104 20 2150 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 Machine Learning Insights into Mobile Phone Usage and Its Effects on Student Health and Academic Achievement Subject The topic of the resource Academic Achievement; Machine Learning Models; Smart Phone; Student Health Description An account of the resource The research intends to find how students' health and academic performance are affected by their smartphone use. Considering how widely smartphones are used among students, it is important to know how they could affect health and learning results. This study aims to create prediction models that can spot trends and links between smartphone usage, health ratings, and academic achievement, thereby offering insightful information for teachers and legislators to encourage better and more efficient use among their charges. Data on students' mobile phone use, health evaluations, and academic achievement were gathered for the study. Preprocessing of the dataset helped to translate categorical variables into numerical forms and manage missing values. Trained and assessed were many machine learning models: Random Forest, SVM, Decision Tree, Gradient Boosting, Logistic Regression, AdaBoost, and K-Nearest Neighbors (KNN). The models' performance was evaluated in line with their accuracy in influencing performance effects and health ratings. Predictive accuracy was improved by use of feature engineering and model optimization methods. With 63.33% of accuracy for estimating health ratings, the SVM model was most successful in capturing the link between smartphone usage and health results. With an accuracy of 50%, logistic regression performed very well in forecasting performance effect, therefore stressing important linear connections between consumption habits and academic success. Random Forest and Decision Tree models were less successful for performance impact even if they showed strong performance in health forecasts. These results highlight the need of customized treatments to reduce the detrimental consequences of too high mobile phone use on students' academic performance and health. 2024 IEEE. Creator An entity primarily responsible for making the resource Biswas P.; Krishnan D.R.; Basha M.S.A.; Sucharitha M.M. Source A related resource from which the described resource is derived 2024 3rd International Conference for Advancement in Technology, ICONAT 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/ICONAT61936.2024.10774742" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONAT61936.2024.10774742</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214978797&doi=10.1109%2FICONAT61936.2024.10774742&partnerID=40&md5=4fa16545454a01d65796c2ab7cbfacd1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214978797&doi=10.1109%2fICONAT61936.2024.10774742&partnerID=40&md5=4fa16545454a01d65796c2ab7cbfacd1</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835035417-1 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 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 Biswas P., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Krishnan D.R., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., GITAM School of Business, (Deemed to Be University), Bengaluru, India; Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Sentiment Analysis on Live Webscraped YouTube Comments Using VADER Sentiment Analyzer Subject The topic of the resource Dataframes; Google API V3; Lemmatization; Live web scraping; NLP; Pandas; Sentiment analysis; Stemming; Tokenization; VADER Description An account of the resource After the covid disease came in the beginning of 2020s, the amount of people using social medias has increased dramatically. So as an effect of that, the viewers and engagement in one of the worlds largest platform by google called YouTube also increased. So many new content creators also born during these times. So this project is getting the sentiment from the audience or user to the content creators by which they can improve their content quality. This research holds promise in harnessing the power of sentiment analysis to enhance the overall YouTube experience and inform content creators and platform administrators in their decision-making processes. Understanding these trends is vital for content creators, as it can offer invaluable insights into viewer engagement and preferences. By gaining a deeper understanding of how viewers react to content, creators can refine their strategies, tailor their content to their audience, and enhance the overall quality of videos. By incorporating sentiment information into recommendations, the platform can suggest videos that resonate more effectively with users, thereby increasing engagement and satisfaction. The identification of negative sentiment and harmful comments enables YouTubes content moderation systems to proactively address issues such as hate speech, harassment, and toxicity. This, in turn, contributes to a safer and more welcoming space for users to share their thoughts and opinions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Creator An entity primarily responsible for making the resource Abhijith S.; Pani A.K. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-1112 LNNS, pp. 115-126. 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/10.1007/978-981-97-6684-0_11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-6684-0_11</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215665758&doi=10.1007%2F978-981-97-6684-0_11&partnerID=40&md5=bc94ae2cd74433d04dcc0e20a4a21ce0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215665758&doi=10.1007%2f978-981-97-6684-0_11&partnerID=40&md5=bc94ae2cd74433d04dcc0e20a4a21ce0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981976683-3 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 Abhijith S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Pani A.K., Department of Computer Science and Engineering, School of Engineering and Technology, 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 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 Impact of Variable Viscosity and Gravity Variations on Rayleigh-Bard Instabilities of Viscoelastic Liquids in Energy Sustainable System Subject The topic of the resource convection; variable gravity; viscoelastic Description An account of the resource Energy sustainability systems are vital for transitioning to a low-carbon economy, addressing climate change, and ensuring a sustainable future for all. Rayleigh-Bard convection (RBC) in viscoelastic liquids is a crucial phenomenon in various industrial and environmental applications, including energy sustainability systems where fluid dynamics play a pivotal role in optimizing heat transfer and system efficiency. The study deals with the combined influence of variable viscosity and variable gravity on RBC in viscoelastic liquids. The influence of space-dependent gravity on the onset of convection is considered. The results are analyzed against the background of constant gravity RBC in viscoelastic/Newtonian liquids with constant/variable viscosity. The possibility of variable gravity accelerating/decelerating the onset of convective instability is examined in this paper. 2024 IEEE. Creator An entity primarily responsible for making the resource Jayalatha G.; Sakshath T.N.; Siddheshwar P.G.; Sekhar G.N. Source A related resource from which the described resource is derived 8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 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/CSITSS64042.2024.10816883" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CSITSS64042.2024.10816883</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216930150&doi=10.1109%2FCSITSS64042.2024.10816883&partnerID=40&md5=43baaff74d836a572f5eaf769cc7509b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216930150&doi=10.1109%2fCSITSS64042.2024.10816883&partnerID=40&md5=43baaff74d836a572f5eaf769cc7509b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-833150546-2 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 Jayalatha G., Rv College of Engineering, Department of Mathematics, Bengaluru, India; Sakshath T.N., Rv College of Engineering, Department of Mathematics, Bengaluru, India; Siddheshwar P.G., Christ (Deemed to be University), Department of Mathematics, Bengaluru, India; Sekhar G.N., B M S College of Engineering, Department of Mathematics, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Decoding Big Data: The Essential Elements Shaping Business Intelligence Subject The topic of the resource Big Data Analytics; Business Intelligence(BI); Competitive Advantage; Modern Enterprises Description An account of the resource In today's Business Intelligence (BI) world, Big Data Analytics integration has become critical, transforming company strategy and decision-making processes. This study investigates the complex influence of Big Data on business intelligence, focusing on important drivers of this transition. It investigates how Big Data's improved data processing capabilities, integration of advanced analytics techniques such as machine learning, and real-time data insights enable businesses to make more informed decisions and achieve a competitive advantage. Furthermore, the paper emphasizes the importance of personalized consumer insights, operational savings, and strategic benefits obtained from predictive analytics when adopting Big Data for BI. 2024 IEEE. Creator An entity primarily responsible for making the resource Chopra D.; Shekhar S.; Christopher A.; Vinay M.; Kavitha S. Source A related resource from which the described resource is derived 8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 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/CSITSS64042.2024.10816944" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CSITSS64042.2024.10816944</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216927014&doi=10.1109%2FCSITSS64042.2024.10816944&partnerID=40&md5=12614261a431a40ec7c979dfce6eaf3c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216927014&doi=10.1109%2fCSITSS64042.2024.10816944&partnerID=40&md5=12614261a431a40ec7c979dfce6eaf3c</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-833150546-2 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 Chopra D., Christ University, Department of Computer Science, Bangalore, India; Shekhar S., Christ University, Department of Computer Science, Bangalore, India; Christopher A., Christ University, Department of Computer Science, Bangalore, India; Vinay M., Christ University, Department of Computer Science, Bangalore, India; Kavitha S., Christ University, Department of Computer Science, 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 Harnessing Machine Learning for Mental Health: A Study on Classifying Depression-Related Social Media Posts Subject The topic of the resource Classification Models; Depression; Mental Health; Social Media Posts Description An account of the resource This study is of particular relevance in the way it identifies depression-related content on social media using a machine learning model to classify posts and comments. This dataset, encompassing around 6500 entries from various platforms including Facebook, was rigorously annotated by four proficient English-speaking undergraduate students together with the final label which is established via majority voting. Data Preprocessing, initial cleaning, normalization and TF-IDF feature creation through vectorization for the output of POS tags. The different machine learning models that were trained and tested are Logistic Regression, Random Forest, SVM (Support Vector Machine), Naive Bayes Gradient Boosting Algorithm K-NN (K nearest Neighbors) AdaBoost Decision Tree. Authors evaluated the models and measured their accuracy, precision score, recall rate (also known as sensitivity) in addition to F1-score. Gradient Boost, Random Forest, and SVM were top performers among which Gradient boosting was found to be an overall best one with almost 98.5%. They show that machine learning model can successfully predict the label of social media posts, as a way for accurately identifying depression from text data. This detailed model performance evaluation is useful in understanding what each approach does well and poorly, shedding light into whether they are / would be actually suitable for real-world applications. This study not only developed discriminative classifiers, but also included detailed analysis of their performance which should hopefully guide future work and help in practical implementations for real-time mental health monitoring. Through this work, this study aim to facilitate timely identification of depression-related posts, ultimately supporting mental health awareness and intervention efforts on social media platforms. 2024 IEEE. Creator An entity primarily responsible for making the resource Varalakshmi C.; Christina S.; Lakshmi M.B.; Parthiban R.; Basha M.S.A.; Sucharitha M.M. Source A related resource from which the described resource is derived Proceedings of NKCon 2024 - 3rd Edition of IEEE NKSS's Flagship International Conference: Digital Transformation: Unleashing the Power of Information 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/NKCon62728.2024.10775133" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NKCon62728.2024.10775133</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215267566&doi=10.1109%2FNKCon62728.2024.10775133&partnerID=40&md5=40454881d15f309d4f58ff19132f489e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215267566&doi=10.1109%2fNKCon62728.2024.10775133&partnerID=40&md5=40454881d15f309d4f58ff19132f489e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835036456-9 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 Varalakshmi C., Andhra Loyola College, Department of Mba, Andhra Pradesh, Vijayawada, India; Christina S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Lakshmi M.B., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Parthiban R., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., Gitam (Deemed to Be University), Gitam School of Business, Bengaluru, India; Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Diabetes Mellitus Classification Using Machine Learning Algorithms with Hyperparameter Tuning Subject The topic of the resource Diabetes Mellitus; GridSearchCV; Hyperparameter Tuning; Machine Learning; One-hot Encoding; Random Forest Description An account of the resource Diabetes Mellitus is a prevalent condition globally, marked by elevated blood sugar levels resulting from either insufficient production of insulin or the body cells' inability to respond appropriately to released insulin. For people with diabetes to lead healthy, normal lives, early identification and treatment of the condition are essential. With the need to move away from current traditional procedures, towards a noninvasive methodology, machine learning and data mining technologies can be very useful in the classification of diabetes. Creating an effective machine learning model for the classification of diabetes mellitus was the primary goal of this research. This work is primarily carried out on combined Pima Indian diabetes dataset and German Frankfurt diabetes dataset. The class imbalance issue has been resolved using Synthetic Minority Oversampling Technique. One-hot encoding is applied to convert categorial features to numerical and various single and ensemble classifiers with the best hyperparameters obtained using GridSearchCV method were employed on the pre-processed dataset. With an AUC of 0.98 and maximum accuracy of 98.79%, the Random Forest ensemble technique outperformed the other models, according to the experimental results. As a result, the algorithm might be used to predict diabetes and alert doctors to serious cases that call for emergency care. 2024 IEEE. Creator An entity primarily responsible for making the resource Karna V.V.R.; Karna V.R.; Beemagani R.; Tummala A.B.; Arigela S.V.; Janamala V.; Flah A. Source A related resource from which the described resource is derived 2024 6th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 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/ISAECT64333.2024.10799744" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ISAECT64333.2024.10799744</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215554826&doi=10.1109%2FISAECT64333.2024.10799744&partnerID=40&md5=a37574f17316b9d549ef87a7d5e5c666" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215554826&doi=10.1109%2fISAECT64333.2024.10799744&partnerID=40&md5=a37574f17316b9d549ef87a7d5e5c666</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-833152998-7 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 Karna V.V.R., Aditya University, Electronics and Communication Engineeirng, Surampalem, India; Karna V.R., R V College of Engineering, Department of Aiml, Bengaluru, India; Beemagani R., Indian Institute of Technology, Dept. of Electrical Engineering, Tirupati, India; Tummala A.B., Chaitanya Bharati Institute of Technology, Electronics and Communication Engineeirng, Hyderabad, India; Arigela S.V., Manipal Academy of Higher Education, Manipal Institute of Technology, Department of Electrical and Electronics Engineering, Manipal, 576104, India; Janamala V., Christ University, School of Engineering and Technology, Dept. of Electrical and Electronics Engineering, Karnataka, Bangalore, 560074, India; Flah A., Energy Processes Environment and Electrical Systems Unit, National Engineering School of Gabes, University of Gabes, Tunis, Gab, 6029, Tunisia, Middle East University, Meu Research Unit, Amman, 11831, Jordan, University of Gabes, Private Higher School of Applied Sciences and Technology of Gabes, Gab, 6029, Tunisia, Vsb - Technical University of Ostrava, Enet Centre, Ostrava, 708 00, Czech Republic, Applied Science Private University, Applied Science Research Center, Amman, 11931, Jordan 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 Advanced Sentiment Analysis: From Lexicon-Enhanced BERT to Dimensionality Reduction Using NLP Subject The topic of the resource BERT Embeddings; Lexicon-Based Sentiment Analysis; Natural Language Processing (NLP); Sentiment Analysis in Many Different Languages Description An account of the resource Social media platforms serve as vital connections for communication, generating massive quantities of data that represent an array of perspectives. Efficient sentiment analysis is necessary for understanding public opinion, particularly in domains such as product reviews and socio-political discussion. This paper develops a novel sentiment analysis model that is customized for social media data by integrating machine learning algorithms, language processing techniques with part-of-speech tagging, and dimensionality reduction methods. The model will improve sentiment analysis performance by tackling challenges like noise and data domain variations. To further improve sentiment representation, it includes convolutional neural networks (CNNs), BERT embeddings, N-grams, and sentiment lexicons. The model's effectiveness is determined on a variety of datasets, which enhances sentiment analysis in social media discussion. This paper goes beyond sentiment analysis in code-mixed, multilingual text and highlights the importance of careful data before treatment and an extensive variety of ML algorithms. This study attempts to explain the nuances of sentiment analysis and its use in social media discussions through methodical research. 2024 IEEE. Creator An entity primarily responsible for making the resource Kumar V.; Chauhan P.S.; Lella J.A.; Momidi M.; Gupta P.; Upreti K. Source A related resource from which the described resource is derived 2024 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings, pp. 441-446. 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/ETNCC63262.2024.10767483" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ETNCC63262.2024.10767483</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214691215&doi=10.1109%2FETNCC63262.2024.10767483&partnerID=40&md5=d44be8ca845a288ff49ed5259752bf2d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214691215&doi=10.1109%2fETNCC63262.2024.10767483&partnerID=40&md5=d44be8ca845a288ff49ed5259752bf2d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835035326-6 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 Kumar V., SEAS, GWU, Washington, DC, United States; Chauhan P.S., Global Payments, Atlanta, United States; Lella J.A., Electrical & Comm. Engg., National Institute of Technology, Calicut, India; Momidi M., Dell Technologies, Bangalore, India; Gupta P., Computer Science Engineering, ADGIPS, Delhi, India; Upreti K., Christ University, Dept. of Computer Science, Delhi NCR Campus, 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 and Analyzing Early Onset of Stroke Using Advanced Machine Learning Classification Technique Subject The topic of the resource Bayes cv search; Decision Tree Classification; K_Nearest neighbors; Logistic Regression; Machine learning; Nae Bayes classification; Random Forest Classification; Stroke; Support Vector Machine Description An account of the resource Around the world, stroke is the leading cause of death. When blood vessels in the brain rupture, they cause damage. Alternatively, blockage in a blood vessel that supplies oxygen and other nutrients may also lead to this disease. This study uses various machine learning models to predict whether someone will have a stroke or not. Different physiological features were taken into account by this study while using Logistic Regression; Decision Tree Classification; Random Forest Classification; K-Nearest Neighbors (KNN); Support Vector Machine (SVM); Nae Bayes classifier algorithm; and XGBoost classification algorithm - these were used for six different models to ensure accurate predictions are made. We will accomplish the finest exactness with Bayes cv look which may be a hyper-tuning classifier with 92.87%. This consideration can be utilized for future work by doing the increase and include designing on the dataset. It is constrained to literary information, so it might not continuously be right for foreseeing stroke. so utilize the datasets that contain pictures and work on those datasets. 2024 IEEE. Creator An entity primarily responsible for making the resource Kumari P.; George J.; Nair A.M.; Paul Alapatt B.; Baby R.; Jose J. Source A related resource from which the described resource is derived 10th International Conference on Electrical Energy Systems, ICEES 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/ICEES61253.2024.10776853" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEES61253.2024.10776853</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215014262&doi=10.1109%2FICEES61253.2024.10776853&partnerID=40&md5=90767300c8fdacef0c0f46d96547044c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215014262&doi=10.1109%2fICEES61253.2024.10776853&partnerID=40&md5=90767300c8fdacef0c0f46d96547044c</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835035377-8 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 Kumari P., CHRIST (Deemed to be University), India; George J., CHRIST (Deemed to be University), India; Nair A.M., Luxsh Technologies Pvt Ltd, United Kingdom; Paul Alapatt B., CHRIST (Deemed to be University), India; Baby R., CHRIST (Deemed to be University), India; Jose J., 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 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 Leveraging Ensemble Methods for Accurate Prediction of Customer Spending Scores in Retail Subject The topic of the resource Academic Achievement; Machine Learning Models; Smart Phone; Student Health Description An account of the resource This study primarily aims to estimate consumer spending trends in a retail context. The goal is to identify the best model for predicting Purchasing Scores, which indicate customer loyalty and potential income, using demographic and financial data. The dataset included information about customers' age, gender, and annual income, and the objective was to analyze their Spending Scores. Several regression models were tested, including Linear Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors (KNN), and Lasso Regression. To improve the models, we engineered features like Age Squared, Income per Age, and Spending Score per Income. Each model was trained and tested using 3fold cross-validation. We evaluated their performance with Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. The results showed significant differences in model performance. The Random Forest model stood out, with the lowest Mean Absolute Error (MAE) of 0.33, Root Mean Square Error (RMSE) of 0.52, and the highest R-squared (R22) score of 0.9997. Gradient Boosting also performed well, achieving a Mean Absolute Error (MAE) of 1.77, Root Mean Square Error (RMSE) of 2.41, and an Rsquared (R2) score of 0.9930. While Linear Regression showed moderate accuracy, KNN and Lasso Regression had higher errors and lower R2 values, indicating less reliable predictions. The findings suggest that ensemble methods, particularly Random Forest, excel at predicting customer Spending Scores. The high accuracy and reliability of this model point to its potential for customer segmentation and targeted marketing strategies, ultimately enhancing customer relationship management and boosting business value. Further refinement and exploration of additional features could further improve these prediction capabilities. 2024 IEEE. Creator An entity primarily responsible for making the resource Prathiba L.; Raja S.; Umadevi A.; Sucharitha M.M.; Basha M.S.A. Source A related resource from which the described resource is derived 2024 3rd International Conference for Advancement in Technology, ICONAT 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/ICONAT61936.2024.10774867" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONAT61936.2024.10774867</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214993398&doi=10.1109%2FICONAT61936.2024.10774867&partnerID=40&md5=8fc29749a1c9a3535fc02c57f8a18e8c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214993398&doi=10.1109%2fICONAT61936.2024.10774867&partnerID=40&md5=8fc29749a1c9a3535fc02c57f8a18e8c</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835035417-1 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 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 Prathiba L., Master of Business Administration Ashoka Women's Engineering College, Andhra Pradesh, Kurnool, India; Raja S., R&D Institute of Science and Technology Avadi, Vel Tech Rangarajan Dr. Sagunthala, Chennai, India; Umadevi A., Alpha College of Engineering, Poonamallee, Department of Management Studies, Chennai, India; Sucharitha M.M., Md Shaik Amzad Basha Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., GITAM (Deemed to Be University), GITAM School of Business, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Cross-Modal Ingredient Recognition and Recipe Suggestion using Computer Vision and Predictive Modeling Subject The topic of the resource Computer vision; Firebase; Flutter; Ingredient recognition; KNN algorithm; Machine learning; Roboflow; YOLOv8 Description An account of the resource This paper is focused on the development of a novel system known as 'IngredEye.' It involves various approaches that can be grouped into categories, such as computer vision, including YOLOv8, a KNN prediction model, and a Flutter framework that hosts all of them in a mobile application environment. Previous studies have analyzed the application of computer vision and OpenCV recognition in cooking and proved that such approaches could enhance the level of convenience in the culinary field. This paper addresses issues like changes in lighting, occlusions, and other factors that have to be solved by the algorithms envisaged for real applications. The objective of this paper solely relies on integrating the OpenCV object detection method with comprehensive machine learning techniques specialized for the culinary field. Presenting the end-user with recipe recommendations based on the visual input they have given. 2024 IEEE. Creator An entity primarily responsible for making the resource Sanin Zulphi M.; Muzzammil Razin P.A.; Rahul Krishna K.R.; Ramasamy G. Source A related resource from which the described resource is derived 8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 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/CSITSS64042.2024.10816851" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CSITSS64042.2024.10816851</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216926745&doi=10.1109%2FCSITSS64042.2024.10816851&partnerID=40&md5=112229662bcea780f5146114833a2d96" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216926745&doi=10.1109%2fCSITSS64042.2024.10816851&partnerID=40&md5=112229662bcea780f5146114833a2d96</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-833150546-2 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 Sanin Zulphi M., School of Science, Christ(Deemed-to-be-University), Department of Computer Science, Karnataka, Bengaluru, 560029, India; Muzzammil Razin P.A., School of Science, Christ(Deemed-to-be-University), Department of Computer Science, Karnataka, Bengaluru, 560029, India; Rahul Krishna K.R., School of Science, Christ(Deemed-to-be-University), Department of Computer Science, Karnataka, Bengaluru, 560029, India; Ramasamy G., School of Science, Christ(Deemed-to-be-University), Department of Computer Science, Karnataka, Bengaluru, 560029, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Predicting Player Engagement in Online Gaming: A Machine Learning Approach Subject The topic of the resource Behavior; Machine Learning models; Online Gaming; Player Engagement Description An account of the resource The aim of this research is to make precise forecasts on player participation in online game using state-of-the-art machine learning algorithms. Player engagement plays a crucial element in determining the success of online games because it affects player retention, satisfaction and monetization. By understanding and predicting engagement levels, game developers and marketers can enhance the gaming experience and develop strategies to keep players invested. This research involves a comprehensive analysis of player behavior data from an online gaming platform. The dataset includes various demographic and behavioral features such as age, gender, location, game genre, playtime hours, in-game purchases, game difficulty, sessions per week, average session duration, player level, achievements unlocked, and engagement level. The data was preprocessed through handling missing values, normalizing numerical features, and encoding categorical variables. Exploratory Data Analysis (EDA) was conducted to understand the distribution and relationships between different features. Multiple machine learning models were evaluated to predict player engagement levels, including Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM). These models were then compared through the accuracy, precision, recall, and F1-score metrics. In the comparison, XGBoost emerged as the best model. Since it is the best-performing model, we can make the feature importance analysis to identify the best factors for predicting engagement in the next step. The XGBoost model achieved the highest accuracy of 91%, demonstrating superior precision, recall, and F1-scores across all engagement levels (High, Medium, Low). Ensemble methods like XGBoost, Gradient Boosting, and Random Forest outperformed the SVM model, highlighting their effectiveness in handling complex datasets. 2024 IEEE. Creator An entity primarily responsible for making the resource Manju Usha Sree T.; Sasikumar P.; Ayesha S.; Amzad Basha M.S.; Martha Sucharitha M. Source A related resource from which the described resource is derived Proceedings of NKCon 2024 - 3rd Edition of IEEE NKSS's Flagship International Conference: Digital Transformation: Unleashing the Power of Information 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/NKCon62728.2024.10774748" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NKCon62728.2024.10774748</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215295742&doi=10.1109%2FNKCon62728.2024.10774748&partnerID=40&md5=da6eafd1a38351897115404278edead7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215295742&doi=10.1109%2fNKCon62728.2024.10774748&partnerID=40&md5=da6eafd1a38351897115404278edead7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835036456-9 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 Manju Usha Sree T., Master of Business Administration Mallareddy Engineering College (Autonomous), Secunderabad, India; Sasikumar P., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Ayesha S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Amzad Basha M.S., Gitam (Deemed to Be University), Gitam School of Business, Bengaluru, India; Martha Sucharitha M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparative Study on GANs and VAEs in Credit Card Fraud Detection Subject The topic of the resource anomaly detection; credit card fraud; fraud detection; generative adversarial networks (GANs); synthetic data generation; variational autoencoders (VAEs) Description An account of the resource In today's world, the major issue credit card sectors encounter is fraud. This comparative study deals with how GANs and VAEs detect fraudulent transactions. The dataset comprised 284807 transactions, of which 492 were fraudulent. These two models, GANs and VAEs, are trained on this dataset, during which, in the training process, the models are learned to deal with the imbalance in the dataset. VAEs are trained so that fraud transactions are considered anomalies, and only legitimate transactions are passed onto the model for training. Conversely, GANs generate synthetic data of fraud by addressing the problem of data imbalance and passed on to the ML model for classification. We can observe that Both the models have very good AUC-ROC scores of around 96%, which indicates their distinguishing capability between the classes. In all other aspects, GANs outperformed VAEs, which makes GANs a better option for fraud detection. 2024 IEEE. Creator An entity primarily responsible for making the resource Sukruth S.; Haripriya M.; Deepa S.; Jayapriya J.; Vinay M. Source A related resource from which the described resource is derived 2024 1st International Conference on Software, Systems and Information Technology, SSITCON 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/SSITCON62437.2024.10796972" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SSITCON62437.2024.10796972</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215688428&doi=10.1109%2FSSITCON62437.2024.10796972&partnerID=40&md5=693147697cb88f36955eb6b9a210929a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215688428&doi=10.1109%2fSSITCON62437.2024.10796972&partnerID=40&md5=693147697cb88f36955eb6b9a210929a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835035293-1 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 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 Sukruth S., Christ University, Department of Computer Science, Bangalore, India; Haripriya M., Christ University, Department of Computer Science, Bangalore, India; Deepa S., Christ University, Department of Computer Science, Bangalore, India; Jayapriya J., Christ University, Department of Computer Science, Bangalore, India; Vinay M., Christ University, Department of Computer Science, 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 Sub-Optimization based Random Forest Algorithm for Accurate and Efficient Land use and Land Cover Classification using Landsat Time Series Data Subject The topic of the resource land use/land cover; pan-sharpening; random forest; remote sensing images; sub-forest optimization Description An account of the resource The land use and land cover (LULC) play an essential role to investigate the impacts of environmental factors and socio-economic development in the Earth's surface. Extracting the hidden information from the remote sensing images in the observed earth environment is the challenging process. In this research, implemented a model that uses Landsat data to investigate the LULC changes. Utilized the Landsat 5,7 and 8 as inputs for the 1985 to 2019 by Google Earth Engine (GEE) is applied for the robust classification. This paper proposed a Sub-forest optimization based Random forest (SO-RF) classifier with faster diagnosis speed for LULC classification. Moreover, to increase the multispectral Landsat band's resolution from 30 m to 15 m, the pan-sharpening algorithm is utilized. In addition, analyzed the various image configurations grounded numerous spectral indices and other supplementary data such as land surface temperature (LST) and digital elevation model (DEM) on final classification accuracy. The proposed SO-RF produced higher accuracy (0.97 for kappa, 96.78% Overall accuracy (OA), 0.94 for f1-score) than Copernicus Global Land Cover Layers (CGLCL) map and state of art methods like K-Nearest Neighbor (KNN), Decision Tree (DT), and Multi-class Support Vector machine (MSVM). 2024 IEEE. Creator An entity primarily responsible for making the resource Parameshachari B.D.; Jasim L.; Shivaraj S.; Suneetha P.; Manjunath C. Source A related resource from which the described resource is derived 2024 1st International Conference on Software, Systems and Information Technology, SSITCON 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/SSITCON62437.2024.10797034" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SSITCON62437.2024.10797034</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215702936&doi=10.1109%2FSSITCON62437.2024.10797034&partnerID=40&md5=a544739d09300e5a37a095c554fcb2b7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215702936&doi=10.1109%2fSSITCON62437.2024.10797034&partnerID=40&md5=a544739d09300e5a37a095c554fcb2b7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835035293-1 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 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 Parameshachari B.D., Nitte Meenakshi Institute of Technology, Department of Electronics and Communication Engineering, Bengaluru, India; Jasim L., The Islamic University of Babylon, Babylon, Iraq; Shivaraj S., Raichur (Affiliated to VTU), Hke Society's Sir. M Visvesvaraya College of Engineering, Department of Civil Engineering, Raichur, India; Suneetha P., S.R.K.R. Engineering College, Department of Information Technology, Bhimavaram, India; Manjunath C., Christ (Deemed to Be University), Department of Computer Science and Engineering, 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 Depth Wise Separable Convolutional Neural Network with Context Axial Reverse Attention Based Sentiment Analysis on Movie Reviews Subject The topic of the resource Bag-Of-Words and Term Frequency-Inverse Document Frequency; Bowerbird Optimization Algorithm; Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network; Sentiment Analysis Description An account of the resource Sentiment Analysis (SA) in movie reviews involves using natural language processing techniques to determine the sentiment expressed in reviews. This analysis helps in understanding the overall audience sentiment towards a movie, categorizing reviews as positive, negative, or neutral. It's useful for filmmakers, marketers, and audiences. The existing methods does not provide sufficient accuracy, error rate and complexity was increased. To overcome the aforementioned problem, Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN) is proposed for accurately classifying SA in movie reviews. In this input image is taken from two datasets such as IMDB dataset and Polarity dataset. The pre-processing is done using six steps namely, Cleaning, Tokenization, Case Folding, Normalization, Stop Word Elimination, and Stemming for the purpose of removing noises. Following that feature extraction are done using Bag-Of-Words and Term Frequency-Inverse Document Frequency (BOW-TF-IDF). After that classification are done using Depth Wise Separable Convolutional Neural Networks with Context Axial Reverse Attention Network (DWSCNN-CARAN)for classifying the AS in movie reviews. The efficiency of the proposed DWSCNN-CARAN-BOA is analyzed using a dataset and attains 99.94% accuracy, 98.76% recall and attains better results compared with the existing methods. In the future, this approach will use the adversarial instances it generated to conduct adversarial training and assess the potential improvement in classification performance. It also looks into the possibilities of creating adversarial examples at the word and sentence levels by combining structured knowledge from high-quality knowledge bases. 2024 IEEE. Creator An entity primarily responsible for making the resource Kapruwan A.; Pandey P.; Tripathi A.P.; Dhaundiyal P.; Patil H.; Maranan R. Source A related resource from which the described resource is derived 4th International Conference on Sustainable Expert Systems, ICSES 2024 - Proceedings, pp. 651-657. 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/ICSES63445.2024.10763067" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSES63445.2024.10763067</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214783688&doi=10.1109%2FICSES63445.2024.10763067&partnerID=40&md5=ec20b8acf81337abae00768f2939bc2b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214783688&doi=10.1109%2fICSES63445.2024.10763067&partnerID=40&md5=ec20b8acf81337abae00768f2939bc2b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-833154036-4 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 Kapruwan A., Graphic Era Deemed to be University, Department of Computer Science & Engineering, Uttarakhand, Dehradun, 248002, India; Pandey P., Akgec Ghaziabad, Department of Mca, Uttar Pradesh, Ghaziabad, 201001, India; Tripathi A.P., School of Business & Management (SBM), Christ (Deemed to be University), Delhi NCR campus, Uttar Pradesh, Ghaziabad, 201003, India; Dhaundiyal P., School of Business & Management (SBM), Christ (Deemed to be University), Delhi NCR campus, Uttar Pradesh, Ghaziabad, 201003, India; Patil H., School of Computer Science & Engineering, Iilm University, Uttar Pradesh, Greater Noida, 201306, India; Maranan R., Saveetha School of Engineering, Simats, Department of Research and Innovation, Tamil Nadu, Chennai, 602105, 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 Improving Groundwater Forecasting Accuracy with a Hybrid ARIMA-XGBoost Approach. Subject The topic of the resource ARIMA-XGB; Decision Trees; Groundwater level prediction; LGBM; MAE; Random Forest; RMSE; Stacking and ensemble; XGB Description An account of the resource In addressing the critical challenge of accurate groundwater level prediction, this study explores the comparative performance of various machine learning models. We implement a novel hybrid model combining ARIMA and Extreme Gradient Boosting (XGB) for the prediction of groundwater levels, and compare it against traditional models including ARIMA, XGBoost, LightGBM, Random Forest, and Decision Trees. Traditional approaches often rely on single models; however, our research seeks to delve into the intricacies of hybrid model architectures. Combining the strengths of ARIMA and XGB, we aim to build a highly accurate and efficient groundwater level prediction system. Comprehensive evaluations were conducted using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), The future scope of machine learning in water resource management includes integrating such models with real-time monitoring systems and expanding their applications to diverse environmental conditions and regions. 2024 IEEE. Creator An entity primarily responsible for making the resource George J.; Yadav J.; Nair A.M.; Peter M.V.; Alapatt B.P.; Baby R. Source A related resource from which the described resource is derived 2024 3rd International Conference for Advancement in Technology, ICONAT 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/ICONAT61936.2024.10775242" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONAT61936.2024.10775242</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215003825&doi=10.1109%2FICONAT61936.2024.10775242&partnerID=40&md5=cd9188f38eeb197b97f0d3b8f2107625" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215003825&doi=10.1109%2fICONAT61936.2024.10775242&partnerID=40&md5=cd9188f38eeb197b97f0d3b8f2107625</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835035417-1 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 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 George J., CHRIST (Deemed to Be University), India; Yadav J., CHRIST (Deemed to Be University), India; Nair A.M., Luxsh Solutions Private Limited, India; Peter M.V., CHRIST (Deemed to Be University), India; Alapatt B.P., CHRIST (Deemed to Be University), India; Baby R., 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 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 Seismic Performance Assessment of Reinforced Concrete Frames: Insights from Pushover Analysis Subject The topic of the resource Ductility; Non-linear analysis; RC frames; Shear capacity Description An account of the resource This paper offers a comprehensive exploration of the seismic response of Reinforced Concrete (RC) frames examined through pushover analysis. The frames analyzed are designed as per IS 13920 and IS 456 for different levels of earthquake intensities and different levels of axial loads. Nonlinear analysis techniques have gained prominence in assessing the response of RC frames, especially when subjected to extreme loading events or when accurate predictions of structural behavior are required beyond the linear elastic range. The study aims to delve into the structural behavior of RC frames under seismic influences, employing pushover analysis as the principal analytical tool. With a focus on assessing the effectiveness and reliability of pushover analysis, the research endeavors to elucidate the seismic performance of RC frames while considering their response to different seismic zones and axial loading scenarios. The methodology involves conducting a series of pushover analyses on RC frames using advanced structural analysis software. The results obtained are meticulously analyzed to discern the shear capacities and ultimate displacements of the frames, by investigating the displacement versus shear capacity relationship across varying seismic zones and axial loading scenarios. Through this comprehensive investigation, the paper aims to enhance our understanding of the seismic behavior of RC frames and will provide valuable insights for seismic design. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. Creator An entity primarily responsible for making the resource Umwi D.; Alex J. Source A related resource from which the described resource is derived Lecture Notes in Civil Engineering, Vol-607, pp. 979-987. 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/10.1007/978-3-031-70431-4_72" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-70431-4_72</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215303286&doi=10.1007%2F978-3-031-70431-4_72&partnerID=40&md5=cf7a44fb195c1ca87e631103b3cf4263" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215303286&doi=10.1007%2f978-3-031-70431-4_72&partnerID=40&md5=cf7a44fb195c1ca87e631103b3cf4263</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23662557; ISBN: 978-303170430-7 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 Umwi D., Department of Civil Engineering, CHRIST (Deemed to be) University, Bengaluru, India; Alex J., Department of Civil Engineering, CHRIST (Deemed to be) University, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 An Innovative Method for Brain Stroke Prediction based on Parallel RELM Model Subject The topic of the resource Brain Stroke Prediction; Extreme learning machine (ELM); Recursive Feature Elimination (RFE) Description An account of the resource Strokes occur when blood supply to the brain is suddenly cut off or severely impaired. Stroke victims may experience cell death as a result of oxygen and food shortages. The effectiveness of various predictive data mining algorithms in illness prediction has been the subject of numerous studies. The three stages that make up this suggested method are feature selection, model training, and preprocessing. Missing value management, numeric value conversion, imbalanced dataset handling, and data scaling are all components of data preparation. The chi-square and RFE methods are utilized in feature selection. The former assesses feature correlation, while the latter recursively seeks for ever-smaller feature sets to choose features. The whole time the model was being trained, a Parallel RELM was used. This new method outperforms both ELM and RELM, achieving an average accuracy of 95.84%. 2024 IEEE. Creator An entity primarily responsible for making the resource Kavitha K.; Devi V.J.; Alam M.S.; Sindhu V.; Gandhewar N.; Kalra G. Source A related resource from which the described resource is derived 4th International Conference on Sustainable Expert Systems, ICSES 2024 - Proceedings, pp. 1637-1643. 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/ICSES63445.2024.10763097" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSES63445.2024.10763097</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214838562&doi=10.1109%2FICSES63445.2024.10763097&partnerID=40&md5=eb53335ae8ea8b6b0b8388c05ba79d27" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214838562&doi=10.1109%2fICSES63445.2024.10763097&partnerID=40&md5=eb53335ae8ea8b6b0b8388c05ba79d27</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-833154036-4 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 Kavitha K., Gmr Institute of Technology, Department of CSE-AI&ML, Andhra Pradesh, Razam, India; Devi V.J., Aditya Institute of Technology and Management, Department of Information Technology, Andhra Pradesh, Tekkali, India; Alam M.S., College of Engineering and Computer Science, Jazan University, Department of Computer Science, Jizan, Saudi Arabia; Sindhu V., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Gandhewar N., Shri Ramdeobaba College of Engineering and Management, Department of Aiml, Nagpur, India; Kalra G., Maharishi Markandeshwar (Deemed to be University), Department of Mechanical Engineering, Mullana, Ambala, 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 Automated Single Responsibility Principle Enforcement: A Step Toward Reusable and Maintainable Code Subject The topic of the resource Code smells; Cyclometric complexity; Halstead volume; Interface analysis; Single responsibility principle Description An account of the resource In this study, we delve into the sphere of automated code scrutiny, specifically concentrating on compliance with the single responsibility principle (SRP), a key principle in software architecture. The SRP proposes that a class should have a singular reason for modification, thereby enhancing code cohesion and facilitating its maintenance and reusability. The study presents a pioneering system that utilizes a holistic strategy to ascertain SRP compliance within code. This system rigorously inspects code interfaces, the interaction points among various software components. Through this process, we extract critical insights into the codes maintainability and reusability. An optimally designed interface can significantly improve code management and foster its reuse, leading to superior software design efficiency. Beyond interface inspection, our system also explores complexity metrics such as cyclomatic complexity and hassel volume. Cyclomatic complexity offers a numerical indicator of the count of linearly independent paths traversing a programs source code, serving as a measure of code complexity. Hassel volume is an additional metric that can quantify code complexity. Moreover, our system employs code smell detection methodologies to identify instances of high interdependence between classes, often a sign of SRP breaches. High interdependence, or tight coupling, complicates code modification and maintenance. The system integrates the conclusions from these varied analyses to determine SRP compliance. The outcomes of this investigation highlight a hopeful trajectory toward automated SRP detection. This could provide developers with tools that proactively foster the development of well-organized and maintainable code, thereby enhancing software design quality. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Creator An entity primarily responsible for making the resource Jacob B.A.; Manohar M.; Roy G. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-1112 LNNS, pp. 343-353. 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/10.1007/978-981-97-6684-0_28" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-6684-0_28</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215655517&doi=10.1007%2F978-981-97-6684-0_28&partnerID=40&md5=7ac1173f69be0421e06c009b900c3c26" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215655517&doi=10.1007%2f978-981-97-6684-0_28&partnerID=40&md5=7ac1173f69be0421e06c009b900c3c26</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981976683-3 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 Jacob B.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Manohar M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Roy G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 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 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 Towards an Improved Model for Stability Score Prediction: Harnessing Machine Learning in National Stability Forecasting Subject The topic of the resource and security; Economy politics; Fragile State of Index; Machine Learning Description An account of the resource In our increasingly interconnected world, national stability holds immense significance, impacting global economics, politics, and security. This study leverages machine learning to forecast stability scores, essential for understanding the intricate dynamics of country stability. By evaluating various regression models, our research aims to identify the most effective methods for predicting these scores, thus deepening our insight into the determinants of national stability. The field of machine learning has seen remarkable progress, with regression models ranging from conventional Linear Regression (LR) to more complex algorithms like Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting (GB). Each model has distinct strengths and weaknesses, necessitating a comparative analysis to determine the most suitable model for specific predictive tasks. Our methodology involves a comparative examination of models such as LR, Polynomial Regression (PR), Lasso, Ridge, Elastic Net (ENR), Decision Tree (DT), RF, GB, K-Nearest Neighbors (KNN), and SVR. Performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared (R2) assess each model's predictive accuracy using a diverse dataset of country stability indicators. This study's comprehensive model comparison adds novelty to predictive analytics literature. Our findings reveal significant variations in the performance of different regression models, with certain models exhibiting exceptional predictive accuracy, as indicated by high R2 values and low error metrics. Notably, models such as LR, SVR, and Elastic Net demonstrate outstanding performance, suggesting their suitability for stability score prediction. 2024 IEEE. Creator An entity primarily responsible for making the resource Maheshwari A.; Malhotra A.; Singh Hada B.; Ranka M.; Amzad Basha M.S. Source A related resource from which the described resource is derived Proceedings of NKCon 2024 - 3rd Edition of IEEE NKSS's Flagship International Conference: Digital Transformation: Unleashing the Power of Information 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/NKCon62728.2024.10775198" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NKCon62728.2024.10775198</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215302501&doi=10.1109%2FNKCon62728.2024.10775198&partnerID=40&md5=5e4e15ab92a68efcf33ab4fdf2362ea5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215302501&doi=10.1109%2fNKCon62728.2024.10775198&partnerID=40&md5=5e4e15ab92a68efcf33ab4fdf2362ea5</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835036456-9 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 Maheshwari A., Christ (Deemed to Be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Malhotra A., Christ (Deemed to Be University), School of Commerce, Finance and Accountancy, Ghaziabad, India; Singh Hada B., Christ (Deemed to Be University), School of Commerce, Finance and Accountancy, Delhi-NCR, India; Ranka M., Dayananda Sagar College of Arts, Science and Commerce, Bengaluru, India; Amzad Basha M.S., Gitam (Deemed to Be University), Gitam School of Business, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Unveiling Powerful Machine Learning Strategies for Detecting Malware in Modern Digital Environment Subject The topic of the resource Binomial logistic regression; KNN; Malware; Random Forest; XGB classifier Description An account of the resource Machine learning has emerged as formidable instrument in realm of malware detection exhibiting capacity to dynamically adapt to ever-shifting topography of digital hazards. This study presents an exhaustive comparative analysis of four intricate machine learning algorithms namely XGBoost Classifier, K-Nearest Neighbors (KNN) Classifier, Binomial Logistic Regression and Random Forest with primary objective of assessing their effectiveness in domain of malware detection. Conventional signature-based detection methodologies have struggled to synchronize with rapid mutations exhibited by malware variants. In sharp contrast machine learning algorithms proffer data-centric approach adept at unraveling intricate data patterns thereby enabling identification of both well-known and hitherto uncharted threats. To meticulously appraise efficacy of these machine learning models we employ stringent set of evaluation metrics. Precision, recall, F1 Score, testing accuracy and training accuracy are meticulously scrutinized to ascertain distinctive strengths and frailties of these algorithms. By providing comparative analysis of machine learning algorithms within milieu of malware detection this research engenders significant contribution to ongoing endeavor of fortifying cybersecurity. Resultant analysis elucidates that each algorithm possesses its unique competencies. XGBoost Classifier showcases remarkable precision (Benign files: 99%, Malicious files: 99%), recall (Benign files: 97%, Malicious files: 99%) and F1 Score (Benign files: 98%, Malicious files: 99%) implying its aptitude for precise malware identification. KNN Classifier excels in discerning benign software exhibiting precision (Benign files: 90%) and recall (Benign files: 91%) to mitigate likelihood of erroneous positives. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. Creator An entity primarily responsible for making the resource Ravindra Babu S.; Leisha R.; Medows K.J.; Sreekumar K.; Girish G.P. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-874 LNNS, pp. 277-286. 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/10.1007/978-3-031-50887-5_28" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-50887-5_28</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215805763&doi=10.1007%2F978-3-031-50887-5_28&partnerID=40&md5=cde0953e31d184c3af78587c0734b9ef" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215805763&doi=10.1007%2f978-3-031-50887-5_28&partnerID=40&md5=cde0953e31d184c3af78587c0734b9ef</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-303150886-8 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 Ravindra Babu S., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, India; Leisha R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Medows K.J., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Sreekumar K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Girish G.P., Department of Finance, ICFAI Business School, IFHE University (a Deemed to-be-University under Sec 3 of UGC Act 1956), Hyderabad, India