@karunya.edu
Assistant Professor , Computer Science and Engineering
Karunya Institute of Technology and Sciences
Artificial Intelligence, Machine Learning, Deep Learning
Scopus Publications
Scholar Citations
Scholar h-index
C. P. Shirley, J Immanuel John Raja, S. V. Evangelin Sonia, and I. Titus
Springer Science and Business Media LLC
Elakkiya Elango, Gnanasankaran Natarajan, Ahamed Lebbe Hanees, and Shirley Chellathurai Pon Anna Bai
IGI Global
Organizations are quickly realizing the transformative possibilities of digital twins and artificial intelligence (AI) in this era of fast technical advancement. This chapter provides a brief synopsis of “The Roadmap to AI and Digital Twin Adoption,” a comprehensive resource that delves into the key elements and techniques necessary for the successful integration of AI and digital twins across a range of sectors. This roadmap explores the mutually beneficial relationship between artificial intelligence (AI) and digital twins, emphasizing how each may enhance overall performance, decision-making, and operational efficiency. It covers the fundamental concepts of artificial intelligence (AI), such as natural language processing, machine learning, and deep learning, and how important they are in relation to digital twins. The guide's emphasis extends to the practical use of AI and digital twins, offering guidance on data collection and management, model training, and algorithm choice.
Kavin S and Shirley C P
IEEE
To guarantee patient safety and regulatory compliance, healthcare providers must accurately enter batch numbers and expiration dates from drug packaging. But manual entry is prone to mistakes, which could have unfavourable effects. This project suggests an automated method for extracting batch numbers and expiration dates from the back of pharmaceutical packaging by using optical character recognition (OCR) technology. In order to improve OCR accuracy, the research makes use of preprocessing techniques and a broad dataset of photos from medical packaging. The retrieved text is analysed to determine the batch number and expiration date, and data correctness is ensured by validation procedures. The end product solution reduces errors in healthcare data management by offering an intuitive user interface for effective data extraction. By means of thorough inspection and evaluation, The experiment shows how the OCR-based system can reliably and effectively extract important information from pharmaceutical packaging. Through the reduction of data entry errors and the facilitation of error-free information retrieval from drug packaging, this effort seeks to improve patient safety and streamline healthcare procedures.
Immanuel John Raja, S. V. Evangelin Sonia, C. P. Shirley, and I. Titus
Springer Science and Business Media LLC
Venkatesan R, Brijit Benny, Shirley C P, and Berin Jeba Jingle I
IEEE
As the virtual and real worlds continue to collide, digital twins may become important tools for finding solutions to some of the most serious challenges facing the sustainability movement. These challenges include making effective use of resources, adjusting to the effects of climate change, and ensuring that everyone has access to a high quality of life. The first steps have been taken on the road to making widespread use of digital twins. It is imperative that their connectedness should be secured if their potential is to be fully utilized. This presents a number of challenging situations. The digital transition may make it possible to achieve several of the United Nations' sustainable development objectives. Data and technology should assist the user to make appropriate judgements that are better suited to their circumstances. It’s feasible that the computer model can see into the future and predict the user about the consequence. This may be used to make predictions about the future, analyze the present, and analyze how a decision will play out in the future. These features may have an effect on a number of different sectors including commerce. The use of dynamic modelling may be beneficial for improving efficiency in the management of supply chains, transportation, and crowds.
Shirley C P, Brijit Benny, Vidhya K, and Berin Jeba Jingle I
IEEE
Agriculture is the activity of cultivating natural resources that humans utilize for survival and economic benefit. It has evolved into one of the most significant industries in the day-to-day existence of all living organisms, providing access to food, a means of subsistence, etc. In reality, a country's agricultural output may have a direct or indirect impact on its food security and overall health. Despite the widespread adoption of these agricultural methods, various obstacles, including soil fertility and climate, can negatively impact the development and output of agricultural goods. As every living thing on Earth depends on agriculture for its existence, there is a constant need to find ways to overcome the problems faced. It is a well-acknowledged reality that contemporary issues demand modern solutions. Thus, it is critical to use developing technology to tackle the challenges that exist. The quality of life has been greatly enhanced by modern technologies. This concept has led to the introduction of several contemporary inventions and technologies that enhance the standard of life and advance the welfare of every individual. The demand for new technologies has been growing over time, and one promising technology with the potential to improve living standards and address long-standing issues in the agricultural sector is digital twin technology. It has made it possible to reduce labor and energy waste, among other things. Examining how digital twins might improve agricultural productivity and promote sustainability is part of the process of understanding digital twins and their place in the agricultural industry. The prerequisites and implementation strategies for encouraging the use of digital twins are covered. Since this is a new technology, a careful analysis of its benefits and limitations is necessary to properly address identified issues.
Sujatha Gaddam, T. R. Vijaya Lakshmi, C. P. Shirley, G Ravivarman, Rajendiran M, and Ramya Maranan
IEEE
The present research applies advanced automated segmentation techniques to MRI data, addressing the urgent need for improved colorectal cancer therapy. An extensive dataset of 2032 images collected from multiple online sources was used to thoroughly train and assess Naive Bayes (NB), Support Vector Machines (SVMs), and Convolutional Neural Networks (CNNs). CNN predicts the response with an outstanding accuracy of 98.76% and capacity to recognize intricate patterns connected to colorectal cancer histology. The SVM and NB models demonstrated good performances with a minimal latency, with accuracy of 94.5% and 89.9%, respectively, suggesting their suitability in specific medical imaging scenarios. After a thorough examination that took into account the Hausdorff distance, memory, accuracy, and F1 score, it was evident what the benefits and drawbacks of each model were. The results show a notable advancement in automated segmentation methods, which may result in more individualized treatment regimens, accurate diagnosis, and medications for colorectal cancer. The findings not only signify a significant progression in the domain of artificial intelligence in medical imaging, but they also offer prospects for additional investigation concerning the amalgamation of multi-modal imaging data and continuous model optimization. In result, our work bridges the knowledge gap between cutting-edge technologies and practical implementation by offering insightful information that can benefit both the larger medical community and patients with colorectal cancer.
Shirley C P and Absin S J
IEEE
Facial expression recognition is becoming a core part of human-computer interaction and emotion detecting systems. However, the real-time scenarios such as the widespread use of face masks presents a big challenge to traditional facial recognition systems. This work proposes a new approach by integrating machine learning concepts, particularly in neural networks and feature fusion. Our main goal is to develop an accurate facial recognition system that can recognize expressions even when the face is partially visible. To achieve this process, we will take a diverse dataset containing images of faces with variable occlusion. Using Data pre-processing techniques, it standardized the training and evaluation procedure. The obtained results conclude the model's ability to accurately recognize facial expressions even in the presence of occluded faces. To achieve this objective, this research work has proposed a Squeeze and Excitation Network (SE-Net) to enhance the relationship in convolutional feature maps. The proposed feature fusion technique combines the local features and global features. Moreover, it creates opportunities for enabling more consideration into scientific research and the synthesis of data from various inputs by eventually validating more complex situations.
Shirley C P and Sujith P
IEEE
In recent years, piracy has become a common practice in the film and gaming industry. Previously, films were pirated, copied and distributed using compact disks (CDs), but now pirated content is shared online. In the gaming industry, competitors and fans often copy textures, characters, maps, and other content from each other. The proposed method aims to address the issue of piracy that occurs within the industry by proposing a security framework that uses cryptography for encryption and digital watermarking techniques for identification. Content creators usually have to share their work with other developers or creators in a work environment, which makes the content vulnerable to piracy. The aim of the proposed work is to put the created content into this security framework, encrypt it, and embed an invisible watermark specific to that user when sharing it with others. If any other creator needs that content, they can request it through the license server, and it will be decrypted using the keys from the server. The proposed method ensures that leaked content will have the watermark, which can help identify the person who leaked it, be it a city map in a game or a scene from a movie. This article will explain the methodologies used in this approach in detail.
D. Mahammad Rafi, T. R. Vijaya Lakshmi, C. P. Shirley, G Ravivarman, G. Senthilkumar, and Natrayan L
IEEE
This work demonstrates the effectiveness of convolutional neural networks (CNNs) and proposes a fresh approach to prostate cancer diagnosis through the application of machine learning models. The research makes use of a dataset of 2498 prostate pictures, of which 40% show benign diseases and 60% show cancerous ones. Many machine learning models are trained using comprehensive feature extraction, with an emphasis on texture- and intensity-based variables. With accuracy of 95.8%, precision, recall, and F1 scores of 96.2%, 95.5%, and 95.8%, respectively, CNN performs remarkably well. K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), and Support Vector Machine (SVM) all show great accuracy with scores of 92.2%, 88.7%, and 86.5%, respectively. A thorough examination can result in more advantages and suitability of various models for prostate cancer screening. CNN is a suitable option for workflow integration in the healthcare industry because of its improved performance metrics and interpretability. With its ability to make better decisions, the accuracy of diagnoses and patient outcomes are improved and also these machine learning models have the potential to fundamentally alter the way prostate cancer is treated. This research shows how computational techniques could revolutionize the way prostate cancer is now identified and open up the opportunity to more accurate and individualized treatment plans.
M. Maheswari, Mohamed Uvaze Ahamed Ayoobkhan, C. P. Shirley, and T. R. Vijaya Lakshmi
Springer Science and Business Media LLC
Gracious S and Shirley C P
IEEE
Heart disease is still the world’s top cause of death, and becoming older is a major risk factor. Age-related risk for heart disease can be decreased by using early detection and prevention strategies. This research study presents a novel machine learning-based method for estimating an individual’s risk of heart disease based on age-related variables and their medical history. This study analyzes the efficacy of ensemble learning methods, namely XGBoost and Random Forest models, in predicting the risk of heart disease by utilizing a tabular dataset that includes data on individuals' health records from various age groups. By means of thorough experimentation and model validation, the proposed model classifies heart disease with 93.26% accuracy. This research adds to the advancement of predictive modeling for age-related heart disease and holds promise for personalized healthcare interventions aimed at improving patient outcomes and lowering healthcare costs. The research findings not only highlight the potential of machine learning in healthcare but also highlight the significance of early intervention strategies tailored to age-related factors.
S. Alvin Jesuraj, S. V. Evangelin Sonia, and C. P. Shirley
IEEE
This research investigates the application of DenseNet-201, a deep convolutional neural network architecture, in the RSNA-MICCAI Brain Tumor Radiogenomic Classification aimed at predicting the genetic subtype of glioblastoma using MRI imaging data. This study demonstrates the effectiveness of DenseNet-201 in accurately classifying glioblastoma cases based on MGMT promoter methylation status, a critical biomarker influencing treatment outcomes. Through comprehensive experimental evaluations, including training, validation, and testing phases, DenseNet-201 exhibits robust performance metrics such as high accuracy, precision, recall, F1-score, and AUC-ROC values. These results highlight the model's ability to effectively distinguish between MGMT promoter methylation-positive and negative glioblastoma cases, offering valuable support for clinical decision-making in treatment planning and prognosis assessment. Leveraging deep learning techniques and MRI imaging data, DenseNet-201 holds promise as a powerful tool for enhancing the understanding of glioblastoma genetics and guiding personalized therapeutic interventions, ultimately contributing to improved patient outcomes in brain cancer management.
J. Immanuel Johnraja, P. Getzi Jeba Leelipushpam, C. P. Shirley, and P. Joyce Beryl Princess
Springer Nature Switzerland
C. P. Shirley, Jagannath Pattar, P. Kavitha Rani, Sumit Saini, Jarabala Ranga, D. Elangovan, and Ch. Venkatakrishna Reddy
Informa UK Limited
Boojhana Elango, C.P. Shirley, Gunadhor Singh Okram, Thiyagarajan Ramesh, Kamala-Kannan Seralathan, and Maghimaa Mathanmohun
Elsevier BV
Shirley C P, Kantilal Rane, Kolli Himantha Rao, Bradley Bright B, Prashant Agrawal, and Neelam Rawat
Anapub Publications
Navigating through an environment can be challenging for visually impaired individuals, especially when they are outdoors or in unfamiliar surroundings. In this research, we propose a multi-robot system equipped with sensors and machine learning algorithms to assist the visually impaired in navigating their surroundings with greater ease and independence. The robot is equipped with sensors, including Lidar, proximity sensors, and a Bluetooth transmitter and receiver, which enable it to sense the environment and deliver information to the user. The presence of obstacles can be detected by the robot, and the user is notified through a Bluetooth interface to their headset. The robot's machine learning algorithm is generated using Python code and is capable of processing the data collected by the sensors to make decisions about how to inform the user about their surroundings. A microcontroller is used to collect data from the sensors, and a Raspberry Pi is used to communicate the information to the system. The visually impaired user can receive instructions about their environment through a speaker, which enables them to navigate their surroundings with greater confidence and independence. Our research shows that a multi-robot system equipped with sensors and machine learning algorithms can assist visually impaired individuals in navigating their environment. The system delivers the user with real-time information about their surroundings, enabling them to make informed decisions about their movements. Additionally, the system can replace the need for a human assistant, providing greater independence and privacy for the visually impaired individual. The system can be improved further by incorporating additional sensors and refining the machine learning algorithms to enhance its functionality and usability. This technology has the possible to greatly advance the value of life for visually impaired individuals by increasing their independence and mobility. It has important implications for the design of future assistive technologies and robotics.
Malaika Samuel and C.P. Shirley
IEEE
The prevalence of mental health problems is increasing globally especially among the youth, leading to a growing need for accessible mental health support. Social issues such as joblessness, family breakdown, destitution, drug addiction, and associated criminal activity are often linked to suboptimal mental health. Impaired immune function is strongly influenced by mental well-being, with individuals suffering from depression experiencing more adverse consequences than those who are not. Mobile health technologies, such as mobile applications, offer a potential solution to this problem. In this research study, the researchers have proposed an Android-based mental well-being support mobile application, Mindset that provides users with various features to improve their mental and emotional well-being. The app provides users with various tools and resources, such as mood tracking, guidance, and educational articles on mental health topics, to help them manage stress, anxiety, and other common mental health concerns. Additionally, Mindset includes features such as journaling, goal-setting, personalized music based on mood, breathing exercises, and reminders to promote positive habits and behaviors. The unique feature of this application is that it provides a medium for its user to communicate with other users via a communication channel, thereby supporting each other, while being anonymous.
Shirley C. P and Sharon Natasha Francis
IEEE
Diseases such as diabetes, high blood pressure, high cholesterol, etc. have grown significantly in importance in recent years. In order to preserve optimum health, the blood pressure measurements, medications, and diet of the patients diagnosed with these diseases must be effectively monitored and controlled. However, frequent monitoring requires a person to go to a healthcare center, which is not feasible due to various real-time interfaces. To address this issue, a mobile application that monitors the factors that influence a patient's blood work, and assists patients in making decisions about their diet, treatment, and medication adjustments based on the data gathered is presented. It also gives alerts and suggestions based on a graph-generated report. In addition, the app features a communication channel that connects the patient and doctor, facilitating easy and efficient monitoring and management of diseases.
C. P. Shirley, S.V. Evangelin Sonia, V. Sathya, N. Manikandan, M.K. Vidhyalakshmi, and Ch. Venkata Krishna Reddy
IEEE
The use of Electrical Vehicles (EVs) is increasing rapidly nowadays. This results in the efficient utilization of electrical energy. This leads to making the grid more economically reliable. Thus the smart charging of electric vehicles is done through blockchain technology with deep learning to meet the demand side management. This helps in the determination of the charging system with higher accuracy the systems. This indudes real-time implementation with higher precision control. This helps to obtain the exact optimal solution for dynamic functions. The deep earning helps to obtain real-time implementation in the system. This also helps to obtain the optimum charging cost consumed at a specific period. The charging station energy management system is implemented based on blockchain technology. This helps to meet the demand side management in the system. They are accompanied by centralized and decentralized energy models in the architecture. This also helps in the reduction of peer-to-peer transactions proceeding from the electricity grid to electric vehicles.
C. P. Shirley, N. R. Ram Mohan, and B. Chitra
Springer Science and Business Media LLC