437 20 11858 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Predicting the Stock Markets Using Neural Network with Auxiliary Input Subject The topic of the resource Deep learning; Prediction; Stock market; Text classification Description An account of the resource Predicting the stock market has always been a challenging task and has always had a certain appeal for researchers all around the world. Stock markets are supposed to be quite random and people with experience in the market strongly agree to the fact. Thus, predicting the stock market accurately paves the way for endless money. To date, no such algorithm has been devised that could even predict the stock market with a 90% accuracy rate. The difficulty lies in the randomness of the markets, and the various complexities involved in modeling market dynamics. Nevertheless, there have been algorithms with a decent success rate and researchers around the world have been in a constant attempt to improve over them. Thus, through this paper we attempt at predicting the return of a stock over a period of 10days after a particular news was out regarding the stock using the headlines of the news and certain other features important in determining the direction of a stock. The model was implemented with a sigma score of 0.81. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Sakshi; Ghosh S. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-190, pp. 743-752. 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 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-16-0882-7_66" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-0882-7_66</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112245198&doi=10.1007%2F978-981-16-0882-7_66&partnerID=40&md5=a27282907642831cc1b0eadea6b85a6a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112245198&doi=10.1007%2f978-981-16-0882-7_66&partnerID=40&md5=a27282907642831cc1b0eadea6b85a6a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981160881-0 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 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 Sakshi, Computer Science and Engineering, Siddaganga Institute of Technology, Tumkur, India; Ghosh S., Computer Science and Engineering, Christ University, Bangalore, India https://archives.christuniversity.in/files/original/a49ba2830ab7ee1ab996fdeb985dd84a.jpg 8180a41ff194c26c6d215b57d6168bfb 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 Predicting the stock price index of yahoo data using elman network / Subject The topic of the resource Yahoo stock data Description An account of the resource International Journal of Control Theory and Applications, Vol.10, Issue 10, pp.481-497, ISSN: 0974-5572. Creator An entity primarily responsible for making the resource Chandar, S Kumar. Source A related resource from which the described resource is derived <a href="https://serialsjournals.com/abstract/36038_50.pdf" target="_blank" title="Predicting the stock price index of yahoo data using elman network" rel="noreferrer noopener">https://serialsjournals.com/abstract/36038_50.pdf</a> Publisher An entity responsible for making the resource available International Science Press Date A point or period of time associated with an event in the lifecycle of the resource 2017-02-16 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 Predicting the Thyroid Disease Using Machine Learning Techniques Description An account of the resource An endocrine gland that is allocated in the front of the neck is called the thyroid, which produces thyroid hormones as its main job. Thyroid hormone may be produced insufficiently or excessively as a result of its potential malfunction. There are various thyroid types including Hyperthyroidism, Hypothyroidism, Thyroid Cancer Thyroiditis, swelling of the thyroid. A goiter is an enlarged thyroid gland. When your thyroid gland produces more thyroid hormones than your body requires, you have hyperthyroidism. When the thyroid gland in our body doesnt provide enough thyroid hormones, then our body has hypothyroidism; when you have euthyroid sick, your thyroid function tests during critical illness taken in an inpatient or intensive care setting show alterations. Hypothyroid, hyperthyroid, and euthyroid conditions are expected from these thyroid conditions. The Three similarly used machine learning algorithms are: Support Vector Machine (SVM), Logistic Regression, and Random Forest methods, were evaluated from among the various machine learning techniques to forecast and evaluate their performance in terms of accuracy. Random forest can perform both regression and classification tasks. Logistic Regression is used to calculate or predict the probability of a binary (yes/no) event occurring. SVM classifiers offers great accuracy and work well with high dimensional space. A thyroid data set from Kaggle is used for this. This study has demonstrated the use of SVM, logistic regression, and random forest as classification tools, as well as the understanding of how to forecast thyroid disease. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Creator An entity primarily responsible for making the resource Krishnasamy L.; Aparnaa M.; Deepa Prabha G.; Kavya T. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-728 LNNS, pp. 49-57. 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-99-3932-9_6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-3932-9_6</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176016196&doi=10.1007%2F978-981-99-3932-9_6&partnerID=40&md5=22c1bbdae346d5c0024294052d29de94" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176016196&doi=10.1007%2f978-981-99-3932-9_6&partnerID=40&md5=22c1bbdae346d5c0024294052d29de94</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981993931-2 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 Krishnasamy L., Department of Computer Science and Engineering, School of Engineering and Technology, Christ (Deemed to be) University, Kengeri Campus, Bengaluru, 560074, India; Aparnaa M., Department of Information Technology, Kongu Engineering College, Tamil Nadu, Erode, India; Deepa Prabha G., Department of Information Technology, Kongu Engineering College, Tamil Nadu, Erode, India; Kavya T., Department of Information Technology, Kongu Engineering College, Tamil Nadu, Erode, India 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 Predicting Wind Energy: Machine Learning from Daily Wind Data Description An account of the resource This paper offers a comprehensive review of the advancements in the realm of renewable energy, specifically focusing on solid oxide fuel cells and electrolysers for green hydrogen production. The review delves into the significance of wind energy as a pivotal renewable energy source and underscores the importance of precise forecasting for efficient energy management and distribution. The integration of machine learning-based approaches, such as Support Vector Regression and Random Forest Regression, has shown promising results in enhancing the accuracy of wind energy production forecasts. Furthermore, the paper explores the broader landscape of renewable energy generation forecasting, emphasizing the rising prominence of machine learning and deep learning techniques. As the penetration of renewable energy sources into the electricity grid intensifies, the need for accurate forecasting becomes paramount. Traditional methods, while valuable, have encountered limitations, paving the way for advanced algorithms capable of deciphering intricate data relationships. The review also touches upon the inherent challenges and prospective research avenues in the domain, including addressing uncertainties in renewable energy generation, ensuring data availability, and enhancing model interpretability. The overarching goal remains the seamless integration of renewable sources into the grid, propelling us towards a greener future. The Authors, published by EDP Sciences, 2024. Creator An entity primarily responsible for making the resource Subramani K.; Sharon Sophia J.; Habelalmateen M.I.; Singh R.; Pahade A.; Ikhar S. Source A related resource from which the described resource is derived E3S Web of Conferences, Vol-540 Publisher An entity responsible for making the resource available EDP Sciences 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/10.1051/e3sconf/202454003009" target="_blank" rel="noreferrer noopener">https://doi.org/10.1051/e3sconf/202454003009</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198130852&doi=10.1051%2Fe3sconf%2F202454003009&partnerID=40&md5=5b6e72985b2c1e9bac6948b40b55e6ee" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198130852&doi=10.1051%2fe3sconf%2f202454003009&partnerID=40&md5=5b6e72985b2c1e9bac6948b40b55e6ee</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 25550403 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 Subramani K., School of Business and Management, CHRIST(Deemed to Be University) Bangalore Yeshwantpur Campus, India; Sharon Sophia J., School of Business and Management, CHRIST(Deemed to Be University) Bangalore Yeshwantpur Campus, India; Habelalmateen M.I., The Islamic University, Najaf, Iraq; Singh R., Department of Electronics and Communication Engineering, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India; Pahade A., Department of Computer Science and Engineering, IES College of Technology, IES University, Madhya Pradesh, Bhopal, 462044, India; Ikhar S., Yashika Journal Publications Pvt Ltd, Maharashtra, Wardha, India 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 Predicting Work Environment and Job Environment Among Employees using Transfer Learning Approach Subject The topic of the resource Environmentally Positive Behavior(EPB); Job S atisfaction; Organizational Citizenship Behavior (OCB) Description An account of the resource Today's enterprises face numerous challenges as a result of the world's rapid evolution. Maintaining a content workforce is crucial to a company's success and survival in today's fast-paced business environment. The efficacy, productivity, efficiency, and dedication of the company's staff are directly associated with the company's capacity to meet the needs of its employees in the workplace. The focus of this system is to identify the factors that contribute to a satisfying work environment for the participants. Preprocessing, feature selection, and model training are the first three steps in the suggested methodology. Data mining systems should get in the habit of normalizing data as a preliminary processing step. The multiple elements assessing company culture and worker satisfaction were consolidated using Principal Components Analysis (PCA) in the feature selection phase. Once features have been selected, KNN-SVM is utilized for model training. When compared to the two most popular alternatives, SVM and KNN, the proposed technique performs better. 2023 IEEE. Creator An entity primarily responsible for making the resource Shukla T.D.; Giri P.; Rana P.; Krishna V.P.; Thulasimani T.; Vanisri S. Source A related resource from which the described resource is derived 2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 771-776. 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/ICACRS58579.2023.10404165" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACRS58579.2023.10404165</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185383619&doi=10.1109%2FICACRS58579.2023.10404165&partnerID=40&md5=8c532fa188bb9a8b74c8b1c708ef2b16" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185383619&doi=10.1109%2fICACRS58579.2023.10404165&partnerID=40&md5=8c532fa188bb9a8b74c8b1c708ef2b16</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835034023-5 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Shukla T.D., University Institute of Teachers Training and Research, Chandigarh University, Mohali, India; Giri P., School of Business and Management, Christ (Deemed to be University,), Delhi NCR, India; Rana P., Department of MBA, School of Commerce and Management, IIMT University, Meerut, India; Krishna V.P., School of Management and Commerce, Malla Reddy University, Hyderabad, India; Thulasimani T., Department of Mathematics, Bannari Amman Institute of Technology, Tamilnadu, India; Vanisri S., Department of MCA, M.Kumarasamy College of Engineering, TamilNadu, Karur, India 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 and analysis of financial crises using machine learning Description An account of the resource This study presents a comparative analysis of various machine learning algorithms for credit risk assessment. The algorithms were tested on two credit datasets: German Credit Dataset and Australian Credit Dataset. The performance of the algorithms was evaluated based on several metrics, including sensitivity, specificity, accuracy, F-score, and Kappa. The results showed that the FCPFS-QDNN algorithm outperformed other algorithms in both datasets, achieving high accuracy, sensitivity, specificity, and F-score. On the other hand, the ACO Algorithm and Multilayer Perceptron algorithms were found to perform poorly in both datasets. The findings of this study have significant implications for credit risk assessment in banking and financial institutions. The study recommends the use of the FCPFS-QDNN algorithm for credit risk assessment due to its superior performance compared to other algorithms. 2023, IGI Global. All rights reserved. Creator An entity primarily responsible for making the resource Baranidharan S.; Rathod H.S. Source A related resource from which the described resource is derived Advancement in Business Analytics Tools for Higher Financial Performance, pp. 200-214. Publisher An entity responsible for making the resource available IGI Global 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/10.4018/978-1-6684-8386-2.ch010" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/978-1-6684-8386-2.ch010</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171232813&doi=10.4018%2F978-1-6684-8386-2.ch010&partnerID=40&md5=dbcd436022763f602f9498bdd739e493" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171232813&doi=10.4018%2f978-1-6684-8386-2.ch010&partnerID=40&md5=dbcd436022763f602f9498bdd739e493</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166848388-6; 1668483866; 978-166848386-2 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 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 Baranidharan S., CHRIST University (Deemed), India; Rathod H.S., Shri Jairambhai Patel Institute of Business Management, 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 and modeling of mechanical properties of concrete modified with ceramic waste using artificial neural network and regression model Subject The topic of the resource ANN; Compressive strength; LR; Rejected ceramics; Splitting tensile strength Description An account of the resource Over two centuries, concrete has been crucial to building. Thus, eco-friendly concrete is being developed. Emulating these tangible traits has recently gained popularity. Ceramic waste concretes mechanical properties were modeled in this study. Ceramic waste percentages ranged from 5 to 20%. Compressive and tensile concrete strengths were modeled. To predict concrete hardness, regression modeling and artificial neural network (ANN) were used. Model performance was evaluated using prediction coefficients and root-mean-square error (RMSE). ANN models outperformed linear prediction with a coefficient for determination (R2) of 0.97. ANN models achieved root-mean-square errors (RMSEs) of 1.22MPa, 1.21MPa, and 1.022MPa after 7, 14, and 28days of retraining, respectively. Linear regression model showed RMSE values of 1.21, 1.32, and 1.27MPa at 7, 14, and 28days, respectively. In determining the compressive and tensile strength, the R2 was 0.70, meanwhile the ANN model achieved 0.87. Given its accuracy in predicting the strength qualities of ceramics cement and structural stiffness, the ANN model presents a promising tool for representing various types of concrete. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Creator An entity primarily responsible for making the resource Kshirsagar P.R.; Upreti K.; Kushwah V.S.; Hundekari S.; Jain D.; Pandey A.K.; Parashar J. Source A related resource from which the described resource is derived Signal, Image and Video Processing, Vol-18, No. Suppl 1, pp. 183-197. 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11760-024-03142-z" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11760-024-03142-z</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189956901&doi=10.1007%2Fs11760-024-03142-z&partnerID=40&md5=4bf410cb78e49e9ffc69fa7a42a1ba90" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189956901&doi=10.1007%2fs11760-024-03142-z&partnerID=40&md5=4bf410cb78e49e9ffc69fa7a42a1ba90</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18631703 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 Kshirsagar P.R., Department of Electronics & amp; Telecommunication Engineering, J D College of Engineering & amp; Management, Nagpur, India; Upreti K., Department of Computer Science, CHRIST (Deemed to Be University), Delhi-NCR, Uttar Pradesh, Ghaziabad, India; Kushwah V.S., School of Computing Science & amp; Engineering, VIT Bhopal University, Bhopal-Indore Highway, Madhya Pradesh, Bhopal, India; Hundekari S., MIT College of Management, MIT ADT University, Loni Kalbhor, Pune, India; Jain D., Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, India; Pandey A.K., Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, India; Parashar J., Department of Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Engineering and Technology, New Delhi, India 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 Answer Keywords using Char-RNN Subject The topic of the resource Gated Recurrent Units; Long Short Term Memory; Natural Language Processing; Neural Networks; Recurrent Neural Network Description An account of the resource Generating sequences of characters using a Recurrent Neural Network (RNN) is a tried and tested method for creating unique and context aware words, and is fundamental in Natural Language Processing tasks. These type of Neural Networks can also be used a question-answering system. The main drawback of most of these systems is that they work from a factoid database of information, and when queried about new and current information, the responses are usually bleak. In this paper, the author proposes a novel approach to finding answer keywords from a given body of news text or headline, based on the query that was asked, where the query would be of the nature of current affairs or recent news, with the use of Gated Recurrent Unit (GRU) variant of RNNs. Thus, this ensures that the answers provided are relevant to the content of query that was put forth. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. Creator An entity primarily responsible for making the resource Pratheek I.; Paulose J. Source A related resource from which the described resource is derived International Journal of Electrical and Computer Engineering, Vol-9, No. 3, pp. 2164-2176. Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science 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/10.11591/ijece.v9i3.pp2164-2176" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijece.v9i3.pp2164-2176</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068008467&doi=10.11591%2Fijece.v9i3.pp2164-2176&partnerID=40&md5=49792670bb5fa3d4d5897c06657bf52d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068008467&doi=10.11591%2fijece.v9i3.pp2164-2176&partnerID=40&md5=49792670bb5fa3d4d5897c06657bf52d</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Relation A related resource ISSN: 20888708 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 Pratheek I., Department of Computer Science, Christ University, Hosur Road, Bangalore, 560029, Karnataka, India; Paulose J., Department of Computer Science, Christ University, Hosur Road, Bangalore, 560029, Karnataka, India 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 DDoS attacks in agriculture 4.0 with the help of prairie dog optimization algorithm with IDSNet Description An account of the resource Integrating cutting-edge technology with conventional farming practices has been dubbed smart agriculture or the agricultural internet of things. Agriculture 4.0, made possible by the merging of Industry 4.0 and Intelligent Agriculture, is the next generation after industrial farming. Agriculture 4.0 introduces several additional risks, but thousands of IoT devices are left vulnerable after deployment. Security investigators are working in this area to ensure the safety of the agricultural apparatus, which may launch several DDoS attacks to render a service inaccessible and then insert bogus data to convince us that the agricultural apparatus is secure when, in fact, it has been stolen. In this paper, we provide an IDS for DDoS attacks that is built on one-dimensional convolutional neural networks (IDSNet). We employed prairie dog optimization (PDO) to fine-tune the IDSNet training settings. The proposed model's efficiency is compared to those already in use using two newly published real-world traffic datasets, CIC-DDoS attacks. 2023, Springer Nature Limited. Creator An entity primarily responsible for making the resource Vatambeti R.; Venkatesh D.; Mamidisetti G.; Damera V.K.; Manohar M.; Yadav N.S. Source A related resource from which the described resource is derived Scientific Reports, Vol-13, No. 1 Publisher An entity responsible for making the resource available Nature Research 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/10.1038/s41598-023-42678-x" target="_blank" rel="noreferrer noopener">https://doi.org/10.1038/s41598-023-42678-x</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171395639&doi=10.1038%2Fs41598-023-42678-x&partnerID=40&md5=2684fc8f35010fa9846fb05c47963833" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171395639&doi=10.1038%2fs41598-023-42678-x&partnerID=40&md5=2684fc8f35010fa9846fb05c47963833</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Relation A related resource ISSN: 20452322; PubMed ID: 37717114 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 Vatambeti R., School of Computer Science and Engineering, VIT-AP University, Vijayawada, India; Venkatesh D., Department of Computer Science and Engineering, GITAM School of Technology, GITAM University-Bengaluru Campus, Bengaluru, India; Mamidisetti G., Department of Computer Science and Engineering, Malla Reddy University, Hyderabad, India; Damera V.K., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, India; Manohar M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Yadav N.S., Department of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, 500075, India 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 Depression in Young Adults Using Supervised Learning Algorithm Subject The topic of the resource Classification; Depression; Oversampling; Random Forest Classifier; Recursive Feature Elimination Description An account of the resource Over the years, mental health has achieved an essential role in the pertinent development of a human being, and a large part of the population is affected by it. The most commonly affected community being college-going students, and the most common disorders being Anxiety and Depression. Depression is a leading cause of suicide in individuals, where suicide is the second most prevailing reason for death among 1529-year-olds. This study aims to identify the different reasons and other factors associated with depression to predict and determine whether an individual faces depressive disorders. For this research purpose, the most appropriate classifier is selected. The absolute accuracy of the proposed model is 91.17%, i.e., the model can correctly predict whether an individual has depression 91.17% of the time. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Chakraborty A.; Shukla S. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-290, pp. 446-460. 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 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-16-4486-3_50" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-4486-3_50</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115086670&doi=10.1007%2F978-981-16-4486-3_50&partnerID=40&md5=7a4a6a1b20bc400b14ff93eeb0ffe9a6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115086670&doi=10.1007%2f978-981-16-4486-3_50&partnerID=40&md5=7a4a6a1b20bc400b14ff93eeb0ffe9a6</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981164485-6 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 Chakraborty A., Christ University, Bangalore, India; Shukla S., Christ University, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 football players performance using machine learning and deep learning algorithms Subject The topic of the resource Deep Learning Algorithm; Machine Learning algorithm Linear Regression; Neural networks; Performance Prediction Description An account of the resource In modern days the margin of error for football game is low, therefore the ultimate aim of the game is to win the match. The performance of the players in the match affects the results of the game. Due to this it is very important to evaluate the player and know his weakness. Manual evaluation tends to generate many errors and take more time. In the current research the statistical model is proposed to predict the stats of the football player based on previous session data by considering various aspects of the game. Through literature reviews it is observed that machine learning and deep learning algorithms can be used predict the performance of football player. But which model would be more efficient considering the positions of the player is not considered in any article. The proposed model has designed separate model as per the position of the player during the game. This can help to predict the player's performance as per their playing position. The current study has successfully implemented various machine learning and deep learning models and provide comparative analysis of the same. Each position has considered different variables associated with that position. The performance of these models is compared for further clarification 2021 IEEE. Creator An entity primarily responsible for making the resource Manish. S.; Bhagat V.; Pramila R.M. Source A related resource from which the described resource is derived 2021 2nd International Conference for Emerging Technology, INCET 2021 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 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/INCET51464.2021.9456424" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/INCET51464.2021.9456424</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113334181&doi=10.1109%2FINCET51464.2021.9456424&partnerID=40&md5=08439906702208501ed7eeae3af94f5b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113334181&doi=10.1109%2fINCET51464.2021.9456424&partnerID=40&md5=08439906702208501ed7eeae3af94f5b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-172817029-9 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 Manish. S., CHRIST(Deemed to Be University), Department of Data Science, Lavasa, Pune, India; Bhagat V., CHRIST(Deemed to Be University), Department of Data Science, Lavasa, Pune, India; Pramila R.M., CHRIST(Deemed to Be University), Department of Data Science, Lavasa, Pune, India 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 Friction Stir Welding Parameters Using Response Surface Methodology Subject The topic of the resource Friction stir welding; H13 tool steel; Response Surface Method Description An account of the resource The Friction Stir Welding (FSW) technique results in mixing and densification of weld joint in a more accurate and localized manner. FSW has been used to create a more significant weld with more structural integrity. In this research work, to join AA 3103 and AA 7075 was carried out. These alloys were preferred due to their wide variety of applications varying from aluminium fabrication to the aerospace industry. AA 7075, being a costlier metal, can be partially replaced with AA 3103, which can be economically justifiable for this research. The study tries to reveal the regression model by considering the FSW parameters like speed feed and offset. Various mechanical tests, impact tests and hardness tests were used for determining the most suitable weld joint. After conducting the tests, the results were analyzed using Minitab 18 software. The mathematical equations were derived out of Response Surface Methodology, which proved to be efficient. The report thus discusses the details in the analysis and study of FSW. 2023 American Institute of Physics Inc.. All rights reserved. Creator An entity primarily responsible for making the resource Raj A.; Kumar J.P.; Rout I.S.; Dulabhai H.P.; Thomas S. Source A related resource from which the described resource is derived AIP Conference Proceedings, Vol-2856, No. 1 Publisher An entity responsible for making the resource available American Institute of Physics Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0165890" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0165890</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177595229&doi=10.1063%2F5.0165890&partnerID=40&md5=780123892a982deabd03cdabbf4214f2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177595229&doi=10.1063%2f5.0165890&partnerID=40&md5=780123892a982deabd03cdabbf4214f2</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 Raj A., Department of Mechanical and Automobile Engineering School of Engineering and Technology, Christ university, Karnataka, Bengaluru, 560074, India; Kumar J.P., Department of Mechanical and Automobile Engineering School of Engineering and Technology, Christ university, Karnataka, Bengaluru, 560074, India; Rout I.S., Department of Mechanical and Automobile Engineering School of Engineering and Technology, Christ university, Karnataka, Bengaluru, 560074, India; Dulabhai H.P., Department of Mechanical and Automobile Engineering School of Engineering and Technology, Christ university, Karnataka, Bengaluru, 560074, India; Thomas S., Department of Mechanical and Automobile Engineering School of Engineering and Technology, Christ university, Karnataka, Bengaluru, 560074, India 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 ground water quality in western regions of Tamilnadu using LSTM network Subject The topic of the resource Groundwater; LSTM; Prediction; Tamil Nadu; Western region Description An account of the resource Assessing and safeguarding groundwater quality is critical for sustaining life in water-scarce regions like western Tamil Nadu. The motivation behind this study stems from the pressing need to address water quality challenges in a region grappling with scarcity. Despite existing efforts, a notable research gap exists in predictive tools that comprehensively capture the nuanced temporal variations and trends in groundwater quality. This is where the LSTM network steps in, showcasing exceptional accuracy in short-term predictions and discerning long-term trends. This research uses Long Short-Term Memory (LSTM) networks, a variant of recurrent neural networks, to predict groundwater quality in South Indian Regions, especially in Tamil Nadu. Extensive data, encompassing parameters such as pH, dissolved oxygen, turbidity, and various chemical constituents, were gathered over an extended timeframe. The LSTM model was then trained on this historical dataset, factoring in temporal dependencies and seasonality inherent in groundwater quality data. The validation process rigorously tests the LSTM model against actual groundwater quality measurements. The results were impressive, as the model demonstrated a remarkable ability to unravel the complex variations in groundwater quality. 2024 Elsevier B.V. Creator An entity primarily responsible for making the resource M K.; C V.S.R.P.; J V.A.; A S.; M S.; K.B P.; Shanmugm M. Source A related resource from which the described resource is derived Groundwater for Sustainable Development, Vol-25 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.gsd.2024.101156" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.gsd.2024.101156</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188555566&doi=10.1016%2Fj.gsd.2024.101156&partnerID=40&md5=1ad8807b4103a20417113b25393cc0b0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188555566&doi=10.1016%2fj.gsd.2024.101156&partnerID=40&md5=1ad8807b4103a20417113b25393cc0b0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2352801X 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 M K., Department of Electronics and Communication Engineering, Sri Ramakrishna Engineering College, Tamil Nadu, Coimbatore, India; C V.S.R.P., Department of Civil Engineering, Malla Reddy Engineering College, Telangana, Secunderabad, 500100, India; J V.A., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; A S., Department of Science and Humanities, Brilliant Grammar School Educational Society's Group of Institutions Integrated Campus, Telangana, Hyderabad, 501505, India; M S., Technical Executive, National Agro Foundation, Tamil Nadu, Chennai, 600113, India; K.B P., Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, BSF Campus, Yelahanka, Govindapura, Karnataka, Bengaluru, 560064, India; Shanmugm M., Department of Electronics and Communication Engineering, Kongu Engineering College, Perundurai, Erode, 638060, India 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 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 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