAI SOWMYA, E.KAVYA SRI, K.VISWA TEJASWI
Parishodh Journal 9 (3), 6587-6593 2020 - ENHANCED AND RELIABLE STOCK PRICE PREDICTION USING MACHINE LEARNING
TV K.CHANDRAKALA,K.MAMATHA ,V.SRAVANI ,R.B.N.SUNITHA
Parishodh Journal 9 (3), 6491-6496 2020 - Component based Development Methodology for Real time Applications
DELL Vasumathi Devi Majety, GL Sravanthi, K.Chandrakala
TEST Engineering and Management 82, 1181-1186 2020 - Constructing a Model to Predict Fraudulent Credit Card Transactions using Machine Learning Techniques
DELL K.Chandra Kala, N.Ashok Kumar, Dr.Vasumathi Devi Majety, G.L.Sravanthi
International Journal of Advanced Science and Technology 29 (9), 2849-2856 2020 - Performance Comparison of Hadoop MapReduce and Apache Pig
CK Kuruba
International Journal of Advanced Research in Computer Science 8 (9) 2017 - CONSTRAINT TIME MINIMIZING ASSIGNMENT PROBLEM – GENETIC ALGORITHM
KSB K.Chandra Kala
International Journal of Advanced Research in Computer Science 8 (9), 438-440 2017 - A Novel Approach To Measure Semantic Similarity Between Words Using Web Search Engine
PEVP Chandrakala. K
International Journal of Engineering Research and Development 3 (5), 62-71 2012 - A Lexi-Search Approach for Variant Mutiple Travelling Salesmen Problem
SB Kappala
International Journal of Advanced Research in Computer Science 1 (3) 2010 - A New Approach for Variant Multi Assignment Problem
K Sobhan Babu, K Chandra Kala, S Purusotham, M Sundara Murthy
International Journal on Computer Science and Engineering 2 (5), 1633-1640 2010
MOST CITED SCHOLAR PUBLICATIONS
- Nuclear reactor application on Jeffrey fluid flow with Falkner-skan factor, Brownian and thermophoresis, non linear thermal radiation impacts past a wedge
K Dharmaiah, G. , Mebarek-Oudina, F. , Sreenivasa Kumar, M. , Chandra Kala
Journal of the Indian Chemical Society 100 (2) 2023
Citations: 107 - Hall and ion slip impact on magneto-titanium alloy nanoliquid with diffusion thermo and radiation absorption
G Dharmaiah, W Sridhar, KS Balamurugan, K Chandra Kala
International Journal of Ambient Energy 43 (1), 3507-3517 2022
Citations: 42 - Data mining and deep learning-based hybrid health care application
C Kuruba, N Pushpalatha, G Ramu, I Suneetha, MR Kumar, P Harish
Applied Nanoscience 13 (3), 2431-2437 2023
Citations: 8 - A New Approach for Variant Multi Assignment Problem
K Sobhan Babu, K Chandra Kala, S Purusotham, M Sundara Murthy
International Journal on Computer Science and Engineering 2 (5), 1633-1640 2010
Citations: 8 - Robust blood vessel detection with image enhancement using relative intensity order transformation and deep learning
C Kuruba, NP Gopalan
Biomedical Signal Processing and Control 86, 105195 2023
Citations: 4 - Paillier Cryptography Based Message Authentication Code for IoMT Security.
SS Devi, C Kuruba, Y Nam, M Abouhawwash
Comput. Syst. Sci. Eng. 44 (3), 2209-2223 2023
Citations: 3 - A Real Time Stock tendency prognostication using Quantopian
P Rajesh, M Alam, M Tahernezhadi, KV Reddy, K Chandrakala
2020 19th IEEE International Conference on Machine Learning and Applications 2020
Citations: 3 - Efficient Transfer Learning Model for Automated Diabetic Retinopathy Grading in Resource-Constrained Environment
K Chandrakala, Y Tulasi, D Shanmukhi, M RaveenaRai, D SriHarshitha
2023 2nd International Conference on Automation, Computing and Renewable 2023
Citations: 1 - Hybrid optimisation with black hole algorithm for improving network lifespan
SS Devi, C Kuruba, Y Nam, M Abouhawwash
Intell. Autom. Soft Comput. 35 (2), 1873-1887 2023
Citations: 1 - Performance Comparison of Hadoop MapReduce and Apache Pig
CK Kuruba
International Journal of Advanced Research in Computer Science 8 (9) 2017
Citations: 1