438 20 11858 https://archives.christuniversity.in/files/original/b817de489ce6c881c6355815d1af57c9.jpg aef8f902d41826f50c5dcaac97c0760b 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 Prediction of stock market price using hybrid of wavelet transform and artificial neural network / Subject The topic of the resource Stock Market Prediction Description An account of the resource Indian Journal of Science and Technology, Vol.9, Issue 8, pp.1-5, ISSN: 0974-5645 (Online) 0974-6846 (Print). Creator An entity primarily responsible for making the resource Chandar, S Kumar., Sumathi, M., Sivanandam S N. Source A related resource from which the described resource is derived <a href="https://indjst.org/articles/prediction-of-stock-market-price-using-hybrid-of-wavelet-transform-and-artificial-neural-network" target="_blank" title="Prediction of stock market price using hybrid of wavelet transform and artificial neural network" rel="noreferrer noopener">https://indjst.org/articles/prediction-of-stock-market-price-using-hybrid-of-wavelet-transform-and-artificial-neural-network</a> Publisher An entity responsible for making the resource available INDJST Date A point or period of time associated with an event in the lifecycle of the resource 2016-02-09 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 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 Prediction of Stock Prices using Prophet Model with Hyperparameters tuning Subject The topic of the resource forecast; hyperparameter tuning; prophet model; share prices; time-series Description An account of the resource As part of the data analytical process, predicting and time - series are crucial. In academics and financial research, anticipating share prices is a prominent and significant subject. A share market would be an uncontrolled environment for anticipating shares since there are no fundamental guidelines for evaluating or anticipating share prices there. As a result, forecasting share prices is a difficult time-series issue. fundamental, technical, time series predictions and analytical strategies are just a few of the various techniques and approaches that machine learning uses to execute stock value predictions. This article implements the stock price prediction, Researchers compared the model of the prophet with the tuned model of the prophet. By utilizing the tuning of hyperparameters using parameter grid search to improve the performance of the model accuracy for the best prediction. The findings of the study demonstrated that tuned model of the prophet with hyperparameters tuning which results in model accuracy and based on the experimental findings mean squared error (MSE) and mean absolute percentage error (MAPE) has significant improvement. 2022 IEEE. Creator An entity primarily responsible for making the resource Maheshwari A.; Malhotra A.; Tuteja S.; Ranka M.; Basha M.S.A. Source A related resource from which the described resource is derived 2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022 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 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/NKCon56289.2022.10126796" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NKCon56289.2022.10126796</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163063097&doi=10.1109%2FNKCon56289.2022.10126796&partnerID=40&md5=2a3c3f6228de9eb36506ccbaf6051c4c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163063097&doi=10.1109%2fNKCon56289.2022.10126796&partnerID=40&md5=2a3c3f6228de9eb36506ccbaf6051c4c</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166545342-4 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 Maheshwari A., School of Commerce, Finance and Accountancy Christ (Deemed to Be University), Department of Commerce, Ghaziabad, India; Malhotra A., School of Commerce, Finance and Accountancy Christ (Deemed to Be University), Department of Commerce, Ghaziabad, India; Tuteja S., Indira Institute of Management Pune (IIMP), Maharashtra, Pune, India; Ranka M., Arch Research College for Higher Education, Jaipur, India; Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management (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 Prediction of the capture and utilization of atmospheric acidic gases by azo-based square-pillared fluorinated MOFs Description An account of the resource More than the permissible limit of acidic gases like CO2, SO2, and NO2 in the atmosphere are responsible for the formation of acid rain, the greenhouse effect and many other undesirable environmental hazards. So, the capture and utilization of these gases are essential for mankind. Herein, we proposed an azo-based square pillared MOF, [Ni(MF5)(1,2-bis(4-pyridy)diazene)2]n, with the CUS metal site, i.e. M = Al/Fe, for the selective capture and conversion of acidic gas molecules into commodity chemicals such as cyclic carbonate, sulphite and nitrite. With the aid of Density Functional Theory (DFT), [Ni(MF5)(1,2-bis(4-pyridy)diazene)2]n has been optimized, and the specific force field is derived via guest-host interaction. The Grand Canonical Monte Carlo (GCMC) simulation has been used to explore the guest-host interactions over a wide range of pressures, and their respective stability under pre-humidification is evaluated. The adsorption prediction reveals that MFFIVE-Ni-apy have a higher adsorptive capacity (37.1 mmol g?1), and especially ALFFIVE-Ni-apy possesses a higher affinity towards guest molecules (CO2, SO2) rather than FEFFIVE-Ni-apy. Additionally, the adsorption of gases in the presence of humidity reveals that ALFFIVE-Ni-apy has an optimal adsorption capacity for all investigated acidic gases even at 38.5 RH%. The absorbed acidic gases on MFFIVE-Ni-apy were used for the theoretical investigations on cycloaddition with the aid of DFT as an application perspective of the toxic gases instead of expelling into atmosphere. The Climbing Image Nudged Elastic Band (CI-NEB) approach was used to discover the transition state in this scenario, in which the cycloaddition of adsorbed CO2, SO2, and NO2 gases with epoxides leads to the formation of cyclic carbonates, sulphites, and nitrates, respectively. 2023 The Royal Society of Chemistry. Creator An entity primarily responsible for making the resource Muthukumar D.; Palakkal A.S.; Pillai R.S. Source A related resource from which the described resource is derived Physical Chemistry Chemical Physics, Vol-25, No. 44, pp. 30458-30468. Publisher An entity responsible for making the resource available Royal Society of Chemistry 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/10.1039/d3cp02365f" target="_blank" rel="noreferrer noopener">https://doi.org/10.1039/d3cp02365f</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176106853&doi=10.1039%2Fd3cp02365f&partnerID=40&md5=51e11898204285ee9fc4c0635336fc93" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176106853&doi=10.1039%2fd3cp02365f&partnerID=40&md5=51e11898204285ee9fc4c0635336fc93</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 14639076; PubMed ID: 37921019; CODEN: PPCPF 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 Muthukumar D., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560, India; Palakkal A.S., Department of Chemistry, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, 603, India; Pillai R.S., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560, India, Analytical and Spectroscopy Division, ASCG/PCM, Vikram Sarabhai Space Center, Indian Space Research Organisation, Kerala, Thiruvananthapuram, 695022, 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 Prediction of Users Behavior on the Social Media Using XGBRegressor Subject The topic of the resource NLP; Social media; XGBRegressor Description An account of the resource The previous decennium has seen the growth and advance with respect to social media and such that has violently also immensely expanded to infiltrate each side of user lives. In addition, mobile network empowers clients to admittance to MSNs at whenever, anyplace, for any character, including job and gathering. Accordingly, the association practices among clients and MSNs are getting completer and more confounded. The goal of this paper is to examine the number of followers, likes, and post for Instagram users. The dataset yielded several fundamental features, which were used to create the model with multimedia social networks (MSNs). Then, natural language processing (NLP) features should be added and finally incorporate features derived in distinction to a machine learning technique like XGBRegressor with TF-IDF technique. We use two performance indicators to compare the different models: root mean square error (RMSE) and the R2 value. We achieved average accuracy using XGBRegressor which is 82%. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Tahseen S.; Danti A. Source A related resource from which the described resource is derived Lecture Notes on Data Engineering and Communications Technologies, Vol-111, pp. 491-502. 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_36" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-9113-3_36</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133544114&doi=10.1007%2F978-981-16-9113-3_36&partnerID=40&md5=4f3dcad166db285093479c34da2f9d3f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133544114&doi=10.1007%2f978-981-16-9113-3_36&partnerID=40&md5=4f3dcad166db285093479c34da2f9d3f</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 Tahseen S., Department of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India; Danti A., Department of Computer Science and Engineering, Christ (Deemed to be 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 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 Predictive analysis in smart agriculture Description An account of the resource Analyzing large databases for hidden connections, correlations and insights is known as big data analytics. Although many countries still use outdated farming methods, technological advancements have allowed for specific improvements (especially in developing countries). Big data analytics has the potential to expand the agricultural sector in this regard significantly. The farmers rely heavily on old methods for deciding what to plant and how to cultivate it. Walking through fields, selecting soil samples for moisture analysis, and visually inspecting plant leaves are typical examples of these time-honored practices. Understanding the significance of technology for acquiring crop information in considerable amounts and turning that data into usable knowledge is crucial for agriculturists (mainly farmers). Integration of big data could help agriculture make changes to its current practices. If used correctly, big data analytics can shed light on the most efficient crop cultivation methods. Extensive developments in three areas-crop prediction, precision farming and seed production-are reshaping the agricultural industry. There are four parts to this chapter. The first part of this paper provides an introduction to analytics on big data in agriculture. The second part will then focus on the various big data methods used in the agricultural sector. The third section provides two examples of how big data analysis methods were put to use in the field of agriculture. In the fourth section, the authors examine the several agricultural research avenues open to scholars and scientists. This chapter concludes with a brief overview. 2023 River Publishers. All rights reserved. Creator An entity primarily responsible for making the resource Balaji S.; Ashok J.; Selvaraju P.; Gangwar V.P. Source A related resource from which the described resource is derived Advanced Technologies for Smart Agriculture, pp. 105-127. Publisher An entity responsible for making the resource available River Publishers 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="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175668892&partnerID=40&md5=1e0f9587cc6c9a4a5997450fde263b3a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175668892&partnerID=40&md5=1e0f9587cc6c9a4a5997450fde263b3a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-877022883-1; 978-877022848-0 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 Balaji S., Department of Electrical and Electronics Engineering, SRM Valliammai Engineering College, India; Ashok J., School of Business and Management, CHRIST (Deemed to be University), India; Selvaraju P., Department of Mathematics, Rajalakshmi Institute of Technology, India; Gangwar V.P., Department of General Marketing, Mittal School of Business, Lovely Professional University, 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 Predictive Analysis of Academic Performance Among Students using A-CNN-BiLSTM Approach Subject The topic of the resource Academic Performance; Convolutional Neural Networks (CNN); Minimum Redundancy Maximum Relevance (mRMR) Description An account of the resource The number of possibilities to analyze educational data using data mining techniques is expanding, with the goal of improving learning outcomes. There is an explosion in data produced by online and virtual education, e-learning platforms, and institutional IT. Using these statistics, teachers could gain valuable insights into their students' learning habits. Academic performance of students and other useful information can be analyzed with the help of educational data mining. Model training consists of three primary steps: data preprocessing, feature selection, and training the model. To eliminate unwanted problems like noise and redundant attributes, data preparation is necessary. By prioritizing which features to calculate, the mRMR algorithm lowers calculation costs. Feature selection plays a crucial role in training A-CNN-BiLSTM models. The suggested approach routinely outperforms BiLSTM and CNN, two state-of-the-art algorithms. With a data accuracy percentage of 96.57%, it's clear that there was a significant improvement. 2024 IEEE. Creator An entity primarily responsible for making the resource Nishant N.; Magadum A.; Pandey V.; Suganthi D.; Rashmi Bh.; Vigneshwaran K. Source A related resource from which the described resource is derived 2024 2nd World Conference on Communication and Computing, WCONF 2024 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/WCONF61366.2024.10692223" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/WCONF61366.2024.10692223</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207377888&doi=10.1109%2FWCONF61366.2024.10692223&partnerID=40&md5=4d45f9a5dec15d1dd1f43576aaa5b829" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207377888&doi=10.1109%2fWCONF61366.2024.10692223&partnerID=40&md5=4d45f9a5dec15d1dd1f43576aaa5b829</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835039532-7 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 Nishant N., Department of CSE, School of Engineering, Babu Banarasi Das University, Lucknow, India; Magadum A., Department of MCA, MIT-ADT University, College of Management, Maharashtra, Pune, India; Pandey V., Amity University, Patna, India; Suganthi D., Department of Computational Intelligence, Saveetha College of Liberal Arts and Sciences, SIMATS, Chennai, India; Rashmi Bh., School of Business and Management, Christ University, Bengaluru, India; Vigneshwaran K., Department of ECE, K.Ramakrishnan College of Engineering, Trichy, 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 Predictive Analysis of Sleep Disorders Using Machine Learning: A Comprehensive Analysis Subject The topic of the resource AdaBoost; classification; machine learning; regression; sleep disorders; XGBoost Description An account of the resource The diagnosis of sleep disorders often relies on subjective patient reports, sleep diaries, and potentially cumbersome polysomnography (PSG) tests. However, these methods have limitations such as subjectivity, sleep diaries require meticulous effort, and expensive PSG tests are expensive, resource-intensive, and may not accurately capture sleep patterns in a non-clinical setting. Sleep disorders pose significant health risks and can impair overall well-being. Predictive analysis plays a crucial role in identifying individuals at risk of developing sleep disorders, enabling timely interventions and personalized treatment plans. In this paper, a comparative analysis of regression and classification models for sleep disorders prediction using machine learning (ML) techniques on insomnia and sleep apnea are discussed. Through extensive experimentation and comparative analysis, XGBoost and AdaBoost demonstrated as the most effective predictive models for insomnia and sleep apnea. AdaBoost and XGBoost classifiers are displaying 93.49% and 92.73% respectively. It is therefore possible to draw the conclusion that AdaBoost and XGBoost are doing well based on the findings as a whole, as indicated by the results. Our findings contribute to advancing the understanding and application of ML techniques in sleep disorder prediction, paving the way for more accurate and timely diagnosis based on ML techniques and personalized interventions in clinical practices. 2024 IEEE. Creator An entity primarily responsible for making the resource Malik N.; Gupta V.; Sharma V. Source A related resource from which the described resource is derived TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TQCEBT59414.2024.10545120" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545120</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204489521&doi=10.1109%2FTQCEBT59414.2024.10545120&partnerID=40&md5=18fc6931ff30743548ec970940b4486a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204489521&doi=10.1109%2fTQCEBT59414.2024.10545120&partnerID=40&md5=18fc6931ff30743548ec970940b4486a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038427-7 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 Malik N., Christ Deemed to Be University, India; Gupta V., Christ Deemed to Be University, India; Sharma V., Christ Deemed to Be University, 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 Predictive analysis of stock prices through scikit-learn: Machine learning in python Subject The topic of the resource Machine learning; Predictive analysis; Scikit-learn; Stock market Description An account of the resource Scikit-learn, a tool for developing machine learning algorithms, is a standard library of python. Through Scikit-learn, a trained model for predictive analysis can be developed. Such models aim to provide accurate predictions. Stock predictions are based on changes and patterns identified in the historical dataset. Following the trends and patterns of the historical changes of stocks, machine learning algorithms can be developed for achieving accurate outcomes. An effective model is developed, which enhance the working pattern or performance of the machine that further helps to draw a precise analysis of stocks. 2023 Scrivener Publishing LLC. Creator An entity primarily responsible for making the resource Mishra V.K.; Binyala R.; Sharma P.; Singh S. Source A related resource from which the described resource is derived Mathematics and Computer Science, Vol-1, pp. 397-404. Publisher An entity responsible for making the resource available Wiley Blackwell 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="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168709100&partnerID=40&md5=5832db983d571848a504fdedd86bcf9b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168709100&partnerID=40&md5=5832db983d571848a504fdedd86bcf9b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-111987983-1; 978-111987967-1 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 Mishra V.K., SCSE, Galgotias University, Uttar Pradesh, India; Binyala R., School of Business and Management, Christ (Deemed to be University), Delhi NCR, India; Sharma P., School of Business and Management, Christ (Deemed to be University), Delhi NCR, India; Singh S., School of Business and Management, Christ (Deemed to be University), Delhi NCR, 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 Predictive Analysis of the Recovery Rate from Coronavirus (COVID-19) Subject The topic of the resource Classification; COVID-19; Predictive analysis; Situational analysis; Summarization Description An account of the resource Estimation of recovery rate of COVID-19 positive persons is significant to measure the severity of the disease for mankind. In this work, prediction of the recovery rate is estimated based on machine learning technology. Standard data set of Kaggle has been used for experimental purpose, and the data sets of COVID cases in Italy, China and India for these countries are considered. Based on that data set and the present scenario, the proposed technique predicts the recovery rate. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Bhattacharya A.; Ghosh G.; Mandal R.; Ghatak S.; Samanta D.; Shukla V.K.; Mukherjee S.; Dutta S.; Mandal A. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-291, pp. 309-320. 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/10.1007/978-981-16-4284-5_27" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-4284-5_27</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116764767&doi=10.1007%2F978-981-16-4284-5_27&partnerID=40&md5=38e5a4a62167b27f27777eb5ef95d0e8" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116764767&doi=10.1007%2f978-981-16-4284-5_27&partnerID=40&md5=38e5a4a62167b27f27777eb5ef95d0e8</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981164283-8 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 Bhattacharya A., Institute of Engineering and Management, Kolkata, India; Ghosh G., Institute of Engineering and Management, Kolkata, India; Mandal R., Institute of Engineering and Management, Kolkata, India; Ghatak S., Institute of Engineering and Management, Kolkata, India; Samanta D., CHRIST (Deemed To Be University), Bengaluru, India; Shukla V.K., Amity University Dubai, Dubai, United Arab Emirates; Mukherjee S., Asansol Engineering College, Asansol, India; Dutta S., Institute of Engineering and Management, Kolkata, India; Mandal A., Institute of Engineering and Management, Kolkata, India https://archives.christuniversity.in/files/original/7544e453b93a1a535b3ea2c05c48c100.pdf c17302fa91541526d6b6686f7f8573a7 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 Predictive analytics for cardiac arrhythmia using machine intelligence Subject The topic of the resource Computer Science Description An account of the resource Myocardial Infarction (MI) is the primary cause of death worldwide. MI occurs when a plaque buildup in the inner surface of the coronary artery suddenly ruptures and prevents the blood flow. A heart attack is medically termed as MI. It is the irreversible damage caused by the prolonged ischemia. Ischemia is nothing but the heart organ doesn’t get enough blood and oxygen which is also termed as coronary artery disease or coronary heart disease. The heart gets damaged if it has not received enough blood or oxygen. In connection to the damage of the heart, arrhythmia would occur. Arrhythmia is the problem based on the heart rhythm or rate of the heartbeat. Tachycardia, when there is a fast beat in the heart. Bradycardia, when the heart beats too slow. The common type of arrhythmia is atrial fibrillation. The great concern is that the patient who has arrhythmia has to be treated immediately. They lose consciousness in a few minutes when the heart is not pumping enough blood mainly to the brain. Death occurs when the patient is not given emergency treatment. Creator An entity primarily responsible for making the resource Merjulah, R - 1650070 Publisher An entity responsible for making the resource available CHRIST (Deemed to be University) 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 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 61000161 Title A name given to the resource Predictive analytics for cardiac arrhythmia using machine intelligence Subject The topic of the resource Computer Science Description An account of the resource Myocardial Infarction (MI) is the primary cause of death worldwide. MI occurs when a plaque buildup in the inner surface of the coronary artery suddenly ruptures and prevents the blood flow. A heart attack is medically termed as MI. It is the irreversible damage caused by the prolonged ischemia. Ischemia is nothing but the heart organ doesn t get enough blood and oxygen which is also termed as coronary artery disease or coronary heart disease. The heart gets damaged if it has not received enough blood or oxygen. In connection to the damage of the heart, arrhythmia would occur. Arrhythmia is the problem based on the heart rhythm or rate of the heartbeat. Tachycardia, when there is a fast beat in the heart. Bradycardia, when the heart beats too slow. The common type of arrhythmia is atrial fibrillation. The great concern is that the patient who has arrhythmia has to be treated immediately. They lose consciousness in a few minutes when the heart is not pumping enough blood mainly to the brain. Death occurs when the patient is not given emergency treatment. newline Treatment which is included in the emergency is defibrillation and Cardiopulmonary Resuscitation (CPR). CPR is an emergency procedure which is combined with the chest compressions. It is through artificial ventilation which gives manual effort, preserves the brain functions until further treatment for the restoration of spontaneous blood circulation. The common symptoms of sudden cardiac death are chest pain, shortness of breath, severe wheezing, irregular heartbeats, fainting etc. newlineHeart Scar tissue which is not like heart muscle. It doesn t contrast like the normal heart muscle. Heart muscles get damaged for the heart attack patient based on the time of the treatment. The damage of the heart is based on the blockage of the artery. Arrhythmia can be predicted based on the volume of the scar region in the heart. Arrhythmia patients are treated by fixing Implantable Cardioverter Defibrillator (ICD). Creator An entity primarily responsible for making the resource Merjulah, R. 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 2020-01-01 Contributor An entity responsible for making contributions to the resource J, Chandra 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/10603/350759" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/350759</a> 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 Predictive Analytics for Network Traffic Management Subject The topic of the resource LSTM networks; machine learning; network traffic management; predictive analytics; real-time processing Description An account of the resource It examines how this can be applied to monitoring network traffic and carrying out predictive analysis to improve the functionality and effectiveness of network management. The study uses historical data of the network traffics and uses machine learning techniques such as the Long Short Term Memory based models and the Ensemble Methods to predict the traffic patterns in the future. It includes data gathering, data pre-processing, feature selection, model choice, model training, model validation, and the architectural setup of the machine learning solution in a real-time stream processing pipeline using Apache Kafka and Apache Flink. It is evident from the results that the proposed models yield a high level of accuracy in terms of prediction and that the Ensemble method alone gives a slightly higher accuracy than LSTM in the specific metrics. Real-time values closely followed actual traffic level, thus allowing real-time adjustments in network usage. In light of this, there is a clear understanding of the significance of having reliable data preprocessing, feature engineering, and model optimization process. The study also notes the need in prediction concerning data quality and scalability issues taking into account that current and future networks are characterized as dynamic and highly complex to offer more effective solutions for intelligent and proactive networking. 2024 IEEE. Creator An entity primarily responsible for making the resource Jain N.; Husain S.O.; Goyal S.; Hariharasudhan S.; Victor M.; Manjula Source A related resource from which the described resource is derived 2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IICCCS61609.2024.10763554" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IICCCS61609.2024.10763554</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214438436&doi=10.1109%2FIICCCS61609.2024.10763554&partnerID=40&md5=d44f26ed7daefa23fc7dd5486e317a68" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214438436&doi=10.1109%2fIICCCS61609.2024.10763554&partnerID=40&md5=d44f26ed7daefa23fc7dd5486e317a68</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835039075-9 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 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 Jain N., Ies College of Technology, Department of Electrical & Electronics Engineering, M.P., Bhopal, India; Husain S.O., The Islamic University, College Of Technical Engineering, Department Of Computers Techniques Engineering, Najaf, Iraq, The Islamic University Of Al Diwaniyah, College Of Technical Engineering, Department Of Computers Techniques Engineering, Al Diwaniyah, Iraq; Goyal S., Chandigarh Group of Colleges, Chandigarh Engineering College, Department of Computer Science Engineering, Punjab, Mohali, 140307, India; Hariharasudhan S., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Victor M., Christ (Deemed to be University), School of Business and Management, India; Manjula, Saveetha Institute of Medical and Technical Sciences, Department of Mathametics, Tamilnadu, Chennai, 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 Predictive Analytics for Stock Market Trends using Machine Learning Subject The topic of the resource Data Engineering; Data Preprocessing; Decision Trees; Deep Learning; Feature Selection; Financial Data; Linear Regression; Machine Learning; Machine Learning Algorithms; Random Forests; Recurrent Neural Networks (RNNs); Stock Market Trends Description An account of the resource Navigating the intricacies of stock market trends demands a novel approach capable of deciphering the web of financial data and market sentiment. This research embarks on a transformative journey into the realm of machine learning, where we harness the power of data to forecast stock market trends with increased precision and accuracy. Commencing with an exploration of stock market dynamics and the inherent limitations of traditional forecasting techniques, this paper takes a bold step into the future by embracing the potential of machine learning. The study begins with an in-depth analysis of data preprocessing, unraveling the complexity of feature selection and engineering, setting the stage for a data-driven odyssey. As our exploration progresses, we dive into the deployment of diverse machine learning algorithms, including linear regression, decision trees, random forests, and the formidable deep learning models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These algorithms act as our guiding lights, revealing intricate patterns concealed within historical stock price data. Our journey reaches new heights as we recognize the significance of augmenting predictive models with external data sources. Incorporating elements like news sentiment analysis and macroeconomic indicators enriches our understanding of the market landscape, enhancing the predictive capabilities of our models. We also delve into the crucial aspects of model evaluation, guarding against overfitting, and selecting appropriate performance metrics to ensure robust and reliable predictions. The research reaches its zenith with a meticulous analysis of real-world case studies, providing a comparative perspective between machine learning models and traditional forecasting methods. The results underscore the remarkable potential of machine learning in predicting stock market trends more accurately. 2023 IEEE. Creator An entity primarily responsible for making the resource Manasa N.; Praveenraj D.W.; Lakshmi S.R. Source A related resource from which the described resource is derived 2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023 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/ICCAKM58659.2023.10449528" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCAKM58659.2023.10449528</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187556499&doi=10.1109%2FICCAKM58659.2023.10449528&partnerID=40&md5=171d658a35aeb60e8ca851d7aaae7a84" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187556499&doi=10.1109%2fICCAKM58659.2023.10449528&partnerID=40&md5=171d658a35aeb60e8ca851d7aaae7a84</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835039324-8 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 Manasa N., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Praveenraj D.W., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Lakshmi S.R., School of Business and Management, Christ (Deemed to Be 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 Predictive analytics in cryptocurrency using neural networks: A comparative study Subject The topic of the resource Convolutional Neural Network (CNN); Cryptocurrency; Gated Recurrent Unit (GRU); Long Short Term Memory (LSTM) Description An account of the resource This paper is concerned with assessing different neural network based predictive models. Each of these predictive models has one goal and that is to predict the price of a cryptocurrency, Bitcoin is the cryptocurrency taken into consideration. The models will be focusing on predicting the USD equivalent value of bitcoin using historical data and live data. The neural network models being assessed are a Convolutional Neural Network, and two variations of the Recurrent Neural Network that are Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The goal is to observe the validation loss of each model and also the time it takes to train or epoch for each training set which basically just determine its efficiency and performance. The results that are achieved are almost what was expected as LSTM outperforms CNN but the when we take a look at GRU, it is at par with LSTM. However, CNN is quicker at training or creating epochs and the validation loss is acceptable and not too high but it looks so when it is compared with the Recurrent Neural Networks such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). BEIESP. Creator An entity primarily responsible for making the resource Khan A.S.; Augustine P. Source A related resource from which the described resource is derived International Journal of Recent Technology and Engineering, Vol-7, No. 6, pp. 425-429. Publisher An entity responsible for making the resource available Blue Eyes Intelligence Engineering and Sciences Publication Date A point or period of time associated with an event in the lifecycle of the resource 2019-01-01 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-s2.0-85067053116&partnerID=40&md5=4755b40d069d16c1fc4ef33c5acb7f5f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067053116&partnerID=40&md5=4755b40d069d16c1fc4ef33c5acb7f5f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 22773878 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 Khan A.S., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, India; Augustine P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, Karnataka, 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 Predictive Machine Learning Approaches for Estimating Residential Rental Rates in India Subject The topic of the resource Decision Tree Algorithm; Gradient Boosting Technique; Linear Regression Model; Random Forest Algorithm; Rental Rate Description An account of the resource As urban areas like Chennai and Bangalore witness a continuous surge in land and housing prices, accurately estimating the market value of houses has become increasingly crucial. This presents a formidable challenge, prompting a growing demand for an accessible and efficient method to predict house rental prices, ensuring dependable forecasts for future generations. In response to this need, this study delves into the core factors influencing rental prices, with a keen focus on location and area. Leveraging a dataset comprising ten essential features tailored for detecting Rental Price in Metropolitan cities, the research meticulously preprocesses the data using a Python library to ensure data cleanliness, laying a robust foundation for constructing the predictive model. Employing a diverse range of Machine Learning algorithms, including Random Forest, Linear Regression, Decision Tree Regression, and Gradient Boosting, the study evaluates their efficacy in forecasting rental prices. Notably, feature extraction underscores the significance of area and property type in shaping rental prices. In comparison with existing methodologies, this research adopts gradient boosting as its preferred approach, achieving the most satisfactory predictive outcomes. Evaluation metrics are meticulously analyzed to validate the model's performance. Through this comprehensive analysis, the study not only offers valuable insights into rental price prediction but also ensures a rigorous comparison with existing approaches, maintaining originality and relevance in addressing the pressing challenges of housing market dynamics. 2024 IEEE. Creator An entity primarily responsible for making the resource Jayadharshini P.; Ragunath R.; Prabhakaran D.; Krishnasamy L.; Abinaya N.; Priyanka S. Source A related resource from which the described resource is derived TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TQCEBT59414.2024.10545253" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545253</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204457681&doi=10.1109%2FTQCEBT59414.2024.10545253&partnerID=40&md5=e1120ea3fb4ecffa3d27417986fdc147" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204457681&doi=10.1109%2fTQCEBT59414.2024.10545253&partnerID=40&md5=e1120ea3fb4ecffa3d27417986fdc147</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038427-7 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 Jayadharshini P., Kongu Engineering College, Department of Artificial Intelligence, Tamilnadu, Erode, India; Ragunath R., Nandha Engineering College, Department of Information Technology, Tamilnadu, Erode, India; Prabhakaran D., Nandha Engineering College, Department of Information Technology, Tamilnadu, Erode, India; Krishnasamy L., Christ University, Department of Cse, Bangalore, India; Abinaya N., Kongu Engineering College, Department of Artificial Intelligence, Erode, India; Priyanka S., Kongu Engineering College, Department of Artificial Intelligence, Tamilnadu, Erode, 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 Predictive Modeling for Uber Ride Cancellation and Price Estimation: An Integrated Approach Subject The topic of the resource decision tree algorithms; fare estimation; gradient boosting; logistic regression; machine learning; predictive modeling; ride cancellation prediction; rideshare; transportation efficiency; Uber Description An account of the resource In the realm of ridesharing services, exemplified by Uber, two formidable challenges have surfaced: ride cancellations and precise fare estimation. This research introduces an innovative, integrated approach that leverages predictive modeling to address both issues. By analyzing historical ride data, we identify the intricate factors influencing cancellations, and through machine learning techniques, we develop predictive models to forecast cancellation likelihood. Additionally, we pioneer a dynamic approach to fare estimation by considering historical data alongside real-time variables. By unifying these strategies, we aim to enhance user satisfaction, optimize driver allocation, and promote trust and transparency within the ridesharing ecosystem. 2024 IEEE. Creator An entity primarily responsible for making the resource Gupta A.; Pipaliya A.; Jacob L.; Balagangadhar K. Source A related resource from which the described resource is derived TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TQCEBT59414.2024.10545287" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545287</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204466108&doi=10.1109%2FTQCEBT59414.2024.10545287&partnerID=40&md5=517f4020d2f61295cb026c7ece051cb5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204466108&doi=10.1109%2fTQCEBT59414.2024.10545287&partnerID=40&md5=517f4020d2f61295cb026c7ece051cb5</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038427-7 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 Gupta A., Christ (Deemed to Be University), School of Science, Lavasa, India; Pipaliya A., Christ (Deemed to Be University), School of Science, Lavasa, India; Jacob L., Christ (Deemed to Be University), Department of Data Science, Lavasa, India; Balagangadhar K., Christ (Deemed to Be University), Department of Data Science, Lavasa, 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 Predictive Modeling of Solar Energy Production: A Comparative Analysis of Machine Learning and Time Series Approaches Subject The topic of the resource Machine Learning approaches; Solar Energy; Time Series and ARIMA Description An account of the resource In this study, we dive into the world of renewable energy, specifically focusing on predicting solar energy output, which is a crucial part of managing renewable energy resources. We recognize that solar energy production is heavily influenced by a range of environmental factors. To effectively manage energy usage and the power grid, it's vital to have accurate forecasting methods. Our main goal here is to delve into various predictive modeling techniques, encompassing both machine learning and time series analysis, and evaluate their effectiveness in forecasting solar energy production. Our study seeks to address this by developing robust models capable of capturing these complex dynamics and providing dependable forecasts. We took a comparative route in this research, putting three different models to the test: Random Forest Regressor, a streamlined version of XGBoost, and ARIMA. Our findings revealed that both the Random Forest and XGBoost models showed similar levels of performance, with XGBoost having a slight edge in terms of RMSE.. By providing a comprehensive comparison of these different modeling techniques, our research makes a significant contribution to the field of renewable energy forecasting. We believe this study will be immensely helpful for professionals and researchers in picking the most suitable models for solar energy prediction, given their unique strengths and limitations. 2024 IEEE. Creator An entity primarily responsible for making the resource Sucharitha M.M.; Sowjanya S.; Sumalatha K.N.; Ayesha S.; Basha M.S.A. Source A related resource from which the described resource is derived Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 235-241. 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICWITE59797.2024.10502994" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICWITE59797.2024.10502994</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192550456&doi=10.1109%2FICWITE59797.2024.10502994&partnerID=40&md5=3bda5cb84c707a27021c6a67004c7a93" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192550456&doi=10.1109%2fICWITE59797.2024.10502994&partnerID=40&md5=3bda5cb84c707a27021c6a67004c7a93</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038328-7 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 Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sowjanya S., Rajeev Gandhi Memorial College of Engineering and Technology, Department of Management Studies, Nandyal, India; Sumalatha K.N., Maris Stella College, Vijayawada, India; Ayesha S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to Be University), Gitam School of Business, 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 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 Predictive Modelling of Heart Disease: Exploring Machine Learning Classification Algorithms Subject The topic of the resource Cross-validation; F1 Score; Grid Search; Heart attack prediction; Machine Learning; overfitting; Recall Description An account of the resource In addressing the critical challenge of early and accurate heart failure diagnosis, this study explores the application of five machine learning models, including XGBoost, Decision Tree, Random Forest, Logistic Regression, and Gaussian Naive Bayes. Employing cross-validation and grid search techniques to enhance generalization, the comparative analysis reveals XGBoost as the standout performer, achieving a remarkable accuracy of 85%. The findings emphasize the significant potential of XGBoost in advancing heart failure diagnosis, paving the way for earlier intervention, and potentially improving patient prognosis. The study suggests that integrating XGBoost into diagnostic processes could represent a valuable and impactful advancement in the realm of heart failure prediction, offering promising avenues for improved healthcare outcomes. 2024 IEEE. Creator An entity primarily responsible for making the resource Yadav J.; Nair A.M.; George J.; Alapatt B.P. Source A related resource from which the described resource is derived International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDCOT61034.2024.10515398" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDCOT61034.2024.10515398</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193242794&doi=10.1109%2FICDCOT61034.2024.10515398&partnerID=40&md5=22f504b8b66edfbc39176235c31f6a3b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193242794&doi=10.1109%2fICDCOT61034.2024.10515398&partnerID=40&md5=22f504b8b66edfbc39176235c31f6a3b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038295-2 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 Yadav J., Christ (Deemed to Be University), Delhi NCR, India; Nair A.M., Christ (Deemed to Be University), Delhi NCR, India; George J., Christ (Deemed to Be University), Delhi NCR, India; Alapatt B.P., Christ (Deemed to Be University), Delhi NCR, 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 Predictors of behavioral and emotional issues in children involved in custody disputes: A cross sectional study in urban Bengaluru Subject The topic of the resource Child Custody Disputes; Child Mental health; Divorce; India; SDQ Description An account of the resource Background: The increasing rates of divorce in urban India has led to the subsequent parental battle for the child's custody. This paper discusses the behavioral and emotional issues of these children in relation to their psychosocial environmental factors and other relevant socio-demographic variables. Methods: We used samples from parent interviews concerning 52 children aged 717-years-old, involved in child custody cases in the Family court of urban Bengaluru. The Strengths and Difficulties Questionnaire was used to measure response variables of behavioral and emotional issues in these children. Predictor models of quantile and multiple linear regression were used to assess the influence of psychosocial environmental factors and socio-demographic variables on the response variables. Results: The predictor models revealed that risk of child suffering emotional and behavioral issues increased with factors such as excessive parental control, change of academic environment, general unrest at school, frequency of child's court visit, child's visitation of non-custodian parent on occasions and vacations, and negatively altered family relationship. The model however intriguingly showed that residing in nuclear household rather than with their grandparents in a non-nuclear household, decreased the risk of mental health issues in these children. Conclusions: This study is a novel attempt to understand the influence of the psychosocial issues on the child's mental health in the context of custody cases in India. Despite the minimum sample size, the findings imply that family-based intervention is the need of the hour in these cases. The implications for clinical practice and research are discussed. 2021 Elsevier B.V. Creator An entity primarily responsible for making the resource Nambiar P.P.; Jangam K.V.; Jose A.; Seshadri S.P. Source A related resource from which the described resource is derived Asian Journal of Psychiatry, Vol-67 Publisher An entity responsible for making the resource available Elsevier B.V. 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/10.1016/j.ajp.2021.102930" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ajp.2021.102930</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120302855&doi=10.1016%2Fj.ajp.2021.102930&partnerID=40&md5=b427065c75a430aff4f6040947cb83d4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120302855&doi=10.1016%2fj.ajp.2021.102930&partnerID=40&md5=b427065c75a430aff4f6040947cb83d4</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18762018; PubMed ID: 34861567 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 Nambiar P.P., Department of Psychiatric Social Work, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India; Jangam K.V., Department of Psychiatric Social Work, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India; Jose A., Department of Statistics, Christ University, Bengaluru, India; Seshadri S.P., Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), 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 Predictors of compassion competence among nurses working in the non-profit healthcare sector in India Subject The topic of the resource compassion competence; engagement; nurse practice environment; the non-profit sector Description An account of the resource Objectives: For many years, the non-profit healthcare sector in India has been able to instil a sense of goodwill in the society through the provision of healthcare services, which are not only affordable and accessible, but also deliver compassionate care. This study was an attempt to evaluate the compassionate care and competence of the nurses working in India's non-profit healthcare sector, and to identify the predictive factors associated with their work environment and engagement. Methods: A cross-sectional survey of nurses working in the medical college hospitals managed by private trusts in the non-profit sector in India was conducted using an online questionnaire. The study was conducted in April 2021 after the second wave of the Covid-19 pandemic. Socio-demographic factors, compassion competence, nurse practice environment, and nurse engagement were assessed. Linear regression analysis was conducted to identify the variance and the predictors of compassion competence among Indian nurses. Results: We found that nurses practice environment (?=0.982, p=< .001) and engagement (?=0.842, p=< .001) predicted compassion competence during the Covid-19 pandemic. Moreover, nurse practice environment and engagement positively influenced compassion competence. Conclusion: There was a considerably high level of compassion competence among nurses working in the non-profit healthcare sector during the Covid-19 pandemic. The compassion phenomenon was statistically significantly impacted by the nurses practice environment and their level of engagement. Consequently, not only does competent compassion behaviour require positive work environments and engaged nurses, but also nurses compassion competence and its relationship with practice environment factors and engagement are critical in the non-profit healthcare sector in India. These findings support previous reviews that a high degree of compassion competence increases healthcare quality. 2024 Jismon, M. G., Rofin T. M., Thekkekkara, J. V., Asha K. C., & Vijesh P. V. Creator An entity primarily responsible for making the resource George J.M.; Rofin T.M.; Thekkekkara J.V.; Asha K.C.; Vijesh P.V. Source A related resource from which the described resource is derived International Journal of Practice-Based Learning in Health and Social Care, Vol-12, No. 2, pp. 34-48. Publisher An entity responsible for making the resource available Coventry University 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/10.18552/ijpblhsc.v12i2.1054" target="_blank" rel="noreferrer noopener">https://doi.org/10.18552/ijpblhsc.v12i2.1054</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212955375&doi=10.18552%2Fijpblhsc.v12i2.1054&partnerID=40&md5=b69c51b00891f061eb4c92a750f47a0e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212955375&doi=10.18552%2fijpblhsc.v12i2.1054&partnerID=40&md5=b69c51b00891f061eb4c92a750f47a0e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 20516223 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 George J.M., Christ University, NCR, Delhi, India; Rofin T.M., Indian Institute of Management, Mumbai, India; Thekkekkara J.V., St. Johns Medical College, Bangalore, India; Asha K.C., St. Thomas Hospital, Kerala, Chethipuzha, India; Vijesh P.V., Rajagiri College of Social Sciences, Kerala, Kalamassery, India