28 20 16481 https://archives.christuniversity.in/files/original/11a0fd0b25f9e4f8db516f54f5e71945.jpg 0b6dfeeadbd8dbf97f5aef738454f6b9 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 Patents Subject The topic of the resource Patent Description An account of the resource Patent Patent Patents 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 A low cost and high actuation speed 3D printed prosthetic arm / Subject The topic of the resource Bio-Medical Engineering Description An account of the resource Patent Number: 202241047867, Applicant: Sujatha A K. Creator An entity primarily responsible for making the resource Anita, E A Mary. Publisher An entity responsible for making the resource available Intellectual Property India Date A point or period of time associated with an event in the lifecycle of the resource 2022-05-22 Language A language of the resource English Type The nature or genre of the resource Patent https://archives.christuniversity.in/files/original/d1ed2b9f6c0b8ce84c5251602f73fa6a.jpg 0b6dfeeadbd8dbf97f5aef738454f6b9 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 Patents Subject The topic of the resource Patent Description An account of the resource Patent Patent Patents 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 A low cost and high actuation speed 3D printed prosthetic arm / Subject The topic of the resource Bio-Medical Engineering Description An account of the resource Patent Number: 202241047867, Applicant: Sujatha A K. Creator An entity primarily responsible for making the resource J, Jenefa. Publisher An entity responsible for making the resource available Intellectual Property India Date A point or period of time associated with an event in the lifecycle of the resource 2022-05-22 Language A language of the resource English Type The nature or genre of the resource Patent 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 A Low Voltage and Low Power Analog Multiplier Subject The topic of the resource Analog Multipliers; flipped voltage follower; gilbert multiplier; low power; low voltage Description An account of the resource In this research work, a low voltage analog multiplier has been realized through the utilization of a flipped voltage follower (FVF). The multiplier is characterized by its capacity to function at low power while exhibiting high gain. The exclusive use of transistors in its implementation renders it highly appropriate for fully integrated circuit applications. The multiplier has been developed using a supply voltage of 500 mV and an operating frequency of 25 KHz. The design consumes power of 8.23 uW. Moreover, a comparative study between the proposed multiplier and the conventional gilbert multiplier is presented in the paper. All simulations and layout designs have been conducted through the virtuoso analog design environment (ADE) of Cadence at 45 nm CMOS technology. 2023 IEEE. Creator An entity primarily responsible for making the resource Nayak A.G.; Bharath G.S.; Bhan I.; Mishra G.; Kumar V. Source A related resource from which the described resource is derived Proceedings of IEEE 2023 5th International Conference on Advances in Electronics, Computers and Communications, ICAECC 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/ICAECC59324.2023.10560170" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAECC59324.2023.10560170</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198432650&doi=10.1109%2FICAECC59324.2023.10560170&partnerID=40&md5=4a1a14529b0603d20614703385efc992" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198432650&doi=10.1109%2fICAECC59324.2023.10560170&partnerID=40&md5=4a1a14529b0603d20614703385efc992</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835030544-9 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 Nayak A.G., BMS College of Engineering, Department of ECE, Bangalore, India; Bharath G.S., BMS College of Engineering, Department of ECE, Bangalore, India; Bhan I., BMS College of Engineering, Department of ECE, Bangalore, India; Mishra G., BMS College of Engineering, Department of ECE, Bangalore, India; Kumar V., Christ University, Department of ECE, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Low-Complexity Multiplier-Less Filter Bank Based on Modified IFIR for the SDR Channelizer Subject The topic of the resource Cosine modulation technique; FIR filters; Frequency response masking (FRM); Interpolated finite impulse response; Modified IFIR filter Description An account of the resource Digital filter banks are extensively used in an SDR channelizer for channelization. The objective of this research work is to design a low computational complexity FIR filter bank for generating sharp transition width channels for SDR. The design of unified and variable bandwidth channels for SDR using the proposed structure is based on the modified IFIR filter structure and cosine modulation technique (CMT). The performance of the proposed structure is demonstrated with the help of an example. The results show that the multiplier complexity of the proposed structure is less than those of other state-of-the art methods. The optimization techniques are incorporated in this work to further reduce the complexity of the proposed structure. With the help of canonical signed digit (CSD), multi-objective artificial bee colony (MOABC) and shift inclusive differential coefficients (SIDC) common sub-expression elimination (CSE) optimization, the filter used in this structure is made multiplier-less. 2024 IETE. Creator An entity primarily responsible for making the resource Varghese R.C.; Amir A. Source A related resource from which the described resource is derived IETE Journal of Research, Vol-70, No. 4, pp. 3334-3346. Publisher An entity responsible for making the resource available Taylor and Francis Ltd. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1080/03772063.2023.2206371" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/03772063.2023.2206371</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158082416&doi=10.1080%2F03772063.2023.2206371&partnerID=40&md5=15fddf537f4c699856f21fc7de812fb9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158082416&doi=10.1080%2f03772063.2023.2206371&partnerID=40&md5=15fddf537f4c699856f21fc7de812fb9</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 3772063 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 Varghese R.C., CHRIST (Deemed to be University), Karnataka, Bangalore, 560 074, India; Amir A., CHRIST (Deemed to be University), Karnataka, Bangalore, 560 074, 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 A LSTM based model for stock price analysis and prediction Subject The topic of the resource LSTM; Machine Learning; Stock Price prediction Description An account of the resource The share market in India is exceedingly unpredictable and volatile, with an infinite range of factors regulating the share market's orientations and tendencies; hence, forecasting the upswing and downturn is a difficult procedure. Because of several essential aspects, the principles of share market have always been unclear for shareholders. This study aims to significantly reduce the likelihood of analysis and forecasting with Long Short-term Memory (LSTM) model approach that is both resilient yet easy is still suggested. LSTM is a complete Learning Model that is a Predictive Method. Conversely, advancements in technology have opened the way for more efficient and precise share market forecasting in current times. Using the provided historical data sets, the results showed that the LSTM model has considerable potential for forecasting. 2023 Author(s). Creator An entity primarily responsible for making the resource Basha M.S.A.; Sucharitha M.M.; Krishnan V.S.; Lakshmi M.B. Source A related resource from which the described resource is derived AIP Conference Proceedings, Vol-2888, 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/10.1063/5.0164524" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0164524</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176772176&doi=10.1063%2F5.0164524&partnerID=40&md5=264f5ba90dd52ae413e34b076aa14af3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176772176&doi=10.1063%2f5.0164524&partnerID=40&md5=264f5ba90dd52ae413e34b076aa14af3</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 Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management, GITAM University, Karnataka, Bengaluru, India; Sucharitha M.M., School of Commerce, Finance and Accountancy, CHRIST University, Karnataka, Bengaluru, India; Krishnan V.S., School of Commerce, Finance and Accountancy, CHRIST University, Karnataka, Bengaluru, India; Lakshmi M.B., School of Commerce, Finance and Accountancy, CHRIST University, Karnataka, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 A Machine Learning Approach for Revving Up Revenue of Indian Tech Companies Subject The topic of the resource arima; fb prophet; indian tech companies; machine learning; revenue; v.a.r model Description An account of the resource This study addresses a critical gap in research by examining the effectiveness of various machine learning models in predicting revenue for Indian tech companies. The V.A.R, ARIMA, simple moving average, weighted moving average, and FB Prophet models were employed and their performances was compared. The findings demonstrate that FB Prophet consistently outperforms other models, exhibiting superior accuracy in revenue forecasting. This underscores FB Prophet's potential to offer precise revenue predictions, enabling companies to gain insights into their financial health, anticipate market trends, and optimize decision-making. Future research could further enhance accuracy by incorporating economic indicators, providing a more holistic view of revenue dynamics and empowering companies to make more informed strategic decisions. 2024 IEEE. Creator An entity primarily responsible for making the resource Tungar S.K.; Umme Salma M. Source A related resource from which the described resource is derived 2nd IEEE International Conference on Data Science and Information System, ICDSIS 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/ICDSIS61070.2024.10594491" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSIS61070.2024.10594491</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200126947&doi=10.1109%2FICDSIS61070.2024.10594491&partnerID=40&md5=839ff3e2b69f9adccb24eb4e56707556" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200126947&doi=10.1109%2fICDSIS61070.2024.10594491&partnerID=40&md5=839ff3e2b69f9adccb24eb4e56707556</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038166-5 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 Tungar S.K., Christ (Deemed To Be University), Department Of Statistics And Data Science, Karnataka, Bengaluru, India; Umme Salma M., Christ (Deemed To Be University), Department Of Statistics And Data Science, Karnataka, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Tripathi, Sachin; Tiwari, Akhilesh; Upreti, Kamal; Radhakrishnan, G.V. Title A name given to the resource A Machine Learning Approach to Consumer Behavior Analysis in Social Media-Influenced E-Book Markets Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Applied Science and Technology Trends;Volume;6;Issue;2;pp.242-250 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.38094/jastt62318" target="_blank" rel="noreferrer noopener">https://doi.org/10.38094/jastt62318</a> <a href="https://www.scopus.com/pages/publications/105016567771?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016567771?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Tripathi S., Department of Business and Management, Christ University, Delhi NCR Campus, Ghaziabad, India; Tiwari A., Department of Business and Management, Christ University, Delhi NCR Campus, Ghaziabad, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Radhakrishnan G.V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India Description An account of the resource Social media has emerged as a dominant marketing channel, significantly influencing consumer purchase decisions. Despite extensive global research, little is known about region-specific dynamics in emerging markets such as India. This study addresses this gap by applying Random Forest and Gradient Boosting models to survey data from 386 respondents in the Delhi-NCR region to analyze e-book purchasing behavior. Data were preprocessed through encoding, normalization, and stratified traintest splitting (80:20), with reproducibility ensured via a fixed random seed. Model evaluation employed R, RMSE, and MAE metrics, alongside a paired-sample t-test. Results showed that Gradient Boosting (R = 0.82) outperformed Random Forest (R = 0.78; p = 0.038). Feature importance analysis revealed that behavioral variablespurchase intention, brand awareness, and social media engagementwere the strongest predictors, whereas demographic features contributed minimally. These findings emphasize the primacy of behavioral traits in social mediadriven e-book markets and provide evidence for designing region-specific digital marketing strategies in emerging economies. 2025, Interdisciplinary Publishing Academia. All rights reserved. Subject The topic of the resource classification; Cognitive Learning; Education; Machine Learning; Non-cognitive Learning; student performance Publisher An entity responsible for making the resource available Interdisciplinary Publishing Academia Relation A related resource ISSN: 27080757; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Punjabi, Hitesh; Kumar Chandar, S.; Malik, Mayur Title A name given to the resource A Machine Learning Approach to Crude Oil Price Prediction Using Support Vector Machine (SVM) Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1755 LNNS;pp.509-526 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-13177-5_40" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-13177-5_40</a> <a href="https://www.scopus.com/pages/publications/105028757415?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028757415?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Punjabi H., Maharashtra, Mumbai, India; Kumar Chandar S., School of Business and Management, Christ University, Karnataka, Bengaluru, India; Malik M., Research Scholar, International Open University, Banjul, Gambia Description An account of the resource Crude oil is one of the most important energy sources, and fluctuations in its international prices affect all aspects of the economy. The price of crude oil is influenced by several variables, and the length of time that each component has an effect differs giving an increase in non-linear oil price features. Although it is a complex task, identifying the most essential factor influencing for precise predicting, crude oil prices are essential. Therefore, this study aims to employ a machine learning model to address the intricate relationships among different factors. Primarily, it gathers data regarding West Texas Intermediate (WTI) and Brent crude oil prices as well as macroeconomic variables. Secondly, the data is normalized to prepare it for further analysis. Finally, a crude oil prediction model is constructed using Support Vector Machine (SVM) to predict future international crude oil prices. The daily, weekly, and monthly prices are used to confirm the models efficacy developed using WTI and Brent oil. The models performance is also evaluated by incorporating various combinations of macroeconomic variables to find the most influential factor. Results from experiments show indicates the benchmark model was much exceeded by the developed model and performed very well in terms of prediction accuracy. The findings reveal that selecting the appropriate variables can greatly enhance prediction accuracy. This model has the potential to provide valuable insights for traders, investors, and energy-related enterprises, offering beneficial guidance for decision-making purposes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Crude oil; energy market; machine learning; macroeconomic variables; normalization; support vector machine Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303213176-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Theres, Abidha Maria; George, Jossy; Kumar, Pawan Title A name given to the resource A Machine Learning Approach to Modelling Sales Performance Based on Social Media Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1827 LNNS;pp.243-256 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-18138-1_21" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-18138-1_21</a> <a href="https://www.scopus.com/pages/publications/105038095719?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038095719?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Theres A.M., CHRIST (Deemed to be University), Karnataka, Bengaluru, India; George J., CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Kumar P., CHRIST (Deemed to be University), Karnataka, Bengaluru, India Description An account of the resource By address the strategic challenges faced while maximising the advertising effectiveness, this paper examines the predictive relationship between different advertising sales platform and the brand sales performance. Four different supervised machine learning models - Random Forest, XGBoost, LightGBM, and a voting classifier ensemble model are applied to categorise the brand sales performance. The models are trained on a multi-platform advertising dataset. This research focuses on both the accuracy and interpretability of the data to make it easier identifying which platform affects the brand sales the most, which is quite different from the existing approaches that concentrates on finding predictive accuracy. This study aims to help marketers and businesses to make better and well informed data-driven decisions for marketing strategies by finding the most effective advertising platform. Each of the models were evaluated using Classification metrics which includes Accuracy, Precision, Recall and F1 score, along with confusion matrix. Future scope of the paper includes merging the models in real time systems and also by expanding it to carefully examine different time periods and customer groups. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Advertising Analytics; Brand Sales Prediction; Classification.; Ensemble Learning; Feature Importance; LightGBM; Random Forest; Social Media Marketing; Voting Classifier; XGBoost Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303218137-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Machine Learning Entrenched Brain Tumor Recognition Framework Subject The topic of the resource Brain tumor; Confusion matrix; Convolutional Neural Networks; Image processing; Machine learning; Mobile application Description An account of the resource Brain tumor detection plays a significant role in medical image processing. Treatment for patients with brain tumors is primarily dependent on faster detection of these tumors. More rapid detection of brain tumors will help in the improvement of the patient's life chances. Diagnosis of brain tumors by doctors most commonly follow manual segmentation, which is difficult and time-consuming; instead, automatic detection is necessary. Nowadays, automatic detection plays a vital role and can be a solution to detecting brain tumors with better performance. Brain tumor detection using the MRI images method is an essential diagnostic tool for predicting brain tumors; the implementation for these kinds of detection can be done using various machine learning algorithms and methodologies. It helps the doctors understand the actual progression of the evolving tumor, allowing the doctors to decide how the treatment has to be given for that particular patient and measures required to follow up. Therefore, the intention is to create a framework to detect brain tumors in MRI images using a machine learning algorithm and analyze the performance of the brain tumor detection using sensitivity and specificity, which helps us to analyze how well the algorithm has performed in detecting the brain tumors accurately and develop a mobile application framework in which the MRI images can be directly scanned to know whether the cancer is present in a scanned MRI image or not. 2022 IEEE. Creator An entity primarily responsible for making the resource Surya S.; Aurelia S. Source A related resource from which the described resource is derived Proceedings of the International Conference on Electronics and Renewable Systems, ICEARS 2022, pp. 1372-1376. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICEARS53579.2022.9752195" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICEARS53579.2022.9752195</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128934305&doi=10.1109%2FICEARS53579.2022.9752195&partnerID=40&md5=ff25dcfe71bdddc3401add20e04b2c34" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128934305&doi=10.1109%2fICEARS53579.2022.9752195&partnerID=40&md5=ff25dcfe71bdddc3401add20e04b2c34</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166548425-1 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 Surya S., Christ University, Dept. Computer Science, Bangalore, India; Aurelia S., Christ University, Dept. 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 A Machine Learning Model for Augmenting the Media Accessibility for the Disabled People Subject The topic of the resource captioning; Digital data; disabled individuals; machine learning; media augmentation; similarity score; tags; vector; words Description An account of the resource In an era characterized by the proliferation of digital media, the need to efficiently use multimedia content has become paramount. This article discusses an innovative technique called 'Fast Captioning (FC)' to improve media accessibility, especially for people with disabilities and others with time restrictions. Modern Machine Learning (ML) algorithms are incorporated into the framework, which speeds up video consumption while maintaining content coherence. The procedure includes extracting complex features like Word2Vec embeddings, part-of-speech tags, named entities, and syntactic relationships. Using annotated data, a ML model is trained to forecast semantic similarity scores between words and frames. The predicted scores seamlessly integrate into equations that calculate similarity, thus enhancing content comprehension. Through this all-encompassing approach, the article offers a comprehensive solution that balances the requirements of contemporary media with the accessibility requirements of people with disabilities, producing a more inclusive digital environment. Machine Learning-based Media Augmentation (ML-MA) has achieved the highest accuracy of 96%, and the captioning is accurate. 2023 IEEE. Creator An entity primarily responsible for making the resource Andi H.K.; Senbagam S.; Sharma D.K.; Shelke M.; Kumar N.K.S.; Poonia R.C. Source A related resource from which the described resource is derived 2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023, pp. 438-443. 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/ICCSAI59793.2023.10420881" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCSAI59793.2023.10420881</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186639436&doi=10.1109%2FICCSAI59793.2023.10420881&partnerID=40&md5=fa550daf75dc62eee3252365a758b0fc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186639436&doi=10.1109%2fICCSAI59793.2023.10420881&partnerID=40&md5=fa550daf75dc62eee3252365a758b0fc</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835036996-0 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 Andi H.K., Asia Metropolitan University, Centre for Postgraduate Studies, Malaysia; Senbagam S., Dhanalakshmi Srinivasan College of Engineering and Technology, Department of Computer Science and Engineering, Tamil Nandu, Mamallapuram, India; Sharma D.K., Jaypee University of Engineering and Technology, Department of Mathematics, Madhya Pradesh, Guna, 473226, India; Shelke M., Aissms Ioit, Department of Artificial Intelligence and Data Science, Pune, India; Kumar N.K.S., School of Computing, Vel Tech Rangarajan Dr. Sagunthala Rd Institute of Science and Technology (Deemed to Be University), Department of Computer Science and Engineering, Tamil Nadu, Chennai, India; Poonia R.C., Christ (Deemed to Be University), Department of Computer Science, Delhi-NCR, Uttar Pradesh, Ghaziabad, 201003, 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 A machine learning model for population analysis among different states in India which influences the socio, demographic and economic needs of society Description An account of the resource In this work Data from 2011 census is taken to identify the state which influences more in Population census among the different states identified. The data is considered from Madhya Pradesh, followed with Utter Pradesh, then to Bihar, Bengal and Orissa. Similarly other case studies are also done for Southern Indian states and North Eastern States. Genetic algorithm will be tried to find the optimal location for the given study. A fitting function is calculated for the population data of 2011 using Lagrange Interpolation technique. This fitting function is given as input to Genetic algorithm to find the optimal state which have maximum influence in the population growth among different states of India as per the Case studies done. BEIESP. Creator An entity primarily responsible for making the resource Krishna A.V.N.; Bala Murugan M. Source A related resource from which the described resource is derived International Journal of Engineering and Advanced Technology, Vol-9, No. 1, pp. 123-126. 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/ijeat.A1066.109119" target="_blank" rel="noreferrer noopener">https://doi.org/10.35940/ijeat.A1066.109119</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074580782&doi=10.35940%2Fijeat.A1066.109119&partnerID=40&md5=b7a53fe8fa3efc8e6128635383647d72" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074580782&doi=10.35940%2fijeat.A1066.109119&partnerID=40&md5=b7a53fe8fa3efc8e6128635383647d72</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 22498958 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 Krishna A.V.N., Faculty of Engineering, CHRIST, Bengalurur, India; Bala Murugan M., Faculty of Engineering, CHRIST, Bengalurur, 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 Letters Letter Faculty Publications-Letters 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 A machine learning model to predict suicidal tendencies in students Description An account of the resource [No abstract available] Creator An entity primarily responsible for making the resource Mukku L.; Thomas J. Source A related resource from which the described resource is derived Asian Journal of Psychiatry, Vol-79 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.ajp.2022.103363" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ajp.2022.103363</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144018882&doi=10.1016%2Fj.ajp.2022.103363&partnerID=40&md5=db6739754dbc82fc0e2cfdf065fedb26" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144018882&doi=10.1016%2fj.ajp.2022.103363&partnerID=40&md5=db6739754dbc82fc0e2cfdf065fedb26</a> Rights Information about rights held in and over the resource All Open Access; Bronze Open Access Relation A related resource ISSN: 18762018; PubMed ID: 36481568 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 Letter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mukku L., CHRIST (Deemed to be University), Kengeri Campus, Bengaluru, 560074, India; Thomas J., CHRIST (Deemed to be University), Kengeri Campus, 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 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 A Machine Learning- Based Driving Assistance System for Lane and Drowsiness Monitoring Description An account of the resource Lane line detection is a vital component when driving heavy vehicles; this concept follows the path for driving a vehicle to prevent the risk of accidentally entering another lane without the drivers knowledge, which could result in an accident. To detect the lane, use frame masking and Hough line transformation with efficient machine learning algorithms, pre-processed and trained adequately for optimum accuracy as per the provided dataset to spot the white markings on both sides of the lane. Long-distance truck drivers suffer from sleep deprivation, making driving extremely dangerous while tired and they ignore the line markings and wander into the wrong lane. This chapter proposes a portable system that does not require any sensors or interference with the vehicles wiring system; instead, a system that fits on a windshield or any surface to monitor the actions of the driver, using computer vision and feature-extracted datasets within a trained neural network model using cameras. This driver-assisted system can detect drowsiness and give an alarm to wake up the driver by identifying the Region of Interest. These predictions are made based on eye movements, and the algorithm generates a score. The higher the score, the longer the time between alarms. 2024 Taylor & Francis Group, LLC. Creator An entity primarily responsible for making the resource Ramasamy S.A.G. Source A related resource from which the described resource is derived Machine Intelligence: Computer Vision and Natural Language Processing, pp. 193-208. Publisher An entity responsible for making the resource available CRC Press Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003424550-10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003424550-10</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173415740&doi=10.1201%2F9781003424550-10&partnerID=40&md5=18e5d624a1730fadfbe572b2c357a582" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173415740&doi=10.1201%2f9781003424550-10&partnerID=40&md5=18e5d624a1730fadfbe572b2c357a582</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-100096031-0; 978-103220199-3 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 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 Ramasamy S.A.G., 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 Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Paul, Aditi; Chaudhary, Shirashti; Sinha, Somnath Title A name given to the resource A Machine Learning-Based Cross-Layer DoS Attack Detection Technique for IoT Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1073;pp.11-23 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-5703-9_2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-5703-9_2</a> <a href="https://www.scopus.com/pages/publications/105000326371?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000326371?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Paul A., Banasthali Vidyapith, Rajasthan, Tonk, 304022, India; Chaudhary S., Banasthali Vidyapith, Rajasthan, Tonk, 304022, India; Sinha S., CHRIST (Deemed to be University), Bengaluru, 560029, India Description An account of the resource The most potent and common attacks on Internet of Things (IoT) are Denial of Service (DoS) attacks. Unfortunately, because the attack occurs on numerous layers, a single layer detection method is insufficient and ineffective to counteract these attacks. The current work focuses on the detection of cross-layer DoS assaults using Machine Learning-based multiclass classifiers. Three attacks against Routing Protocol (RPL) and Transmission Control Protocol (TCP) are detected using three ML Classifiers (KNN, Gradient Boosting, Random Forest). The novelty of the study is the design of cross-layer attack datasets using feature engineering technique. The performances of the classifiers are analyzed in presence of both balanced and imbalanced datasets. The results show that Gradient Boosting classifier has highest accuracy of up to 98% with deviation of up to 97%. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Cross-layer DoS attacks; IoT security; ML classifier; RPL attacks; Sinkhole attack Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981975702-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 A Malicious Botnet Traffic Detection Using Machine Learning Subject The topic of the resource Bot-IoT; Botnet; Machine learning; TOPSIS Description An account of the resource Detection of incorrect and malign data transfers in the Internet of Things (IoT) network is important for IoT safety to observe an eye on and prevent unwelcomed traffic flow to the network of IoT. For it, Machine Learning (ML) strategic methods are produced by several researchers to prevent malign data flows through the network of IoT. Nonetheless, because of the wrong choice of feature, a few malign Machine Learning models differentiate especially the movement of malign traffic. Still, what matters is the problem that needs to be deliberated in-depth to select the best features for better malign traffic acquisition in the network of IoT. Dealing with the challenge, a new process was proposed. 1st, the metric method of selecting a novel feature called the proposed CorrAUC, and hinged on CorrAUC, a new highlight for choosing the Corrauc algorithm name is also being developed, designed hinged on the system folding filter features precisely and select the active features of the choose ML method using AUC metric. After that, we apply a combined application Order of Preference by Similarity to Ideal Solution Using Shannon Entropy (TOPSIS) built on a bijective set which is soft to verify selected features for identification of malign 1traffic in IoT network. We test our method using data set of Bot-IoT and 4 dissimilar ML classifiers. Practical outcomeanalysis showed that our proposed approach works as well and can achieve greater than 96% results on average. 2022 Wolters Kluwer Medknow Publications. All rights reserved. Creator An entity primarily responsible for making the resource Sakthivel M.; Sivanantham S.; Akshaya V.; Sivakumar D.; Karthikeyan H. Source A related resource from which the described resource is derived Journal of Pharmaceutical Negative Results, Vol-13, No. 4, pp. 968-977. Publisher An entity responsible for making the resource available ResearchTrentz Academy Publishing Education Services Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.47750/pnr.2022.13.04.131" target="_blank" rel="noreferrer noopener">https://doi.org/10.47750/pnr.2022.13.04.131</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143358107&doi=10.47750%2Fpnr.2022.13.04.131&partnerID=40&md5=c089677658bbb8045492361f9e52b34e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143358107&doi=10.47750%2fpnr.2022.13.04.131&partnerID=40&md5=c089677658bbb8045492361f9e52b34e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9769234 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 Sakthivel M., Department of CSE, Sree Vidyanikethan Engineering College, Andhra Pradesh, Tirupati, India; Sivanantham S., Department of CSSE, Sree Vidyanikethan Engineering College, Andhra Pradesh, Tirupati, India; Akshaya V., Department of CSE, Sree Vidyanikethan Engineering College, Andhra Pradesh, Tirupati, India; Sivakumar D., Department of CSSE, Sree Vidyanikethan Engineering College, Andhra Pradesh, Tirupati, India; Karthikeyan H., School of Engineering and Technology, CHRIST (Deemed To Be University), Karnataka, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 A Markovian risk model with possible by-claims and dividend barrier Subject The topic of the resource by-claims; dividend barrier; Gerber-Shiu function; GSF; Lundberg equation; MAP claim arrivals; phase type claims; risk reserve process Description An account of the resource A MAP/PH risk model with possible by-claims and a dividend barrier is considered. Along with the main claim, a by-claim also can occur with a certain probability but by-claims are settled only after an inquiry and hence delayed. The model is analysed considering associated Markovian fluid models under the original timeline and an auxiliary timeline. Systems of integro differential equations (IDE) are developed for the Gerber-Shiu function (GSF) and the total dividends paid until ruin. Explicit expressions are obtained for the GSF of the models without and then with the barrier. Expressions are also provided for the moments of the total dividends paid until ruin. A dividends-penalty identity is given. The method is numerically illustrated with a two-phase model and sensitivity analysis of the model is done by varying some of the parameters involved. 2023 Inderscience Enterprises Ltd.. All rights reserved. Creator An entity primarily responsible for making the resource Sreeshamim P.P.; Jacob M.J.; Dibu A.S. Source A related resource from which the described resource is derived International Journal of Mathematics in Operational Research, Vol-26, No. 4, pp. 475-501. Publisher An entity responsible for making the resource available Inderscience Publishers Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJMOR.2023.135545" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJMOR.2023.135545</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180991955&doi=10.1504%2FIJMOR.2023.135545&partnerID=40&md5=2911bdcc17f287330748a48476f2635d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180991955&doi=10.1504%2fIJMOR.2023.135545&partnerID=40&md5=2911bdcc17f287330748a48476f2635d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 17575850 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 Sreeshamim P.P., Department of Mathematics, National Institute of Technology, Kerala, Calicut, 673601, India; Jacob M.J., Department of Mathematics, National Institute of Technology, Kerala, Calicut, 673601, India; Dibu A.S., Department of Data Science and Statistics, Christ (Deemed to be University, Bangalore, 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 A mathematical approach to the study on alkylating agents Subject The topic of the resource QSPR; Topological Index; Zagreb Index Description An account of the resource There are several classes of anticancer drugs, among which our study focuses on alkylating agents. As a chemical graph invariant number, topological index, has crucial role in predicting the physical, chemical, biological and toxicity properties of a molecule. Different versions of Zagreb indices correlate well with various physio-chemical properties of a molecule. We are analysing physio-chemical properties of the class of alkylating agents using various Zagreb indices. In this paper we are able to predict the physico-chemical properties of a molecule which is not yet discovered using the Zagreb class. 2022 Author(s). Creator An entity primarily responsible for making the resource Mathew S.E.; Kalayathankal S.J.; Kureethara J.V. Source A related resource from which the described resource is derived AIP Conference Proceedings, Vol-2357 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 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0080519" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0080519</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130241424&doi=10.1063%2F5.0080519&partnerID=40&md5=459acae36d29ec5ddb92b0579f7d3b43" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130241424&doi=10.1063%2f5.0080519&partnerID=40&md5=459acae36d29ec5ddb92b0579f7d3b43</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 0094243X; ISBN: 978-073544189-7 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 Mathew S.E., Department of Mathematics, Catholicate College, M.G University, Kerala, Pathanamthitta, India; Kalayathankal S.J., Department of Mathematics, Jyothi Engineering College, Kerala, Thrissur, India; Kureethara J.V., Department of Mathematics, Christ University, Karnataka, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 A Mathematical Correlation of Compressive Strength Among Silica, Alumina and Calcia Present in Composite Red Mud and Iron Ore Tailingbricks Subject The topic of the resource Ground granulated blast slag (GGBS); Iron ore tailings (IOT); Lime stabilizer; Red Mud Description An account of the resource Waste Red Mud generated from bauxite beneficiation in aluminium industry contains sodium oxide in minor amount along with silica and alumina in significant quantities. Waste iron ore tailings from beneficiation of iron ore in steel industry contain silica and alumina in significant quantities. A combination of both these materials in different amounts along with GGBS and lime addition resulted in complex alkali-activated reaction products consisting of (Si/Al), (Ca/Si) and (Ca/(Si+Al)) complexes which influence compressive strength of the test samples on curing for extended time periods at room temperature. Individual correlation coefficients of these complexes with compressive strength yielded high values with (Si/Al) and (Ca/Si+Al) ratios (0.92 and 0.96, respectively) while showing a poor correlation coefficient with (Ca/Si) ratio (0.88). A direct regression analysis between compressive strength and (Si/Al) ratio and (Ca/Si+Al) ratio indicated negative values with (Si/Al) ratios but positive values with (Ca/ (Si+Al)) ratios. It is therefore concluded that the addition of lime and GGBS (contributed from both GGBS and lime addition) resulted in Ca-Si-Al complex formations which are responsible for improved compressive strength of the samples. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Beulah M.; Das P.; Gayathri G.; Sudhir M.R.; Sarath Chandra K.; Sasha Rai P. Source A related resource from which the described resource is derived Lecture Notes in Civil Engineering, Vol-143 LNCE, pp. 313-327. 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/10.1007/978-981-33-6969-6_29" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-33-6969-6_29</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106447147&doi=10.1007%2F978-981-33-6969-6_29&partnerID=40&md5=b50cbe1fc34109fec1418a3dc653fc35" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106447147&doi=10.1007%2f978-981-33-6969-6_29&partnerID=40&md5=b50cbe1fc34109fec1418a3dc653fc35</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23662557; ISBN: 978-981336968-9 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 Beulah M., School of Engineering and Technology, Christ (Deemed to be University), Bangalore, India; Das P., ACS College of Engineering, Bangalore, India; Gayathri G., ACS College of Engineering, Bangalore, India; Sudhir M.R., SOET, Christ (Deemed to be University), Bangalore, India; Sarath Chandra K., School of Engineering and Technology, Christ (Deemed to be University), Bangalore, India; Sasha Rai P., KS School of Engineering and Management, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A mathematical model that describes the relation of low-density lipoprotein and oxygen concentrations in a stenosed artery Subject The topic of the resource Endothelium; Finite element method; Hypoxia; Recirculation; Wall shear stress Description An account of the resource The cellular activities of the endothelium layer between lumen and intima are significantly linked to the origin of the disease atherosclerosis. Three stages of atherosclerosis were investigated in this study (40%-mild, 50%-modest, and 60%-acute) concerning the coronary arterial segment. The essence of the hemodynamic factors like flow velocity, pressure, and wall shear stress has been investigated, as well as the interrelationships between them. At all degrees of stenosis, the biophysical relationship between convection-diffusion of low-density lipoproteins (LDL) and convection-diffusion of oxygen in the bloodstream is investigated. The Finite Element Methods (FEM) are used to solve the modeled partial differential equation systems. The method adopted is numerical in nature providing accurate graphical solutions to the framed systems. The physical effects of the deposition of LDL on the arterial wall, like a decrease in the diameter of the lumen, and toughening of the walls, are analyzed through the evaluation of the physical parameters. The study revealed that the deposition of LDL molecules in the post stenotic region leads to the depletion of oxygen in the region leading to the rapid dysfunctioning of the endothelial layer of the lumen-intima boundary. 2022 World Scientific Publishing Company. Creator An entity primarily responsible for making the resource Shankar Narayan S.; Anuradha B.; Sunanda S.; Puneeth V.; Ijaz Khan M.; Abdullah A.; Guedri K.; Jameel M. Source A related resource from which the described resource is derived International Journal of Modern Physics B, Vol-36, No. 26 Publisher An entity responsible for making the resource available World Scientific Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S0217979222501739" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S0217979222501739</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135392519&doi=10.1142%2FS0217979222501739&partnerID=40&md5=8606de7314a334525170b0db24ae6970" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135392519&doi=10.1142%2fS0217979222501739&partnerID=40&md5=8606de7314a334525170b0db24ae6970</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2179792; CODEN: IJPBE 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 Shankar Narayan S., Department of Mathematics, Cmr Institute of Technology, Bangalore, 560 037, India, Department of Mathematics, Dayananda Sagar University, Bangalore, 560 114, India; Anuradha B., Department of Mathematics, Dayananda Sagar University, Bangalore, 560 114, India; Sunanda S., Department of Mathematics, Vellore Institute of Technology, Vellore, 632 014, India; Puneeth V., Department of Mathematics, Christ (Deemed to Be University), Bangalore, 560 029, India; Ijaz Khan M., Department of Mathematics and Statistics, Ripah International University I-14, Islamabad, 44000, Pakistan, Mathematical Modelling and Applied Computation Research Group (MMAC), Department of Mathematics, King Abdulaziz University, Jeddah, Saudi Arabia; Abdullah A., Department of Mathematics, Taif University, Taif, 21944, Saudi Arabia; Guedri K., Mechanical Engineering Department, College of Engineering and Islamic Architecture, Umm Al-Qura University, P.O. Box 5555, Makkah, 21955, Saudi Arabia; Jameel M., Department of Civil Engineering, College of Engineering, King Khalid University, Asir, P.O. Box 960, Abha, 61421, Saudi Arabia