S.Vijayalakshmi
@lavasa.christuniversity.in
Associate Professor, Department of Data Science
christ university
RESEARCH INTERESTS
Image Processing, Computer Vision and IoT
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
S Vijayalakshmi, P Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand
Chapman and Hall/CRC
S. Vijayalakshmi, P. Durgadevi, and A.S. Mohammed Shariff
Chapman and Hall/CRC
Sushma Tanwar, S. Vijayalakshmi, P. Durgadevi, and R. Girija
Chapman and Hall/CRC
Gayathri S. P., Siva Shankar Ramasamy, and Vijayalakshmi S.
IGI Global
Clinical imaging relies heavily on the current medical services' framework to perform painless demonstrative therapy. It entails creating usable and instructive models of the human body's internal organs and structural systems for use in clinical evaluation. Its various varieties include signal-based techniques such as conventional X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US) imaging, and mammography. Despite these clinical imaging techniques, clinical images are increasingly employed to identify various problems, particularly those that are upsetting the skin. Imaging and processing are the two distinct patterns of clinical imaging. To diagnose diseases, automatic segmentation using deep learning techniques in the field of clinical imaging is becoming vital for identifying evidence and measuring examples in clinical images. The fundamentals of deep learning techniques are discussed in this chapter along with an overview of successful implementations.
A. Anbarasi, M. Revathi, and S. Vijayalakshmi
IEEE
Prior to the creation of the ophthalmoscope by von Helmholtz roughly 150 years ago, eye doctors had no way of inspecting the area behind the pupil. The recent decade has seen significant development in retinal imaging and image processing, opening up new frontiers in the study of the eye. Prevention of permanent vision loss due to ocular illnesses requires prompt diagnosis. In particular, convolutional neural networks (CNNs) have shown encouraging results in the analysis of medical pictures in recent years. This research provides support for the use of convolutional neural networks (CNNs) in the detection of ocular disorders. The proposed method takes retinal fundus images as input and employs a pre-trained convolutional neural network (CNN) model to extract relevant features. There are a number of benefits to using CNNs to forecast eye diseases instead of more conventional methods. Convolutional neural networks (CNNs) can learn complicated characteristics from big datasets, which can boost their accuracy and generalizability. Additionally, they are able to pick up on small changes in medical images that may be ignored by human experts, allowing for earlier diagnosis and treatment of ocular illnesses. Furthermore, CNNs can offer objective and reproducible predictions, which can help to lessen the variability and subjectivity of human evaluations. Convolutional neural networks (CNNs) are a sort of deep neural network that has shown effective for a number of computer vision tasks. Due to their propensity for learning complicated characteristics from big datasets, convolutional neural networks (CNNs) are ideally suited for the analysis of medical pictures like retinal fundus photographs and optical coherence tomography (OCT) images. For the purpose of ocular illness prediction, convolutional neural networks (CNNs) are trained using medical image annotation datasets to learn features that can distinguish between healthy and sick eyes. Then, new photos can be analyzed using these traits to determine the existence and severity of ocular disorders. The CNN model was trained and validated using over four thousand fundus images representing various ocular diseases and conditions. Eighty percent of the images were used for training, while the other twenty percent were used for testing. Training using two convolutional layers and two dense layers resulted in an 80% accuracy in predictions.
Azween Abdullah, S. Nithya, M. Mary Shanthi Rani, S. Vijayalakshmi, and Balamurugan Balusamy
The Science and Information Organization
—Cardiovascular diseases (CVD) are the most prevalent causes of death and disability worldwide. Cardiac arrhythmia is one of the chronic cardiovascular diseases that create panic in human life. Early diagnosis aids physicians in securing life. ECG is a non-stationary physiological signal representing the heart's electrical activity. Automated tools to detect arrhythmia from ECG signals are possible with Machine Learning (ML). The ensemble learning technique combines the power of two or more classifiers to solve a computational intelligence problem. It enhances the performance of the models by fusing two or more models, which extremely increases its strength. The proposed ensemble Machine learning amalgamates the potency of Long Short-Term Memory (LSTM) and ensemble learning, opening up a new direction for research. In this research work, two novel ensemble methods of Extreme Gradient Boosting-LSTM (EXGB-LSTM) are developed, which use LSTM as a base learner and are transformed into an ensemble learner by coalescing with Extreme Gradient Boosting. Kernel Principal Component Analysis (K-PCA) is a significant non-linear dimensionality reduction technique. It can manage high-dimensional datasets with various features by lowering the dimensionality of the data while retaining the most crucial details. It has been applied as a preprocessing step for feature reduction in the dataset, and the performance of EXGB-LSTM is tested with and without K-PCA. Experimental results showed that the first method, fusion of EXG-LSTM, has reached an accuracy of 92.1%, Precision of 90.6%, F1-score of 94%, and Recall of 92.7%. The second proposed method, KPCA with EXGB-LSTM, attained the highest accuracy of 94.3%, with a precision of 92%, F1-score of 98%, and Recall of 94.9% for multi-class cardiac arrhythmia classification.
P. Durgadevi and S. Vijayalakshmi
Inderscience Publishers
S. P. Gayathri and S. Vijayalakshmi
Springer International Publishing
S. Vijayalakshmi, Savita, T. Genish, and Jossy P. George
Springer International Publishing
S. Vijayalakshmi, Savita, and P. Durgadevi
Springer International Publishing
T. Genish, S. Kavitha, and S. Vijayalakshmi
World Scientific Pub Co Pte Ltd
Hippocampus segmentation on magnetic resonance imaging is more significant for diagnosis, treatment and analyzing of neuropsychiatric disorders. Automatic segmentation is an active research field. Previous state-of-the-art hippocampus segmentation methods train their methods on healthy or Alzheimer’s disease patients from public datasets. It arises the question whether these methods are capable for recognizing the hippocampus in a different domain. Therefore, this study proposes a precise computational method for hippocampus segmentation from MRI of brain to assist physicians in the diagnosis of Alzheimer’s disease (HCS-MRI-DAD-LBP). Initially, the input images are pre-processed by Trimmed mean filter for image quality enhancement. Then the pre-processed images are given to ROI detection, ROI detection utilizes Weber’s law which determines the luminance factor of the image. In the region extraction process, Chan–Vese active contour model (ACM) and level sets are used (UACM). Finally, local binary pattern (LBP) is utilized to remove the erroneous pixel that maximizes the segmentation accuracy. The proposed model is implemented in MATLAB, and its performance is analyzed with performance metrics, like precision, recall, mean, variance, standard deviation and disc similarity coefficient. The proposed HCS-MRI-DAD-LBP method attains in OASIS dataset provides high disc similarity coefficient of 12.64%, 10.11% and 1.03% compared with the existing methods, like HCS-DAS-MLT, HCS-DAS-RNN and HCS-DAS-GMM and in ADNI dataset provides high precision of 20%, 9.09% and 1.05% compared with existing methods like HCS-MRI-DAD-CNN-ADNI, HCS-MRI-DAD-MCNN-ADNI and HCS-MRI-DAD-CNN-RNN-ADNI, respectively.
M. Vivek Anand, Dr. Munish Sabharwal, and Dr. Vijayalakshmi S
ENGG Journals Publications
M. Vivek Anand, Munish Sabharwal, and S. Vijayalakshmi
IEEE
Internet of things is an important part of our day-to-day life where all things are connected in the network with the internet. The number of devices linked to the network grows steadily each day in recent years. The innovation in the manufacturing industry also the reason for the production of different devices that uses various technologies to make a possible connection between the devices. Even though the Internet of Things has been developing and demonstrating its potential in recent years, its security when connected to the internet is in doubt. Blockchain is a disruptive technology that provides security to their network without tampering with the data in the network. Researchers and experts have recommended using the blockchain to address security vulnerabilities in the Internet of Things. In this paper, we have analyzed some of the issues which are occurring while integrating blockchain into the Internet of things. The major issues were discussed and which will be helpful to move towards the research direction to solve those problems.
Subham Das, Sourav Saha, S. Vijayalakshmi, and Jitendra Jaiswal
IEEE
Nowadays social media plays a vital role in different fields including business, economic communication and personal. Many person get profit from the different origins of availability of data from these social media, but cyber-crimes are increasing day by day. A person can generate many fake accounts and hence pretenders can easily be made. Instagram, as one of the popular types of online social media site, carries big information and messages through the posts. Most of the person use Instagram as a digital life marketing place because it is a one of the big social media site. The goal of the research paper is to recognize and stop fake IDs and pages. Because through the professional pages of Instagram, many fake cases and things are occurring present days. So the main thing is to recognize fake pages and fake accounts also. In this paper, we work on various IDs of Instagram. We want to observe an ID is real or not using Machine Learning techniques namely Logistic Regression, Naive Bayes, Support vector machine, Decision tree, Random Forest.
Akash Patra, Ramkrishna Khan, and S. Vijayalakshmi
IEEE
Winning in the E-Commerce business race at a competitive age like this requires proper usage of Customer data. Using that database and grouping it in similar segments in terms of spending expenditure, observation time, sex, and location so that every customer falls in a segment of characteristics. This mechanism is called Customer Segmentation. In the modern era of highly compatible technological advancements, Machine Learning Algorithms are being vastly used to bring solutions to these difficult yet essential services. In the field of research methods like simple clustering based on purchase behaviour, buyer targeting or automated customer promotion mechanism by dividing into two major categories, have been worked on. However, ensemble algorithms have come handy where different clustering algorithms are combined to deliver best segmentation. Lately combination techniques like clustering and classification mechanism have also delivered good results where, not only segmentation is done but also classification of existing and new customers are possible into the clusters. Depending on that an effective customer relationship management can really benefit the company to a huge extent. Unlike other studies where clustering was performed directly on RFM table, a different approach was taken in this study where, one dimensional clustering was done individually on Recency, Frequency, Monetary columns, then an overall score was calculated and customers were classified into three segments. However, for a new customer depending on his purchase behaviour he/she also can be classified into any of the categories.
Tushar Bhalerao, S. Vijayalakshmi, and Gokulakrishnan J
IEEE
Sports attracted a lot of people to watch various games all over the world. India is not an exception. Among various games, cricket has special attention. Cricket in India contributes to the Indian economy on a large scale. Cricket is also known for the broad amount of data gathered for each team, season, and player. Hence, cricket is a perfect domain to work on various data analysis and machine learning approaches to acquire useful insights and information. In this paper, algorithms were used to enhance the output of the team in a sports league, particularly, IPL (cricket). It reflects the performance of the team on a deeper analysis of the requirements of T20 cricket.
B Kamalesh and S. Vijayalakshmi
IEEE
Users throughout the world may now access massive amounts of data thanks to the internet and social media platforms. [5] In every facet of human existence, electronic commerce (e-commerce) plays a crucial role. E-commerce is a marketing approach that enables businesses and consumers to buy and sell things via the internet. When buyers look for product information and compare alternatives online, they generally have access to dozens or hundreds of product reviews from alternative shoppers. Machine learning is the most appropriate approach to training a neural network in today’s age of practical artificial intelligence. So implementing a model to polarize those reviews and learn from them would make passing hundreds of comments a lot easier. [24] The interpretation will be a very basic product with positive, neutral, and negative polarization. The product is checked. This study suggests a sentiment evaluation model for shopper reviews based on the object and emotive word mining for emotional level analysis using machine learning approaches.
Suresh Kumar S, Chidambaram G, and S. Vijayalakshmi
IEEE
Advances in digital communication play an important role in our daily lives in today’s world. In general, information security systems fall into two categories: encryption and information hiding. The storage and transmission of sensitive personal data have become a part of everyday life for professional and personal well-being. Therefore, the issue of secure storage and transmission of confidential information is interest to many researchers. Steganography is a collective method to hide sensitive data in unobtrusive digital media such as video, audio, and images. One of the biggest challenges in developing steganography systems is finding the right balance between metrices like reliability, security, and data privacy. This project provides information about how the Bit Plane Complexity Steganography (BPCS) technology is applied to both grayscale and color imaging techniques and assesses performance parameters such as data concealment, security and stability based on the peak signal-to-noise ratio (PSNR) and Mean Square Error (MSE) with various test cases to explain how it works for different files and images.
Suresh Kumar S, Chidambaram G, S. Vijayalakshmi, and Dhayanandh A T
IEEE
Data volumes are increasing due to the growth of technologies such as IoT, Cloud Computing, and mobile internet. Data encryption is the solution, which protects data privacy by giving specific access to encrypted data can be better understood using Attribute-based (ABE) encryption. When patients use web-based commercial systems to store their personal health data, the privacy of personal health records (PHRs) can be a major problem. When it comes to establishing policies to control access and protect data confidentiality, common access control systems, such as Work-Based Access Control, have major limitations. In this research work a novel healthcare application has been developed based on the ABE scheme approach. Advanced Encryption Standard (AES) algorithm and Secure Hash Algorithm (SHA) are used for the encryption and decryption processes in the ABE scheme. This allows only designated authorized personnel, such as a patient or their respective doctors, to access personal data recorded in medical records, with a vision to improve the privacy and security of user details.
Rumal Ragsania and S Vijayalakshmi
IEEE
Face recognition is an AI-based innovation used to find and recognize human appearances in videos and images. Organizations can apply face recognition to many different kinds of fields which may include biometrics, regulation of law, security and individual wellbeing; so as to take observation of individuals in any scenario. Face recognition has advanced from simple vision methods to progress in ML; and further to progressively refined neural networks (ANN) and related advances. It currently assumes an indispensable part as the initial phase in numerous basic applications, including the task of tracking a face. Face recognition is utilized to focus cameras or count the number of individuals present in a particular region. The innovation likewise has showcasing applications, for instance, showing recommended promotions when a specific user is detected.
Rithwik Ramesh and S Vijayalakshmi
IEEE
Currently, recommendation systems are a common tool for providing individualized recommendations and item information to users. For personalization in the recommendation system, there are a variety of strategies that can be used. To improve system performance and offset the shortcomings of individual recommendation strategies, a hybrid recommender system integrates two or even more recommendation techniques. The demand to summarize all of the knowledge on actual methods and algorithms utilized in hybrid recommended systems necessitates the need for a systematic review in the domain. These materials will be employed to aid in the development of an auto-switching hybrid recommender system. In the content-based filtering technique, the algorithm is based on the contents of items and the collaborative filtering technique algorithm combines the relationship between user and item. Both of the approaches of recommendation system are suffers from some limitations, this is a big issue to predict better recommendations to the user. Hybrid systems are introduced to overcome the main limitations of both techniques. These systems are made with a combination of content-based and collaborative filtering techniques and have advantages of both techniques. With the use of hybrid systems, the quality of recommendations is improved. Hybrid recommendation systems use previous data of a user to find his/her interest and then they target the set of an adjacent user which is similar with that user and according to adjacent user recommend things to the user. Hybrid systems offer the items that share the common things that a user rated highly (Content-based filtering) and make suggestions by comparing the interest of a similar user (Collaborative filtering).
Adharsh C and S Vijayalakshmi
IEEE
In today's data communication environment, network and system security is vital. Hackers and intruders can gain unauthorized access to networks and online services, resulting in some successful attempts to knock down networks and web services. With the progress of security systems, new threats and countermeasures to these assaults emerge. Intrusion Detection Systems are one of these choices (IDS). An Intrusion Detection System's primary goal is to protect resources from attacks. It analyses and anticipates user behavior before determining if it is an assault or a common occurrence. We use Rough Set Theory (RST) and Gradient Boosting to identify network breaches (using the boost library). When packets are intercepted from the network, RST is used to pre-process the data and reduce the dimensions. A gradient boosting model will be used to learn and evaluate the features chosen by RST. RST-Gradient boost model provides the greatest results and accuracy when compared to other scale-down strategies like regular scaler.
Komesh Yadav and S. Vijayalakshmi
IEEE
Sentiment categorization is utilized in today's world to analyze social media data about the stock market and estimate its future stock movement. We investigated the possible influence of “public sentiment” on “market trends” using sentiment analysis and machine learning concepts. Due to the enormous number of components involved, such as economic situations, political events, and other environmental factors that may affect the stock price, stock price prediction is an exceedingly complicated and challenging process. Because of these considerations, evaluating a single factor's influence on future pricing and trends is challenging. As more individuals spend time online, the popularity and impact of numerous social media platforms has skyrocketed in recent years. Twitter is one such social media tool that has exploded in popularity. Twitter is a terrific place to stay up to speed on current societal trends and perspectives. The “Twittersphere” is a melting pot that supports diverse viewpoints, emotions, and trends, and it has the potential to be a crucial influencer in influencing and shaping perceptions.
S. Vijayalakshmi, T. Genish, and S. P. Gayathri
Springer Nature Singapore