Chandra J
@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
Nagendra N and Chandra J
Science Publications
G. S. Vijay, M. C. Gowrishanker, and Y. Nayak. Suhas
EDP Sciences
Nidhin Raju, D. Peter Augustine, and J. Chandra
Springer Science and Business Media LLC
J. Chandra, Akshay Singhal, and Alwin Joseph
AIP Publishing
P. Y. Preema and J. Chandra
AIP Publishing
Malviya Meenakshi and J. Chandra
AIP Publishing
Somesh Kumar Sahu, Chandra J., Kiran Muloor, and Debabrata Samanta
IGI Global
Cloud computing has caused some companies to modify their IT infrastructure and maintenance procedures and may eliminate their current hardware altogether. Conventional methods of setting up a switch or router may be error-prone and unable to make full use of the capabilities of current network architectures. As many intelligent networking designs as possible must be developed for intellectualization, activation, and customization in future networks. Due to software-defined networking (SDN) technology, it's possible to control, secure, and optimize network resources, eliminating the rigid coupling between the control plane and the data plane in traditional network architectures. Here, the chapter explores the problems, difficulties, and potential solutions associated with software-defined networks (SDN), a novel concept in computer networking. Through SDN, the network gains the ability to be programmable, quick, and adaptable thanks to its separation of data and its ability to control traffic.
Kaushik Pratim Das and Chandra J
Frontiers Media SA
Cancer is a life-threatening disease, resulting in nearly 10 million deaths worldwide. There are various causes of cancer, and the prognostic information varies in each patient because of unique molecular signatures in the human body. However, genetic heterogeneity occurs due to different cancer types and changes in the neoplasms, which complicates the diagnosis and treatment. Targeted drug delivery is considered a pivotal contributor to precision medicine for cancer treatments as this method helps deliver medication to patients by systematically increasing the drug concentration on the targeted body parts. In such cases, nanoparticle-mediated drug delivery and the integration of artificial intelligence (AI) can help bridge the gap and enhance localized drug delivery systems capable of biomarker sensing. Diagnostic assays using nanoparticles (NPs) enable biomarker identification by accumulating in the specific cancer sites and ensuring accurate drug delivery planning. Integrating NPs for cancer targeting and AI can help devise sophisticated systems that further classify cancer types and understand complex disease patterns. Advanced AI algorithms can also help in biomarker detection, predicting different NP interactions of the targeted drug, and evaluating drug efficacy. Considering the advantages of the convergence of NPs and AI for targeted drug delivery, there has been significantly limited research focusing on the specific research theme, with most of the research being proposed on AI and drug discovery. Thus, the study's primary objective is to highlight the recent advances in drug delivery using NPs, and their impact on personalized treatment plans for cancer patients. In addition, a focal point of the study is also to highlight how integrating AI, and NPs can help address some of the existing challenges in drug delivery by conducting a collective survey.
Kaushik Pratim Das and J. Chandra
Springer Nature Singapore
J. Chandra, Madhavi Rangaswamy, Bonny Banerjee, Ambar Prajapati, Zahid Akhtar, Kenneth Sakauye, and Alwin Joseph
Elsevier
Meenakshi Malviya and Chandra J
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
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
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
Inderscience Publishers
Sherly Maria, J. Chandra, Bonny Banerjee, and Madhavi Rangaswamy
Springer Singapore
R. Merjulah and J. Chandra
Inderscience Publishers
Alwin Joseph, J. Chandra, and S. Siddharthan
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
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
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
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.
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.
R. Merjulah and J. Chandra
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.