Chandra J
Verified email at christuniversity.in
Associate Professor
CHRIST(Deemed to be University)
EDUCATION
MCA,MPhil,PhD
RESEARCH INTERESTS
Data Analysis, Artificial Intelligence,Neural Network,Machine Learning,Business Intelligence and Medical Image Analysis
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Kaushik Pratim Das and J. Chandra
Lecture Notes on Data Engineering and Communications Technologies, ISSN: 23674512, eISSN: 23674520, Volume: 111, Pages: 455-475, Published: 2022
Springer Nature Singapore
J. Chandra, Madhavi Rangaswamy, Bonny Banerjee, Ambar Prajapati, Zahid Akhtar, Kenneth Sakauye, and Alwin Joseph
Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence, Pages: 243-272, Published: 1 January 2022
Elsevier
Meenakshi Malviya and Chandra J
ECS Transactions, ISSN: 19386737, eISSN: 19385862, Volume: 107, Pages: 11623-11632, Published: 2022
The Electrochemical Society
Quality of life (QoL) and QoL predictors have become crucial in the pandemic. Neurological anomalies are at the highest level of QoL threats. Autism is a multi-system disorder that causes behavioural, neurological, cognitive, and physical differences. Recent studies state that neurological disorders can result in dysfunction of brain or whole nervous system, which may cause other symptoms of Autism. The paper focuses on reviewing various Machine Learning techniques used for diagnosing Autism at an early age with the help of multiple datasets. The study of brain Magnetic Resonance Imaging (MRI) provides astute knowledge of brain structure that helps to study any minor to significant changes inside the brain that have emerged due to the disorder. Early diagnosis leads to a healthy life by getting timely treatment and training. "Early diagnosis of Autism Spectrum Disorder" is an objective and one of the prime goals of health establishments worldwide. The research paper aims to systematically review and find which machine learning algorithms are efficient for prognosis of Autism.
Nagendra N and Chandra J
ECS Transactions, ISSN: 19386737, eISSN: 19385862, Volume: 107, Pages: 2503-2514, Published: 2022
The Electrochemical Society
Aspect extraction is an important and challenging and meaningful task in aspect-based text classification analysis. To apply variants of topic models on task, while reasonably successful, these methods usually do not produce highly coherent aspects. In this review, present a novel neural/cognitive approach to discover coherent aspects methods. Exploiting the distribution of word co-occurrences through neural/cognitive word embeddings. Unlike topics that typically assume independently generated words, word embedding models encourage words that appear in similar factors close to each other in the embedding space. Also, use an attention mechanism to de-emphasize irrelevant words during training, further improving aspects coherence. Methods results on datasets demonstrate that approach discovers more meaningful and coherent aspects and substantially outperforms baseline. Aspect-based text analysis aims to determine people's attitudes towards different aspects in a review.
Kaushik Pratim Das and Chandra J
ECS Transactions, ISSN: 19386737, eISSN: 19385862, Volume: 107, Pages: 3649-3673, Published: 2022
The Electrochemical Society
Medical image fusion has become essential for accurate diagnosis. For example, a lung cancer diagnosis is currently conducted with the help of multimodality image fusion to find anatomical and functional information about the tumor and metabolic measurements to identify the lung cancer stage and metastatic information of the disease. Generally, the success of multimodality imaging for lung cancer diagnosis is due to the combination of PET and CT imaging advantages while minimizing their respective limitations. However, medical image fusion involves the registration of two different modalities, which is time-consuming and technically challenging, and it is a cause of concern in a clinical setting. Therefore, the paper's main objective is to identify the most efficient medical image fusion techniques and the recent advances by conducting a collective survey. In addition, the study delves into the impact of deep learning techniques for image fusion and their effectiveness in automating the image fusion procedure with better image quality while preserving essential clinical information.
Sanjay Vincent and J. Chandra
International Journal of Biomedical Engineering and Technology, ISSN: 17526418, eISSN: 17526426, Pages: 249-266, Published: 2022
Inderscience Publishers
Sherly Maria, J. Chandra, Bonny Banerjee, and Madhavi Rangaswamy
Smart Innovation, Systems and Technologies, ISSN: 21903018, eISSN: 21903026, Volume: 251, Pages: 561-568, Published: 2022
Springer Singapore
R. Merjulah and J. Chandra
International Journal of Computer Aided Engineering and Technology, ISSN: 17572657, eISSN: 17572665, Issue: 2-3, Pages: 269-280, Published: 2021
Inderscience Publishers
Alwin Joseph, J. Chandra, and S. Siddharthan
Lecture Notes in Networks and Systems, ISSN: 23673370, eISSN: 23673389, Volume: 132, Pages: 221-226, Published: 2021
Springer Singapore
Precision agriculture is a farm management technique which uses the help with the help of information technology to ensure that the crops and soil receive exactly what is required for optimum health and productivity. Genome analysis in plants helps to identify the plant structure and physiological traits. The identification of the right plant genome and the resulting traits help to optimize the cultivation of the plant for better productivity and adaptability. Genome analysis helps the biologist edit the plant genetic makeup structure to make the plant to adapt to the current conditions and thereby reducing the use of fertilizers. For precision agriculture, artificial intelligence techniques help to understand the relationships between plant genome and soil nutrient conditions that help in precision farming effectively reducing the usage of fertilizers by modifying the plants to adapt with the current soil characteristics.
R. Merjulah and J. Chandra
Pattern Recognition and Image Analysis, ISSN: 10546618, eISSN: 15556212, Pages: 530-540, Published: 1 July 2020
Pleiades Publishing Ltd
Abstract Myocardial Ischemia segmentation is a challenging task for basic and translational research on cardiovascular, as it provides ultimately “realistic” in heart muscle model. The main objective of the research work is to find an efficient segmentation technique for the myocardial ischemia based on the myocardial infarcted MRI data set for the accurate classification of scar volume. The paper will give an insight about the segmentation technique based on myocardial ischemia and discusses essential cellular components. The paper provides an integrated approach which comprises of fuzzy c-means and morphological operations along with median filtering enhancement technique help in detecting the myocardial ischemia. The developed model is tested with 2D and 3D enhanced myocardial ischemia MRI and also with normal heart. The purpose of segmentation in myocardial ischemia is to identify the scar region in the heart. The integrated model is evaluated based on statistical measures and validated based on manual segmentation done by clinical expert. The scar classification is done based on the myocardial ischemia segmentation which leads to better prediction of arrhythmia in heart patient. The integrated model is considered as one of the best model for segmenting myocardial ischemia.
J. Chandra, Akshay Santhanam, and Alwin Joseph
International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020, Published: February 2020
IEEE
Data mining is the primary method of gathering large volumes of knowledge. To make use of such data to implementation requires the use of effective machine learning strategies. Semantic Textual Similarity is one of the primary machine learning strategies. At its core, semantic textual similarity is the identification of words with similar context. Initial work in STS involved text reuse, word search among others. The proposed research work uses a specific method of determining textual similarity using Google’s Word2Vec framework and the Continuous-bag-of-words algorithm for identifying word similarity in rap records. A large data set consisting of over 50,000 rap records is used. The key aspect of proposed methodology is to determine the words with similar context and cluster them into different word clusters also called bags. To achieve the desired result, the dataset is first processed to obtain the features. Once the features are selected, a model is generated by passing the data onto the Word2Vec framework. The research work on semantic textual similarity was repeated across three different runs, with the data set size changing in every run. At the end of each the accuracy of similarity obtained by the model was determined. The current research work has achieved average accuracy as 85%.
Benny Thomas and J. Chandra
International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020, Published: February 2020
IEEE
With the advancement in internet, the efficiency of e-learning increased and currently e-learning is one of the primary method of learning for most learners after the regular academics studies. The knowledge delivery through e-learning web sites increased exponentially over the years because of the advancement in internet and e-learning technologies. The learner can find many website with lots of information on the relevant domain. However learners often found it difficult to Figure out the right leaning content from the humongous availability of e-content. In the proposed work an intelligent framework is developed to address this issue. The framework recommend the right learning content to a user from the e-learning web sites with the knowledge level of the user. The e-contents available in web sites were divided in to three cognitive levels such as beginner, intermediate and advanced level. The current work uses Blooms Taxonomy verbs and its synonyms to improve the accuracy of the classifier used in the framework.
Benny Thomas and Chandra J.
International Journal of Electrical and Computer Engineering, ISSN: 20888708, Pages: 4372-4380, Published: 2020
Institute of Advanced Engineering and Science
The e-learning is the primary method of learning for most learners after the regular academics studies. The knowledge delivery through e-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never have been possible without the e-learning technologies. Most of the working professional do focused studies for carrier advancement, promotion or to improve the domain knowledge. These learner can find many free e-learning web sites from the internet easily in the domain of interest. However it is quite difficult to find the best e-learning content suitable for their learning based on their domain knowledge level. User spent most of the time figuring out the right content from a plethora of available content and end up learning nothing. An intelligent framework using machine learning algorithms with Random Forest Classifier is proposed to address this issue, which classifies the e-learning content based on its difficulty levels and provide the learner the best content suitable based on the knowledge level .The frame work is trained with the data set collected from multiple popular e-learning web sites. The model is tested with real time e-learning web sites links and found that the e-contents in the web sites are recommended to the user based on its difficulty levels as beginner level, intermediate level and advanced level.
International Journal of Innovative Technology and Exploring Engineering, eISSN: 22783075, Pages: 1456-1462, Published: May 2019
R. Merjulah and J. Chandra
Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions, Pages: 209-235, Published: 1 January 2019
Elsevier
Abstract This chapter addresses the classification of myocardial ischemia in delayed contrast enhancement using machine-learning techniques for magnetic resonance imaging which solves the social issue of a sudden cardiac death. To automate the classification of myocardial ischemia, the computer-aided design has a crucial path on the mixture ensemble of machine learning. The mixture ensemble of machine learning can partition a high-dimensional image in a simultaneous and competitive way. The detection and the segmentation processes are carried out through Fuzzy C-Means multispectral and single-channel algorithms along with a morphological filtering technique for feature extraction. Furthermore, the feed forward neural network (FFNN) technique is trained through the Levenberg-Marquardt Back Propagation algorithm for the classification of myocardial ischemia in delayed contrast enhancement. The proposed classification model performs well for the classification of myocardial ischemia. The rigorous process of the proposed result reveals that the FFNN classifier produces 99.9% accuracy on the classification of myocardial ischemia and also shows that the given classifier is considered one of the best methods in classifying medical myocardial ischemia.
International Journal of Engineering Research and Technology, ISSN: 09743154, Pages: 596-601, Published: 2019
International Journal of Engineering Research and Technology, ISSN: 09743154, Pages: 440-445, Published: 2019
R. Merjulah and J. Chandra
Lecture Notes in Computational Vision and Biomechanics, ISSN: 22129391, eISSN: 22129413, Pages: 1135-1146, Published: 2019
Springer International Publishing
The main objective of the paper is to review the performance of various machine learning classification technique currently used for magnetic resonance imaging. The prerequisite for the best classification technique is the main drive for the paper. In magnetic resonance imaging, detection of various diseases might be simple but the physicians need quantification for further treatment. So, the machine learning along with digital image processing aids for the diagnosis of the diseases and synergizes between the computer and the radiologist. The review of machine learning classification based on the support vector machine, discrete wavelet transform, artificial neural network, and principal component analysis reveals that discrete wavelet transform combined with other highly used method like PCA, ANN, etc., will bring high accuracy rate of 100%. The hybrid technique provides the second opinion to the radiologist on taking the decision.
R. Merjulah and J. Chandra
Proceedings of the International Conference on Inventive Computing and Informatics, ICICI 2017, Pages: 1055-1061, Published: 24 May 2018
IEEE
Segmentation is one of the popular and efficient technique in context to medical image analysis. The purpose of the segmentation is to clearly extract the Region of Interest from the medical images. The main focus of this paper is to review and summarize an efficient segmentation method. While doing the comparison study on segmentation methods using the Support Vector Machine, K-Nearest Neighbors, Random Forest and the Convolutional Neural Network for medical image analysis identifies that Convolutional Neural Network works efficiently for doing in-depth analysis. The Convolutional Neural Network can be used as segmentation technique for achieving the high accuracy on medical image analysis.
Meenakshi Malviya, J Chandra, and Manjunatha Hiremath
Proceedings of the 2017 International Conference On Smart Technology for Smart Nation, SmartTechCon 2017, Pages: 872-878, Published: 11 May 2018
IEEE
Down Syndrome is a chromosomal disease which causes many physical and cognitive disabilities. Down Syndrome patients are more vulnerable than any other patient. Medical experts started knowing it now with keen awareness. In recent years it has become a field of interest for many researchers, medical experts and social organisation. For the researchers it is an area of interest where very little work is done and a lot to be explored. Machine Learning consists of different processing levels like pre-processing, segmentation, feature selection and classification. Each level contains a vast set of techniques like filters, segmentation algorithms and classifiers. Machine Learning is one of the most popular algorithm, which is used to automate the decision making process with higher rate of accuracy in less time with least error rate. Machine Learning proved its significance with highest rate of accuracy in decision making and problem solving in almost all the fields but automated decision making in medical science is still a challenge. This paper reviews the different works done in the field of Down Syndrome using Machine Learning applied on different medical images, and the techniques like pre-processing, segmentation, feature selection and classification. The aim of this research work is to analyse and identify the Machine Learning methodologies that works efficiently to detect Down Sundrome.
Tinu Anand Singh and J. Chandra
Journal of Computer Science, ISSN: 15493636, Pages: 639-644, Published: 2018
Science Publications
With industrialization and continuously evolving climatic conditions, the urge to practice agriculture with the fusion of technology has become a necessity. In the era of Internet of Things where all eyes are witnessing the evolution of machine to machine interaction, there is also a lack of clarity in considering the type of protocol to be used in building a particular system like Green House. A green house is a regulated environment for agriculture where critical parameters like temperature, light, humidity, ph level of soil can be monitored with the help of sensor systems using Internet of Things protocols. Message Queue Telemetry Transfer protocol was chosen over Constrained Application Protocol and Extensible Messaging and Presence Protocol in the experiment conducted in terms of its light weight transmission, resource consumption and effectively providing the different quality of services to detect the temperature and humidity as well as the gas leaks encountered in a greenhouse environment.
Journal of Advanced Research in Dynamical and Control Systems, eISSN: 1943023X, Issue: Special issue 14, Pages: 2532-2542, Published: 2017
J. Chandra, M. Nachamai, and Anitha S. Pillai
Lecture Notes in Electrical Engineering, ISSN: 18761100, eISSN: 18761119, Volume: 362, Pages: 747-758, Published: 2016
Springer International Publishing
The return on investment of stock market index is used to estimate the effectiveness of an investment in different savings schemes. To calculate Return on Investment, profit of an investment is divided by the cost of investment. The purpose of the paper is to perform empirical evaluation of various multilayer perceptron neural networks that are used for obtaining high quality prediction for Return on Investment based on stock market indexes. Many researchers have already implemented different methods to forecast stock prices, but accuracy of the stock prices are a major concern. The multilayer perceptron feed forward neural network model is implemented and compared against multilayer perceptron back propagation neural network models on various stock market indexes. The estimated values are checked against the original values of next business day to measure the actual accuracy. The uniqueness of the research is to achieve maximum accuracy in the Indian stock market indexes. The comparative analysis is done with the help of data set NSEindia historical data for Indian share market. Based on the comparative analysis, the multilayer perceptron feed forward neural network performs better prediction with higher accuracy than multilayer perceptron back propagation. A number of variations have been found by this comparative experiment to analyze the future values of the stock prices. With the experimental comparison, the multilayer perceptron feed forward neural network is able to forecast quality decision on return on investment on stock indexes with average accuracy rate as 95 % which is higher than back propagation neural network. So the results obtained by the multilayer perceptron feed forward neural networks are more satisfactory when compared to multilayer perceptron back propagation neural network.
J. Chandra, M. Nachamai, and Anitha S. Pillai
Journal of Computer Science, ISSN: 15493636, Pages: 863-871, Published: 2015
Science Publications
Commodity trading is one of the most popular resources owning to its eminent predictable return on investment to earn money through trading. The trading includes all kinds of commodities like agricultural goods such as wheat, coffee, cocoa etc. and hard products like gold, rubber, crude oils etc.,. The investment decision can be made very easily with the help of the proposed model. The proposed model correlation based multi layer perceptron feed forward adaline neural network is an integrated method to forecast the future values of all commodity trading. The correlation based adaline neuron is used as an optimized predictor in the multi layer perceptron feed forward neural network. The correlation is used for feature selection before building the predictive model. The aim of the paper is to build the predictive model for commodity trading. The model is created using correlation based feature selection and adaline neural network to prognosticate all future values of commodities. The adaptive linear neuron is formed with the help of linear regression. To implement the proposed model the live data is captured from mcxindia. The mcxindia is considered as one the popular website for doing commodities and derivatives in India. To train the proposed model, few random samples are used and the model is evaluated with the help of few test samples from the same data set.
J. Chandra, G. Da Prato, D. Kannan, G. Ladde, E. Roxin, and M. Sambandham
Stochastic Analysis and Applications, ISSN: 07362994, eISSN: 15329356, Pages: 753-754, Published: September 2012
Informa UK Limited