719 20 16481 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Sreya, Pampati; Yashaswi, D.; Stephen, R.; Gobinath, R.; Ramkumar, S. Title A name given to the resource Indian Stock Market Prediction Using Neural Networks: A Comparative Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1469 LNNS;pp.101-112 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-7807-5_10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-7807-5_10</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020263014?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020263014?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sreya P., CHRIST University, Bangalore, India; Yashaswi D., CHRIST University, Bangalore, India; Stephen R., CHRIST University, Bangalore, India; Gobinath R., CHRIST University, Bangalore, India; Ramkumar S., CHRIST University, Bangalore, India Description An account of the resource Predicting stock prices remains a challenging problem due to the highly dynamic and nonlinear nature of financial markets. Traditional statistical models like ARIMA and GARCH often fail to capture the complexities inherent in stock market data. This paper investigates the use of deep learning techniques, focusing on Convolutional Neural Networks (CNNs) and a hybrid CNN-LSTM ensemble model for stock price prediction in the Indian stock market. The CNN model efficiently extracts temporal patterns from sequential data, while the CNN-LSTM ensemble leverages temporal dependencies for improved long-term prediction accuracy. Historical data from Tata motors, spanning over two decades, was used to train and evaluate the models. Experimental results highlight the CNN-LSTM ensemble's superior performance in capturing volatile trends and long-term dependencies, with a notable decrease in test loss compared to standalone CNN. This study underscores the effectiveness of hybrid deep learning architectures in enhancing prediction reliability, paving the way for more adaptive and robust financial forecasting systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Convolutional Neural Networks (CNNs); Deep learning; Ensemble model; Long Short-Term Memory (LSTM); Machine learning (ML); Stock price prediction Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981967806-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Bala, Nishita Shekhar; Bandi, Sree Vani; Stephen, R.; Dandu, Ravi; Balakrishnan, C. Title A name given to the resource Impact of the Internet on Human Life a Data-Driven Analysis Using Machine Learning and Statistical Correlations Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1469 LNNS;pp.147-158 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-7807-5_14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-7807-5_14</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020259774?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020259774?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bala N.S., CHRIST University, Bangalore, India; Bandi S.V., CHRIST University, Bangalore, India; Stephen R., CHRIST University, Bangalore, India; Dandu R., CHRIST University, Bangalore, India; Balakrishnan C., CHRIST University, Bangalore, India Description An account of the resource These days Internet is became an essential part of human life and affects various domains which includes education, business, social interactions, mental health. It pushes the society ahead through increasing innovations, amplifying learning techniques, connecting people across the globe and access to vast resources which makes it a valuable tool in this modern society. But it comes with problems such as Internet addiction, sleeping disorders, health complications. This abstract discusses about dual impact of Internet uses, focusing on its significant benefits and possible dangers. Hence, there is need to manage use of Internet so one can make use of its benefits at the same time reducing the affects which are caused by Internet on human life. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Academic performance; Correlation studies; Health condition; Internet usage patterns; Regression; Statistical analysis Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981967806-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource D, Bijo M; Unnikrishnan, Sujatha Title A name given to the resource A Review on Development and Properties of Ultra-High-Performance Concrete Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Civil Engineering;Volume;666 LNCE;pp.95-114 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-7971-3_8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-7971-3_8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021000803?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021000803?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant D B.M., Christ University, Bengaluru, India; Unnikrishnan S., Christ University, Bengaluru, India Description An account of the resource This study presents a review of literature on ultra-high-performance concrete that brings in information regarding the preparation of UHPC, mix design of UHPC on the basis of various particle packing density models, microstructural analysis, durability studies, and strength characteristics. A data base is collected to study the performance, mechanical strength and durability of UHPC from various research works. UHPC can found to be a long-term solution for present day challenges that is faced in construction industry when conventional concrete is used and makes this concrete a novel concept in concrete technology. The advantages of UHPC are: less porosity, high abrasion resistance, greater mechanical properties, high packing density, and improvement in fatigue behavior though the cost of UHPC is high. The non-availability of a standard code for mix design of UHPC makes it difficult to arrive at consistent and comparable mix. The cost of UHPC can be controlled with the use of naturally available materials and utilizing agricultural and industrial waste materials in UHPC. From the data collected, it is observed that the binder content can be optimized and cement which is residual in UHPC can be replaced by industrial residue like fly ash, GGBS, glass powder, etc. and thereby brings down the cost without compromising on the strength and performance. The information shared in this paper will help the contractors, consultants, engineers, industry stakeholders and researchers to alleviate the confusions regarding the use of UHPC in construction industry and to encourage research to make this concrete construction-industry friendly. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Microstructural analysis; Particle packing density; Ultra-high-performance concrete Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23662557; ISBN: 978-981967970-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Joseph, Eldo Ephraim; Sharma, Vandana; Shoran, Preety Title A name given to the resource A Comparative Study on IOT Security Using Machine Learning Techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1496 LNNS;pp.439-450 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8107-5_32" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8107-5_32</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028314790?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028314790?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joseph E.E., Christ University, Bengaluru, India; Sharma V., Christ University, Bengaluru, India; Shoran P., Christ University, Bengaluru, India Description An account of the resource This increased reliance on networks has made the security of IoT devices a highly burning issue. Among the sea of threats, the threats associated with DDoS attacks are at a high level since they do damage to the functionality of devices and unavailability of networks. Detection and mitigation of DDoS attacks will demand IoT environments based on powerful classification algorithms. This paper evaluates the performances of three prominent algorithms: Decision Tree, Random Forest, and Histogram-Based Gradient Boosting for the classification of DDoS attack traffic within IoT networks. An IoT-23 dataset comprising a subset of attacks, including DDoS, is used herein for the purpose of achieving high classification accuracy to ensure a reliable evaluation of attacks. The results clearly show that all three algorithms are pretty good in terms of detection performance, and Histogram-Based Gradient Boosting is the best in terms of generalization accuracy. These results open new perspectives for the implementation of machine learning, generally, and Histogram-Based Gradient Boosting, specifically, directed to improving security in IoT networks against DDoS attacks, which is an extremely promising result when working within the light of some insights for future research and development within this critical area of security. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Artificial intelligence; Cybersecurity; Internet of Things (IoT); Machine learning Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981968106-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Shrotriya, Shobhit; Banu, P. K. Nizar; Kulkarni, Avi; Kumar, Vinod G. Title A name given to the resource Real-World Breast Cancer Imaging DataLLM Led Analytics for Insights and Evidence Generation Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Smart Innovation, Systems and Technologies;Volume;448 SIST;pp.17-28 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8126-6_2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8126-6_2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028362364?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028362364?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Shrotriya S., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Banu P.K.N., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Kulkarni A., ThoughtSphere, CA, United States; Kumar V.G., Accenture, Bengaluru, India Description An account of the resource Breast cancer remains one of the most prevalent and deadly forms of cancer worldwide, affecting individuals across all ages and sexes. In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in the field of medical diagnostics, offering the potential to enhance the detection, diagnosis, and prediction of breast cancer. Despite these advancements, challenges remain, including the need for large, diverse datasets to train robust models, the integration of AI tools into clinical workflows, and addressing ethical concerns related to AI in healthcare. This paper explores the application of Large Language Models (LLMs) using embeddings in breast cancer management, focusing on its ability to analyze medical data, including imaging, histopathology datasets to identify patterns that may be imperceptible to human experts. Datasets from real-world setting have been secured for analysis across multiple models. Convolutional Neural Network (CNN) model and custom-built large language model are employed to demonstrate the precision and accuracy of Generative AI techniques and observed that custom-built LLM with 98.44% outperforms the traditional AI approaches such as CNN with 61.72%. Future studies can further establish how these models can assist in stratifying patients based on risk, thereby enabling personalized treatment plans that can reduce overtreatment and improve quality of life. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Artificial intelligence (AI); Breast cancer; Embeddings; Generative AI; Large language models (LLM); Machine learning (ML) Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-981968125-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Bose, Manoswita; Chatterjee, Moumita; Sarkar, Dhrubasish Title A name given to the resource Leveraging Machine Learning and Streamlit for Real-Time Stock Analysis and Prediction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1488 LNNS;pp.497-507 Identifier An unambiguous reference to the resource within a given context <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> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 An account of the resource 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 The topic of the resource Analytics; Prediction; Stocks; Visualization Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981968308-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Augustine, Praveen; John, Nimitha Title A name given to the resource Autoregressive Model with Students t-Errors Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1501 LNNS;pp.351-364 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8350-5_25" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8350-5_25</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028365077?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028365077?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Augustine P., Department of Data Science and Statistics, CHRIST (Deemed to be University), Bengaluru, India; John N., Department of Data Science and Statistics, CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource This paper examines a first-order autoregressive model that incorporates students t-distributed errors. The estimation procedure is developed using the maximum likelihood method, with the solutions demonstrated using a simulation approach. As the estimating equations were not in a closed-form expression, we obtained the parameter estimates using the NewtonRaphson method. For a finite sample size, a parametric bootstrap procedure for the unit root test has been illustrated. To demonstrate the applications of the proposed model, a time series of quarterly GDP percentage changes are analyzed. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Autoregressive model; Bootstrapping; Likelihood ratio test; Maximum likelihood; Students t-distribution Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981968349-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Augustine, Praveen; John, Nimitha Title A name given to the resource Detection andDealing ofMulticollinearity Using aNew RidgeEstimator Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1501 LNNS;pp.365-378 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8350-5_26" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8350-5_26</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028352404?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028352404?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Augustine P., Department of Data Science and Statistics, CHRIST (Deemed to Be University), Bengaluru, India; John N., Department of Data Science and Statistics, CHRIST (Deemed to Be University), Bengaluru, India Description An account of the resource The Ordinary Least Square (OLS) method is a widely used technique for estimating regression coefficients that describe the relationship between the independent variables and the dependent variable. The regression estimates obtained using this approach give a poor result in the presence of multicollinearity. Ridge regression is employed to address this problem. A ridge estimator, also called a biasing constant, plays a notable role in the parameter estimation of ridge regression. In this paper, we introduced a new ridge estimator and studied its properties. A comparative study of the proposed estimator with some of the existing ridge estimators under extreme multicollinearity has been illustrated by using a simulation technique. The estimator developed in this paper seems to perform better than all the other estimators because of the smaller ratio of average mean square error. The performance of the proposed estimator is analysed and verified using real-life data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Generalised ridge regression; Multicollinearity; Ridge estimator; Ridge regression Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981968349-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Joy, Sebin; Arun Kumar, Arti Title A name given to the resource Evaluating Technostress: Work-Life Balance and Well-Being in Varied Work Contexts Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Springer Proceedings in Business and Economics;pp.83-101 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8582-0_5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8582-0_5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011987635?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011987635?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joy S., School of Business and Management, Indian Institute of Management Bangalore, Bengaluru, India; Arun Kumar A., School of Business and Management, Christ University Bangalore, Bengaluru, India Description An account of the resource In the contemporary digital landscape, the phenomenon of technostress, defined as stress induced by technology usage, has emerged as a crucial factor influencing work-life balance and employee well-being. This study will explore the impact of technostress in varied work modes such as traditional office-based, remote, and hybrid models. Employing a quantitative approach, the researcher conducted surveys on a representative sample of employees across multiple industries. The results indicate that technostress adversely influences work-life balance and well-being, and the difference in various work modes is observable. Further, it has also been observed that there are significant differences in technostress and well-being concerning the various work modes; working from home comes out to be a positive option, which is related to lesser levels of technostress and higher outcomes of well-being. The present study shows how organisational interventions may be implemented in mitigating technostress-inducing effects: induction of digital literacy, instillation of appropriate communication policies, and embedding of supportive work culture. Essentially, an intervention could help organisations improve well-being 0f employees and achieve better work-life balance in a digital environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Digital overload; Technostress; Well-being; Work modes; Work-life balance; Workplace stressors Publisher An entity responsible for making the resource available Springer Nature Relation A related resource ISSN: 21987246; ISBN: 978-981968581-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Chattopadhyay, Shrabana; Bhattacharjee, Ripon; Jana, Aritra; De, Pubali Title A name given to the resource Mobilizing Automated Vehicles: Harmonizing the Intersection of Technological Innovations and Legal Regulations Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1513 LNNS;pp.107-129 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8632-2_7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8632-2_7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028260534?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028260534?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chattopadhyay S., Department of Law (IIIJS), University of Engineering and Management, Kolkata, India; Bhattacharjee R., National Law University, Tripura, India; Jana A., Christ University, Bangalore, India; De P., Institute of Engineering and Management (IEM), Kolkata, University of Engineering and Management, Kolkata, India Description An account of the resource There will be significant shifts in transportation with the introduction of autonomous vehicles (AVs), which will increase efficiency, safety, and environmental friendliness. But these technologies can only be used to their full potential if technological breakthroughs are seamlessly integrated with strong legal regulations. The article delves into the ways in which technological advances and legal frameworks meet, highlighting how important regulatory measures are for ensuring the secure use of AVs. In addition, the article delves into the current legal framework around AVs, drawing attention to the difficulties caused by inconsistent regulations and the necessity for flexible rules that can stay up with the fast-paced advancements in technology. The purpose of this article is to examine current policies and case studies to shed light on how to effectively integrate technology advancements with legal requirements to create conditions that are favorable to the broad use of autonomous vehicles. The results highlight the need for manufacturers, lawmakers, and the general public to work together for the sake of society's safety and well-being during the shift to autonomous transportation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Automated vehicle; Infrastructure; Legal regulation; Policy measures; Sustainability; Technological innovation Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981968631-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource James, Jimmi; Alapatt, Bosco Paul; George, Fr. Jossy P.; Gupta, Varuna Title A name given to the resource Improving the Accuracy of Cardiovascular Disease Classification Using CardioAugmentNet Technique Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1477 LNNS;pp.295-305 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8639-1_24" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8639-1_24</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020765274?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020765274?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant James J., Christ University, Bengaluru, India; Alapatt B.P., Christ University, Bengaluru, India; George F.J.P., Christ University, Bengaluru, India; Gupta V., Christ University, Bengaluru, India Description An account of the resource Cardiovascular disease is the leading cause of death and mortality worldwide. Thus, early diagnosis of CVDs is crucial since the disease can be managed with optimal care. In the current study, we consider CardioAugmentNet, which is a CNN model augmented with data augmentation strategies for the classification of several cardiovascular pathologies in ECG images. A proposed method was designed to provide a robust algorithm for the detection of irregular heart rhythms, myocardial infarction and other cardiac diseases. The model is trained and tested on the dataset of ECG images from individuals with various prevalent cardiovascular diseases as well as normal hearts. Therefore, the CardioAugmentNet state-of-the-art model classifies different cardiac abnormalities with high accuracy, suggesting that it can be used in clinical practice. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource CardioAugmentNet; Cardiovascular diseases; Deep learning; ECG classification Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981968638-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Mana, Niveditha Cherukattu; Mathew, Ansa; Baby, Riya; Joseph, Jerush John Title A name given to the resource Technological Interventions in Unpaid Care Work and Gender Dynamics Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1507 LNNS;pp.607-617 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8750-3_49" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8750-3_49</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023592493?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023592493?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mana N.C., School of Sciences, CHRIST University, Bengaluru, India; Mathew A., School of Sciences, CHRIST University, Bengaluru, India; Baby R., School of Sciences, CHRIST University, Bengaluru, India; Joseph J.J., School of Commerce, Finance and Accountancy, CHRIST University, Bengaluru, India Description An account of the resource Unpaid care work, frequently centered on women, is an important yet neglected component of the world economy. This study strives to address the potential and flexibility that technology brings in curbing gender disparities, along with the bodily burden associated with unpaid care work. An examination is made for smart home devices, telehealth solutions, and caregiving applications that will further be evaluated for their effectiveness in minimizing the time and effort invested in unpaid care responsibilities. Through existing theoretical frameworks, empirical evidence, and case studies, this paper aims to determine how technological innovation can more effectively redistribute care work between genders and enhance the economic value of unpaid care to further improve gender equality. For instance, in Japan, smart home appliances such as automated pill dispensers and remote monitoring devices have become crucial solutions to a caregiving burden largely imposed on women. Telemedicine services similar to these have transported rural India from its unfavorable health care situation, thereby significantly shortening the time women spend on activities related to health care. Caregiver applications have assisted in the United States in achieving an equal distribution of caregiver responsibilities between male and female caregivers. Sophisticated robotic assistants in South Korea may fill gaps in the workforce while tending to older populations; thus, potentially minimizing housework hours for women. The education systems operating online across Sub-Saharan Africa enable girls to juggle learning with caring effectively and hence strike long-lasting gender parity. Such socio-economic advantages were purely garnered through wearable health monitors in Europe that eased family members burdens while experiencing economic benefits at a broader level. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource AI; Automation; Gender dynamics; Gender inequality; Technological interventions; Unpaid care work Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981968749-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Subhadha, B.; Jaganathan, Siva Title A name given to the resource Efficacy of AI for Three-Dimensional Point Cloud Semantic Segmentation of Heritage Data for XR Environments Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Civil Engineering;Volume;683 LNCE;pp.67-84 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8761-9_5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8761-9_5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022165240?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022165240?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Subhadha B., Christ University, Bengaluru, India, Communication, Arch + The Arts, Florida International University, Miami, United States; Jaganathan S., Christ University, Bengaluru, India Description An account of the resource In heritage documentation, three-dimensional (3D) models created using Scan-to-BIM processes are essential for interpreting and presenting historic structures. Point cloud data derived from 3D laser scanning and photogrammetry facilitate realistic digital models used for immersive experiences. For this, raw point clouds, which are unstructured, are processed, semantically classified, and segmented to create parametric architectural objects in modeling platforms. Three-Dimensional Point Cloud Semantic Segmentation (3DPCSS) refers to segmenting point clouds into classes like walls, columns, etc. Automating 3DPCSS using Artificial Intelligence (AI) has gained importance in current research activities because of its versatility and efficiency over manual segmentation. However, implementing it solely with AI presents various operational and conceptual challenges, particularly for XR models in digital heritage. Automated segmentation often fails to capture the unique characteristics and intricate geometries, leading to misrepresentations or oversimplifications. Selecting an appropriate algorithmic framework for automating 3DPCSS is essential to address this gap. This paper aims to understand the efficacy of AI algorithms in recent research for 3DPCSS, particularly those tailored for 3D modeling. A study of Dwarakadesh Haveli, Ahmedabad, India, highlights the workflow and challenges of integrating point clouds into 3D models. The findings indicate the need for a detailed approach tailored to the projects specific characteristics, emphasizing the importance of systematic algorithm ensemble experimentation to refine segmentation, leading to the development of 3D parametric objects. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource 3D Point Cloud Semantic Segmentation (3DPSS); AI in cultural heritage; Digital heritage; Immersive experience; Scan-to-BIM; XR in heritage conservation Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23662557; ISBN: 978-981968760-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Siby, Amala; Jha, Babita Title A name given to the resource Managing Retail Credit Default Risk Using Artificial Intelligence and Machine Learning: Ethical Considerations and Challenges Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1509 LNNS;pp.351-359 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8793-0_28" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8793-0_28</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028314421?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028314421?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Siby A., School of Commerce, Finance and Accountancy, Christ University, Bengaluru, India; Jha B., School of Business and Management, Christ University, Bengaluru, India Description An account of the resource Credit risk management is an integral aspect of financial stability of financial institutions. The determinants of credit default behavior help in identifying types of credit risk based upon customers habits or actions such as purchasing behavior, spending patterns, social interactions, and moral hazard. The awareness on such behavioral factors helps in getting a complete picture in analyzing the credit worthiness of any individual. The onset of artificial intelligence (AI) and machine learning (ML) has remodeled the methodologies of credit risk assessment. Training and developing learning models to predict the borrowers behavior evolved along with AI and ML, developing advanced mechanisms for managing credit default risks. Using risk modeling to determine behavioral scoring enables the banks to acquire creditworthy retail borrowers, alongside its inherent demerits of the models. This study is focused on the employment of AI and ML techniques along with qualitative determinants in managing credit risk, drawing attention to the advantages and disadvantages and applications and implications for the financial service providers. The paper examines contemporary literature on modern methods of credit risk management and sheds light on the technological accuracy of credit risk calculations and automated decision-making processes alongside methods to alleviate biases. The study also outlines the sequential phases of the conventional models along with the regulatory and ethical considerations of AI-ML-driven credit risk assessment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Artificial intelligence; Credit risk assessment models; Data-driven decision models; Ethical considerations in credit default models; Machine learning; Retail credit Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981968792-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Mukhopadhyay, Debarka; Bhattacharyya, Siddhartha; Plato, Jan; Zelinka, Ivan; Snasel, Vaclav Title A name given to the resource Development ofNew Fidelity Theorems inQuantum Communication Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1508 LNNS;pp.405-417 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8998-9_33" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8998-9_33</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028250224?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028250224?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mukhopadhyay D., Department of Computer Science and Engineering, Christ University, Bangalore, India; Bhattacharyya S., VSB Technical University of Ostrava, Ostrava, Czech Republic; Plato J., VSB Technical University of Ostrava, Ostrava, Czech Republic; Zelinka I., VSB Technical University of Ostrava, Ostrava, Czech Republic; Snasel V., VSB Technical University of Ostrava, Ostrava, Czech Republic Description An account of the resource Fidelity estimates the closeness or degree of overlap between a pair of unknown quantum systems. In quantum information processing study, a primary task is to estimate the fidelity of a pure states pair in a 2-dimensional Hilbert space or between a mixed state, ?, and pure state existing in N dimensional Hilbert space while transmitting them through noisy environments. This paper suggests two theorems to estimate fidelity while proving and justifying them numerically. The first theorem computes fidelity between pure states and the second estimates fidelity between mixed and pure states. The mathematical basis of the theorems confirms accuracy in estimation while dealing with single- and multiqubit system environments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Fidelity; No cloning theorem; Quantum computing; Quantum state; Qubits Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981968997-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Joseph, Jasmin; Philip, Samuel; Siby, Amala Title A name given to the resource Financial Inclusion as a Tool for Social Equity: A Focus on the Elderly and Underprivileged Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1525 LNNS;pp.283-290 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-9048-0_26" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-9048-0_26</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028313191?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028313191?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joseph J., CHRIST University, Bengaluru, India; Philip S., CHRIST University, Bengaluru, India; Siby A., CHRIST University, Bengaluru, India Description An account of the resource Financial inclusion is a key element in reducing poverty and promoting economic growth, particularly for vulnerable groups such as the elderly and the poor. This article explores the multiple barriers that these groups face in accessing financial services, including economic constraints, discrimination, and lack of identification and credit history. The report highlights the special challenges that older people face due to limited mobility and digital literacy. The study emphasizes the importance of a multi-faceted policy approach to improving financial inclusion, including developing simplified financial products, expanding financial literacy programs, and working with NGOs. The paper focuses on effective strategies and proposes interventions aimed at empowering these populations and improving their access to financial resources, ultimately supporting economic stability and reducing inequality. The findings highlight the need for policymakers and financial institutions to work together to address systemic barriers that impede the financial autonomy of vulnerable groups. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Digital literacy; Economic development; Financial inclusion; Inclusive banking; Sustainable development goals Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981969047-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Saranya, R.; Sandhika, G. Title A name given to the resource Leveraging Machine Learning for Epidermal Ailment Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1497 LNNS;pp.275-288 Identifier An unambiguous reference to the resource within a given context <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> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 The topic of the resource Deep learning; Diagnosis; Image processing; Machine learning; Skin ailment detection Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981969183-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Dkhar, Josaiah Murfeal; John, Tegil; Rai, Pranab Title A name given to the resource Harnessing Machine Learning toOptimize Social Media Marketing Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1497 LNNS;pp.581-598 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-9184-5_43" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-9184-5_43</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028353338?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028353338?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dkhar J.M., Christ University, Bengaluru, India; John T., Christ University, Bengaluru, India; Rai P., Christ University, Bengaluru, India Description An account of the resource Machine learning (ML) has transformed the way digital advertising is done and analyzed by social media data. This paper explores ML in targeted advertising, including techniques such as supervised learning, neural networks, and NLP. While ML improves campaign precision and consumer engagement, it also presents challenges such as algorithmic bias, data privacy concerns, and computational scalability. This study is a synthesis of existing research and explores real-world applications, providing a critical analysis of MLs capacity to optimize social media advertising. It argues that while ML may provide exceptional possibilities for customization and engagement, its success can only be ensured through appropriate ethical practices, transparency, and innovation in technology. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Digital advertisement; Machine learning; NLP; Social media marketing Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981969183-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Rai, Pranab; John, Tegil; Dkhar, Josaiah Murfreal Title A name given to the resource The Quantum Shift inMachine Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1497 LNNS;pp.611-628 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-9184-5_45" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-9184-5_45</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028364802?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028364802?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rai P., Christ University, Bengaluru, India; John T., Christ University, Bengaluru, India; Dkhar J.M., Christ University, Bengaluru, India Description An account of the resource Quantum machine learning (QML) leverages the power of quantum computing to extensively enhance the performance of simple ML algorithms. This kind of enhancement efficiently avoids complicated high-dimensional and computationally expensive applications. Following exciting discoveries of some key algorithms such as Grovers and Shors algorithm, QML came up with advanced models such as quantum neural networks (QNNs), quantum support vector machines (QSVMs), and quantum deep architecture. The discussed models give better results in data processing, pattern recognition, and optimization in comparison to their classical variants. This survey discusses the most important developments in QML with focus on Quantum Fourier Transform for encoding the data. Applications of QML in quantum chemistry, secure distributed computing, and high-dimensional data analysis are shown. It will present new breakthroughs and continue to challenge practical, scalable QML solutions toward mapping out the course toward quantum advantage in machine learning tasks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Deep architecture; Quantum machine learning; Quantum neural networks Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981969183-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library 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 A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Marriwala, Nikhil Kumar; Kaushik, Ruchi; Appathurai, Ahilan; Shukla, Vinod Kumar; Sandeep, J. Title A name given to the resource Deep Learning-Based Consolidated Disease Classification in Health Data Management Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1537 LNNS;pp.197-207 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-9242-2_15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-9242-2_15</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023289381?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023289381?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Marriwala N.K., Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra University, Haryana, Kurukshetra, India; Kaushik R., Xavier Institute of Management and Entrepreneurship, Bengaluru, India; Appathurai A., Department of Electronics and Communication Engineering, PSN College of Engineering and Technology, Tamilnadu, Tirunelveli, India; Shukla V.K., School of Engineering Architecture Interior Design, Amity University Dubai, Dubai International Academic City, Dubai, United Arab Emirates; Sandeep J., CHRIST (Deemed to be University), Bangalore, India Description An account of the resource Healthcare data management is critical for ensuring comprehensive and high-quality medical treatment. Sensitive patient data management has a potential new option thanks to blockchain technology. However, existing blockchain-based healthcare data management systems face challenges in scalability, integration, and regulatory compliance. To address these issues, a novel blockchain-based healthcare data management system has been proposed to provide a secure, decentralized, and interoperable platform for managing sensitive patients medical information. Proposed approach involves collecting comprehensive health measurements from patients using wearable sensors and ensuring the security and integrity of patient data through robust user verification protocols. Artificial Neural Networks (ANNs) are employed to consolidate disease symptoms, enhancing the efficiency and accuracy of data analysis. The results and comparative analysis showcase the efficiency of the proposed method in terms of precision, accuracy, recall, search accuracy and F1-score. The accuracy of the proposed method is improved by 12.9%, 6.07%, and 14.28% when related to the existing ACTION-EHR, BSDMF, and BlockMedCare techniques respectively. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Artificial Neural Networks; Blockchain; Deep learning; Healthcare; Medical data Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981969241-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library