608 20 16481 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Hazardous Asteroids Using Machine Learning Subject The topic of the resource Asteroid Impact; data analysis - minor planets; Machine Learning; Planetary defense; Potentially Hazardous Asteroids; Prediction; Random Forest Classification Description An account of the resource As the need for early detection and mitigation of potential threats from near-Earth objects continues to grow, this study presents a comprehensive approach to predicting hazardous asteroids through the application of machine learning techniques. With the increasing interest in safeguarding our planet from potential impact events, the accurate classification and prediction of hazardous asteroids is of paramount importance. This research leverages a diverse dataset comprising a wide array of asteroid characteristics, including orbital parameters, physical properties, and historical impact data, to train and validate machine learning models. The study employs a combination of feature engineering, data preprocessing, and state-of-the-art machine learning algorithms to assess the risk posed by asteroids in near-Earth space. 2024 IEEE. Creator An entity primarily responsible for making the resource Surekha N.P.; Nanjundan P.; Bashir S. Source A related resource from which the described resource is derived ESIC 2024 - 4th International Conference on Emerging Systems and Intelligent Computing, Proceedings, pp. 169-174. 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/ESIC60604.2024.10481589" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ESIC60604.2024.10481589</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190992971&doi=10.1109%2FESIC60604.2024.10481589&partnerID=40&md5=3a4c445df8078adc6d2ead14b9dd5358" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190992971&doi=10.1109%2fESIC60604.2024.10481589&partnerID=40&md5=3a4c445df8078adc6d2ead14b9dd5358</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835034985-6 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 Surekha N.P., Christ University Pune, Department of Data Science, Lavasa, India; Nanjundan P., Christ University Pune, Department of Data Science, Lavasa, India; Bashir S., Christ University Pune, 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 Faculty Publications 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/. Creator An entity primarily responsible for making the resource Jangid, Harsh; Kannan, M.; Dua, Arush; Jaiswal, Kush; Singh, Gurinder; Agrawal, Rayan Title A name given to the resource Prediction of health insurance premium using bidirectional long short-term memory network with local interpretable model-agnostic explanations Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Artificial Intelligence, Computational Intelligence, and Inclusive Technologies: Proceedings of International Conference on Artificial Intelligence, Computational Intelligence, and Inclusive Technologies (ICRAIC2IT 2025);pp.621-630 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003740100-77" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003740100-77</a> <a href="https://www.scopus.com/pages/publications/105030796312?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030796312?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jangid H., Department of Computer Science, CHRIST University, Bengaluru, India; Kannan M., Department of Computer Science, CHRIST University, Bengaluru, India; Dua A., Department of Computer Science, CHRIST University, Bengaluru, India; Jaiswal K., Department of Computer Science, CHRIST University, Bengaluru, India; Singh G., Department of Computer Science, CHRIST University, Bengaluru, India; Agrawal R., Department of Computer Science, CHRIST University, Bengaluru, India Description An account of the resource This research proposes an application of deep learning techniques towards the prediction of insurance premiums using ConvLSTM, BI-LSTM, and CNN-LSTM models. Nowadays, Insurance is becoming more sophisticated, there is a need for better models that predict premiums so that risk factors that can be properly valued. The aim of this study is to improve the accuracy and reliability of insurance premium prediction using deep learning methods. The main challenge is the shallow traditional models, whose capturing of temporal dependencies is ineffective and results are not explainable resulting in very few stakeholders having any trust to the predictions. To solve this, this study compared three models: ConvLSTM model, BI-LSTM and CNN LSTM. Of these, the BI-LSTM model was the most effective because it was able to learn bidirectional sequential patterns. These patterns were enhanced using L2 regularization, dropout and dense layers to improve generalization. The dataset used comes from a Kaggle repository, which contained actual insurance data incorporating age, BMI, region and smoking as attributes. Results showed that BI-LSTM had performed the best as compare to other models in terms of accuracy and loss minimization. Important findings highlighted features such as age, smoking, and BMI as pivotal to estimating premiums. Also, to make the model explainable, we incorporated Explainable AI using LIME which delivers interpretable explanations by showing and visualizing the most important features for single predictions. 2026 selection and editorial matter, K. V. Sambasivarao, and Anasuya Sesha Roopa Devi Bhima; individual chapters, the contributors. All rights reserved. Subject The topic of the resource Deep learning; Health insurance; LSTM; Machine learning; XAI Publisher An entity responsible for making the resource available Taylor and Francis Relation A related resource ISBN: 978-104089003-5; 978-104124091-4; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 heart disease using XGB classifier Description An account of the resource Predicting heart disease in advance could be a significant medical breakthrough because it is widespread. A reliable strategy that can be utilized to do this is machine learning. Decision tree classifiers, random forests, and multilayer perceptron have all been used in studies to predict heart disease. However, several of these techniques could be improved, like poor precision. In our research, we have taken the South African heart Disease dataset and implemented a few models, which include Support Vector Machine (SVM), K Neighbors (KNN), Artificial neural network and XG Boost Classifier. We have used different methods for measuring performance. SVM with 69.0 accuracy, KNN with 86.0 accuracy, and ANN with 80.0 accuracy. However, the XGB classifier has shown some promising results in predicting heart disease with an accuracy of 90%. Further, when the hyperparameters were tuned using the random search method, the accuracy increased to 92.8%. The benefit of this work is that it uses machine-learning approaches to enhance the performance of coronary heart disease prediction. 2024 Author(s). Creator An entity primarily responsible for making the resource Vijayalakshmi S.; Sivakumar V.; Nataraj C.; Kanth P.C. Source A related resource from which the described resource is derived AIP Conference Proceedings, Vol-3161, No. 1 Publisher An entity responsible for making the resource available American Institute of Physics 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/10.1063/5.0229493" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0229493</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203980484&doi=10.1063%2F5.0229493&partnerID=40&md5=693fe65e4eb2ab89af2338d538b95d93" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203980484&doi=10.1063%2f5.0229493&partnerID=40&md5=693fe65e4eb2ab89af2338d538b95d93</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 Vijayalakshmi S., Department of Data Science, Christ University, Pune, India; Sivakumar V., School of Computing, Asia Pacific University of Technology and Innovation (APU), Kuala Lumpur, Malaysia; Nataraj C., School of Engineering, Asia Pacific University of Technology and Innovation (APU), Kuala Lumpur, Malaysia; Kanth P.C., Department of Data Science, Christ 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 Prediction of indoor air circulation of residential room with adaptation of solar chimney using numerical technique Subject The topic of the resource Natural Convection; Numerical Technique; Solar chimney; Ventilation Description An account of the resource With the exponential increase in consumption of electrical power during the summer season by household, there is a great need for households to withhold sustainability. To maintain the temperature of the household a passive heating and cooling system is used i.e. Solar Chimney. Ventilation, through a natural convection process, is gaining a lot of attention to be an alternative technique for mechanical air conditioning ventilation because of its reduced power usage when compared to the external cooling devices used in residential buildings of hot regions. The present study, involve solar chimney of horizontal and vertical designs in comparison with different width and height. The following paper studies the effect of a solar chimney on the indoor thermal behavior using Numerical Technique for a prototype of a residential room. The performance on the ventilation velocity and air temperature operation inside the room with varying air gap width is studied based on multiple numerical analysis solutions. The present study deals with two different architectures of a two dimensional model and results have shown that the ventilation velocity has increased to 0.017626444 kg/s and operative air temperature has been decreased by 7.26C for the vertical model while the horizontal model has shown a mass flow rate of 0.018027636 kg/s and a temperature decrease of 9.15C. The most efficient chimney was found to be model 7 which is horizontal solar chimney 3 with an air gap width of 0.05625m and a height of 0.3175 m, when compared to the other models from model number one to six. BEIESP. Creator An entity primarily responsible for making the resource Kongi P.; Thejaraju R.; Madan Kumar T.S.; Joseph P. Source A related resource from which the described resource is derived International Journal of Innovative Technology and Exploring Engineering, Vol-8, No. 11, pp. 4110-4116. 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="https://doi.org/10.35940/ijitee.K1477.0881119" target="_blank" rel="noreferrer noopener">https://doi.org/10.35940/ijitee.K1477.0881119</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073382269&doi=10.35940%2Fijitee.K1477.0881119&partnerID=40&md5=a5e5d1f16db122c43704dbbedafc62ca" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073382269&doi=10.35940%2fijitee.K1477.0881119&partnerID=40&md5=a5e5d1f16db122c43704dbbedafc62ca</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 22783075 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 Kongi P., B.V.B College of Engineering and Technology, India; Thejaraju R., Department of Mechanical and Automobile Engineering in the area of Heat Transfer, Fluid Dynamics and Energy, India; Madan Kumar T.S., Department of Mechanical and Automobile Engineering, India; Joseph P., CHRIST (Deemed to Be University), Faculty of Engineering, 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 Material Removal Rate and Surface Roughness in Hot Air Assisted Hybrid Machining on Soda-Lime-Silica Glass using Regression Analysis and Artificial Neural Network Subject The topic of the resource Artificial neural network (ANN); Hot air assisted hybrid machining (HAAHM); Material removal rate; Regression analysis (RA); Surface roughness Description An account of the resource Hybrid machining is a combination of conventional with the non-conventional process or two non-conventional processes. In the present work, an attempt has been made to combine hot air with a conventional cutting tool to form a novel Hot Air Assisted Hybrid Machining (HAAHM) for the machining of soda-lime-silica glass. The mathematical model for the Material Removal Rate (MRR) and Surface Roughness (Ra) using Regression Analysis (RA) and the Artificial Neural Network (ANN) models has been developed for the grooving process. The deviation of 8.24% and 7.70% were found in the prediction of MRR and Ra by regression analysis and the deviation of 1.89% and 1.70% for MRR and Ra using an artificial neural network model. The deviation between the predicted and the experimental results of both the models are found to be within the permissible limit. Higher predictive capabilities were observed in ANN model than the regression model. However, both models demonstrated good agreement with the MRR of soda-lime-silica glass by this hybrid machining process. 2020, Springer Nature B.V. Creator An entity primarily responsible for making the resource Nagaraj Y.; Jagannatha N.; Sathisha N.; Niranjana S.J. Source A related resource from which the described resource is derived Silicon, Vol-13, No. 11, pp. 4163-4175. Publisher An entity responsible for making the resource available Springer Science and Business Media B.V. 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/10.1007/s12633-020-00729-2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s12633-020-00729-2</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091683594&doi=10.1007%2Fs12633-020-00729-2&partnerID=40&md5=3e25985e70844c5871167ce2568f14f2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091683594&doi=10.1007%2fs12633-020-00729-2&partnerID=40&md5=3e25985e70844c5871167ce2568f14f2</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 1876990X 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 Nagaraj Y., Department of Mechanical Engineering, SJM Institute of Technology, Chitradurga, Karnataka, India; Jagannatha N., Department of Mechanical Engineering, SJM Institute of Technology, Chitradurga, Karnataka, India; Sathisha N., Department of Mechanical Engineering, Yenepoya Institute of Technology, Moodabidri, Mangaluru, Karnataka, India; Niranjana S.J., Department of Mechanical Engineering, Christ (Deemed to be University), Bangalore, 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 Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Joseph, Amal; Pambhar, Bansi; George, Allen; Kokatnoor, Sujatha Arun; Kumar, Sandeep Title A name given to the resource Prediction of Next-Day Stock Price Using Stacked Ensemble Learning TechniquesAn Exploration of Model Compatibility Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1239 LNNS;pp.253-265 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-1188-1_19" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-1188-1_19</a> <a href="https://www.scopus.com/pages/publications/105008496219?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008496219?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joseph A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Pambhar B., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; George A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India Description An account of the resource Trading professionals can make well-informed decisions about what to purchase or sell in order to maximize short-term gains by forecasting stock prices for the next day. This research study focuses on exploring the compatibility of ensemble learning techniques through stacking to predict next-day stock prices. The models involvedRandom Forest, Extra Trees, AdaBoost, and Gradient Boosting, were paired two at a time, and their predictions were used as inputs to a Multi-Layer Perceptron (MLP) Regressor, which served as the meta-learner. The results revealed that the combination of Extra Trees Regressor and Gradient Boosting outperformed the individual base models, due to their complementary strengths and ability to capture non-linear relationships effectively. However, other model combinations showed only average performance. This outcome was attributed to overlapping model strengths, leading to increase in error and overfitting. The findings highlight the importance of thoughtful model selection in ensemble methods and suggest that not all combinations are equally beneficial. Understanding the compatibility of different models is crucial to improving performance in ensemble learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource AdaBoost; Ensemble machine learning models; Extra trees; Gradient boosting; Multi-Layer Perceptron (MLP); Random forest; Stacking Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981961187-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Rainfall Using Seasonal Auto Regressive Integrated Moving Average and Transductive Long Short-Term Model Subject The topic of the resource Accuracy rate; Rainfall prediction; Seasonal Auto Regressive Integrated Moving Average; Time Series Analysis; Transductive Long Short-Term Model Description An account of the resource One of the most crucial parts of the practical application in recent years has been the analysis of time series data for forecasting. Because of the extreme climate variations, it is now harder than ever to estimate rainfall accurately. It is possible to forecast rainfall using a number of time series models that uncover hidden patterns in past meteorological data. Choosing the right Time Series Analysis Models for predicting is a challenging task. This study suggests using a Seasonal Auto Regressive Integrated Moving Average (SARIMA) to forecast values that are similar to historical values that exhibit seasonal patterns. Twelve years of historical weather data for the city of Lahore (from 2005 to 2017) and Blora Regency are taken into account for the prediction. The dataset underwent pre-processing operations like cleaning and normalisation before to the classification procedure. For classification, Transductive Long Short-Term Model (TLSTM) is employed which has learned the dependency values where the memory blocks are recurring and capable of learning long-term dependencies on this model. Further, TLSTM's goal is to increase accuracy close to the test point, where test points are selected as a validation group. The performance of the models has been assessed based on accuracy (99%), precision (98%), recall (96%) and fl-score (98%). Proposed SARIMA model showed optimistic results when compared to existing models. 2023 IEEE. Creator An entity primarily responsible for making the resource Phadke R.; Mohamad Ramadan G.; Archana Reddy R.; Kumar Pani A.; Al-Jawahry H.M. Source A related resource from which the described resource is derived IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 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/AIKIIE60097.2023.10390346" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIKIIE60097.2023.10390346</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187257517&doi=10.1109%2FAIKIIE60097.2023.10390346&partnerID=40&md5=eefe494d87dd726dbda70f9945426acb" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187257517&doi=10.1109%2fAIKIIE60097.2023.10390346&partnerID=40&md5=eefe494d87dd726dbda70f9945426acb</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835031646-9 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 Phadke R., Nitte Meenakshi Institute of Technology, Department of Electronics and Communication Engineering, Bangalore, India; Mohamad Ramadan G., College of Mlt Ahl Al Bayt University, Karbala, Iraq; Archana Reddy R., School of Computer Science and Artificial Intelligence Sr University, Warangal, India; Kumar Pani A., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Al-Jawahry H.M., The Islamic University, Najaf, Iraq 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 software defects using object-oriented metrics Subject The topic of the resource CK metrics; Object-oriented metrics; Software defects Description An account of the resource In recent years, many of the object-oriented software metrics were proposed for increasing the quality of software design such as prediction of defects and the maintainability of classes and methods. As the word metrics is frequently used for specific measurements taken on a particular process or item and in object-oriented metrics the metrics are the unit of measurements that is used to characterize the data.The fundamental point of this research is to identify the significance difference between software metrics which observes defect prediction and also study about their relation involving in the object oriented metrics that is named as "Chidamber and Kemerer metric suite" which is also known as "CK metrics suite", the number of defects and then finally decide the differences of the metrics in ordering to Eclipse classes as defective and selected with regard to defect prediction. IAEME Publication. Creator An entity primarily responsible for making the resource Pooja U.; Nizar Banu P.K. Source A related resource from which the described resource is derived International Journal of Civil Engineering and Technology, Vol-9, No. 1, pp. 889-899. Publisher An entity responsible for making the resource available IAEME Publication Date A point or period of time associated with an event in the lifecycle of the resource 2018-01-01 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-s2.0-85041682486&partnerID=40&md5=18d1d38943b165464954d33c6f069f47" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041682486&partnerID=40&md5=18d1d38943b165464954d33c6f069f47</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9766308 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 Pooja U., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, India; Nizar Banu P.K., Department of Computer Science, 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 Prediction of stock market price using hybrid of wavelet transform and artificial neural network Subject The topic of the resource Artificial neural network; Discrete Wavelet Transform (DWT); Time series and stock market prediction Description An account of the resource Background/Objectives: Accurate prediction of stock market is highly challenging. This paper presents a forecasting model based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) for predicting financial time series. Methods/Statistical analysis: The idea of forecasting stock market prices with discrete wavelet transform is the central element of this paper. The proposed forecasting model uses the Discrete Wavelet Transform to decompose the financial time series data. The obtained approximation and detail coefficients after decomposition of the original time series data are used as input variables of back propagation neural network to forecast future stock prices. Approximation coefficients can characterize the coarse structure of the data and detail coefficients capture ruptures, discontinuities and singularities in the original data, to recognize the long-term trends in the original data. Findings: The proposed model was applied to five datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model. It also proved that the hybrid forecasting technique has achieved better results compared with the approach which is not using the wavelet transform. Applications/Improvements: The accuracy of the proposed hybrid method can also be improved by developing a model using artificial neural network with Adaptive Neuro Fuzzy Interference System. Creator An entity primarily responsible for making the resource Chandar S.K.; Sumathi M.; Sivanandam S.N. Source A related resource from which the described resource is derived Indian Journal of Science and Technology, Vol-9, No. 8 Publisher An entity responsible for making the resource available Indian Society for Education and Environment 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/10.17485/ijst/2016/v9i8/87905" target="_blank" rel="noreferrer noopener">https://doi.org/10.17485/ijst/2016/v9i8/87905</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961990702&doi=10.17485%2Fijst%2F2016%2Fv9i8%2F87905&partnerID=40&md5=cc3ed64f826a1bf9ad0e40616099e3dd" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961990702&doi=10.17485%2fijst%2f2016%2fv9i8%2f87905&partnerID=40&md5=cc3ed64f826a1bf9ad0e40616099e3dd</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 9746846 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, Karnataka, 560029, India; Sumathi M., Sri Meenakshi Government College for Arts for Women (Autonomous), Madurai, Tamil Nadu, 625002, India; Sivanandam S.N., Karpagam College of Engineering, Coimbatore, Tamil Nadu, 641032, India 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 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Olakkengil, Divya Mathews; Aurelia, Sagaya P. Title A name given to the resource Predictive Analysis of Voice Pathology Using Logistic Regression: Insights and Challenges Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Engineering Proceedings;Volume;107;Issue;1;Article No.;28; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3390/engproc2025107028" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/engproc2025107028</a> <a href="https://www.scopus.com/pages/publications/105025653560?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105025653560?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Olakkengil D.M., Department of Computer Science, CHRIST University, Bangalore, 560029, India, Department of Computer Science, Vimala College (Autonomous), Thrissur, 680009, India; Aurelia S.P., Department of Computer Science, CHRIST University, Bangalore, 560029, India Description An account of the resource Voice pathology diagnosis is essential for the timely detection and management of voice disorders, which can significantly impact an individuals quality of life. This study employed logistic regression to evaluate the predictive power of variables that include age, severity, loudness, breathiness, pitch, roughness, strain, and gender on a binary diagnosis outcome (Yes/No). The analysis was performed on the Perceptual Voice Qualities Database (PVQD), a comprehensive dataset containing voice samples with perceptual ratings. Two widely used voice quality assessment tools, CAPE-V (Consensus Auditory-Perceptual Evaluation of Voice) and GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain), were employed to annotate voice qualities, ensuring systematic and clinically relevant perceptual evaluations. The model revealed that age (odds ratio: 1.033, p < 0.001), loudness (odds ratio: 1.071, p = 0.005), and gender (male) (odds ratio: 1.904, p = 0.043) were statistically significant predictors of voice pathology. In contrast, severity and voice quality-related features like breathiness, pitch, roughness, and strain did not show statistical significance, suggesting their limited predictive contributions within this model. While the results provide valuable insights, the study underscores notable limitations of logistic regression. The model assumes a linear relationship between the independent variables and the log odds of the outcome, which restricts its ability to capture complex, non-linear patterns within the data. Additionally, logistic regression does not inherently account for interactions between predictors or feature dependencies, potentially limiting its performance in more intricate datasets. Furthermore, a fixed classification threshold (0.5) may lead to misclassification, particularly in datasets with imbalanced classes or skewed predictor distributions. These findings highlight that although logistic regression serves as a useful tool for identifying significant predictors, its results are dataset-dependent and cannot be generalized across diverse populations. Future research should validate these findings using heterogeneous datasets and employ advanced machine learning techniques to address the limitations of logistic regression. Integrating non-linear models or feature interaction analyses may enhance diagnostic accuracy, ensuring more reliable and robust voice pathology predictions. 2025 by the authors. Subject The topic of the resource logistic regression; machine learning; voice pathology Publisher An entity responsible for making the resource available Multidisciplinary Digital Publishing Institute (MDPI) Relation A related resource ISSN: 26734591; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications 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/. Creator An entity primarily responsible for making the resource Sheriff, Naadia Ghalib; Bhattacharjee, Pragya; Kumar, B. R. Gagan Title A name given to the resource Predictive Analytics as a Catalyst for Disruption in the Fashion Ecosystem Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Smart Business and Education in the Age of VUCA and BANI;pp.243-272 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-7922-7.ch010" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-7922-7.ch010</a> <a href="https://www.scopus.com/pages/publications/105027118569?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027118569?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sheriff N.G., Christ University, Bangalore, India; Bhattacharjee P., Christ University, Bangalore, India; Kumar B.R.G., Christ University, Bangalore, India Description An account of the resource The fashion industry is one of the most dynamic industries, with trends that change constantly being moulded by the changing preferences of consumers. In this dynamic environment it is crucial for companies to adjust to the current market demand, failing which companies might plunge into losses and even face shutdowns. Predictive analytics plays an important role in trend prediction, demand forecasting, optimizing the supply chain and inventory management, personalization, dynamic pricing, waste minimization and prediction of sustainable materials. This, of course, is not without challenges. Data privacy concerns and over reliance on AI algorithms leading to inhibition of human creativity stand out as the most concerning challenges that need to be addressed to ensure that predictive analytics is utilised to its fullest potential in the fashion industry. Predictive analytics and AI can ensure that the fashion industry is not just profitable but also consumer centric and sustainable in its operations thus ensuring long term growth and sustenance of businesses in this industry. 2026 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 979-833737924-1; 979-833737922-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library