273 20 11858 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 Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Football Player Substitution Analysis using NLP and Survival Analysis Subject The topic of the resource Kaplan-Meier Fitter; Natural language Processing; player analysis; player comparison.; Sentimental analysis; Survival Analysis; team's performance Description An account of the resource Football player substitution is extremely significant in situations where the team is down by goals or attempting to retain a lead that can add value to the team's performance. However, substituting players based on their prior performance would not assist the squad in making good decisions. In one of the papers, they used an inverse gaussian hazard model to determine the survival rate of players. However, the main issue arises when players do not give their all due to their mental state, which plays a critical role during the game. Furthermore, most of the research papers relied solely on past performance of players and various analyses, which was insufficient. This study discovered that the player's mindset should be mentally stable and competitive which is also very crucial during the match by reading various research articles. Hence, this study proposes a framework which comprises of two models, namely Survival Analysis (Kaplan-Meier Fitter) and Natural Language Processing (Sentimental Analysis). Sentimental Analysis would hel p in determining a player's mindset before the match and Kaplan-Meier Fitter is used to find out the survival rate of player's performance based on several factors like goal scored, passing accuracy etc. which would allow the team to make better informed decisions. Comparison of these two models would yield the best results for substitute players on the bench on the basis of their past performance and their mental health which will allow them to make team management to make better judgments. 2023 IEEE. Creator An entity primarily responsible for making the resource Bera A.; Joshi S.; Nair A.M. Source A related resource from which the described resource is derived Proceedings - 5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023, pp. 753-757. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSSIT55814.2023.10061118" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSSIT55814.2023.10061118</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150676946&doi=10.1109%2FICSSIT55814.2023.10061118&partnerID=40&md5=687da57f3179caf1ff6a199ba58dd910" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150676946&doi=10.1109%2fICSSIT55814.2023.10061118&partnerID=40&md5=687da57f3179caf1ff6a199ba58dd910</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166547467-2 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bera A., Deemed to Be University, Department of Data Science Christ, Pune, India; Joshi S., Deemed to Be University, Department of Data Science Christ, Pune, India; Nair A.M., Deemed to Be University, Department of Data Science Christ, Pune, India 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 Editorials Editorial Faculty Publications- Editorial 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 Footloose Culture: Migrant Workers and Cultural Meanings of Labour Description An account of the resource [No abstract available] Creator An entity primarily responsible for making the resource Ness I.; Das A.K.; Kumar M. Source A related resource from which the described resource is derived Society and Culture in South Asia, Vol-10, No. 2, pp. 161-176. Publisher An entity responsible for making the resource available Sage Publications India Pvt. Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1177/23938617241264963" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/23938617241264963</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201555519&doi=10.1177%2F23938617241264963&partnerID=40&md5=27c3acdaf1b935682bb03c84a37cd504" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201555519&doi=10.1177%2f23938617241264963&partnerID=40&md5=27c3acdaf1b935682bb03c84a37cd504</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23938617 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Editorial Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ness I., City University of New York, NY, United States; Das A.K., Department of Sociology, Tezpur University, Assam, Tezpur, India; Kumar M., Christ University, IN-KA, Karnataka, Bengaluru, 560029, India 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 Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource FOPID controller tuning: A comparative study of optimization techniques for an automatic voltage regulator Subject The topic of the resource Ant Lion Optimization; Automated Voltage Regulator; FOPID Controller; Particle swarm Optimization; Salp Swarm Algorithm Description An account of the resource This study evaluated a fractional order proportional-integral-derivative (FOPID) controller optimization with a fractional filter for an automated voltage regulator (AVR) system. For the suggested controller, a variety of different parameters can be changed. For the purpose of creating the optimum PID controller for an automated voltage regulator system, comparative analysis using multiple optimization methodologies is carried out. The Salp Swarm Algorithm (SSA), Ant Lion Optimization (ALO), and Particle Swarm Optimization algorithm (PSO) are the techniques that are being examined in this study. The settling time, rising time, and overshoot performance indices is being used. The transient responsiveness of the AVR system was increased by each of the recommended optimization techniques in a different way, and early results were optimistic. The comparison with the most ideally tuned FOPID controllers for the AVR system also serves to support the superiority of the suggested controller. 2023 Author(s). Creator An entity primarily responsible for making the resource Athira S.; Aneesh V. Source A related resource from which the described resource is derived AIP Conference Proceedings, Vol-2904, No. 1 Publisher An entity responsible for making the resource available American Institute of Physics Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0170829" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0170829</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177575208&doi=10.1063%2F5.0170829&partnerID=40&md5=906fa044644a5149b548981522fdf38f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177575208&doi=10.1063%2f5.0170829&partnerID=40&md5=906fa044644a5149b548981522fdf38f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 0094243X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Athira S., Department of Electronics and Communication Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Aneesh V., Department of Electronics and Communication Engineering, CHRIST (Deemed to Be University), Bengaluru, India 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 Articles Article Faculty Publications -Articles 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 Forbidden Cravings: Exploring socio-cultural ramifications of food practices in Aamis Subject The topic of the resource Aamis; Assamese cuisine; cultural appropriation; food; food politics Description An account of the resource Food choices represent conscious affirmation and expression of personal, group, ethnic or national identity. Due to its multidimensional role, food that we rely on sustenance is often politicised and used as a tool to create conflict amongst and within diverse social groups. Assamese cuisine includes a rich platter of authentic food varieties, often limited to the north-eastern region. Although food consumption is a subjective experience, cultural taboos within a community might be acceptable practices in another culture, creating conflicting notions of food practices. The balance between the twin axis of culture and politics regarding food is disrupted when heterogeneous cultural patterns and opposing political notions are in discord. Similarly, the solidarity within a cultural group becomes hostile when the authority of the individual concerning food choices is not aligned with the authority of the social structure. This discord from a political and cultural standpoint is evident in the Assamese socio-cultural scenario. Taking Bhaskar Hazarika's Ravening/Aamis (2019) as a case study, this paper proposes to analyse the representational troupe of food, through a structuralist anthropological lens, with respect to food politics to understand socio-cultural ramifications of Assamese food patterns. 2022 Aesthetics Media Services. All rights reserved. Creator An entity primarily responsible for making the resource Jacob A.; Chattaraj D. Source A related resource from which the described resource is derived Rupkatha Journal on Interdisciplinary Studies in Humanities, Vol-14, No. 2 Publisher An entity responsible for making the resource available Aesthetics Media Services Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.21659/rupkatha.v14n2.ne37" target="_blank" rel="noreferrer noopener">https://doi.org/10.21659/rupkatha.v14n2.ne37</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134808390&doi=10.21659%2Frupkatha.v14n2.ne37&partnerID=40&md5=4c58240ff579bc92415d9a1b2ec386c4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134808390&doi=10.21659%2frupkatha.v14n2.ne37&partnerID=40&md5=4c58240ff579bc92415d9a1b2ec386c4</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 9752935 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jacob A., Department of English and Cultural Studies, Christ University, Bangalore, India; Chattaraj D., Department of English and Cultural Studies, Christ University, Bangalore, India 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 Articles Article Faculty Publications -Articles 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 Forced Labour, Global Supply Chain and TNCs: Recent Trends and Practices Subject The topic of the resource Forced Labour; Human Rights; International Labour Organization; Modern Slavery; Supply Chain; The Forced Labour Convention; United Nations Description An account of the resource The abolition of forced labour is a fundamental element of contemporary international human rights law, but the idea has undergone a protracted and complex history, and the scope of the various international mechanisms that handle different aspects of it is not always precisely defined. Slavery, forced labour, and related practices are strictly prohibited under international law. Forced labour is a longstanding and complex obstacle in global supply chains, frequently associated with the desire for inexpensive products and the outsourcing of manufacturing processes to nations with lax labour regulations. The growing power of transnational corporations (TNCs) poses significant challenges to workers at the bottom of supply chains. However, disagreements have made it unclear how to deal with new forms of forced labour, or modern forms of slavery. This confusion highlights the need for a comprehensive approach to combating these issues. Efforts to stop or restrict forced labour will be made easier with a clear legal definition at both the national and international levels, particularly with an emphasis on the human rights perspective. 2024 Kluwer Law International BV, The Netherlands Creator An entity primarily responsible for making the resource Singh A.; Mishra A. Source A related resource from which the described resource is derived Global Trade and Customs Journal, Vol-19, No. 10, pp. 627-637. Publisher An entity responsible for making the resource available Kluwer Law International Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.54648/GTCJ2024073" target="_blank" rel="noreferrer noopener">https://doi.org/10.54648/GTCJ2024073</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204432812&doi=10.54648%2FGTCJ2024073&partnerID=40&md5=72634d08666d432a7a6a3a00f92c1325" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204432812&doi=10.54648%2fGTCJ2024073&partnerID=40&md5=72634d08666d432a7a6a3a00f92c1325</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 1569755X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singh A., Christ University, Karnataka, Bengaluru, India; Mishra A., Christ University, Karnataka, Bengaluru, India 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 PHD PhD PhD Thesis 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/. Relation A related resource 61000309 Title A name given to the resource Forcing Parameters and Propagation Time of Certain Graph Classes Subject The topic of the resource Mathematics and Statistics Description An account of the resource A branch of mathematics that treats vertices and edges of a graph is called graph theory. This theory is used to replicate many real-life situations and physical problems. Graph coloring problem is one of the prominent studies in extremal graph theory. Suppose information has to be communicated in a network or some product has to be marketed to all the people in a cluster then there are two types of cost that needs to be encountered, one the cost of selecting the initial set of vertices in a network and the second is, time it takes to propagate the information through the entire network. The sum of these two parameters is known as the total cost. Optimizing the total cost, which is the sum of vertices and the time it takes to propagate information through the entire network, is a challenging problem for any newlinegraph. Such an interesting and well-studied problem is called the dynamic coloring newlineproblem. The forcing problem also known as infecting or spreading problem is one newlinesuch dynamic coloring problem where two colors- white and black are used. Assume that a fxed set of vertices in a graph G are initially black and that the remaining vertices are considered white vertices. The aim of the forcing process is to obtain, fully black-colored vertices of the graph G by progressively applying the color change law, making sure that at least one white vertex is forced black in every discrete time interval. The forcing index is the minimum cardinality of the forcing sets. Diand#64256;erent types of forcing, such as one forcing, connected one forcing, k-forcing connected k-forcing etc., are defned based on the color change law. The one forcing is the basic form of forcing. A generalised form of one forcing is known as k-forcing where k lt V (G). The time taken by a forcing set to force the entire vertices of the graph G black is the propagation time or iteration index. The subject of study aims to fnd the one forcing number and k-forcing number of diand#64256;erent types of graph classes and derived graph classes. Creator An entity primarily responsible for making the resource M R, Raksha Source A related resource from which the described resource is derived Author's Submission Publisher An entity responsible for making the resource available Christ(Deemed to be University) Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Contributor An entity responsible for making contributions to the resource Dominic, Charles Rights Information about rights held in and over the resource Open Access Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource PhD Identifier An unambiguous reference to the resource within a given context <a href="http://hdl.handle.net/10603/547693" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/547693</a> https://archives.christuniversity.in/files/original/b7910f958f2bbe8a7af18b3a61e99c57.pdf e4a08cd508bc8cb49d9450daf6801e6e 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 Thesis PhD PhD Thesis 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 Forcing parameters and propagation time of certain graph classes / Subject The topic of the resource Mathematics Creator An entity primarily responsible for making the resource Raksha M R - 1942072 Publisher An entity responsible for making the resource available CHRIST (Deemed to be University) Date A point or period of time associated with an event in the lifecycle of the resource 2024-07-08 Language A language of the resource English Type The nature or genre of the resource PhD 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 Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Forecasting a Fast-Moving Consumer Goods (FMCG) Company's Customer Repurchase Behavior via Classification Machine Learning Models Description An account of the resource With numerous businesses offering clients equivalent products, the FMCG (Fast Moving Consumer Goods) industry is very competitive. Retaining client loyalty and encouraging them to return to make product purchases is a big concern for businesses in this sector. One of the main issues this bleak business needs to overcome is customer retention. Failure to repurchase by customers is a sign that they do not trust the brand, which will increase attrition rates and have an adverse effect on the company's revenue. These issues were addressed by attempting to predict the customer repurchase rate and approaching the target segments in accordance with that prediction, but this was done entirely from the perspective of the consumer and not from the retailer, and it ignores other factors like location, the salespeople they work with, the wholesaler they are affiliated with, and the customer programme they have chosen. The retailer's repurchase pattern must be predicted using a more accurate and effective model that considers all the variables. Retailers play a significant role in the supply chain for FMCG businesses. Different models like KNN, Nae Bayes and Logistic Regression was explored to find the best fit. By keeping them, the business can forge enduring connections that are crucial for preserving stabilityand dependability in the distribution network and having the resources necessary to serve its clients. 2023 ACM. Creator An entity primarily responsible for making the resource Helen Josephine V.L.; Joy J.; Rajan D. Source A related resource from which the described resource is derived ACM International Conference Proceeding Series Publisher An entity responsible for making the resource available Association for Computing Machinery Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1145/3647444.3647840" target="_blank" rel="noreferrer noopener">https://doi.org/10.1145/3647444.3647840</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194181096&doi=10.1145%2F3647444.3647840&partnerID=40&md5=3d781565c676efe2ef3e76e3c3e11826" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194181096&doi=10.1145%2f3647444.3647840&partnerID=40&md5=3d781565c676efe2ef3e76e3c3e11826</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-840070941-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Helen Josephine V.L., School of Business and Management, Christ University, Bangalore, India; Joy J., School of Business and Management, Christ University, Bangalore, India; Rajan D., Department of MCA, CMR Institute of Technology, Bangalore, India 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 Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Forecasting Bitcoin Price During Covid-19 Pandemic Using Prophet and ARIMA: An Empirical Research Subject The topic of the resource ADF; Bitcoin; Blockchain; Bubble; Covid-19; Cryptocurrency; Fintech Description An account of the resource Bitcoin and other cryptocurrencies are the alternative and speculative digital financial assets in today's growing fintech economy. Blockchain technology is essential for ensuring ownership of bitcoin, a decentralized technology. These coins display high volatility and bubble-like behavior. The widespread acceptance of cryptocurrencies poses new challenges to the corporate community and the general public. Currency market traders and fintech researchers have classified cryptocurrencies as speculative bubbles. The study has identified the bitcoin bubble and its breaks during the COVID-19 pandemic. From 1st April 2018 to 31st March 2021, we used high-frequency data to calculate the daily closing price of bitcoin. The prophet model and Arima forecasting methods have both been taken. We also examined the explosive bubble and found structural cracks in the bitcoin using the ADF, RADF, and SADF tests. It found five multiple breaks detected from 2018 to 2021 in bitcoin prices. ARIMA(1,1,0) fitted the best model for price prediction. The ARIMA and Facebook Prophet model is applied in the forecasting, and found that the Prophet model is best in forecasting prices. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. Creator An entity primarily responsible for making the resource Dhaku C.R.; Arumugam S.K. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-717 LNNS, pp. 487-495. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-35510-3_46" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-35510-3_46</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173568415&doi=10.1007%2F978-3-031-35510-3_46&partnerID=40&md5=9e9dec97b2406162055da5c01a59ede6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173568415&doi=10.1007%2f978-3-031-35510-3_46&partnerID=40&md5=9e9dec97b2406162055da5c01a59ede6</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-303135509-7 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dhaku C.R., Department of Commerce, CHRIST (Deemed to Be University), Central Campus, Bangalore, India; Arumugam S.K., Department of Professional Studies, CHRIST (Deemed to be University), Central Campus, Bangalore, India 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 Book Chapter Book Chapter Faculty Publications- Book Chapter 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 Forecasting Demand for Paddy and Cotton in India: Empirical Analysis Using Machine Learning Models Description An account of the resource India has a thriving and varied agricultural sector, which has long served as the foundation of the economy. Agriculture contributes significantly to Indias economy and is essential to the nations food security because a sizable percentage of the countrys agricultural population works in farming and associated industries. Indian farmers have managed to successfully produce a variety of commodities, including cash crops like cotton and sugarcane as well as staples like rice and wheat, despite confronting numerous obstacles like small landholdings, poor infrastructure, and unpredictable weather. In this context, it is crucial to examine the status of Indian agriculture at the moment, its advantages and disadvantages, and the possibilities and difficulties confronting farmers and policymakers. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. Creator An entity primarily responsible for making the resource Jahnavi A.G.; Bashir S. Source A related resource from which the described resource is derived Artificial Intelligence in Forecasting: Tools and Techniques, pp. 219-232. Publisher An entity responsible for making the resource available CRC Press Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003399292-14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003399292-14</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195925902&doi=10.1201%2F9781003399292-14&partnerID=40&md5=918adafd0e760e4a2a4a6fdc3fce92b8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195925902&doi=10.1201%2f9781003399292-14&partnerID=40&md5=918adafd0e760e4a2a4a6fdc3fce92b8</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-104005150-4; 978-103250615-9 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jahnavi A.G., CHRIST (Deemed to be University), Pune, India; Bashir S., Department of Data Science, CHRIST (Deemed to be University), Pune, India 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 Articles Article Faculty Publications -Articles 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 Forecasting gold prices based on extreme learning machine Subject The topic of the resource Extreme learning machine; Feed forward neural networks; Gold price forecasting Description An account of the resource In recent years, the investors pay major attention to invest in gold market because of huge profits in the future. Gold is the only commodity which maintains its value even in the economic and financial crisis. Also, the gold prices are closely related with other commodities. The future gold price prediction becomes the warning system for the investors due to unforeseen risk in the market. Hence, an accurate gold price forecasting is required to foresee the business trends. This paper concentrates on forecasting the future gold prices from four commodities like historical data's of gold prices, silver prices, Crude oil prices, Standard and Poor's 500 stock index (S & P500) index and foreign exchange rate. The period used for the study is from 1st January 2000 to 31st April 2014. In this paper, a learning algorithm for single hidden layered Feed forward neural networks called Extreme Learning Machine (ELM) is used which has good learning ability. Also, this study compares the five models namely Feed forward networks without feedback, Feed forward back propagation networks, Radial basis function, ELMAN networks and ELM learning model. The results prove that the ELM learning performs better than the other methods. 2006-2016 by CCC Publications. Creator An entity primarily responsible for making the resource Chandar S.K.; Sumathi M.; Sivanadam S.N. Source A related resource from which the described resource is derived International Journal of Computers, Communications and Control, Vol-11, No. 3, pp. 372-380. Publisher An entity responsible for making the resource available Agora University Date A point or period of time associated with an event in the lifecycle of the resource 2016-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.15837/ijccc.2016.3.2009" target="_blank" rel="noreferrer noopener">https://doi.org/10.15837/ijccc.2016.3.2009</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962439513&doi=10.15837%2Fijccc.2016.3.2009&partnerID=40&md5=fa9701d9ed74c52a195f9c916daddd6e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962439513&doi=10.15837%2fijccc.2016.3.2009&partnerID=40&md5=fa9701d9ed74c52a195f9c916daddd6e</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 18419836 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandar S.K., Christ University, Bangalore, India; Sumathi M., Sri Meenakshi Government College For Arts For Women (Autonomous), Madurai, India; Sivanadam S.N., Karpagam College Of Engineering, Coimbatore, India https://archives.christuniversity.in/files/original/3d6c6131b4c4499a7c7b0819321704ac.png dff3d3bdddff12ea6b1cef24016a12b1 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 Articles Article Faculty Publications -Articles 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 Forecasting gold prices based on extreme learning machine / Subject The topic of the resource Extreme Learning Machine Description An account of the resource International Journal Of Computers Communications & Control, Vol.11, Issue 3, pp.372-380, ISSN: 1841-9836. Creator An entity primarily responsible for making the resource Chandar, S Kumar., Sumathi, M., Sivanadam, S N. Source A related resource from which the described resource is derived <a href="https://univagora.ro/jour/index.php/ijccc/article/view/2009" target="_blank" title="Forecasting gold prices based on extreme learning machine" rel="noreferrer noopener">https://univagora.ro/jour/index.php/ijccc/article/view/2009</a> Publisher An entity responsible for making the resource available CCC Publications Date A point or period of time associated with an event in the lifecycle of the resource 2016-02-14 Language A language of the resource English Type The nature or genre of the resource Journal Article 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 Articles Article Faculty Publications -Articles 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 Forecasting intraday stock price using ANFIS and bio-inspired algorithms Subject The topic of the resource Adaptive neuro-fuzzy inference system; ANFIS; Bio-inspired algorithm; Genetic algorithm; Intraday day; Minute price; Particle swarm optimisation; PSO Description An account of the resource The main focus of this study is to explore the predictability of stock price with variants of adaptive neuro-fuzzy inference system (ANFIS) and suggests a hybrid model to enhance the prediction accuracy. Two variants of ANFIS model are designed which includes genetic algorithm-ANFIS (GA-ANFIS) and particle swarm optimisation-ANFIS (PSO-ANFIS) to forecast stock price more accurately. The standard ANFIS is tuned employing GA and PSO algorithm. The experimental data used in this investigation are stocks traded per minute price of four companies from NSE. Sixteen technical indicators are calculated from the historical prices and used as inputs to the developed models. Prediction ability of the developed models is analysed by varying number of input samples. Numerical results obtained from the simulation confirmed that the PSO-ANFIS model has the potential to predict the future stock price more precisely than GA-ANFIS as well as other earlier methods. Copyright 2021 Inderscience Enterprises Ltd. Creator An entity primarily responsible for making the resource Chandar S.K. Source A related resource from which the described resource is derived International Journal of Networking and Virtual Organisations, Vol-25, No. 1, pp. 29-47. Publisher An entity responsible for making the resource available Inderscience Publishers Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJNVO.2021.117754" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJNVO.2021.117754</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116032336&doi=10.1504%2FIJNVO.2021.117754&partnerID=40&md5=53f2d695cabbbb83cb85efa610a25d16" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116032336&doi=10.1504%2fIJNVO.2021.117754&partnerID=40&md5=53f2d695cabbbb83cb85efa610a25d16</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 14709503 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandar S.K., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, 560029, India 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 Articles Article Faculty Publications -Articles 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 Forecasting NIFTY 50 in Volatile Markets Using RNNLSTM: A Study on the Performance of Neural Network Models During the COVID-19 Pandemic Subject The topic of the resource forecasting; LSTM; neural network; NIFTY50; RNN Description An account of the resource The COVID-19 pandemic has shown us how the market can be highly uncertain and volatile at certain times. This brings a new level of challenges to all the investors and active traders in the market, as they have not seen such a movement in the past. However, as technology is evolving, highly sophisticated tools and techniques are being used by hedge funds and other investment banks to track down these movements and turn this into an opportunity. In this paper, we try to analyse how recurrent neural network (RNN) with long- and short-term memory architecture performs under volatile market conditions. For this study, we tried to perform a comparative analysis between two models within two successive time periods, where one is trained in a volatile market condition and the other in a relatively low volatile market condition. The results showed that the RNN model is less accurate in predicting the prices in a volatile market compared to a relatively low volatile market. We also compared these two models to a separate model where we trained using the combined data from the two successive time periods. Even though the addition in data points for the neural network produced a better result compared to the model trained under volatile conditions, it did not significantly perform better than the model, which was trained in the low volatile period. 2022 Management Development Institute. Creator An entity primarily responsible for making the resource Gnanendra M.; Sainath A.R.; Jijo P. Source A related resource from which the described resource is derived Vision Publisher An entity responsible for making the resource available Sage Publications India Pvt. Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1177/09722629221106251" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/09722629221106251</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132909080&doi=10.1177%2F09722629221106251&partnerID=40&md5=f643b0d94845590951de418c01b876c3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132909080&doi=10.1177%2f09722629221106251&partnerID=40&md5=f643b0d94845590951de418c01b876c3</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9722629 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gnanendra M., Department of Management Studies and Centre for Research, New Horizon College of Engineering, Karnataka, Bangalore, India; Sainath A.R., Department of Management Studies and Centre for Research, New Horizon College of Engineering, Karnataka, Bangalore, India; Jijo P., School of Business and Management, CHRIST (Deemed to Be University), Karnataka, Bengaluru, India 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 Book Chapter Book Chapter Faculty Publications- Book Chapter 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 Forecasting of Environmental Sustainability through Green Innovation of E-Vehicle Industry Description An account of the resource E-mobility sustainability forecasting is getting more detailed with study, taking into account social cost in addition to technological, economic, or environmental factors. One solution for reducing greenhouse gas emissions is to implement green innovation in the transportation sector. The citizenrys view and acceptance of electric cars must be improved, more research into the social cost of these innovations is required. Consequently, the transportation industry might decarbonize more quickly. Another approach to do it is to advocate for more all-encompassing green innovations that can enhance sustainable development. Using Our Common Future, published in 1987 by the World Commission on Environment and Development [1], the commission emphasized the importance of sustainability while integrating social and economic development. Additionally, it recommended that governments take environmental factors into account while making decisions. The significance of sustainability was then increased and institutionalized, which meant that nations began passing laws that promoted sustainability. Consumer awareness of sustainability is rising largely from an economic and environmental standpoint. This also has an impact on the transportation industry and poses significant environmental, social, and economic difficulties. However, given that it generates close to 5% of the GDP and employs almost 11 million people, transportation is crucial from an economic standpoint. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. Creator An entity primarily responsible for making the resource Jacob J.; Neelu P.; Antony A.C. Source A related resource from which the described resource is derived Artificial Intelligence in Forecasting: Tools and Techniques, pp. 62-72. Publisher An entity responsible for making the resource available CRC Press Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003399292-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003399292-4</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195960352&doi=10.1201%2F9781003399292-4&partnerID=40&md5=0b9c18422af1fc4732a344deee779175" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195960352&doi=10.1201%2f9781003399292-4&partnerID=40&md5=0b9c18422af1fc4732a344deee779175</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-104005150-4; 978-103250615-9 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jacob J., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Neelu P., School of Business and Management, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Antony A.C., Christ College, Vadgaon Sheri, Off Pune Nagar Road, Maharashtra, Pune, India 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 Articles Article Faculty Publications -Articles 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 Forecasting of foreign currency exchange rate using neural network Subject The topic of the resource Artificial neural network; Back propagation algorithm; Forecasting; Foreign exchange rate and training function Description An account of the resource Foreign exchange market is the largest and the most important one in the world. Foreign exchange transaction is the simultaneous selling of one currency and buying of another currency. It is essential for currency trading in the international market. In this paper, we have investigated Artificial Neural Networks based prediction modelling of foreign exchange rates using five different training algorithms. The model was trained using historical data to predict four foreign currency exchange rates against Indian Rupee. The forecasting performance of the proposed system is evaluated by using statistical metric and compared. From the results, it is confirmed that the new approach provided an improve technique to forecast foreign exchange rate. It is also an effective tool and significantly close prediction can be made using simple structure. Among the five models, Levenberg-Marquardt based model outperforms than other models and attains comparable results. It also demonstrates the power of the proposed approach and produces more accurate prediction. In conclusion, the proposed scheme can improve the forecasting performance significantly when measured on three commonly used metrics. Creator An entity primarily responsible for making the resource Kumar Chandar S.; Sumathi M.; Sivanandam S.N. Source A related resource from which the described resource is derived International Journal of Engineering and Technology, Vol-7, No. 1, pp. 99-108. Publisher An entity responsible for making the resource available Engg Journals Publications Date A point or period of time associated with an event in the lifecycle of the resource 2015-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925342759&partnerID=40&md5=c5ab94e4e56f705fb376f24826f647f6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925342759&partnerID=40&md5=c5ab94e4e56f705fb376f24826f647f6</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23198613 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar Chandar S., Christ University, India, Madurai Kamaraj University, Madurai, India; Sumathi M., Sri Meenakshi Government College for Arts for Women (Autonomous), Madurai, India; Sivanandam S.N., Karpagam College of Engineering, Coimbatore, India 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 Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Forecasting Prices of Black Pepper in Kerala and Karnataka using Univariate and Multivariate Recurrent Neural Networks Subject The topic of the resource Black Pepper; Gated Recurrent Unit (GRU); Long Short-Term Memory (LSTM); Macroeconomic; Singular Spectral Analysis (SSA); Time series Description An account of the resource Our country has a high level of agricultural employment. Price swings harm the economy of our country. To combat this impact, forecasting the selling price of agricultural products has become a need. Forecasts of agricultural prices assist farmers, government officials, businesses, central banks, policymakers, and consumers. Price prediction can then assist in making better selections in this area. Black pepper, sometimes known as the "King of Spices, " is a popular spice farmed and exported in India. The largest producers of black pepper are Karnataka and Kerala. For black pepper in Kerala and Karnataka, this study provides a univariate and multivariate price prediction model using Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data is denoised using Singular Spectral Analysis (SSA). The most accurate method is the multivariate variate LSTM technique, which uses macroeconomic variables. It has a Mean Absolute Percentage Error (MAPE) of 0.012 and 0.040 for Kerala and Karnataka, respectively. Grenze Scientific Society, 2022. Creator An entity primarily responsible for making the resource Kiranjith K.A.; Pramila R.M. Source A related resource from which the described resource is derived 13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022, Vol-8, pp. 803-810. Publisher An entity responsible for making the resource available Grenze Scientific Society Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143317391&partnerID=40&md5=960b6a7fa8f0f5c4588e41289ee87564" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143317391&partnerID=40&md5=960b6a7fa8f0f5c4588e41289ee87564</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-171385793-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kiranjith K.A., Christ (Deemed to be University), Pune Lavasa Campus, Maharashtra, India; Pramila R.M., Christ (Deemed to be University), Pune Lavasa Campus, Maharashtra, India 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 Book Chapter Book Chapter Faculty Publications- Book Chapter 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 Forecasting Stock Market Indexes Through Machine Learning Using Technical Analysis Indicators and DWT Subject The topic of the resource Discrete wavelet transform; Machine learning; Nifty 50; S&P 500; Stock market forecasting; SVR; Technical analysis indicators Description An account of the resource In recent years, the stock market prices have become more volatile due to refinement in technology and a rise in trading volume. As these seemingly unpredictable price trends continue, the stock market investors and consumers refer to the security indices to assess these financial markets. To maximise their return on investment, the investors could employ appropriate methods to forecast the stock market trends, taking into account the nonlinearity and nonstationarity of the stock market data. This research aims to assess the predictive capability of supervised machine learning models for the stock market regression analysis. The dataset utilised in this research includes the daily prices and additional technical indicator data of S&P 500 Index of US stock exchange and Nifty50 Index of Indian stock exchange from January 2008 to June 2016; both the indexes are weighted measurements of the top companies listed on respective stock exchanges. The model proposed in this research combines the discrete wavelet transform and support vector regression (SVR) with various kernels such as Linear, Poly and Radial basis function kernel (RBF) of the support vector machine. The results show that using the RBF kernel on Nifty 50 index data, the proposed model achieves the lowest MSE and RMSE error during testing are 0.0019 and 0.0431, respectively, and on S&P 500 index data, it achieves 0.0027 and 0.0523, respectively. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Patel S.; Surya B.D.V.; Manjunath C.; Marimuthu B.; Ghosh B. Source A related resource from which the described resource is derived Lecture Notes on Data Engineering and Communications Technologies, Vol-111, pp. 625-638. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-16-9113-3_46" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-9113-3_46</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133556520&doi=10.1007%2F978-981-16-9113-3_46&partnerID=40&md5=9f116fa2103c56642aa354758affe57b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133556520&doi=10.1007%2f978-981-16-9113-3_46&partnerID=40&md5=9f116fa2103c56642aa354758affe57b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23674512 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Patel S., Department of Computer Science and Engineering, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Surya B.D.V., Department of Computer Science and Engineering, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Manjunath C., Department of Computer Science and Engineering, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Marimuthu B., Department of Computer Science and Engineering, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India; Ghosh B., Department of Finance, RV Institute of Management, Karnataka, Bengaluru, India 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 Articles Article Faculty Publications -Articles 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 Forecasting stock market volatility in India - Using linear and non - Linear models Subject The topic of the resource BSE Sensex; Emerging markets; Forecasting; GARCH model; Volatility Description An account of the resource Volatility models and their forecasting performance attracted the interest of many economic agents, especially for financial risk management. The role of economic agents is to decide which one will be best model for forecasting volatility. This paper examines the modeling and forecasting performance of BSE Sensex daily stock market returns over the period from 1 July 1997 to 31 October 2008, by using simple Random Walk, GARCH, EGARCH and TGARCH models. The out-of-sample forecasts are evaluated by using MAE, RMSE, MAPE and Theil - U Statistics. The result suggests the standardized residual of white noise series strongly rejects the null hypothesis for GARCH model and capture the serial dependence and inherent nonlinearity series. Moreover, Random walk model dominates the forecasting performance and it is considered as the best model followed by the TGARCH model. International Economic Society. Creator An entity primarily responsible for making the resource Srinivasan K.; Deo M. Source A related resource from which the described resource is derived International Journal of Economic Perspectives, Vol-4, No. 4, pp. 617-622. Date A point or period of time associated with an event in the lifecycle of the resource 2010-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959400849&partnerID=40&md5=0500421aa7e9bc6a56feaca6d0375861" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959400849&partnerID=40&md5=0500421aa7e9bc6a56feaca6d0375861</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 13071637 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Srinivasan K., Department of Management Studies, Christ University, Bangalore, Karnataka - 560 029, India; Deo M., Department of Commerce (SOM), Pondicherry Central University, Kalapet, Puducherry, 605 014, India 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 Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Forecasting the Academic Horizon: Machine Learning Models Unraveling the Complex Web of Student Well-being Determinants Subject The topic of the resource Academic Attributes; Economic Indicators; Graduate; Machine Learning; Student Well Being Description An account of the resource In the contemporary academic landscape, the well-being of students is pivotal not only for their individual success but also for the broader educational ecosystem. This study meticulously delves into a rich dataset encompassing diverse student attributes, academic performance metrics, and economic indicators to discern patterns and predictors affecting student well-being. Leveraging a multi-faceted research methodology, we employed various machine learning models, ranging from logistic regression to advanced ensemble methods, aiming to forecast and comprehend the intricate determinants of student outcomes. The research design, underpinned by rigorous exploratory data analysis, revealed intriguing correlations between economic conditions, academic achievements, and students' well-being. The Gradient Boosting model, in particular, showed a significant improvement post hyperparameter tuning, with an accuracy reaching up to 77.63%. On the other hand, models like the Random Forest achieved a base accuracy of 77.29%. These insights highlight the potential of data-driven methodologies in understanding and predicting student well-being. As we stride into an era where data-driven decisions in education are paramount, our findings offer a robust foundation for future endeavors in this realm. Future directions of this study encompass refining prediction models with more granular data, exploring the psychological facets of student well-being, and devising actionable interventions based on the identified predictors. 2023 IEEE. Creator An entity primarily responsible for making the resource Basha M.S.A.; Biswas P.; Christina S.; Krishnan D.R.; Martha Sucharitha M. Source A related resource from which the described resource is derived Proceedings of NKCon 2023 - 2nd IEEE North Karnataka Subsection Flagship International Conference Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/NKCon59507.2023.10396455" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NKCon59507.2023.10396455</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185008823&doi=10.1109%2FNKCon59507.2023.10396455&partnerID=40&md5=6a09e0dbdb007a0a8370b67610def319" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185008823&doi=10.1109%2fNKCon59507.2023.10396455&partnerID=40&md5=6a09e0dbdb007a0a8370b67610def319</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835031404-5 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to Be University), Gitam School of Business, Bengaluru, India; Biswas P., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Christina S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Krishnan D.R., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Martha Sucharitha M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India