607
20
16481
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Articles
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Faculty Publications -Articles
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Predicting the financial behavior of Indian salaried-class individuals
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behavioral finance; Bengaluru; conservatism; financial decision-making; financial selfefficacy; herding; India
Description
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COVID-19 has caused not only unprecedented health crises but also economic crises among individuals across the world. White-collar (salaried-class) employees with a fixed salary face financial insecurity due to job loss, pay cuts and uncertainty in retaining a job. This study examines the financial behavior of Indian white-collar salariedclass investors to their cognitive biases. In addition, the mediating effect of financial self-efficacy on cognitive biases and financial behavior is examined. Respondents were given structured questionnaires (google forms) through emails and WhatsApp for data collection. SPSS and R-PLS are used to analyze the data. Conservatism (r = -.603, p < 0.05) and herding bias (r = -.703, p < 0.05) have a significant negative correlation with financial behavior. Financial self-efficacy has a significant positive correlation (r =.621. p < 0.050). Conservatism and herding predicted 60.5% and 62.2% of the variance, respectively. The direct and indirect paths between conservatism bias, financial self-efficacy, and financial behavior are significant. The paths between herding, financial self-efficacy and financial behavior are also significant. Ankita Mulasi, Jain Mathew, Kavitha Desai, 2022.
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Mulasi A.; Mathew J.; Desai K.
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Investment Management and Financial Innovations, Vol-20, No. 1, pp. 26-37.
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LLC CPC Business Perspectives
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2023-01-01
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<a href="https://doi.org/10.21511/imfi.20(1).2023.03" target="_blank" rel="noreferrer noopener">https://doi.org/10.21511/imfi.20(1).2023.03</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148227348&doi=10.21511%2Fimfi.20%281%29.2023.03&partnerID=40&md5=c3552dd08ae79147d12aafd938f97a63" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148227348&doi=10.21511%2fimfi.20%281%29.2023.03&partnerID=40&md5=c3552dd08ae79147d12aafd938f97a63</a>
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All Open Access; Gold Open Access
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ISSN: 18104967
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Online
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English
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Article
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Mulasi A., School of Business and Management, CHRIST (Deemed to be University), India; Mathew J., School of Business and Management, CHRIST (Deemed to be University), India; Desai K., Department of Professional Studies, CHRIST (Deemed to be University), India
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Conference Papers
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Title
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Predicting the Stock Markets Using Neural Network with Auxiliary Input
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Deep learning; Prediction; Stock market; Text classification
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Predicting the stock market has always been a challenging task and has always had a certain appeal for researchers all around the world. Stock markets are supposed to be quite random and people with experience in the market strongly agree to the fact. Thus, predicting the stock market accurately paves the way for endless money. To date, no such algorithm has been devised that could even predict the stock market with a 90% accuracy rate. The difficulty lies in the randomness of the markets, and the various complexities involved in modeling market dynamics. Nevertheless, there have been algorithms with a decent success rate and researchers around the world have been in a constant attempt to improve over them. Thus, through this paper we attempt at predicting the return of a stock over a period of 10days after a particular news was out regarding the stock using the headlines of the news and certain other features important in determining the direction of a stock. The model was implemented with a sigma score of 0.81. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Sakshi; Ghosh S.
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Lecture Notes in Networks and Systems, Vol-190, pp. 743-752.
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Springer Science and Business Media Deutschland GmbH
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2021-01-01
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<a href="https://doi.org/10.1007/978-981-16-0882-7_66" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-0882-7_66</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112245198&doi=10.1007%2F978-981-16-0882-7_66&partnerID=40&md5=a27282907642831cc1b0eadea6b85a6a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112245198&doi=10.1007%2f978-981-16-0882-7_66&partnerID=40&md5=a27282907642831cc1b0eadea6b85a6a</a>
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ISSN: 23673370; ISBN: 978-981160881-0
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Online
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English
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Conference paper
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Sakshi, Computer Science and Engineering, Siddaganga Institute of Technology, Tumkur, India; Ghosh S., Computer Science and Engineering, Christ University, Bangalore, India
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https://archives.christuniversity.in/files/original/a49ba2830ab7ee1ab996fdeb985dd84a.jpg
8180a41ff194c26c6d215b57d6168bfb
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Articles
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Faculty Publications -Articles
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Title
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Predicting the stock price index of yahoo data using elman network /
Subject
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Yahoo stock data
Description
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International Journal of Control Theory and Applications, Vol.10, Issue 10, pp.481-497, ISSN: 0974-5572.
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Chandar, S Kumar.
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<a href="https://serialsjournals.com/abstract/36038_50.pdf" target="_blank" title="Predicting the stock price index of yahoo data using elman network" rel="noreferrer noopener">https://serialsjournals.com/abstract/36038_50.pdf</a>
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International Science Press
Date
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2017-02-16
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English
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Journal Article
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Title
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Conference Papers
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Faculty Publications- Conference Papers
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Predicting the Thyroid Disease Using Machine Learning Techniques
Description
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An endocrine gland that is allocated in the front of the neck is called the thyroid, which produces thyroid hormones as its main job. Thyroid hormone may be produced insufficiently or excessively as a result of its potential malfunction. There are various thyroid types including Hyperthyroidism, Hypothyroidism, Thyroid Cancer Thyroiditis, swelling of the thyroid. A goiter is an enlarged thyroid gland. When your thyroid gland produces more thyroid hormones than your body requires, you have hyperthyroidism. When the thyroid gland in our body doesnt provide enough thyroid hormones, then our body has hypothyroidism; when you have euthyroid sick, your thyroid function tests during critical illness taken in an inpatient or intensive care setting show alterations. Hypothyroid, hyperthyroid, and euthyroid conditions are expected from these thyroid conditions. The Three similarly used machine learning algorithms are: Support Vector Machine (SVM), Logistic Regression, and Random Forest methods, were evaluated from among the various machine learning techniques to forecast and evaluate their performance in terms of accuracy. Random forest can perform both regression and classification tasks. Logistic Regression is used to calculate or predict the probability of a binary (yes/no) event occurring. SVM classifiers offers great accuracy and work well with high dimensional space. A thyroid data set from Kaggle is used for this. This study has demonstrated the use of SVM, logistic regression, and random forest as classification tools, as well as the understanding of how to forecast thyroid disease. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Krishnasamy L.; Aparnaa M.; Deepa Prabha G.; Kavya T.
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Lecture Notes in Networks and Systems, Vol-728 LNNS, pp. 49-57.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-99-3932-9_6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-3932-9_6</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176016196&doi=10.1007%2F978-981-99-3932-9_6&partnerID=40&md5=22c1bbdae346d5c0024294052d29de94" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176016196&doi=10.1007%2f978-981-99-3932-9_6&partnerID=40&md5=22c1bbdae346d5c0024294052d29de94</a>
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ISSN: 23673370; ISBN: 978-981993931-2
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Online
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English
Type
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Conference paper
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Krishnasamy L., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to be) University, Kengeri Campus, Bengaluru, 560074, India; Aparnaa M., Department of Information Technology, Kongu Engineering College, Tamil Nadu, Erode, India; Deepa Prabha G., Department of Information Technology, Kongu Engineering College, Tamil Nadu, Erode, India; Kavya T., Department of Information Technology, Kongu Engineering College, Tamil Nadu, Erode, India
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Conference Papers
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Faculty Publications- Conference Papers
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Predicting Wind Energy: Machine Learning from Daily Wind Data
Description
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This paper offers a comprehensive review of the advancements in the realm of renewable energy, specifically focusing on solid oxide fuel cells and electrolysers for green hydrogen production. The review delves into the significance of wind energy as a pivotal renewable energy source and underscores the importance of precise forecasting for efficient energy management and distribution. The integration of machine learning-based approaches, such as Support Vector Regression and Random Forest Regression, has shown promising results in enhancing the accuracy of wind energy production forecasts. Furthermore, the paper explores the broader landscape of renewable energy generation forecasting, emphasizing the rising prominence of machine learning and deep learning techniques. As the penetration of renewable energy sources into the electricity grid intensifies, the need for accurate forecasting becomes paramount. Traditional methods, while valuable, have encountered limitations, paving the way for advanced algorithms capable of deciphering intricate data relationships. The review also touches upon the inherent challenges and prospective research avenues in the domain, including addressing uncertainties in renewable energy generation, ensuring data availability, and enhancing model interpretability. The overarching goal remains the seamless integration of renewable sources into the grid, propelling us towards a greener future. The Authors, published by EDP Sciences, 2024.
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Subramani K.; Sharon Sophia J.; Habelalmateen M.I.; Singh R.; Pahade A.; Ikhar S.
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E3S Web of Conferences, Vol-540
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EDP Sciences
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2024-01-01
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<a href="https://doi.org/10.1051/e3sconf/202454003009" target="_blank" rel="noreferrer noopener">https://doi.org/10.1051/e3sconf/202454003009</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198130852&doi=10.1051%2Fe3sconf%2F202454003009&partnerID=40&md5=5b6e72985b2c1e9bac6948b40b55e6ee" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198130852&doi=10.1051%2fe3sconf%2f202454003009&partnerID=40&md5=5b6e72985b2c1e9bac6948b40b55e6ee</a>
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All Open Access; Gold Open Access
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ISSN: 25550403
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Online
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English
Type
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Conference paper
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Subramani K., School of Business and Management, CHRIST(Deemed to Be University) Bangalore Yeshwantpur Campus, India; Sharon Sophia J., School of Business and Management, CHRIST(Deemed to Be University) Bangalore Yeshwantpur Campus, India; Habelalmateen M.I., The Islamic University, Najaf, Iraq; Singh R., Department of Electronics and Communication Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India; Pahade A., Department of Computer Science and Engineering, IES College of Technology, IES University, Madhya Pradesh, Bhopal, 462044, India; Ikhar S., Yashika Journal Publications Pvt Ltd, Maharashtra, Wardha, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Predicting Work Environment and Job Environment Among Employees using Transfer Learning Approach
Subject
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Environmentally Positive Behavior(EPB); Job S atisfaction; Organizational Citizenship Behavior (OCB)
Description
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Today's enterprises face numerous challenges as a result of the world's rapid evolution. Maintaining a content workforce is crucial to a company's success and survival in today's fast-paced business environment. The efficacy, productivity, efficiency, and dedication of the company's staff are directly associated with the company's capacity to meet the needs of its employees in the workplace. The focus of this system is to identify the factors that contribute to a satisfying work environment for the participants. Preprocessing, feature selection, and model training are the first three steps in the suggested methodology. Data mining systems should get in the habit of normalizing data as a preliminary processing step. The multiple elements assessing company culture and worker satisfaction were consolidated using Principal Components Analysis (PCA) in the feature selection phase. Once features have been selected, KNN-SVM is utilized for model training. When compared to the two most popular alternatives, SVM and KNN, the proposed technique performs better. 2023 IEEE.
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Shukla T.D.; Giri P.; Rana P.; Krishna V.P.; Thulasimani T.; Vanisri S.
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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 771-776.
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Institute of Electrical and Electronics Engineers Inc.
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2023-01-01
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<a href="https://doi.org/10.1109/ICACRS58579.2023.10404165" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACRS58579.2023.10404165</a>
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ISBN: 979-835034023-5
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Online
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English
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Conference paper
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Shukla T.D., University Institute of Teachers Training and Research, Chandigarh University, Mohali, India; Giri P., School of Business and Management, Christ (Deemed to be University,), Delhi NCR, India; Rana P., Department of MBA, School of Commerce and Management, IIMT University, Meerut, India; Krishna V.P., School of Management and Commerce, Malla Reddy University, Hyderabad, India; Thulasimani T., Department of Mathematics, Bannari Amman Institute of Technology, Tamilnadu, India; Vanisri S., Department of MCA, M.Kumarasamy College of Engineering, TamilNadu, Karur, India
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Book Chapter
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Faculty Publications- Book Chapter
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Prediction and analysis of financial crises using machine learning
Description
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This study presents a comparative analysis of various machine learning algorithms for credit risk assessment. The algorithms were tested on two credit datasets: German Credit Dataset and Australian Credit Dataset. The performance of the algorithms was evaluated based on several metrics, including sensitivity, specificity, accuracy, F-score, and Kappa. The results showed that the FCPFS-QDNN algorithm outperformed other algorithms in both datasets, achieving high accuracy, sensitivity, specificity, and F-score. On the other hand, the ACO Algorithm and Multilayer Perceptron algorithms were found to perform poorly in both datasets. The findings of this study have significant implications for credit risk assessment in banking and financial institutions. The study recommends the use of the FCPFS-QDNN algorithm for credit risk assessment due to its superior performance compared to other algorithms. 2023, IGI Global. All rights reserved.
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Baranidharan S.; Rathod H.S.
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Advancement in Business Analytics Tools for Higher Financial Performance, pp. 200-214.
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IGI Global
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2023-01-01
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<a href="https://doi.org/10.4018/978-1-6684-8386-2.ch010" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/978-1-6684-8386-2.ch010</a>
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ISBN: 978-166848388-6; 1668483866; 978-166848386-2
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Online
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English
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Book chapter
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Baranidharan S., CHRIST University (Deemed), India; Rathod H.S., Shri Jairambhai Patel Institute of Business Management, India
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Articles
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Prediction and modeling of mechanical properties of concrete modified with ceramic waste using artificial neural network and regression model
Subject
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ANN; Compressive strength; LR; Rejected ceramics; Splitting tensile strength
Description
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Over two centuries, concrete has been crucial to building. Thus, eco-friendly concrete is being developed. Emulating these tangible traits has recently gained popularity. Ceramic waste concretes mechanical properties were modeled in this study. Ceramic waste percentages ranged from 5 to 20%. Compressive and tensile concrete strengths were modeled. To predict concrete hardness, regression modeling and artificial neural network (ANN) were used. Model performance was evaluated using prediction coefficients and root-mean-square error (RMSE). ANN models outperformed linear prediction with a coefficient for determination (R2) of 0.97. ANN models achieved root-mean-square errors (RMSEs) of 1.22MPa, 1.21MPa, and 1.022MPa after 7, 14, and 28days of retraining, respectively. Linear regression model showed RMSE values of 1.21, 1.32, and 1.27MPa at 7, 14, and 28days, respectively. In determining the compressive and tensile strength, the R2 was 0.70, meanwhile the ANN model achieved 0.87. Given its accuracy in predicting the strength qualities of ceramics cement and structural stiffness, the ANN model presents a promising tool for representing various types of concrete. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
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Kshirsagar P.R.; Upreti K.; Kushwah V.S.; Hundekari S.; Jain D.; Pandey A.K.; Parashar J.
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Signal, Image and Video Processing, Vol-18, No. Suppl 1, pp. 183-197.
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Springer Science and Business Media Deutschland GmbH
Date
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2024-01-01
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<a href="https://doi.org/10.1007/s11760-024-03142-z" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11760-024-03142-z</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189956901&doi=10.1007%2Fs11760-024-03142-z&partnerID=40&md5=4bf410cb78e49e9ffc69fa7a42a1ba90" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189956901&doi=10.1007%2fs11760-024-03142-z&partnerID=40&md5=4bf410cb78e49e9ffc69fa7a42a1ba90</a>
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ISSN: 18631703
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Online
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English
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Article
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Kshirsagar P.R., Department of Electronics & amp; Telecommunication Engineering, J D College of Engineering & amp; Management, Nagpur, India; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Delhi-NCR, Uttar Pradesh, Ghaziabad, India; Kushwah V.S., School of Computing Science & amp; Engineering, VIT Bhopal University, Bhopal-Indore Highway, Madhya Pradesh, Bhopal, India; Hundekari S., MIT College of Management, MIT ADT University, Loni Kalbhor, Pune, India; Jain D., Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, India; Pandey A.K., Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, India; Parashar J., Department of Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Engineering and Technology, New Delhi, India
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Articles
Article
Faculty Publications -Articles
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Prediction of Answer Keywords using Char-RNN
Subject
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Gated Recurrent Units; Long Short Term Memory; Natural Language Processing; Neural Networks; Recurrent Neural Network
Description
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Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved.
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Pratheek I.; Paulose J.
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International Journal of Electrical and Computer Engineering, Vol-9, No. 3, pp. 2164-2176.
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Institute of Advanced Engineering and Science
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2019-01-01
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<a href="https://doi.org/10.11591/ijece.v9i3.pp2164-2176" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijece.v9i3.pp2164-2176</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068008467&doi=10.11591%2Fijece.v9i3.pp2164-2176&partnerID=40&md5=49792670bb5fa3d4d5897c06657bf52d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068008467&doi=10.11591%2fijece.v9i3.pp2164-2176&partnerID=40&md5=49792670bb5fa3d4d5897c06657bf52d</a>
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All Open Access; Gold Open Access; Green Open Access
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ISSN: 20888708
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Online
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English
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Article
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Pratheek I., Department of Computer Science, Christ University, Hosur Road, Bangalore, 560029, Karnataka, India; Paulose J., Department of Computer Science, Christ University, Hosur Road, Bangalore, 560029, Karnataka, India
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Nwibo, Ezekiel Gabriel; Iwendi, Celestine; Sharma, Vandana; Nwigwe, Simon; Ojo, Olayinka Anthony; Odirichukwu, Jacinta Chioma
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Prediction of Campus Energy Consumption Patterns Using Machine Learning Techniques
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01-01-2025
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2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025;
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<a href="https://doi.org/10.1109/ICRITO66076.2025.11241252" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICRITO66076.2025.11241252</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029841206?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029841206?origin=resultslist</a>
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Nwibo E.G., School of Arts and Creative Technologies, University of Greater Manchester, Department of Computing, Bolton, United Kingdom; Iwendi C., School of Arts and Creative Technologies, University of Greater Manchester, Department of Computing, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Nwigwe S., Doctoral College, University of Greater Manchester, Department of Research, Bolton, United Kingdom; Ojo O.A., University of Greater Manchester, Department of Computing, Bolton, United Kingdom; Odirichukwu J.C., Federal University of Technology Owerri, Department of Computer Science, Nigeria
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The exponential increase in campus energy consumption results from the rise in population density, leading to urbanisation and the use of higher energy-intensive devices within the environment. This study explored high-performance data analytics techniques to visualise energy consumption across buildings using datasets obtained from a load audit of the entire distribution network within the Federal University of Technology, Owerri (FUTO). Advanced time series models were used to predict and forecast the consumption patterns for a year. Visualisations for this research provided detailed insights into the energy profile across all the clusters, while the SARIMA, ARIMA, and Prophet models predicted the energy demands. The heatmap for the correlation matrix reveals a constant energy scale throughout the week (weekend average energy usage is at least 40% of the weekday). A comparative performance was done to analyse the scalability and predictive abilities of the individual models. Results from the study indicate that SARIMA has the lowest mean square error (4.4896) and the highest R2 score (0.8362). The study concludes that the adoption of machine learning models for energy forecasting and prediction is vital for modern-day energy management in the University. 2025 IEEE.
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ARIMA; Artificial Intelligence; Energy Management; Machine Learning; Prophet; Renewable Energy; SARIMA
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833155421-7;
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English
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Conference paper
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Faculty Publications
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Faculty Publications -Articles
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Sudhir, M.R.; Beulah, M.
Title
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Prediction of CFRP and NSM-Wrapped Composite Column Capacities Using Experimentation and an Ensemble Machine-Learning Approach with SHAP Interpretation
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01-01-2026
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Journal of Structural Design and Construction Practice;Volume;31;Issue;3;Article No.;4026046;
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<a href="https://doi.org/10.1061/JSDCCC.SCENG-2094" target="_blank" rel="noreferrer noopener">https://doi.org/10.1061/JSDCCC.SCENG-2094</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035086761?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035086761?origin=resultslist</a>
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Sudhir M.R., Dept. of Civil Engineering, School of Engineering and Technology, Christ (Deemed to Be Univ.), Bangalore, Karnataka, 560074, India; Beulah M., Dept. of Civil Engineering, School of Engineering and Technology, Christ (Deemed to Be Univ.), Bangalore, Karnataka, 560074, India
Description
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Column capacity is an essential parameter in structural design, and its accurate determination is critical for a safe load-Transfer mechanism in structures. Also, experimental and accurate model-based assessments are critical to the column capacity evaluation. The main objective of this study is to experimentally and analytically investigate near-surface mounted (NSM) wrapped columns of different configurations and compare their capacity enhancements with the capacity enhancements of carbon fiber-reinforced polymer (CFRP). The study is also focused on developing a statistical regression model and extreme gradient boost (XG Boost), an ensemble machine-learning (ML) approach-based model, and examining both models developed for the experimental results by Shapley additive explanations (SHAP) interpretations. Therefore, the study experimentally reviewed the behavior of 24 composite columns to gain insights into experimental and code-recommended column capacities, stress-strain responses, axial stiffness, ductility factors, and failure modes. NSM-wrapped columns gained 10% strength increments, and, in comparison, the full-wrapped CFRP columns achieved 22% strength enhancement. The structural columns in a structure typically require various levels or types of strengthening, depending on their loading conditions, geometry, and material properties. With a 10% increment, the NSM technique suits columns needing lesser strength enhancements. Therefore, a key finding of the study is that the contribution of NSM longitudinal wrapping to column capacity is significant and cannot be ignored. A statistical regression model is developed for column capacity with four key parameters: percentage steel reinforcement, the extent of epoxy adhesion, the weight of the specimen, and the concrete clear cover. A model based on XG Boost, an ensemble ML approach, is also developed for the same four key parameters. The models developed are evaluated by SHAP interpretations. The SHAP analysis technique interpreted this improved model for various input-output features. The XG Boost machine-learning algorithm, developed with a coefficient of determination of 0.99, is found to be a refined regression model. Also, the study establishes that the ensemble ML approach used in tandem with SHAP analysis is a robust prediction and model interpretation tool, highlighting the significance of the percentage of steel reinforcement and the extent of epoxy adhesion over the other variables for the experimental dataset. 2026 American Society of Civil Engineers.
Subject
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Carbon fiber-reinforced polymer wrapping; Column capacity; Composite columns; Extreme gradient boost; Machine learning in structural engineering; Near-surface mounted wrapping; Shapley additive explanations
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American Society of Civil Engineers (ASCE)
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ISSN: 29965136;
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
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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Karthick, K.; Aruna, S.K.; Ravivarman, S.
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Prediction of CO and NOx Emission from Gas Turbine Using Machine Learning
Date
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01-01-2025
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AIP Conference Proceedings;Volume;3137;Issue;1;Article No.;20037;
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<a href="https://doi.org/10.1063/5.0262087" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0262087</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105001959194?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105001959194?origin=resultslist</a>
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Karthick K., Department of Electrical and Electronics Engineering, GMR Institute of Technology, Andhra Pradesh, Rajam, India; Aruna S.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Ravivarman S., Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Shamshabad, Telangana, Hyderabad, India
Description
An account of the resource
In gas-turbine-based power plants, predictive emission monitoring systems (PEMS) are used to validate and back up the expensive continuous emission monitoring systems. Increasing energy consumption increased deforestation and carbon and flue gas emissions, harming the environment. The availability of relevant and ecologically sound data is crucial to their successful deployment. In this article, we adopted the Gas Turbine CO and NOx Emission Data Set Data Set from UCI machine learning repository to predict the CO And NOx emission from gas turbine using machine learning (ML). We developed the model using random forest and support vector algorithms. The random forest algorithm performs better for the data. 2025 Author(s).
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Machine Learning; Prediction Model; Prediction of CO and NOx Emission
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American Institute of Physics
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ISSN: 0094243X;
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
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Pala, Allansius E.; Moses, Joseph; Ahamed, Shaik Sardar Zubair; Kokatnoor, Sujatha Arun; Kumar, Sandeep
Title
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Prediction of Crime Hotspots Using Machine-Learning Techniques
Date
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01-01-2025
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Lecture Notes in Networks and Systems;Volume;1263 LNNS;pp.549-560
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<a href="https://doi.org/10.1007/978-981-96-2724-0_40" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-2724-0_40</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011981111?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011981111?origin=resultslist</a>
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Pala A.E., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Moses J., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Ahamed S.S.Z., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India
Description
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Crime prediction is critical in improving police strategies and implementing measures for crime prevention and control. In recent years, machine learning has emerged as a critical way to predictive analytics in this domain. However, few studies have thoroughly compared various machine-learning algorithms for crime prediction. This study investigates the predicting capacities of various machine learning and ensemble approaches using historical public property crime data from a large city in India. Five ensemble models, Random Forest, AdaBoost, CatBoost, Gradient Boosting Machine (GBM) and eXtreme Gradient Boosting (XGBoost) and Four machine learning models, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Nae Bayes and Decision Trees are used for crime predictive analysis in this study. The XGBoost model outperformed the other models tested, based primarily on historical crime data. XGBoost being an ensemble approachcombines multiple weak classifiers to create an effective classifier. Every weak learner concentrates on the faults made by the preceding ones, enabling the model to refine its predictions and fix errors repeatedly. When compared with other models used in the study, this resultedin higher accuracy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Subject
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AdaBoost; CatBoost; Crime prediction; Decision trees; Gradient boosting machine (GBM); K-nearest neighbor; Nae Bayes; Random forest; Support vector machine (SVM); XGBoost
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Springer Science and Business Media Deutschland GmbH
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ISSN: 23673370; ISBN: 978-981962723-3;
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
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Articles
Article
Faculty Publications -Articles
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Prediction of DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet
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Integrating cutting-edge technology with conventional farming practices has been dubbed smart agriculture or the agricultural internet of things. Agriculture 4.0, made possible by the merging of Industry 4.0 and Intelligent Agriculture, is the next generation after industrial farming. Agriculture 4.0 introduces several additional risks, but thousands of IoT devices are left vulnerable after deployment. Security investigators are working in this area to ensure the safety of the agricultural apparatus, which may launch several DDoS attacks to render a service inaccessible and then insert bogus data to convince us that the agricultural apparatus is secure when, in fact, it has been stolen. In this paper, we provide an IDS for DDoS attacks that is built on one-dimensional convolutional neural networks (IDSNet). We employed prairie dog optimization (PDO) to fine-tune the IDSNet training settings. The proposed model's efficiency is compared to those already in use using two newly published real-world traffic datasets, CIC-DDoS attacks. 2023, Springer Nature Limited.
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Vatambeti R.; Venkatesh D.; Mamidisetti G.; Damera V.K.; Manohar M.; Yadav N.S.
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Scientific Reports, Vol-13, No. 1
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Nature Research
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2023-01-01
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<a href="https://doi.org/10.1038/s41598-023-42678-x" target="_blank" rel="noreferrer noopener">https://doi.org/10.1038/s41598-023-42678-x</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171395639&doi=10.1038%2Fs41598-023-42678-x&partnerID=40&md5=2684fc8f35010fa9846fb05c47963833" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171395639&doi=10.1038%2fs41598-023-42678-x&partnerID=40&md5=2684fc8f35010fa9846fb05c47963833</a>
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All Open Access; Gold Open Access; Green Open Access
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ISSN: 20452322; PubMed ID: 37717114
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Online
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English
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Article
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Vatambeti R., School of Computer Science and Engineering, VIT-AP University, Vijayawada, India; Venkatesh D., Department of Computer Science and Engineering, GITAM School of Technology, GITAM University-Bengaluru Campus, Bengaluru, India; Mamidisetti G., Department of Computer Science and Engineering, Malla Reddy University, Hyderabad, India; Damera V.K., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, India; Manohar M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Yadav N.S., Department of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, 500075, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Prediction of Depression in Young Adults Using Supervised Learning Algorithm
Subject
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Classification; Depression; Oversampling; Random Forest Classifier; Recursive Feature Elimination
Description
An account of the resource
Over the years, mental health has achieved an essential role in the pertinent development of a human being, and a large part of the population is affected by it. The most commonly affected community being college-going students, and the most common disorders being Anxiety and Depression. Depression is a leading cause of suicide in individuals, where suicide is the second most prevailing reason for death among 1529-year-olds. This study aims to identify the different reasons and other factors associated with depression to predict and determine whether an individual faces depressive disorders. For this research purpose, the most appropriate classifier is selected. The absolute accuracy of the proposed model is 91.17%, i.e., the model can correctly predict whether an individual has depression 91.17% of the time. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Chakraborty A.; Shukla S.
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Lecture Notes in Networks and Systems, Vol-290, pp. 446-460.
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Springer Science and Business Media Deutschland GmbH
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2021-01-01
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<a href="https://doi.org/10.1007/978-981-16-4486-3_50" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-4486-3_50</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115086670&doi=10.1007%2F978-981-16-4486-3_50&partnerID=40&md5=7a4a6a1b20bc400b14ff93eeb0ffe9a6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115086670&doi=10.1007%2f978-981-16-4486-3_50&partnerID=40&md5=7a4a6a1b20bc400b14ff93eeb0ffe9a6</a>
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Restricted Access
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ISSN: 23673370; ISBN: 978-981164485-6
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Online
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English
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Conference paper
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Chakraborty A., Christ University, Bangalore, India; Shukla S., Christ University, Bangalore, India
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Ajayraj, C.; Ms, Asha; Pk, Sathish
Title
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Prediction of Facial Emotions using Deep Learning and Machine Learning Techniques
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01-01-2025
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2025 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2025;
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<a href="https://doi.org/10.1109/ICDSAAI65575.2025.11011786" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSAAI65575.2025.11011786</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007976191?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007976191?origin=resultslist</a>
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Ajayraj C., Christ University, Department of CSE, Bangalore, India; Ms A., Christ University, Department of CSE, Bangalore, India; Pk S., Christ University, Department of CSE, Bangalore, India
Description
An account of the resource
Facial expression prediction has gained considerable attention in recent years, particularly because of its applications in human-computer interaction. This paper compares a wide range of deep learning and machine learning models for the prediction of face emotion using the CK+ dataset proposed by Cohn-Kanade. The dataset is characterized by seven classes of emotions represented by the labels, namely surprise, happiness, disgust, anger, sadness, fear, and contempt, on 784 training, 98 validation, and 99 testing images. To further improve model performance, preprocessing techniques were employed that enhanced data efficiency. To increase variability in the data and reduce overfitting, all images were scaled to a 48*48 pixel resolution, pixel values were scaled to be between 0 and 1 for uniformity and the following data augmentation techniques were implemented: 10-degree rotation, horizontal flip, 0.15 zoom. The five models that were tested were CNN, SVM, VGG16, InceptionV3 and VGG19. The results demonstrate the high accuracy achieved by the CNN model which showed an accuracy of 98.98%, 99% and 99% in training, validation and test respectively. The SVM classifier got an accuracy of 99%. Both InceptionV3 and VGG19 on the other hand achieved competitive testing accuracy values of 90.91% and 97.98% respectively, while VGG16 got tested and reached an accuracy of 85.86%. 2025 IEEE.
Subject
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CK+Dataset; Deep Learning; Emotion Prediction; Machine Learning; Model Comparison; Pre-processing Techniques
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Institute of Electrical and Electronics Engineers Inc.
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A related resource
ISBN: 979-833153755-5;
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
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Title
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Prediction of football players performance using machine learning and deep learning algorithms
Subject
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Deep Learning Algorithm; Machine Learning algorithm Linear Regression; Neural networks; Performance Prediction
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In modern days the margin of error for football game is low, therefore the ultimate aim of the game is to win the match. The performance of the players in the match affects the results of the game. Due to this it is very important to evaluate the player and know his weakness. Manual evaluation tends to generate many errors and take more time. In the current research the statistical model is proposed to predict the stats of the football player based on previous session data by considering various aspects of the game. Through literature reviews it is observed that machine learning and deep learning algorithms can be used predict the performance of football player. But which model would be more efficient considering the positions of the player is not considered in any article. The proposed model has designed separate model as per the position of the player during the game. This can help to predict the player's performance as per their playing position. The current study has successfully implemented various machine learning and deep learning models and provide comparative analysis of the same. Each position has considered different variables associated with that position. The performance of these models is compared for further clarification 2021 IEEE.
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Manish. S.; Bhagat V.; Pramila R.M.
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2021 2nd International Conference for Emerging Technology, INCET 2021
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Institute of Electrical and Electronics Engineers Inc.
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2021-01-01
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<a href="https://doi.org/10.1109/INCET51464.2021.9456424" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/INCET51464.2021.9456424</a>
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ISBN: 978-172817029-9
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Online
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English
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Conference paper
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Manish. S., CHRIST(Deemed to Be University), Department of Data Science, Lavasa, Pune, India; Bhagat V., CHRIST(Deemed to Be University), Department of Data Science, Lavasa, Pune, India; Pramila R.M., CHRIST(Deemed to Be University), Department of Data Science, Lavasa, Pune, India
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Conference Papers
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Faculty Publications- Conference Papers
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Prediction of Friction Stir Welding Parameters Using Response Surface Methodology
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Friction stir welding; H13 tool steel; Response Surface Method
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The Friction Stir Welding (FSW) technique results in mixing and densification of weld joint in a more accurate and localized manner. FSW has been used to create a more significant weld with more structural integrity. In this research work, to join AA 3103 and AA 7075 was carried out. These alloys were preferred due to their wide variety of applications varying from aluminium fabrication to the aerospace industry. AA 7075, being a costlier metal, can be partially replaced with AA 3103, which can be economically justifiable for this research. The study tries to reveal the regression model by considering the FSW parameters like speed feed and offset. Various mechanical tests, impact tests and hardness tests were used for determining the most suitable weld joint. After conducting the tests, the results were analyzed using Minitab 18 software. The mathematical equations were derived out of Response Surface Methodology, which proved to be efficient. The report thus discusses the details in the analysis and study of FSW. 2023 American Institute of Physics Inc.. All rights reserved.
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Raj A.; Kumar J.P.; Rout I.S.; Dulabhai H.P.; Thomas S.
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AIP Conference Proceedings, Vol-2856, No. 1
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American Institute of Physics Inc.
Date
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2023-01-01
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<a href="https://doi.org/10.1063/5.0165890" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0165890</a>
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ISSN: 0094243X
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Online
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English
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Conference paper
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Raj A., Department of Mechanical and Automobile Engineering School of Engineering and Technology, Christ university, Karnataka, Bengaluru, 560074, India; Kumar J.P., Department of Mechanical and Automobile Engineering School of Engineering and Technology, Christ university, Karnataka, Bengaluru, 560074, India; Rout I.S., Department of Mechanical and Automobile Engineering School of Engineering and Technology, Christ university, Karnataka, Bengaluru, 560074, India; Dulabhai H.P., Department of Mechanical and Automobile Engineering School of Engineering and Technology, Christ university, Karnataka, Bengaluru, 560074, India; Thomas S., Department of Mechanical and Automobile Engineering School of Engineering and Technology, Christ university, Karnataka, Bengaluru, 560074, India
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Aman, Arnisha; Sundaram, Divya; Bhavesh, K.; Anthony, Madalaimuthu
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Prediction of Grandiose Narcissism Using Machine Learning
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01-01-2025
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Lecture Notes in Networks and Systems;Volume;1288 LNNS;pp.249-257
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<a href="https://doi.org/10.1007/978-981-96-3102-5_18" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-3102-5_18</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010812465?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010812465?origin=resultslist</a>
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Aman A., Christ University, Bengaluru, India; Sundaram D., Christ University, Bengaluru, India; Bhavesh K., Christ University, Bengaluru, India; Anthony M., Christ University, Bengaluru, India
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The Gen-Zs have the tendency to exhibit a sense of self-importance and superiority excessively over social media. This study intends to predict Grandiose Narcissism based on Instagram usage and Fear of Missing Out (FoMO) among young adults. The study was conducted on a sample size of 300 young adults, recruited using convenient sampling, residing in the state of Assam, India. This study employed various machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Gradient Boosting, K-Nearest Neighbours (KNNs), and Gaussian Naive Bayes, to analyse the predictors of Grandiose Narcissism. Results showed that machine learning algorithms, especially KNN (90.7%) and Random Forest (88.70%) predicted Grandiose Narcissism accurately based onFoMO, Self-Esteem, PAUM. Additionally, Area Under Curve (AUC) in the range of 0.850.91 indicated that the variables in the data set are being discriminated in the context of specificity and sensitivity thoroughly. Significant influence of grandiose narcissism and FoMO on Instagram usage highlighted the role of social validation in enhancing online engagement. Future studies can include these algorithms to deduce patterns and develop real timebots to provide psychologically safe online environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
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Grandiose narcissism; Instagram usage; Machine learning; Social media
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Springer Science and Business Media Deutschland GmbH
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ISSN: 23673370; ISBN: 978-981963101-8;
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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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Articles
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Faculty Publications -Articles
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Prediction of ground water quality in western regions of Tamilnadu using LSTM network
Subject
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Groundwater; LSTM; Prediction; Tamil Nadu; Western region
Description
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Assessing and safeguarding groundwater quality is critical for sustaining life in water-scarce regions like western Tamil Nadu. The motivation behind this study stems from the pressing need to address water quality challenges in a region grappling with scarcity. Despite existing efforts, a notable research gap exists in predictive tools that comprehensively capture the nuanced temporal variations and trends in groundwater quality. This is where the LSTM network steps in, showcasing exceptional accuracy in short-term predictions and discerning long-term trends. This research uses Long Short-Term Memory (LSTM) networks, a variant of recurrent neural networks, to predict groundwater quality in South Indian Regions, especially in Tamil Nadu. Extensive data, encompassing parameters such as pH, dissolved oxygen, turbidity, and various chemical constituents, were gathered over an extended timeframe. The LSTM model was then trained on this historical dataset, factoring in temporal dependencies and seasonality inherent in groundwater quality data. The validation process rigorously tests the LSTM model against actual groundwater quality measurements. The results were impressive, as the model demonstrated a remarkable ability to unravel the complex variations in groundwater quality. 2024 Elsevier B.V.
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M K.; C V.S.R.P.; J V.A.; A S.; M S.; K.B P.; Shanmugm M.
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Groundwater for Sustainable Development, Vol-25
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Elsevier B.V.
Date
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2024-01-01
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<a href="https://doi.org/10.1016/j.gsd.2024.101156" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.gsd.2024.101156</a>
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ISSN: 2352801X
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Online
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English
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Article
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M K., Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Tamil Nadu, Coimbatore, India; C V.S.R.P., Department of Civil Engineering, Malla Reddy Engineering College, Telangana, Secunderabad, 500100, India; J V.A., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; A S., Department of Science and Humanities, Brilliant Grammar School Educational Society's Group of Institutions Integrated Campus, Telangana, Hyderabad, 501505, India; M S., Technical Executive, National Agro Foundation, Tamil Nadu, Chennai, 600113, India; K.B P., Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, BSF Campus, Yelahanka, Govindapura, Karnataka, Bengaluru, 560064, India; Shanmugm M., Department of Electronics and Communication Engineering, Kongu Engineering College, Perundurai, Erode, 638060, India