492
20
16488
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Articles
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Faculty Publications -Articles
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Leveraging digital yarn dyeing for colour consistency in apparel weaving
Description
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When compared to traditional processes, digital yarn dyeing provides substantial benefits in terms of color control, versatility, and environmental impact. However, technological obstacles and constraints exist. The promise of digital dyeing may be realized by carefully selecting technology, optimizing ink consumption, and adopting stringent quality control methods, resulting in improved colour constancy and a more sustainable textile sector.
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Punniyamoorthy R.; Sugashwar Prashanth R.S.; Girish Babu M.; Arun Krishnan P.
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Asian Textile Journal, Vol-33, No. 45908, pp. 49-53.
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G P S Kwatra
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2024-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207519199&partnerID=40&md5=6e509b9b7e1b28ec239c313681de16b8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207519199&partnerID=40&md5=6e509b9b7e1b28ec239c313681de16b8</a>
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ISSN: 9713425
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Online
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English
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Article
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Punniyamoorthy R., School of Business and Management, CHRIST (Deemed To Be University), Central Campus, Bangalore, India; Sugashwar Prashanth R.S., School of Management Studies, Tamilnadu Open University, Chennai, India; Girish Babu M., School of Business and Management, CHRIST (Deemed To Be University), Central Campus, Bangalore, India; Arun Krishnan P., School of Business and Management, CHRIST (Deemed To Be University), Central Campus, Bangalore, India
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Title
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Book Chapter
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Title
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Leveraging Employee Data to Optimize Overall Performance: Using Workforce Analytics
Description
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Consistent employee performance is necessary for timely achievement and business success. Many key performance indicators influence an employees organizational performance, such as employee satisfaction, employee work environment, relationship with managers and coworkers, work-life balance, and many more. It becomes critical to regularly understand how these factors are connected to employee performance. One such method that is commonly used in companies is workforce analytics. It is a process that uses data-based intelligence for improving and enhancing management decisions in hiring and constructing compensations in alignment with employee performance. This also helps the management make data-based decisions and predictions, which helps in cost reductions and increases the overall profit. This chapter aims to analyze and report the workforce-related data and visualize the performance of 1,470 employees using published IBM human resources (HR) data made available at https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781003357070/bb25a486-c036-4524-ab00-446f8eda3fd1/content/www.Kaggle.com xmlns:xlink=https://www.w3.org/1999/xlink>Kaggle.com. The chapter considers the following factors - job involvement, job satisfaction, performance rating, relationship satisfaction, environmental satisfaction, employee tenure, work-life balance, and income level - for data analysis and visualization of employee performance. The chapter aims to adopt descriptive, diagnostic, and predictive analysis using various software like Python, the Konstanz Information Miner (KNIME), and Orange. The visualization will be made using Tableau, Power BI, and Google Data Studio. Thus, the chapter gives a comprehensive insight into the meaning and importance of workforce analytics, different technologies used in workforce analytics, workforce analytics trends and tools, challenges of workforce analytics, and the process of implementation of workforce analytics. 2024 selection and editorial matter, Alex Khang, Sita Rani, Rashmi Gujrati, Hayri Uygun, and Shashi Kant Gupta; individual chapters, the contributors.
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Saravanakrishnan V.; George G.; Joy A.K.; Anusha B.
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Designing Workforce Management Systems for Industry 4.0: Data-Centric and AI-Enabled Approaches, pp. 235-249.
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CRC Press
Date
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2023-01-01
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<a href="https://doi.org/10.1201/9781003357070-15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003357070-15</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173820973&doi=10.1201%2F9781003357070-15&partnerID=40&md5=9381eb82af35e8d2dbdedaff930b6508" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173820973&doi=10.1201%2f9781003357070-15&partnerID=40&md5=9381eb82af35e8d2dbdedaff930b6508</a>
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ISBN: 978-100092334-6; 978-103240824-8
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Online
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English
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Book chapter
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Saravanakrishnan V., Department of Commerce, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; George G., Department of Commerce, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Joy A.K., Department of Commerce, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Anusha B., Department of Commerce (PG), Krupanidhi Degree College, Karnataka, Bengaluru, India
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Title
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Articles
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Faculty Publications -Articles
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Title
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Leveraging ensemble learning for enhanced security in credit card transaction fraudulent within smart cities for cybersecurity challenges
Subject
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Cyber security; Ensemble learning algorithms; Fraud; Fraud detection; Machine learning; Smart cities
Description
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In the age of digital transactions, credit cards have emerged as a prevalent form of payment in smart cities. However, the surge in online transactions has heightened the challenge of accurately discerning legitimate from fraudulent activities. This paper addresses this crucial concern by introducing a pioneering system for detecting fraudulent credit card transactions, particularly within highly imbalanced datasets, in the realm of cybersecurity. This paper proposes a hybrid model to effectively manage imbalanced data and enhance the detection of fraudulent transactions. This paper emphasizes the efficacy of the hybrid approach in proficiently identifying and mitigating fraudulent activities within highly imbalanced datasets, thereby contributing to the reduction of financial losses for both merchants and customers in smart cities. As cybersecurity in smart cities evolves, this paper underscores the significance of ensemble learning and cross-validation techniques in optimizing credit card transaction analysis and fortifying the security of digital payment systems. 2024, Taru Publications. All rights reserved.
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Padhi B.K.; Chakravarty S.; Naik B.; Nayak S.R.; Poonia R.C.
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Journal of Discrete Mathematical Sciences and Cryptography, Vol-27, No. 4, pp. 1233-1246.
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Taru Publications
Date
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2024-01-01
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<a href="https://doi.org/10.47974/JDMSC-1978" target="_blank" rel="noreferrer noopener">https://doi.org/10.47974/JDMSC-1978</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197740854&doi=10.47974%2FJDMSC-1978&partnerID=40&md5=29845c968f21895411f72513c9cbd593" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197740854&doi=10.47974%2fJDMSC-1978&partnerID=40&md5=29845c968f21895411f72513c9cbd593</a>
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ISSN: 9720529
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Online
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English
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Article
Coverage
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Padhi B.K., Department of Computer Science & Engineering, Centurion University of Technology & Management, Odisha, Bhubaneswar, 761211, India; Chakravarty S., Department of Computer Science & Engineering, Centurion University of Technology & Management, Odisha, Bhubaneswar, 761211, India; Naik B., Department of Computer Application, Veer Surendra Sai University of Technology, Odisha, Burla, 768018, India; Nayak S.R., School of Computer Engineering, KIIT Deemed to be University, Odisha, Bhubaneswar, 751024, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University) Delhi-NCR, Uttar Pradesh, Ghaziabad, 201003, India
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Title
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Conference Papers
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Faculty Publications- Conference Papers
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Title
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Leveraging Ensemble Methods for Accurate Prediction of Customer Spending Scores in Retail
Subject
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Academic Achievement; Machine Learning Models; Smart Phone; Student Health
Description
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This study primarily aims to estimate consumer spending trends in a retail context. The goal is to identify the best model for predicting Purchasing Scores, which indicate customer loyalty and potential income, using demographic and financial data. The dataset included information about customers' age, gender, and annual income, and the objective was to analyze their Spending Scores. Several regression models were tested, including Linear Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors (KNN), and Lasso Regression. To improve the models, we engineered features like Age Squared, Income per Age, and Spending Score per Income. Each model was trained and tested using 3fold cross-validation. We evaluated their performance with Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. The results showed significant differences in model performance. The Random Forest model stood out, with the lowest Mean Absolute Error (MAE) of 0.33, Root Mean Square Error (RMSE) of 0.52, and the highest R-squared (R22) score of 0.9997. Gradient Boosting also performed well, achieving a Mean Absolute Error (MAE) of 1.77, Root Mean Square Error (RMSE) of 2.41, and an Rsquared (R2) score of 0.9930. While Linear Regression showed moderate accuracy, KNN and Lasso Regression had higher errors and lower R2 values, indicating less reliable predictions. The findings suggest that ensemble methods, particularly Random Forest, excel at predicting customer Spending Scores. The high accuracy and reliability of this model point to its potential for customer segmentation and targeted marketing strategies, ultimately enhancing customer relationship management and boosting business value. Further refinement and exploration of additional features could further improve these prediction capabilities. 2024 IEEE.
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Prathiba L.; Raja S.; Umadevi A.; Sucharitha M.M.; Basha M.S.A.
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2024 3rd International Conference for Advancement in Technology, ICONAT 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/ICONAT61936.2024.10774867" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONAT61936.2024.10774867</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214993398&doi=10.1109%2FICONAT61936.2024.10774867&partnerID=40&md5=8fc29749a1c9a3535fc02c57f8a18e8c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214993398&doi=10.1109%2fICONAT61936.2024.10774867&partnerID=40&md5=8fc29749a1c9a3535fc02c57f8a18e8c</a>
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ISBN: 979-835035417-1
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Online
Language
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English
Type
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Conference paper
Coverage
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Prathiba L., Master of Business Administration Ashoka Women's Engineering College, Andhra Pradesh, Kurnool, India; Raja S., R&D Institute of Science and Technology Avadi, Vel Tech Rangarajan Dr. Sagunthala, Chennai, India; Umadevi A., Alpha College of Engineering, Poonamallee, Department of Management Studies, Chennai, India; Sucharitha M.M., Md Shaik Amzad Basha Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., GITAM (Deemed to Be University), GITAM School of Business, Bengaluru, India
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Title
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Book Chapter
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Faculty Publications- Book Chapter
Dublin Core
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Title
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Leveraging Financial Data to Optimize Automation: An Industry 4.0 approach
Description
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Industry 4.0 is a transformative approach that leverages advanced technologies to enhance business efficiency and productivity. Automation is a crucial aspect of next-generation industry, and leveraging financial data is essential to optimizing the automation process. This chapter discusses the role of financial data in optimizing automation processes using an I-4.0 approach. Financial data is derived from various sources and can be collected through different methods, such as automated data collection, manual entry, or using sensors and Internet of Things (IoT) devices. The integration of these sources can pose challenges for businesses. The chapter outlines techniques for automation optimization, such as machine learning, predictive analytics, and business process reengineering. Optimizing automation using financial data offers various benefits for businesses, including cost savings, improved quality, and increased profitability. However, there are challenges that businesses face in leveraging financial data, including the integration of various data sources and formats and the need for skilled personnel to analyze and interpret the data. The successful implementation of automation and optimization of processes can lead to sustainable growth and enhanced operations, making it crucial for businesses to remain competitive in the I-4.0 era. By leveraging financial data to optimize automation processes, businesses can maximize their potential and drive growth. Overall, this chapter highlights the significance of financial data in automation optimization and provides insights into the benefits and challenges that businesses must consider when leveraging financial data for optimization. 2024 selection and editorial matter, Nidhi Sindhwani, Rohit Anand, A. Shaji George and Digvijay Pandey; individual chapters, the contributors.
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Mehta K.; Kulshrestha N.; Sisodia D.R.; Dornadula V.H.R.; Priya P.K.; Ranjan R.
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Robotics and Automation in Industry 4.0: Smart Industries and Intelligent Technologies, Vol-45689, pp. 198-214.
Publisher
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CRC Press
Date
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2024-01-01
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<a href="https://doi.org/10.1201/9781003317456-10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003317456-10</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191789502&doi=10.1201%2F9781003317456-10&partnerID=40&md5=5036da12a3f27d345d72ef79674c7047" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191789502&doi=10.1201%2f9781003317456-10&partnerID=40&md5=5036da12a3f27d345d72ef79674c7047</a>
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ISBN: 978-100383735-0; 978-103232943-7
Format
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Online
Language
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English
Type
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Book chapter
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Mehta K., TAPMI School of Business Manipal University, Jaipur, India; Kulshrestha N., Christ Deemed to be University, Bangalore, India; Sisodia D.R., Department of Management Army Institute of Management and Technology, Greater Noida, India; Dornadula V.H.R., Startups and IIC, Chairman Office, Sree Venkateswara College of Engineering, Nellore, India; Priya P.K., KL Business School, Koneru Lakshmaiah Education Foundation, Guntur, India; Ranjan R., School of Business and Commerce, Manipal University, Jaipur, India
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Title
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Book Chapter
Book Chapter
Faculty Publications- Book Chapter
Dublin Core
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Title
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Leveraging FinTech for the Advancement of Circular Economy
Subject
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challenges related to FinTech; circular economy; FinTech; FinTech in circular economy; Sustainable development
Description
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During the past six decades, there has been a lot of emphasis on increasing production and fulfilling the demands of the fast-growing population. As a result, there has been unprecedented utilization and depletion of natural resources and harm to the environment. It was rightly realized by government and policymakers that there is an indispensable need to align economic development with the environment. In other words, the world needs to pursue environmentally friendly economic development. In order to achieve sustainable development, the thought leaders devised a new approach called circular economy. The circular economy focuses on reusing and recycling materials to reduce the consumption of natural resources and minimize waste creation. In recent years, financial technology commonly known as FinTech has become a significant part of commercial activities across many industries. FinTech has benefited organizations and users in terms of cost and time saving with a high degree of reliability. This article outlines the ways in which FinTech supports the cause of a circular economy. It also explores the impediments in this path. 2024 Scrivener Publishing LLC.
Creator
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Agarwa R.; Fatima M.; Malhotra A.
Source
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Fintech for Esg and the Circular Economy, pp. 49-62.
Publisher
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wiley
Date
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2024-01-01
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<a href="https://doi.org/10.1002/9781394234035.ch4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394234035.ch4</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214636296&doi=10.1002%2F9781394234035.ch4&partnerID=40&md5=7287674fcc50295ecdaf1739cf9bb4d4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214636296&doi=10.1002%2f9781394234035.ch4&partnerID=40&md5=7287674fcc50295ecdaf1739cf9bb4d4</a>
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ISBN: 978-139423403-5; 978-139423396-0
Format
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Online
Language
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English
Type
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Book chapter
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Agarwa R., School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Uttar Pradesh, Ghaziabad, India; Fatima M., School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Uttar Pradesh, Ghaziabad, India; Malhotra A., School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Uttar Pradesh, Ghaziabad, India
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
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Kandpal, Saumya Raman; Giri, Pratibha
Title
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Leveraging gamification in the metaverse: Strategies for consumer engagement, innovation, and problem-solving across fashion industries
Date
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01-01-2025
Source
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Navigating AI and the Metaverse in Scientific Research;pp.449-473
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<a href="https://doi.org/10.4018/979-8-3373-0340-6.ch022" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-0340-6.ch022</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105005388420?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005388420?origin=resultslist</a>
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Kandpal S.R., Christ University, India; Giri P., Christ University, India
Description
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As the world adapted during the pandemic, virtual platforms became groundbreaking in terms of popularity, which brought forth the Metaverse, a transformative digital universe. This development is blurring the lines between gaming and the consumer internet and providing immersive, emotional, and socialized experiences. The variables driving deeper connections are technology readiness, user experience, and social influences. This study explores the effective use of animated agents in VR as an advertising strategy, linking findings to existing research. Much attention is given to gamification and VR, but little focus exists on how these experiences resonate with India's unique cultural context. The integration of Indian traditions into the Metaverse can revolutionize brand engagement, reshaping consumer perceptions and interactions. The paper discusses consumer engagement, readiness, and problem- solving, and it is based on the potential of culturally aligned VR experiences to transform industries, enhance connections, and create new avenues for brand immersion in the digital era. 2025, IGI Global Scientific Publishing. All rights reserved.
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IGI Global
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ISBN: 979-833730342-0; 979-833730340-6;
Language
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English
Type
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Book chapter
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
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Grover, Diya; Sangal, Surabhi; Shabir, Suheela Shabir
Title
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Leveraging Generative AI to Address Behavioral Biases in the Investment Decisions of Gen Z and Millennials
Date
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01-01-2025
Source
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Generative AI Insights for Financial Decision Making;pp.361-385
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<a href="https://doi.org/10.4018/979-8-3373-1250-7.ch014" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-1250-7.ch014</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015502412?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015502412?origin=resultslist</a>
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Grover D., Christ University, India; Sangal S., Christ University, India; Shabir S.S., Princess Noura Bint Abdulrahman University, Riyadh, Saudi Arabia
Description
An account of the resource
GAI alters financial decision-making behavior through advanced data analytics, trend prediction, and personal recommendation. Generation Z and the Millennials are more susceptible to behavioral biases like herd mentality, loss aversion, overconfidence, and fear. Such tendencies make people prone to frequently exhibiting instinctive or irrational investment behaviors, thus severely impacting their long-term financial outcome. In the context of this book, the relationship between behavioral finance and GAI is discussed with the benefits of enhancing investment literacy and in guiding the younger investor towards data-driven decisions. Other areas on data quality and transparency, ethical concerns, and regulatory compliance are discussed. Hence, this can result in intelligent and rational investment decisions. The subsequent section explains how GAI successfully eliminates the effects of cognitive biases through an enhancement of the capabilities concerning decision-making in respect of financial choices related to Generation Z and millennials under the everchanging finance landscape. 2026, IGI Global Scientific Publishing. All rights reserved.
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IGI Global
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ISBN: 979-833731252-1; 979-833731250-7;
Language
A language of the resource
English
Type
The nature or genre of the resource
Book chapter
Rights
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Restricted Access; Hardcopy may be available in the library
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Faculty Publications
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Dixit, Pragti; Bhatnagar, Mukul; Kaur, Rajinder; Varghese, Nikhil; Jain, Vibhuti
Title
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Leveraging Green Finance for Sustainable Development: An Empirical Analysis of Economic Growth and Environmental Sustainability of Asian OECD Economies
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01-01-2025
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Utilizing Green Finance for Sustainable Development;pp.217-251
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<a href="https://doi.org/10.4018/979-8-3373-1240-8.ch008" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-1240-8.ch008</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039971796?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039971796?origin=resultslist</a>
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Dixit P., Chandigarh University, India; Bhatnagar M., Graphic Era University, India; Kaur R., Chandigarh University, India; Varghese N., Christ University, India; Jain V., Graphic Era University, India
Description
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Present study investigates the impact of Green Finance and CO2 emissions on GDP per capita of four Asian OECDeconomies controlling for Expenditure on Education, and Foreign Direct Investment using panel data for the time-period 2015 to 2023, applying pooled OLS, Fixed Effects, and Random Effects Models, and ultimately selecting the Fixed Effects Model based on robust statistical tests (Hausman and Breusch-Pagan LM), revealing that Green Finance significantly enhances GDP per capita, Expenditure on Education unexpectedly hinders it in the short term, and both CO2 emissions and Foreign Direct Investment lack statistically significant effects within countries, thereby underscoring the importance of internal structural factors and advocating for tailored, sustainability-driven, and context-sensitive economic growth strategies. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development.
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IGI Global Scientific Publishing
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ISBN: 979-833731242-2; 979-833731240-8; 979-833731241-5;
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English
Type
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Book chapter
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online
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Articles
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Leveraging history to invoke nationalism: from the annals of history to social engineering of present and future in Hindi cinema
Subject
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critical discourse analysis; Hindi cinema; Hindu nationalism; Nationalism; populism; propaganda
Description
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Nationalism calls for ones loyalty and affiliation towards their chosen nation. Various versions of nationalism emphasise that one must prioritise said nation above themselves and their personal ethics, hence, allowing the nation to overpower the nationalists individuality. In this article, we use Critical Discourse Analysis to deconstruct the narratives of nationalism as portrayed in two popular films, viz. The Kashmir Files and Uri: The Surgical Strike, which are based on real historical eventsthe exodus of Kashmiri Hindus and the surgical strike by the Indian Army in retaliation to the Uri attack. Both films use narrative strategies to frame key historical events into certain ideological contexts, and hence they serve the populist purpose of swaying viewers opinion in favour of the dominant socio-political class. 2024 Informa UK Limited, trading as Taylor & Francis Group.
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Iyer J.B.; Das S.
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National Identities
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Routledge
Date
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2024-01-01
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<a href="https://doi.org/10.1080/14608944.2024.2365914" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/14608944.2024.2365914</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199370382&doi=10.1080%2F14608944.2024.2365914&partnerID=40&md5=6d0d198c5609165b6f73f1b0e7f91b68" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199370382&doi=10.1080%2f14608944.2024.2365914&partnerID=40&md5=6d0d198c5609165b6f73f1b0e7f91b68</a>
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ISSN: 14608944
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Online
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English
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Article
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Iyer J.B., Department of Media Studies, Christ University, Bangalore, India; Das S., Department of Media Studies, Christ University, Bangalore, India
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Faculty Publications
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Iyer, Jyotsna B.; Das, Saurabh
Title
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Leveraging history to invoke nationalism: from the annals of history to social engineering of present and future in Hindi cinema
Date
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01-01-2025
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National Identities;Volume;27;Issue;2026-02-01 00:00:00;pp.155-174
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<a href="https://doi.org/10.1080/14608944.2024.2365914" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/14608944.2024.2365914</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85199370382?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85199370382?origin=resultslist</a>
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Iyer J.B., Department of Media Studies, Christ University, Bangalore, India; Das S., Department of Media Studies, Christ University, Bangalore, India
Description
An account of the resource
Nationalism calls for ones loyalty and affiliation towards their chosen nation. Various versions of nationalism emphasise that one must prioritise said nation above themselves and their personal ethics, hence, allowing the nation to overpower the nationalists individuality. In this article, we use Critical Discourse Analysis to deconstruct the narratives of nationalism as portrayed in two popular films, viz. The Kashmir Files and Uri: The Surgical Strike, which are based on real historical eventsthe exodus of Kashmiri Hindus and the surgical strike by the Indian Army in retaliation to the Uri attack. Both films use narrative strategies to frame key historical events into certain ideological contexts, and hence they serve the populist purpose of swaying viewers opinion in favour of the dominant socio-political class. 2024 Informa UK Limited, trading as Taylor & Francis Group.
Subject
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critical discourse analysis; Hindi cinema; Hindu nationalism; Nationalism; populism; propaganda
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Routledge
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ISSN: 14608944;
Language
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English
Type
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Article
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Restricted Access; Hardcopy may be available in the library
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online
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Title
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Faculty Publications
Conference Paper
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George, Shiju; Joseph, Asha; Raj, E. Babu; Shiju, Angel
Title
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Leveraging Hybrid Dual-Level Contextual Attention and Spiking Neural Networks for Effective Hepatic Malignancy Diagnosis
Date
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01-01-2025
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16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;2;pp.14252-14258
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<a href="https://www.scopus.com/pages/publications/105034865412?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034865412?origin=resultslist</a>
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George S., Christ University, Karnataka, Bengaluru, India; Joseph A., New Horizon College of Engineering, Karnataka, Bengaluru, India; Raj E.B., Christ University, Karnataka, Bengaluru, India; Shiju A., Christ University, Karnataka, Bengaluru, India
Description
An account of the resource
s Abstract Liver cancer remains the leading cause of cancer-related mortality, prompt-ing advanced diagnostic techniques for early detection and accurate classification in the health sector worldwide. Multifaceted deep learning methods have shown significant potential in medical imaging, but challenges exist in capturing intricate contextual information. In our research, we propose a novel hybrid framework that integrates Dual-Level Contextual Attention (DLCA) with Spiking Neural Networks (SNNs) to enhance the diagnosis of liver cancer. The proposed framework uses a DLCA mechanism that effectively extracts both local and global contextual features within the medical images and aids in precise lesion differentiation. The SNNs module supports computational efficiency and robust pattern recognition, enabling precise identification of subtle cancerous patterns by reducing redundant activations while preserving critical diagnostic information. Experimental evaluations on publically available datasets demonstrate the effectiveness of our work, showcasing its reliability in clinical applications. Moreover, the model offers a direction for future AI-assisted diagnostic tools in medical imaging and oncology. Grenze Scientific Society, 2025.
Subject
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Dual-Level Contextual Attention; Hepatic Malignancy; Liver Cancer; Medical Data Processing; Medical Imaging; Spiking Neural Networks
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Grenze Scientific Society
Language
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
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Title
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Sneha, M.; Augustine, Peter; Beaulah Soundarabai, P.
Title
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Leveraging Java for Developing Privacy-Preserving and Cross-Platform Machine Learning Applications
Date
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01-01-2025
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2025 IEEE 7th International Conference on Computing, Communication and Automation, ICCCA 2025;
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<a href="https://doi.org/10.1109/ICCCA66364.2025.11325318" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCCA66364.2025.11325318</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035186652?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035186652?origin=resultslist</a>
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Sneha M., Christ University, Dept. Computer Science, Karanataka, Bengaluru, India; Augustine P., Christ University, Dept. Computer Science, Karanataka, Bengaluru, India; Beaulah Soundarabai P., Christ University, Dept. Computer Science, Karanataka, Bengaluru, India
Description
An account of the resource
The growing focus on data privacy and system interoperability has created a clear need for machine learning (ML) applications. These applications must be able to protect sensitive data while maintaining consistent performance in various computing environments. Java is considered as a strong choice due to its platform independence, strong static typing, and rich ecosystem of development tools and libraries. This study checks how Java supports privacy-preserving techniques such as federated learning, differential privacy, and homomorphic encryption, instilling confidence in its role for secure ML development. It also reviews Java-based libraries, including Weka, Deeplearning4j, and Apache Spark, highlighting their role in building safe, scalable, and portable ML solutions. Through architectural analysis, benchmark-based evaluation, and comparisons with other programming languages, the study demonstrates the strengths of Java to deliver secure, scalable, and interoperable privacy-sensitive machine learning applications. 2025 IEEE.
Subject
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Cross-Platform Development; Differential Privacy; Federated Learning; Homomorphic Encryption; Java; Machine Learning; Privacy Preservation
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833156980-8;
Language
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English
Type
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Conference paper
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online
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Faculty Publications
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Bose, Manoswita; Chatterjee, Moumita; Sarkar, Dhrubasish
Title
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Leveraging Machine Learning and Streamlit for Real-Time Stock Analysis and Prediction
Date
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01-01-2026
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Lecture Notes in Networks and Systems;Volume;1488 LNNS;pp.497-507
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<a href="https://doi.org/10.1007/978-981-96-8309-3_35" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8309-3_35</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028271532?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028271532?origin=resultslist</a>
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Bose M., CHRIST (Deemed to be University), Bengaluru, India; Chatterjee M., Aliah University, Kolkata, India; Sarkar D., Supreme Institute of Management and Technology, West Bengal, Hooghly, India
Description
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This paper introduces StockNavigator, an interactive web application developed using Streamlit, designed to offer a comprehensive solution for stock performance analysis, real-time stock price monitoring, and stock price prediction. Users can compare the performance of multiple stocks over a specified period, visualize data through various chart types, and gain insights into stock trends and relative returns. The proposed models user-friendly interface allows investors to make informed data-driven decisions, regardless of whether them being seasoned traders or beginners. This article demonstrates the effectiveness of using modern machine learning models like Prophet in the domain of financial forecasting and highlights the flexibility of Python-based frameworks for developing interactive, data-centric web applications. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Subject
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Analytics; Prediction; Stocks; Visualization
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Springer Science and Business Media Deutschland GmbH
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ISSN: 23673370; ISBN: 978-981968308-6;
Language
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English
Type
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Conference paper
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Format
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online
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Faculty Publications
Conference Paper
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Saranya, R.; Sandhika, G.
Title
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Leveraging Machine Learning for Epidermal Ailment Detection
Date
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01-01-2026
Source
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Lecture Notes in Networks and Systems;Volume;1497 LNNS;pp.275-288
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<a href="https://doi.org/10.1007/978-981-96-9184-5_22" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-9184-5_22</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028358148?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028358148?origin=resultslist</a>
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Saranya R., Department of Computer Science with Data Analytics, PSG College of Arts and Science, Coimbatore, India; Sandhika G., Department of Statistics and Data Science, Christ University, Bangalore, India
Description
An account of the resource
Skin disorders are common across the globe, often proving to be difficult to diagnose because of coexisting signs and symptoms. In this paper, we study the feasibility of using machine learning (ML) techniques for automatic skin disease detection. We look at the emerging patterns in fundamental studies within the scope of focus that deals with image processing for feature extraction and employing classification methods for disease detection. We focus on feature extraction and the classification of images. One of the major strengths is the ML-based approach with better access and usability and higher chances of them being detected at an early stage. In addition, we consider some of the drawbacks and problems of these methods, including biased data and lack of sufficient professional oversight. We also consider other aspects, whereby one of them is further analysis of the requirement in the case of the absence of the adequate data, standard models, and unambiguous explanations of the inner processes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Subject
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Deep learning; Diagnosis; Image processing; Machine learning; Skin ailment detection
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Springer Science and Business Media Deutschland GmbH
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ISSN: 23673370; ISBN: 978-981969183-8;
Language
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English
Type
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Conference paper
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Format
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online
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Book Chapter
Book Chapter
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Leveraging machine learning models for intelligent hazard management
Description
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[No abstract available]
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Nanjundan P.; George Jossy P.; Karpagam C.
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AI for Climate Change and Environmental Sustainability, pp. 109-120.
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CRC Press
Date
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2024-01-01
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<a href="https://doi.org/10.1201/9781003452393-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003452393-9</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200430063&doi=10.1201%2F9781003452393-9&partnerID=40&md5=5a073baf695e6c22dfbfbb2f7075cc6e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200430063&doi=10.1201%2f9781003452393-9&partnerID=40&md5=5a073baf695e6c22dfbfbb2f7075cc6e</a>
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Restricted Access
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ISBN: 978-104008886-9; 978-103258906-0
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Online
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English
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Book chapter
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Nanjundan P., Department of Data Science, Christ University, India; George Jossy P., Department of Data Analytics, Dr N.G.P. Arts and Science College, India; Karpagam C.
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Faculty Publications
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Yadav, Sangeeta; Ranka, Monica; Ojha, Shikha; Malhotra, Amit; Basha, Md Shaik Amzad
Title
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Leveraging Machine Learning to Predict Revenue-Generating Sessions in E-Commerce Platforms
Date
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01-01-2025
Source
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2025 International Conference on Information, Implementation, and Innovation in Technology, I2ITCON 2025;
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<a href="https://doi.org/10.1109/I2ITCON65200.2025.11210485" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/I2ITCON65200.2025.11210485</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026287123?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026287123?origin=resultslist</a>
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Yadav S., New Delhi Institute of Management, Ndim, New Delhi, India; Ranka M., Dayananda Sagar College of Arts, Science and Commerce, Bangalore, India; Ojha S., Ifim college, Bangalore, India; Malhotra A., School of Commerce, Finance and Accountancy, Christ (Deemed to be University), Ghaziabad, India; Basha M.S.A., Gitam School of Business, Gandhi Institute of Technology and Management (Deemed to be University), Hyderabad, India
Description
An account of the resource
Due to the rapid growth of e commerce, develops effective predictive models of online shopper behavior has become important. The goal of this study is to use dataset of online shopping sessions to predict purchase intentions based on session characteristics, user behavior and site metrics. This research aims to apply machine learning and deep learning models to predict online purchasing intentions to assist businesses to improve their strategies of maximizing conversion rates. Using a dataset having numerical and categorical features, features like page views, session duration, bounce rates etc., and the presence of some special days near the user session, we used. We evaluated nine models, including the traditional methods: Logistic Regression, Decision Tree, Naive Bayes, ensemble methods: Random Forest, Gradient Boosting, XGBoost, and more advanced ones like Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Neural Networks. Then, key metrics including Accuracy, Precision, Recall, F1 Score and ROC AUC were used to asses each model. We find that ensemble models perform best (ROC AUC = 0.9245) with Gradient Boosting performing best, with XGBoost and Random Forest close behind. With a competitive ROC AUC of 0.9000, neural networks showed strong potential, but fell slightly behind in recall compared with ensemble methods. Logistic Regression and Decision Tree were simpler models that did not achieve as strongly in predictive accuracy as more complex model; however they provided a baseline insight. Through this analysis, ensemble models and deep learning showed to be very efficient to predict online purchase intentions and provide actionable insights to optimize e-commerce platforms. 2025 IEEE.
Subject
The topic of the resource
Customer Behavior Analysis; Online Shopping Behavior; Predictive Analytics; Purchase Intention Prediction
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Relation
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ISBN: 979-833153482-0;
Language
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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Leveraging Machine Learning: Advanced Algorithms for Soil Data Analysis and Feature Extraction in Arid and Semi-arid Regions with Expert Systems
Subject
The topic of the resource
Classification algorithms and expert systems; Machine learning; Precision agriculture; Soil characteristics
Description
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India is culturally diverse nation at large. There are two words of symphony one is tradition and second one is inherited agriculture. India has long historical advantage having conventional agricultural practices and the scope for it to dive into day to day life as agriculturist. Happiness shrinks as people grow into modern world current trend of agriculture faces a monument challenge and needs immediate address to survive. Now withstanding with this phrase of human life on earth its necessary to give more importance to soil rather than the existence. Soil health is more paramount in this equation, as it directly influences crop growth and yield. Traditionally, analysing a few key soil properties has been the cornerstone of soil treatment practices. However, this approach often overlooks the complex interplay between various soil characteristics. To overcome the above hurdle present research incorporates the method of multivariate data analysis with selective advanced algorithms in machine learning to find suitability to predict best fit algorithm in real time data sets in arid and semi-arid zones of kolar district in Karnataka. The purpose is to draw the attention of stake holders to leveraging the new technology to deploying them into effective assessment in building expert system to incorporate in regular use on handy devices. This penetrates the results by two extremely good classifications algorithms Decision Tree and Gradient Boosting emerged as winner with 99% accuracy. In contrast, Passive Aggressive and Linear SVC produced below average of 36% accuracy. The ensemble algorithms of SMOTE on Random Forest and Stochastic Decent Gradient produced the acceptable accuracy of 83%. This input helped dynamically to build ready to use expert systems for farmers. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.
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Gulledmath S.; Hemanth K.S.
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SN Computer Science, Vol-5, No. 7
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Springer
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2024-01-01
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<a href="https://doi.org/10.1007/s42979-024-03257-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-024-03257-7</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204903024&doi=10.1007%2Fs42979-024-03257-7&partnerID=40&md5=24c3513c192a9ddfc8c163a08d5e2774" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204903024&doi=10.1007%2fs42979-024-03257-7&partnerID=40&md5=24c3513c192a9ddfc8c163a08d5e2774</a>
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Restricted Access
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ISSN: 2662995X
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Online
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English
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Article
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Gulledmath S., School of CSA, REVA University, Karnataka, Bengaluru, India; Hemanth K.S., Department of Computer Science, Christ University, Yashwanthpur Campus, Karnataka, Bengaluru, India
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Faculty Publications
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Patel, Dharma; Sambandam, Rakoth Kandan; Vetriveeran, Divya; Jenefa, J.; Aruna, S.K.; Vinodha, D.
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Leveraging ML Based Technique for Mobile Sales Forecasting
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01-01-2025
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Proceedings of the 2025 12th International Conference on Computing for Sustainable Global Development, INDIACom 2025;
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<a href="https://doi.org/10.23919/INDIACom66777.2025.11115351" target="_blank" rel="noreferrer noopener">https://doi.org/10.23919/INDIACom66777.2025.11115351</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016114906?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016114906?origin=resultslist</a>
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Patel D., Soet, Christ University, Dept. of Cse, Bangalore, India; Sambandam R.K., Soet, Christ University, Dept. of Cse, Bangalore, India; Vetriveeran D., Soet, Christ University, Dept. of Cse, Bangalore, India; Jenefa J., Soet, Christ University, Dept. of Cse, Bangalore, India; Aruna S.K., Soet, Christ University, Dept. of Cse, Bangalore, India; Vinodha D., Soet, Christ University, Dept. of Cse, Bangalore, India
Description
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The mobile phone industry is very competitive, so mobile sales forecasting is now imperative for businesses to forecast demand and order inventory in advance to plan strategically. This research focuses on the higher accuracy of mobile sales prediction and studies several machine learning models like Brand, Ratings, RAM, ROM, Battery- Power, pixel- height- and width, and targets alongside Camera Details as an alternate set to association rule mining. A real-time dataset that covers real-world mobile phone sales data has been collected and had its features pre-processed to fill in missing values and do the definite column encoding. Dataset were tested to understand the model performance of several predictive models, such as Decision Trees, Support Vector Machine (SVM), and ensemble methods (Random Forest and Gradient Boosting). The performance of each model was measured by accuracy, precision, recall, and F1-score. To address the issue of class in the sales categories (Low, Medium, High), stratified sampling and Synthetic Minority Over-sampling Technique (SMOTE) techniques were used. The results showed the predictive solid abilities of all the models in forecasting sales for different segments, with ensemble models performing better than individual classifiers in terms of prediction accuracy and robustness. This approach was further strengthened by applying hyperparameter tuning and cross-validation to improve the model's performance. The results are predicted to drive mobile retailers in the direction of improving demand forecasting and making data-driven decisions towards operational efficiency. 2025 Bharati Vidyapeeth, New Delhi.
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Artificial Intelligence; Decision Tree; Forecasting; Machine Learning; Mobile
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 978-938054460-1;
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English
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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online
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The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Leveraging Model Distillation as a Defense Against Adversarial Attacks Based on Deep Learning
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Adversarial Attacks; Deep Learning; Defensive Approach; Machine Learning; Model Distillation
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Adversarial attacks on deep learning models threaten machine learning system security and reliability. The above attacks use modest data alterations to produce erroneous model results while being undetected by humans. This work suggests model distillation to prevent adversarial perturbations. The student model is taught to emulate the teacher model in model distillation. This is done using teacher model soft outputs. Our idea is that this strategy organically strengthens the student model against adversarial assaults by keeping the teacher model's essential knowledge and generalization capabilities while reducing weaknesses. Distilled models are more resilient to adversarial assaults than non-distilled models, according to experiments. These models also perform similarly on undamaged, uncorrupted data. The results show that model distillation may be a powerful defense against machine learning adversaries. This method protects model resilience and performance. 2023 IEEE.
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Suresh K.; Radha J.; Thilagaraj T.; Subramani R.; Lakineni P.K.; Taqui S.N.
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2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023, pp. 921-925.
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Institute of Electrical and Electronics Engineers Inc.
Date
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2023-01-01
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<a href="https://doi.org/10.1109/ICCSAI59793.2023.10421670" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCSAI59793.2023.10421670</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186657477&doi=10.1109%2FICCSAI59793.2023.10421670&partnerID=40&md5=3351519ea3a7a01e55974e76f192607d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186657477&doi=10.1109%2fICCSAI59793.2023.10421670&partnerID=40&md5=3351519ea3a7a01e55974e76f192607d</a>
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ISBN: 979-835036996-0
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Online
Language
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English
Type
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Conference paper
Coverage
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Suresh K., Christ (Deemed University), Department of Computer Science, Bangalore, India; Radha J., Nandha Engineering College, Department of Computer Science and Engineering, Tamilnadu, Perundurai, India; Thilagaraj T., Moodlakatte Institute of Technology, Department of Computer Science and Engineering, Moodalakatte, Karnataka, Kundapura, India; Subramani R., Christ (Deemed to Be University), Department of Mathematics, Bangalore, India; Lakineni P.K., Gitam School of Technology, Gitam University, Department of Computer Science and Engineering, Visakhapatnam, India; Taqui S.N., Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Department of Vlsi Microelectronics, Tamil Nadu, Chennai, India