409 20 11858 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Optimized deep maxout for breast cancer detection: consideration of pre-treatment and in-treatment aspect Subject The topic of the resource CT images; Fuzzy filter; Hierarchical DBSCAN (density-based clustering algorithm); Shark optimized deep Maxout neural network; Smell indulged shuffled shepherd optimization (SSISSO) Description An account of the resource Breast cancer is one of the deadliest diseases, accounting for the second-highest rate of cancer mortality among females. Breast tissue begins to develop cancerous, malignant lumps as the disease progresses. Self-examinations and routine clinical checks aid in early diagnosis, which considerably increases the likelihood of survival. Because of this, we have created a revolutionary method for finding breast cancer that has the following four steps. Fuzzy filters are used in the initial pre-processing stage to reduce noise and improve outcomes from the incoming data. In the second stage, we have presented an Improved Hierarchical DBSCAN (Density-based clustering algorithm) for the segmentation of anomalous areas. Feature extraction will be carried out following segmentation. We have also developed a better kurtosis-based feature to complement traditional statistical and shape-based features and deliver better results. The Optimized Deep Maxout Neural Network is used for classification in the final step, with the suggested Shark Smell Indulged Shuffled Shepherd Optimization used to optimize the weight parameter (SSISSO). At 90% the learning percentage of the proposed model SSISSO model has achieved 0.984391 accuracy, which is superior to 22.54%, 28.46%, 17.44%, 17%, 15.04%, 13.28%, 29.45%, 28.59%, 21.58%, and 30.72% as compared to other methods like SVM-BS1, CNN-BS7, LSTM, NN, Bi-GRU, RNN, ARCHO, AOA, HGS, CMBO, SSOA, and SSO. Finally, the results of the proposed breast cancer detection technique are compared with conventional techniques. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Creator An entity primarily responsible for making the resource Rajput D.; Bejoy B.J. Source A related resource from which the described resource is derived Multimedia Tools and Applications, Vol-83, No. 10, pp. 31017-31047. Publisher An entity responsible for making the resource available Springer 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/s11042-023-16505-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11042-023-16505-4</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171287675&doi=10.1007%2Fs11042-023-16505-4&partnerID=40&md5=6f342ff1b2febc31a687f467770366a7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171287675&doi=10.1007%2fs11042-023-16505-4&partnerID=40&md5=6f342ff1b2febc31a687f467770366a7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 13807501; CODEN: MTAPF 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 Rajput D., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560074, India; Bejoy B.J., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), 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 State-of-art Techniques for Classification of Breast Cancer: A Review Subject The topic of the resource Breast Cancer; Classification; Deep Learning (DL); Machine Learning (ML); Mammogram; Neural Network (NN) Description An account of the resource Cancer is an unexpected and unclear disease that puts many people at risk. Breast cancer has surpassed prostate cancer as the most common cancer in women, as well as the main cause of cancer-related mortality in women. Breast cancer rates have been rising in India for several years, with 100,000 new cases recorded each year. In India, there are up to one million breast cancer patients at any given moment. The survival rate of breast cancer has increased in recent years as a result of advances in technology, effective treatment, and medical care delivery. It extends the lives of the sufferers and improves their quality of life. Breast cancer can be detected using a variety of imaging methods. Radiologists can utilize a computer-aided diagnostic technique to discover and diagnose irregularities earlier and more quickly. Many Computer-Aided Diagnosis methods have been developed to identify breast cancer in its early stages using mammography images. The computer aided diagnostics systems mostly focus on identifying and detecting breast nodules. Staging breast cancer at its detection needs to be focused on, as the treatment is based on the stage of cancer. As a result, this study focuses on producing evaluations on computer aided diagnostics approaches for segmenting nodules and identifying different stages of breast cancer, thereby assisting radiologists in assessing the illness. 2022 IEEE. Creator An entity primarily responsible for making the resource Rajput D.; Bejoy B.J. Source A related resource from which the described resource is derived Proceedings of 5th International Conference on Contemporary Computing and Informatics, IC3I 2022, pp. 433-437. 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/IC3I56241.2022.10073435" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC3I56241.2022.10073435</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152000610&doi=10.1109%2FIC3I56241.2022.10073435&partnerID=40&md5=aa958d86e900d9e5f4ec1922018f51b4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152000610&doi=10.1109%2fIC3I56241.2022.10073435&partnerID=40&md5=aa958d86e900d9e5f4ec1922018f51b4</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835039826-7 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 Rajput D., CHRIST (Deemed to Be University), Department of CSE, Bengaluru, India; Bejoy B.J., CHRIST (Deemed to Be University), Department of CSE, 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 Using machine learning architecture to optimize and model the treatment process for saline water level analysis Subject The topic of the resource machine learning; saline water; water level data analysis; water saline particle; water treatment plants Description An account of the resource Water is a vital resource that makes it possible for human life forms to exist. The need for freshwater consumption has significantly increased in recent years. Seawater treatment facilities are less dependable and efficient. Deep learning systems have the potential to increase the efficiency as well as the accuracy of salt particle analysis in saltwater, which will benefit water treatment plant performance. This research proposed a novel method for optimization and modelling of the treatment process for saline water based on water level data analysis using machine learning (ML) techniques. Here, the optimization and modelling are carried out using molecular separation-based reverse osmosis Bayesian optimization. Then the modelled water saline particle analysis has been carried out using back propagation with Kernelized support swarm machine. Experimental analysis is carried out based on water salinity data in terms of accuracy, precision, recall, and specificity, computational cost, and Kappa coefficient. The proposed technique attained an accuracy of 92%, precision of 83%, recall of 78%, specificity of 81%, computational cost of 59%, and Kappa coefficient of 78%. 2023, IWA Publishing. All rights reserved. Creator An entity primarily responsible for making the resource Rajput S.P.S.; Webber J.L.; Bostani A.; Mehbodniya A.; Arumugam M.; Nanjundan P.; Wendimagegen A. Source A related resource from which the described resource is derived Water Reuse, Vol-13, No. 1, pp. 51-67. Publisher An entity responsible for making the resource available IWA Publishing 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/10.2166/wrd.2022.069" target="_blank" rel="noreferrer noopener">https://doi.org/10.2166/wrd.2022.069</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153201481&doi=10.2166%2Fwrd.2022.069&partnerID=40&md5=46d67b909d8db382ba4c8085ab1813ec" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153201481&doi=10.2166%2fwrd.2022.069&partnerID=40&md5=46d67b909d8db382ba4c8085ab1813ec</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 27096092 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 Rajput S.P.S., Department of Civil Engineering, Maulana Azad National Institute of Technology, MP, Bhopal, India; Webber J.L., Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), 7th Ring Road, Doha Area, Kuwait; Bostani A., College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait; Mehbodniya A., Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), 7th Ring Road, Doha Area, Kuwait; Arumugam M., Center for Transdisciplinary Research, Saveetha Dental College, Saveetha Institute of Medical and Technical Science, Chennai, India; Nanjundan P., Department of Data Science, Christ University, Lavasa, Maharashtra, Pune, India; Wendimagegen A., College of Natural and Computational Science, Debre Berhan University, Debre Birha, Ethiopia 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 Demography-Based Hybrid Recommender System for Movie Recommendations Subject The topic of the resource Cold start; Collaborative filtering; Content filtering; Demography filtering; SVDpp Description An account of the resource Recommender systems have been explored with different research techniques including content-based filtering and collaborative filtering. The main issue is with the cold start problem of how recommendations have to be suggested to a new user in the platform. There is a need for a system which has the ability to recommend items similar to the users demographic category by considering the collaborative interactions of similar categories of users. The proposed hybrid model solves the cold start problem using collaborative, demography, and content-based approaches. The base algorithm for the hybrid model SVDpp produced a root mean squared error (RMSE) of 0.92 on the test data. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Raju B.K.; Ummesalma M. Source A related resource from which the described resource is derived Lecture Notes in Electrical Engineering, Vol-840, pp. 49-58. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-16-9012-9_5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-9012-9_5</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128613560&doi=10.1007%2F978-981-16-9012-9_5&partnerID=40&md5=4ffd38778a771f14158bd2c8acacd2fe" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128613560&doi=10.1007%2f978-981-16-9012-9_5&partnerID=40&md5=4ffd38778a771f14158bd2c8acacd2fe</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18761100; ISBN: 978-981169011-2 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 Raju B.K., Computer Science Department, Christ Deemed to be University, Bengaluru, Karnataka, India; Ummesalma M., Computer Science Department, Christ Deemed to be University, Bengaluru, Karnataka, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Cross diffusion effects on magnetohydrodynamic slip flow of Carreau liquid over a slendering sheet with non-uniform heat source/sink Subject The topic of the resource Carreau fluid; Cross diffusion; Non-uniform heat source/sink; Slendering surface; Slip parameters Description An account of the resource Magnetohydrodynamic flow of Carreau fluid over a slendering sheet (variable thickness) has been numerically studied by considering the multiple slips effect. Thermosolutal boundary layer analysis is also accounted in the presence of cross diffusion and non-uniform heat source/sink. The governing nonlinear coupled partial differential equations are transformed to nonlinear coupled ordinary differential equations before being integrated numerically using RungeKutta based Newtons schemes. The effects of various parameters involved in the present problem were elaborately discussed with help of graphs and tables. The present results in a limiting sense are found to accord with the previous study. The present results indicate that the cross diffusion and slip parameters had a tendency to control the flow. The influence of slip is more evident in Carreau fluid case on contrast with the Newtonian fluid case. 2018, The Brazilian Society of Mechanical Sciences and Engineering. Creator An entity primarily responsible for making the resource Raju C.S.K.; Hoque M.M.; Priyadharshini P.; Mahanthesh B.; Gireesha B.J. Source A related resource from which the described resource is derived Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol-40, No. 4 Publisher An entity responsible for making the resource available Springer Verlag Date A point or period of time associated with an event in the lifecycle of the resource 2018-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s40430-018-1142-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s40430-018-1142-4</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044830713&doi=10.1007%2Fs40430-018-1142-4&partnerID=40&md5=6a87dd3cd585803aa63405e5d289dd16" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044830713&doi=10.1007%2fs40430-018-1142-4&partnerID=40&md5=6a87dd3cd585803aa63405e5d289dd16</a> Rights Information about rights held in and over the resource All Open Access; Bronze Open Access Relation A related resource ISSN: 16785878 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 Raju C.S.K., Department of Mathematics, GITAM School of Technology, Bangalore, 562163, India; Hoque M.M., Department of Chemical Engineering, The University of Newcastle, Callaghan, Australia; Priyadharshini P., Department of Mathematics, Bharatiar University, Coimbatore, India; Mahanthesh B., Department of Mathematics, Christ University, Bangalore, 560029, India, Department of Studies and Research in Mathematics, Kuvempu University, Shankarghatta, Shimoga, 577451, India; Gireesha B.J., Department of Studies and Research in Mathematics, Kuvempu University, Shankarghatta, Shimoga, 577451, 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 Advanced Machine Learning Techniques for Detecting Irregularities in Skin Lesion Borders: Enhancing Early Skin Cancer Detection Subject The topic of the resource arc-based features; dermatogram analysis; ensemble learning; feature extraction; Skin cancer detection Description An account of the resource Dermatograms are pivotal in the early detection of skin cancer, a disease with significant mortality rates. This paper introduces a novel feature extraction method that captures irregularities in the boundaries of abnormal skin regions. Each raw dermatogram is converted into a binary mask image using an effective segmentation algorithm. The boundary of the lesion region is extracted from the mask. The boundary, together with the centroid of the lesion mask, is used to define a set of directional vectors. An Arc is defined using these directional vectors, and a new Arc feature is calculated based on the number of times the lesion boundary crosses the arc. The proposed Arc feature is evaluated using three standard skin lesion datasets: ISBI 2016, HAM10000, and PH2. Additionally, color features and Local Binary Pattern (LBP) features are implemented for comparison. Classical machine learning algorithms are employed to evaluate these features. Results indicate that for the ISBI 2016 and HAM10000 datasets, the Arc feature set demonstrates superior classification accuracy. In contrast, the PH2 dataset benefits more from the LBP feature. Comparative analysis with recent studies highlights the dependency of accuracy on datasets and classifiers, underscoring the necessity for models incorporating feature fusion and ensemble classifiers. The proposed method outperforms traditional color and texture features and shows competitive results against deep learning models, particularly in scenarios with limited computational resources. These findings suggest that the Arc feature is a promising approach for improving skin cancer detection, although further investigation is needed to fine-Tune performance, optimize classifier selection, and explore feature fusion strategies. 2024 World Scientific Publishing Company. Creator An entity primarily responsible for making the resource Raju G.; Nikesh P.; Resmi K.R.; Swain D.; Acharya B.; Gerogiannis V.C.; Kanavos A. Source A related resource from which the described resource is derived International Journal on Artificial Intelligence Tools 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S0218213024500246" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S0218213024500246</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210081548&doi=10.1142%2FS0218213024500246&partnerID=40&md5=300b51cec3d14f05b8837663f3f8a20f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210081548&doi=10.1142%2fS0218213024500246&partnerID=40&md5=300b51cec3d14f05b8837663f3f8a20f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2182130 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 Raju G., Department of Computer Science and Engineering, Christ University, Bengaluru, Karnataka, India; Nikesh P., Department of Computer Science and Engineering, Government Engineering College, Wayanad, Kerala, India; Resmi K.R., Department of Computer Science and Engineering, Christ University, Bengaluru, Karnataka, India; Swain D., Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India; Acharya B., Department of Computer Engineering-AI, Marwadi University, Gujarat, Rajkot, India; Gerogiannis V.C., Department of Digital Systems, University of Thessaly, Larissa, Greece; Kanavos A., Department of Informatics, Ionian University, Corfu, Greece 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 Determinants of Banks' Profitability: An Empirical Study on Select Indian Public and Private Sector Banks Description An account of the resource In this study the determinants of banking profitability has been studied based on the secondary data. The entire study is classified into two parts (i) Public Sector Banks and (ii) Private Sector Banks. Various variables such as NPA, Operating Profit, Credit Size, ROA, Operating Expense, Total Income, Capital etc. and their interrelationship is studied through correlation coefficients, regression analysis, anova etc. The research observes that a large number of independent factors are responsible in determining banking profitability and that in those determinants some create a significant effect on profitability but some factors do not create any significant effect. It is observed that though macroeconomic variables are not so important to determine the profitability of a bank but the GDP growth rate creates a significant effect on determining the profitability of a bank. According to the study based on facts and figures collected, private sector banks performance is better than public sector banks. Indian Institute of Finance. Creator An entity primarily responsible for making the resource Raju M. Source A related resource from which the described resource is derived Finance India, Vol-35, No. 2, pp. 467-479. Publisher An entity responsible for making the resource available Indian Institute of Finance 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="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131519452&partnerID=40&md5=58ecf91899b09919a032f2fb8c88bfc6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131519452&partnerID=40&md5=58ecf91899b09919a032f2fb8c88bfc6</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9703772 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 Raju M., Christ University, Department of Management Studies, Hosur Road, Bhavani Nagar, Suddagunte Palya 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 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 Impact of macroeconomic variables on the stock performance of select companies in manufacturing industry Subject The topic of the resource Economic value added; Financial performance; Firm value; Market value added; Shareholders value; Strategic investment Description An account of the resource The efficient functioning of a stock market is influenced by different macro economic factors like Inflation, Interest rates, exchange rate etc. The favourable Macro Economic Variables both domestic economy and global economy inspire the organisations to go for strategic investment activities in domestic and global markets and reflect positively on the company financial performance and firms fundamentals like Revenues, Operating margins, Earnings Per Share, the Economic Value , Market value, and the Firms overall Value. These positive indicators in the fundamentals of the firms send positive signals into stock markets and generate positive perceptions about the company's stock prices in the market. Markets become so attractive to domestic and foreign investors which drive the share price of different companies , specially Blue chips upwards and creates value to the shareholders .According to the study organized the impact of macro economic variables is not uniform and the impact varies betweem various macro economic variables on the stock market performance. Serials Publications Pvt. Ltd. Creator An entity primarily responsible for making the resource Raju M.A. Source A related resource from which the described resource is derived International Journal of Economic Research, Vol-14, No. 8, pp. 321-328. Publisher An entity responsible for making the resource available Serials Publications Date A point or period of time associated with an event in the lifecycle of the resource 2017-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026365001&partnerID=40&md5=2400239b14e3fc1fa5a1abb90b5b9c91" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026365001&partnerID=40&md5=2400239b14e3fc1fa5a1abb90b5b9c91</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9729380 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 Raju M.A., Department of Management Studies, Christ 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 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 The behaviour of macro and micro economic variables and the impact on systematic risk of non-banking finance companies Subject The topic of the resource Financial Sector; Government Deficit; Inflation; Leverage; Macro Economic Factors; Systematic Risk; Systemic Description An account of the resource The reforms initiated by the Indian Government during 1990 have brought in drastic changes in the financial sector functioning so as to make competitive with financial markets worldwide. The Indian financial Sector has expanded and acquired greater depth to get itself competitive with new participants in the market. The new liberal business policies introduced by the government due to financial reforms have brought in fundamental changes in the structure and functioning of Banking and Non Banking Institutions, their business models and the products and services offered by them. Global economic developments have altered the macro economic conditions of the respective nations and make the nations and their economies vulnerable to economic shocks in the form of systematic risk associated with their business activities. Macro economic factors at broader level and micro economic factors at the firm level have had effect on the risk level, assessing risk, measuring and managing risk has become paramount for banks and others Non Banking financial Institutions alike. Individual influence of factors on the systematic risk as there is weak relationship with Beta but combined effect of factors is very positive on the systematic risk of the companies. Creator An entity primarily responsible for making the resource Raju M.A. Source A related resource from which the described resource is derived International Journal of Applied Business and Economic Research, Vol-14, No. 6, pp. 4345-4367. Publisher An entity responsible for making the resource available Serials Publications Date A point or period of time associated with an event in the lifecycle of the resource 2016-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989344617&partnerID=40&md5=4e00642ac29b47b8b0032fe6c9990d79" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989344617&partnerID=40&md5=4e00642ac29b47b8b0032fe6c9990d79</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9727302 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 Raju M.A., Department of Management Studies, Christ 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 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 Text extraction from video images Subject The topic of the resource Discrete cosine transform; Fast fourier transform; J48; Random forest; Simple logistic; Text extraction Description An account of the resource Video data contains beneficial textual information such as scene text and caption text. The different types of videos like movies, news videos, and TV programs video etc. are created by various video frames based on its purpose. In a country like India, there are only fewer studies has done on text extraction from video data especially in south Indian languages like Malayalam, Telugu, Kannada, and Tamil. The extracted text has many useful applications in video indexing, video key searching and assisting visually challenged people. Malayalam news channel named Mathrubhumi News videos data are considered for the proposed study. It is very beneficial to Kerala people as it is one of the most media-centric regions in the world. In this proposed paper, a new method for text extraction experiments. The anticipated method extracts 13 different features for classifying the image consists of text or not. Both spatial and frequency domain features are extracted to classify. The different types of classification techniques are used to validate the algorithm. Simple Logistic, J48 and Random Forest classification techniques are giving a good result when compared to other methods. Results are encouraging, the average success rate found to be 98%. Research India Publications. Creator An entity primarily responsible for making the resource Raju N.; Anita H.B. Source A related resource from which the described resource is derived International Journal of Applied Engineering Research, Vol-12, No. 24, pp. 14750-14754. Publisher An entity responsible for making the resource available Research India Publications Date A point or period of time associated with an event in the lifecycle of the resource 2017-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057640769&partnerID=40&md5=722800c6cc6a8b34ea7aae76e90e2c83" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057640769&partnerID=40&md5=722800c6cc6a8b34ea7aae76e90e2c83</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9734562 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 Raju N., Department of Computer Science, Christ University, Bengaluru, 560029, Karnataka, India; Anita H.B., Department of Computer Science, Christ University, Bengaluru, 560029, Karnataka, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Identification of interstitial lung diseases using deep learning Subject The topic of the resource Classification; Deep learning; Medical image analysis; Neural network Description An account of the resource The advanced medical imaging provides various advantages to both the patients and the healthcare providers. Medical Imaging truly helps the doctor to determine the inconveniences in a human body and empowers them to make better choices. Deep learning has an important role in the medical field especially for medical image analysis today. It is an advanced technique in the machine learning concept which can be used to get efficient output than using any other previous techniques. In the anticipated work deep learning is used to find the presence of interstitial lung diseases (ILD) by analyzing high-resolution computed tomography (HRCT) images and identifying the ILD category. The efficiency of the diagnosis of ILD through clinical history is less than 20%. Currently, an open chest biopsy is the best way of confirming the presence of ILD. HRCT images can be used effectively to avoid open chest biopsy and improve accuracy. In this proposed work multi-label classification is done for 17 different categories of ILD. The average accuracy of 95% is obtained by extracting features with the help of a convolutional neural network (CNN) architecture called SmallerVGGNet. 2020 Institute of Advanced Engineering and Science. All rights reserved. Creator An entity primarily responsible for making the resource Raju N.; Anita H.B.; Augustine P. Source A related resource from which the described resource is derived International Journal of Electrical and Computer Engineering, Vol-10, No. 6, pp. 6283-6291. Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/IJECE.V10I6.PP6283-6291" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/IJECE.V10I6.PP6283-6291</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092283268&doi=10.11591%2FIJECE.V10I6.PP6283-6291&partnerID=40&md5=8753b56c556f202486500af7553ccf5e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092283268&doi=10.11591%2fIJECE.V10I6.PP6283-6291&partnerID=40&md5=8753b56c556f202486500af7553ccf5e</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Relation A related resource ISSN: 20888708 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Raju N., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, 560029, India; Anita H.B., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, 560029, India; Augustine P., Department of Computer Science, CHRIST (Deemed to Be University), Bengaluru, Karnataka, 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 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 An efficient deep learning approach for identifying interstitial lung diseases using HRCT images Subject The topic of the resource deep learning; DL; high-resolution computed tomography; HRCT; ILD; interstitial lung disease; multi-label classification; transfer learning Description An account of the resource Interstitial lung disease (ILD) encompasses over 200 fatal lung disorders affecting the interstitium, leading to significant mortality rates. We propose an AI-driven approach to diagnose and classify ILD from high-resolution computed tomography (HRCT) images. The research utilises a dataset of 3,045 HRCT images and employs a two-tier ensemble method that combines various machine learning (ML) models, convolutional neural networks (CNNs), and transfer learning. Initially, ML models achieve high accuracy, with the J48 model at 93.08% accuracy, mainly highlighting the importance of diagonal-wise standard deviation. Deep learning techniques are then applied, with three CNN models achieving test accuracies of 94.08%, 92.04%, and 93.72%. Transfer learning models also show promise, with InceptionV3 at 92.48% accuracy. Ensembling these models further boosts accuracy, with the ensemble of three CNN models reaching 97.42%. This research has the potential to advance ILD diagnosis, offering a robust computational framework that enhances accuracy and ultimately improves patient outcomes. Copyright 2024 Inderscience Enterprises Ltd. Creator An entity primarily responsible for making the resource Raju N.; Augustine D.P. Source A related resource from which the described resource is derived International Journal of Computational Science and Engineering, Vol-27, No. 3, pp. 286-301. 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJCSE.2024.138427" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJCSE.2024.138427</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192870037&doi=10.1504%2FIJCSE.2024.138427&partnerID=40&md5=b3c6ded0e2833e6e04cba94656d8187a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192870037&doi=10.1504%2fIJCSE.2024.138427&partnerID=40&md5=b3c6ded0e2833e6e04cba94656d8187a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 17427185 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 Raju N., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Augustine D.P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Reduce Overfitting and Improve Deep Learning Models Performance in Medical Image Classification Description An account of the resource A significant role in clinical treatment and educational tasks is played by clinical image classification. However, the traditional approach has reached its peak in terms of implementation. Additionally, using traditional approaches requires a lot of time and effort to remove and choose arrangement features. The deep learning (DL) model is a new machine learning (ML) technique that has proven effective for various classification problems. To alter image classification problems, the convolutional neural network performs well, with the best results. This chapter discusses the importance and challenges of deep learning models in medical image classification and explains some techniques for reducing overfitting and leveraging model performance during model training. 2024 Taylor & Francis Group, LLC. Creator An entity primarily responsible for making the resource Raju N.; Augustine D.P. Source A related resource from which the described resource is derived Machine Intelligence: Computer Vision and Natural Language Processing, pp. 65-83. 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/10.1201/9781003424550-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003424550-4</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173400574&doi=10.1201%2F9781003424550-4&partnerID=40&md5=b656c1319f8ec359761c2e47e75cc90b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173400574&doi=10.1201%2f9781003424550-4&partnerID=40&md5=b656c1319f8ec359761c2e47e75cc90b</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 Raju N., Department of Computer Science, Christ University, Bangalore, India; Augustine D.P., Department of Computer Science, 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 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 Novel Deep Learning Approach for Identifying Interstitial Lung Diseases from HRCT Images Subject The topic of the resource Binary classification; Deep learning; HRCT; ILD; Multi-label classification Description An account of the resource Interstitial lung diseases (ILDs) are defined as a group of lung diseases that affect the interstitium and cause death among humans worldwide. It is more serious in underdeveloped countries as it is hard to diagnose due to the absence of specialists. Detecting and classifying ILD is a challenging task and many research activities are still ongoing. High-resolution computed tomography (HRCT) images have essentially been utilized in the diagnosis of this disease. Examining HRCT images is a difficult task, even for an experienced doctor. Information Technology, especially Artificial Intelligence, has started contributing to the accurate diagnosis of ILD from HRCT images. Similar patterns of different categories of ILD confuse doctors in making quick decisions. Recent studies have shown that corona patients with ILD also go on to sudden death. Therefore, the diagnosis of ILD is more critical today. Different deep learning approaches have positively impacted various image classification problems recently. The main objective of this proposed research work was to develop a deep learning model to classify the ILD categories from HRCT images. This proposed work aims to perform binary and multi-label classification of ILD using HRCT images on a customized VGG architecture. The proposed model achieved a high test accuracy of 95.18% on untrained data. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Raju N.; Augustine D.P.; Anita H.B. Source A related resource from which the described resource is derived SN Computer Science, Vol-4, No. 2 Publisher An entity responsible for making the resource available Springer 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/10.1007/s42979-022-01579-y" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-022-01579-y</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145388976&doi=10.1007%2Fs42979-022-01579-y&partnerID=40&md5=f86e32cc9e314ad800bc4ebf2c7f778b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145388976&doi=10.1007%2fs42979-022-01579-y&partnerID=40&md5=f86e32cc9e314ad800bc4ebf2c7f778b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2662995X 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 Raju N., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Augustine D.P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Anita H.B., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Novel Artificial Intelligence System for the Prediction of Interstitial Lung Diseases Subject The topic of the resource Deep learning; HRCT; ILD; Multi-label classification; Transfer learning Description An account of the resource Interstitial lung disease (ILD) encompasses a spectrum of more than 200 fatal lung disorders affecting the interstitium, contributing to substantial mortality rates. The intricate process of diagnosing ILDs is compounded by their diverse symptomatology and resemblance to other pulmonary conditions. High-resolution computed tomography (HRCT) assumes the role of the primary diagnostic tool for ILD, playing a pivotal role in the medical landscape. In response, this study introduces a computational framework powered by artificial intelligence (AI) to support medical professionals in the identification and classification of ILD from HRCT images. Our dataset comprises 3045 HRCT images sourced from distinct patient cases. The proposed framework presents a novel approach to predicting ILD categories using a two-tier ensemble strategy that integrates outcomes from convolutional neural networks (CNNs), transfer learning, and machine learning (ML) models. This approach outperforms existing methods when evaluated on previously unseen data. Initially, ML models, including Logistic Regression, BayesNet, Stochastic Gradient Descent (SGD), RandomForest, and J48, are deployed to detect ILD based on statistical measures derived from HRCT images. Notably, the J48 model achieves a notable accuracy of 93.08%, with the diagnostic significance of diagonal-wise standard deviation emphasized through feature analysis. Further refinement is achieved through the application of Marker-controlled Watershed Transformation Segmentation and Morphological Masking techniques to HRCT images, elevating accuracy to 95.73% with the J48 model. The computational framework also embraces deep learning techniques, introducing three innovative CNN models that achieve test accuracies of 94.08%, 92.04%, and 93.72%. Additionally, we evaluate five full-training and transfer learning models (InceptionV3, VGG16, MobileNetV2, VGG19, and ResNet50), with the InceptionV3 model achieving peak accuracy at 78.41% for full training and 92.48% for transfer learning. In the concluding phase, a soft-voting ensemble mechanism amplifies training outcomes, yielding ensemble test accuracies of 76.56% for full-training models and 92.81% for transfer learning models. Notably, the ensemble comprising the three newly introduced CNN models attains the pinnacle of test accuracy at 97.42%. This research is poised to drive advancements in ILD diagnosis, presenting a resilient computational framework that enhances accuracy and ultimately betters patient outcomes within the medical domain. 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Raju N.; Augustine D.P.; Chandra J. Source A related resource from which the described resource is derived SN Computer Science, Vol-5, No. 1 Publisher An entity responsible for making the resource available Springer 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/s42979-023-02524-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-023-02524-3</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181497206&doi=10.1007%2Fs42979-023-02524-3&partnerID=40&md5=a4f3647e56761e09098ce41e53c92520" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181497206&doi=10.1007%2fs42979-023-02524-3&partnerID=40&md5=a4f3647e56761e09098ce41e53c92520</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2662995X | CS; 2023-2024; Vol-1; 00091-00092 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 Raju N., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Augustine D.P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Chandra J., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Acculturative stress: Psychological health and coping strategies Description An account of the resource There is an increasing shift in focus from the causes of immigration to the consequences of immigration, a major aspect being the stress triggered by the myriad changes and challenges experienced during the process of moving into a different culture and settling in. The main aim of this chapter is to introduce the reader to the concept of acculturative stress in detail. The author has gathered the content by doing a keyword search of relevant terms on Google Scholar and choosing articles that provide insight into acculturation, acculturative stress, and psychological health. The chapter will delve into how the different strategies of acculturation are associated with the level of acculturative stress experienced and consequent mental health problems as well as strategies to manage or reduce acculturative stress. 2023, IGI Global. All rights reserved. Creator An entity primarily responsible for making the resource Raju P. Source A related resource from which the described resource is derived Strategies for Cultural Assimilation of Immigrants and Their Children: Social, Economic, and Political Considerations, pp. 1-24. Publisher An entity responsible for making the resource available IGI Global Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/978-1-6684-4839-7.ch001" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/978-1-6684-4839-7.ch001</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169520121&doi=10.4018%2F978-1-6684-4839-7.ch001&partnerID=40&md5=2f8e987450e26284cf6252310d6d9cf4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169520121&doi=10.4018%2f978-1-6684-4839-7.ch001&partnerID=40&md5=2f8e987450e26284cf6252310d6d9cf4</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166844841-0; 978-166844839-7 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 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 Raju P., CHRIST University (Deemed), 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 Perfectionism and self-compassion among emerging adults: The role of disciplining experiences Subject The topic of the resource Disciplining experiences; Parental warmth; Perfectionism; Self-compassion Description An account of the resource Although the influence of disciplining experiences on a variety of personality factors has been studied, there is less clarity on how disciplining experiences influence the traits of perfectionism and self-compassion in individuals. The purpose of this study was to examine the relationships between different domains of perfectionism and self-compassion, as well as the influence of specific aspects of disciplining experiences, such as parental warmth and punishment experiences, on perfectionism and self-compassion. In this study, a quantitative cross-sectional correlational design was used. A total of 220 Indian emerging adults from the city of Bangalore were surveyed via convenience sampling. The following scales were administered: Disciplining Experiences Measure, Multidimensional Perfectionism Scale, and Self-Compassion Scale. The results showed that (1) Self Compassion has a significant positive relationship with Perfectionism; (2) Punishment experience has an influence on Other-oriented and Socially Prescribed Perfectionism; (3) Disciplining Helped positively predicted Self-oriented Perfectionism; and (4) Parental Warmth positively predicted Self-compassion in individuals. The findings contribute to the literature emphasizing the influence of disciplining experiences on ones self and personality, as well as the potential benefits of self-compassion-based interventions. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Creator An entity primarily responsible for making the resource Raju P.; Sudhesh N.T. Source A related resource from which the described resource is derived Current Psychology, Vol-43, No. 5, pp. 4191-4197. Publisher An entity responsible for making the resource available Springer 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/s12144-023-04531-2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s12144-023-04531-2</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153049381&doi=10.1007%2Fs12144-023-04531-2&partnerID=40&md5=41c45c153e4bed062a344959794f37c1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153049381&doi=10.1007%2fs12144-023-04531-2&partnerID=40&md5=41c45c153e4bed062a344959794f37c1</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 10461310 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 Raju P., CHRIST (Deemed to be University), Bengaluru, India; Sudhesh N.T., CHRIST (Deemed to be University), Bengaluru, India https://archives.christuniversity.in/files/original/65bc9b034921dda669f33c2a476ca071.jpg d8e3e32d9a35cf8399a0f6065ea3e4bf 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 Impact of macroeconomic variables on the stock performance of select companies in manufacturing industry / Subject The topic of the resource Management Description An account of the resource International Journal Of Economic Research, Vol.14(8), pp.321-328, ISSN: 0972-9380. Creator An entity primarily responsible for making the resource Raju, M Appala. Source A related resource from which the described resource is derived <a href="https://serialsjournals.com/abstract/44928_24.pdf" target="_blank" title="Impact of macroeconomic variables on the stock performance of select companies in manufacturing industry" rel="noreferrer noopener">https://serialsjournals.com/abstract/44928_24.pdf</a> Publisher An entity responsible for making the resource available Serials Publications Date A point or period of time associated with an event in the lifecycle of the resource 2017-04-12 Language A language of the resource English Type The nature or genre of the resource Article https://archives.christuniversity.in/files/original/371fa30e5efc7bf8c9a10a1065db6c66.jpg 8bbfbf751c7b63a897e3cdef373f62ea 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 Asset liability management to control the volatility in net interest income and economic value through gap analysis of selected public and private sector banks / Subject The topic of the resource Integration of Economies Description An account of the resource International Journal Of Academic Research In Business And Social Science, Vol.6, Issue 1, pp.123-142, ISSN: 2222-6990. Creator An entity primarily responsible for making the resource Raju, Middi Appala. Source A related resource from which the described resource is derived <a href="https://journals.indexcopernicus.com/search/article?articleId=1463815" target="_blank" title="Asset liability management to control the volatility in net interest income and economic value through gap analysis of selected public and private sector banks" rel="noreferrer noopener">https://journals.indexcopernicus.com/search/article?articleId=1463815</a> Publisher An entity responsible for making the resource available HR Mars Exploring Intellectual Capital Date A point or period of time associated with an event in the lifecycle of the resource 2016-02-11 Language A language of the resource English Type The nature or genre of the resource Journal Article Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Variations in the l-dopa content, phytochemical constituents and antioxidant activity of different germlines of mucuna pruriens (l.) dc. Subject The topic of the resource Antioxidant activity; Germplasms; L-DOPA; Mucuna pruriens; Phytochemicals Description An account of the resource In this study a 'wonder plant' Mucuna pruriens (L.) DC., which is commercially important medicinal plant of the Fabaceae family known for its treatment in Central Nervous System disorders like Dementia, Parkinson's, Alzheimer's, etc. have been selected. Different germplasms have been collected to analyze the phytochemical variations between them and quantify the L-DOPA in root, stem, leaves and seeds of all the five germlines using HPLC. Along with the biochemical assays, antioxidant activity by DPPH, phosphomolybdneum method, the metal chelating and reductive potential activity of all the germplasms were studied. All parts of the plant have shown the presence of L-DOPA but, seeds have the highest quantity followed by the roots, stem and leaves. Arka Shubra seeds showed high L-DOPA content (51.9 mg/g) while the other germplasms showed L-DOPA ranging from 43-45 mg/g. Highest content of carbohydrates (258.8 mg/g) and phenolics (157.0 mg/g) was seen in the seeds of Arka Aswini. While the seeds and leaves of Arka Charaka showed high protein (332.2 mg/g) and flavonoid (10.2 mg/g) content, respectively. High proline (1.74 mg/g) was observed in the seeds of Arka Shubra. Antioxidant studies revealed that Arka Charaka and Arka Daksha to be having high reductive power and free radical scavenging activity by phosphomolybdate method while high metal chelating activity was observed in Arka Aswini (88.7%) and high antioxidant activity by DPPH method was seen in Arka Shubra (86.5%). 2021 Chemical Publishing Co.. All rights reserved. Creator An entity primarily responsible for making the resource RAKESH B.; HIMA BINDU K.; PRAVEEN N. Source A related resource from which the described resource is derived Asian Journal of Chemistry, Vol-33, No. 8, pp. 1881-1890. Publisher An entity responsible for making the resource available Asian Publication Corporation 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/10.14233/ajchem.2021.23293" target="_blank" rel="noreferrer noopener">https://doi.org/10.14233/ajchem.2021.23293</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111231035&doi=10.14233%2Fajchem.2021.23293&partnerID=40&md5=78097769f782030c7b78cebe559a8d61" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111231035&doi=10.14233%2fajchem.2021.23293&partnerID=40&md5=78097769f782030c7b78cebe559a8d61</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 9707077; CODEN: AJCHE 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 RAKESH B., Department of Life Sciences, CHRIST (Deemed To Be University), Hosur Road, Bengaluru, 560 029, India; HIMA BINDU K., Principal Scientist, Flower and Medicinal Crops, ICAR-Indian Institute of Horticultural Research, Bengaluru, 560089, India; PRAVEEN N., Department of Life Sciences, CHRIST (Deemed To Be University), Hosur Road, Bengaluru, 560 029, India