456 20 16481 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Nagashruthi, M.K.; Hemanth, K.S. Title A name given to the resource Integrating Diverse Approaches in Medical Image Analysis: PAA-CNN and Feature Extraction Fusion for Classiation of Psychological Disorders using Anatomical Scans 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 Procedia Computer Science;Volume;260;pp.40-47 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.procs.2025.03.175" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2025.03.175</a> <a href="https://www.scopus.com/pages/publications/105008868634?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008868634?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 Nagashruthi M.K., School Of Computer Science and Applications, REVA University, Bengaluru, 560064, India; Hemanth K.S., Department of Computer Science, Christ University, Bengaluru, 560073, India Description An account of the resource A psychological disorder is a condition that impacts a persons behavior. Due to the contemporary way of life, a large number of individuals suffer from disorders like stress, depression, and other similar ones. These might turn into severe issues that would signiantly impact a persons quality of life. We present a sample framework that uses an Anatomical scan captured along with fMRI. Anatomical scans were used to extract characteristics, which were then utilized to classify using a random forest classir. In a follow-up experiment, CNN is applied to features obtained from the piecewise aggregate approximation method for multi-class classiation of psychological disorders. This method performs noticeably better than the conventional feature extraction techniques, and with this approach, obtained an accuracy of up to 79%. Combining several approaches may boost the classiation and prediction accuracy of medical data. 2025 The Authors. Published by Elsevier B.V. Subject The topic of the resource anatomical images; feature extraction; machine learning; peicewise aggregate approximation; prominent volumes Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 18770509; 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 All Open Access; Gold Open Access 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 Nagashruthi, M.K.; Hemanth, K.S. Title A name given to the resource Interpretable Deep Learning for Multiclass Psychological Disorder Classification Using CNN and TCAV Using fMRI 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 Conference Proceedings - 2025 IEEE 4th International Conference on Data, Decision and Systems, ICDDS 2025;pp.147-152 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDDS67737.2025.11344721" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDDS67737.2025.11344721</a> <a href="https://www.scopus.com/pages/publications/105033365376?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033365376?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 Nagashruthi M.K., School of Computer Science and Applications, Reva University, Bengaluru, India; Hemanth K.S., Christ University, Department of Computer Science, Bengaluru, India Description An account of the resource In this paper a 3D convolutional neural network model is presented that has been improved with squeeze-and-excitation blocks and residual blocks to categorize functional MRI data across healthy controls, schizophrenia, and attention deficit hyperactivity disorder classes. A post hoc explainability framework was applied to concept activation vectors based on mean activation for each class and testing using TCAV. The merging of CAV and TCAV for fMRI data was to improve transparency by providing an interpretable model. This helps in understanding the prediction sensitivity of the models. Concept vectors are defined through the extraction and analysis of intermediate activations from the CNN-SE model. These vectors are then used to calculate the TCAV scores, which indicate the degree to which each brain region influences the model output. The precision of the deep learning model was up to 79%. Similarity matrices indicate the degree of overlap and correlate with the model's results. Visualizations such as activation heat maps and glass brain overlays based on the AAL atlas further support the interpretability of the model, making it more transparent and suitable for clinical applications. Variations in activation between classes can be observed using a visual feature plot. This framework allows mapping model predictions to interpretable neuroanatomical regions and identifies classspecific dependencies on particular brain networks. 2025 IEEE. Subject The topic of the resource classification; concept vector; explainability; fMRI; interpretability Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155479-8; 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 Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Nagashruthi, M.K.; Hemanth, K.S. Title A name given to the resource Artificial Intelligence and Machine Learning in Clinical Care: Revolutionizing Decision Support 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 Innovation in HealthTech: a Roadmap for Empowering Healthcare;pp.95-116 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003516163-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003516163-6</a> <a href="https://www.scopus.com/pages/publications/105002517479?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002517479?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 Nagashruthi M.K., School of Computer Science and Application, REVA University, Karnataka, Bangalore, India; Hemanth K.S., Department of Computer Science, Christ University, Karnataka, Bangalore, India Description An account of the resource The potential of artificial intelligence (AI) and machine learning to significantly modify clinical decision support is examined in this chapter. AI algorithms can use extensive databases of imaging outcomes, clinical trials, and medical records to identify complex patterns that lead to precise diagnoses, treatment plans, and progressively affected patient outcomes. A diagnosis includes evaluating the patients condition leveraging information gathered from multiple kinds of tests and their past medical history. AI-driven systems in the healthcare industry are constrained by the difficulty of handling tiny volumes and poor-quality medical data. A better prediction system for low-quality data and the analysis of unusual and sensitive medical cases can be analyzed by more powerful AI technologies. The chapter shows how AI-powered equipment is presently affecting healthcare. With excellent accuracy, device getting-to-know algorithms can examine medical images and identify potential abnormalities in X-rays, mammograms, or other imaging modes that a human might overlook. Furthermore, AI may review an affected persons records and show fitness facts to estimate a patients vulnerability to specific diseases, allowing for active intervention and preventative measures. The chapter concludes with critical tips for optimizing AIs complete range of applications in scientific care. To ensure the ideal and ethical application of these effective technologies, the responsibilities consist of defensive record safety and privacy, tackling algorithmic bias, and inspiring cooperation among clinical experts and AI developers. The healthcare zone can enter a modern section of using statistics to make educated selections through the implementation of AI and machine-gaining knowledge. 2025 selection and editorial matter, Rakesh Kumar and Meenu Gupta individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104033782-0; 978-103285027-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Business Forecasting and Error Handling Using AI Description An account of the resource Business forecasting is the technique of accurately predicting the future of a business and outcomes using historical data and present trends. To evaluate historical data and find patterns, trends, and other elements that might be used to forecast future events, a variety of analytical tools and techniques are used. Business forecasting is a crucial component of strategic planning because it enables businesses to foresee market changes, spot possible risks and opportunities that may arise in the future, and make wise resource allocation and investment decisions. Businesses that use effective business forecasting can plan and carry out their programs that help them stay competitive, expand their operations, and meet their objectives. According to Glueck [1], Forecasting is a formal process of predicting future events that will significantly affect the functioning of an enterprise.. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. Creator An entity primarily responsible for making the resource Nagasri V.K.; Vasantha M.; Nanjundan P.; George J.P. Source A related resource from which the described resource is derived Artificial Intelligence in Forecasting: Tools and Techniques, pp. 233-241. 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 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003399292-15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003399292-15</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195975936&doi=10.1201%2F9781003399292-15&partnerID=40&md5=d28c1a494392f17fb4978261a4aae764" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195975936&doi=10.1201%2f9781003399292-15&partnerID=40&md5=d28c1a494392f17fb4978261a4aae764</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-104005150-4; 978-103250615-9 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 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 Nagasri V.K., School of Commerce Finance and Accountancy, CHRIST (Deemed to be University), Pune Lavasa, India; Vasantha M., School of Commerce Finance and Accountancy, CHRIST (Deemed to be University), Pune Lavasa, India; Nanjundan P., Department of Data Science, CHRIST (Deemed to be University), Pune Lavasa, India; George J.P., Department of Data Science, CHRIST (Deemed to be University), Pune Lavasa, India https://archives.christuniversity.in/files/original/78af9b6ec725c58defd3425867680f09.pdf d8290985f95fbeed389aebe2e48783d6 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 Digital Collections Description An account of the resource Rare books archived by the library Creator An entity primarily responsible for making the resource Christ University Book Faculty Publications- Book 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 Aviswasi / Subject The topic of the resource Malayalam Play Creator An entity primarily responsible for making the resource Nagavalli Publisher An entity responsible for making the resource available Sahitya Pravarthaka Co-operative Language A language of the resource Malayalam 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 Machine Learning Technology-Based Heart Disease Detection Models Description An account of the resource At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Nae Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared. Copyright 2022 Umarani Nagavelli et al. Creator An entity primarily responsible for making the resource Nagavelli U.; Samanta D.; Chakraborty P. Source A related resource from which the described resource is derived Journal of Healthcare Engineering, Vol-2022 Publisher An entity responsible for making the resource available Hindawi Limited 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/10.1155/2022/7351061" target="_blank" rel="noreferrer noopener">https://doi.org/10.1155/2022/7351061</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126079724&doi=10.1155%2F2022%2F7351061&partnerID=40&md5=6ac9dd4d1c658011717e93dcc65fbc39" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126079724&doi=10.1155%2f2022%2f7351061&partnerID=40&md5=6ac9dd4d1c658011717e93dcc65fbc39</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: 20402295; PubMed ID: 35265303 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 Nagavelli U., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India; Samanta D., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India, Department of Computer Science, CHRIST (Deemed to be) University, Karnataka, Bangalore, India; Chakraborty P., Department of Computer Science and Engineering, Comilla University, Cumilla, 3506, Bangladesh 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 ML-Based Prediction Model for Cardiovascular Disease Subject The topic of the resource Cardiovascular disease (CVD); Logistic regression; Nae Bayes; Random forest Description An account of the resource In this paper, the prediction of cardiovascular disease model based on the machine learning algorithm is implemented. In medical system applications, data mining and machine learning play an important role. Machine learning algorithms will predict heart disease or cardiovascular disease. Initially, online datasets are applied to preprocessing stage. Preprocessing stage will divide the data from baseline data. In the same way, CVD events are collected from data follow-ups. After that, data will be screened using the regression model. The regression model consists of logistic regression, support vector machine, nae Bayes, random forest, and K-nearest neighbors. Based on the techniques, the disease will be classified. Before classification, a testing procedure will be performed. At last from results, it can observe that accuracy, misclassification, and reliability will be increased in a very effective way. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Nagavelli U.; Samanta D.; Thomas B. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-490, pp. 91-98. 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-19-4052-1_11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-4052-1_11</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138760730&doi=10.1007%2F978-981-19-4052-1_11&partnerID=40&md5=d149662a92dcb6a3c3e504032593a768" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138760730&doi=10.1007%2f978-981-19-4052-1_11&partnerID=40&md5=d149662a92dcb6a3c3e504032593a768</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981194051-4 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nagavelli U., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India; Samanta D., Dayananda Sagar Research Foundation, University of Mysore (UoM), Karnataka, Mysore, India, Department of Computer Science, CHRIST Deemed to be University, Karnataka, Bangalore, India; Thomas B., Department of Computer Science, CHRIST Deemed to be University, Karnataka, 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 Bio-Inspired Energy Storage Electrode: Utilizing Co3O4 Hollow Spheres Derived from Sugarcane Bagasse Extract Synthesis Via Hydrothermal Route Subject The topic of the resource ASC device; Co3O4-hollow spheres; Energy storage; Sugarcane bagasse Description An account of the resource Recent research has explored the utilization of sugarcane bagasse, a bio-industrial waste, to fabricate energy storage devices due to ecofriendly nature, low cost with industrial scale production. In this investigation, cobalt oxide hollow spheres (Co3O4 HSs) were synthesized from waste sugarcane bagasse extract with the carbon spheres (CSs) act as template. The main component of sucrose (C12H22O11) linked with cellulose fibers and other oxygenic functional groups were used to prepare CSs. Previously, a metal precursor (Co(NO3)2.6H2O) was mixed with sugarcane bagasse extract and subjected to a hydrothermal process, resulting in uniform-sized metal CSs. The uniform sized Co3O4 HSs were formed by calcined metal CSs. The calcination temperature plays a crucial role to eliminating implanted carbon material on inter surface area of the metal oxide, shaping the Co3O4 HSs. Structural, vibrational, morphology and elemental analyses were confirmed by X-ray diffraction (XRD), Fourier transformed infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDX), respectively. Electrochemical tests show improved ion transport and low resistance, leading to high capacitance in asymmetric supercapacitor (ASC) devices. Subsequently, for asymmetric supercapacitor (ASC) devices, using with Co3O4 HSs has function of cathode and activated carbon (AC) as anode, the devices demonstrated impressive results of 33.1 Fg? 1 at 1 Ag? 1, 86.8% retention after 4,000 cycles, as well as the energy density and power density of 5.9W h kg? 1 at 1500W kg? 1. The Co3O4 HSs||AC device exhibits promising energy storage properties for future applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Creator An entity primarily responsible for making the resource Nagavenkatesh K.R.; Murugesan M.; Sambathkumar C.; Devendran P.; Nallamuthu N.; Krishna Kumar M. Source A related resource from which the described resource is derived Journal of Inorganic and Organometallic Polymers and Materials, Vol-34, No. 12, pp. 6088-6101. 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/s10904-024-03237-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10904-024-03237-7</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200028479&doi=10.1007%2Fs10904-024-03237-7&partnerID=40&md5=ad7604a23750fbae0ead00d400fda20b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200028479&doi=10.1007%2fs10904-024-03237-7&partnerID=40&md5=ad7604a23750fbae0ead00d400fda20b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 15741443 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 Nagavenkatesh K.R., Department of Physics, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, Virudhunagar, 626 126, India; Murugesan M., Department of Physics, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, Virudhunagar, 626 126, India; Sambathkumar C., Department of Physics, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, Virudhunagar, 626 126, India; Devendran P., Department of Physics, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, Virudhunagar, 626 126, India; Nallamuthu N., Department of Physics, Dayananda Sagar Academy of Technology and Management, Udayapura, Karnataka, Bangalore, 560 082, India; Krishna Kumar M., Department of Physics and Electronics, CHRIST (Deemed to be University, Karnataka, Bangalore, 560 029, 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 Comparative electrochemical investigation for scheelite structured metals tungstate (MWO4 (M = Ni, Cu and Co)) nanocubes for high dense supercapacitors application Subject The topic of the resource Chemical reflux; Energy storage; Metal tungstate; retentivity; Scheelite structure; Specific capacitance Description An account of the resource Scheelite structured metal tungstate MWO4 (M = Ni, Cu and Co) nanocubes were synthesized through the chemical reflux for supercapacitors application and ceyltrimethylammonium bromide (C-TAB) as surfactant. In X-ray diffraction (XRD) result are fit with relevant JCPDS cards, synthesized materials are closely matched with monoclinic and triclinic crystal phase corresponding to NiWO4, CoWO4 and CuWO4 with Scheelite type structure. To resist the growth of the particles and succeeding nanocubes morphology were achieving by using PEG-400 and C-TAB act as a surfactant. The prepared modified electrodes were examined electrochemical analysis after successive coating of working material in empty Ni foil. From the galvanostatic charge-discharge (GCD) comparative analysis, fast ions movements are interacts through the aqueous electrolyte medium with nanocubes NiWO4 electrode are achieving specific capacitance of 1185 Fg?1 at 0.5 Ag?1 and cyclic stability 93.084 % (retentivity) formerly compare to CuWO4 and CoWO4 electrodes. 2023 Creator An entity primarily responsible for making the resource Nagavenkatesh K.R.; Murugesan M.; Sambathkumar C.; Nallamuthu N.; Devendran P.; Krishna kumar M. Source A related resource from which the described resource is derived Journal of Energy Storage, Vol-79 Publisher An entity responsible for making the resource available Elsevier 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/10.1016/j.est.2023.110153" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.est.2023.110153</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180533299&doi=10.1016%2Fj.est.2023.110153&partnerID=40&md5=7d44993194bb1f5d369822ef1c5cccc0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180533299&doi=10.1016%2fj.est.2023.110153&partnerID=40&md5=7d44993194bb1f5d369822ef1c5cccc0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2352152X 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 Nagavenkatesh K.R., Department of Physics, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India; Murugesan M., Department of Physics, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India; Sambathkumar C., Department of Physics, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India; Nallamuthu N., Dayananda Sagar Academy of Technology and Management, Bangalore, Uthayapura, 560082, India; Devendran P., Department of Physics, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India; Krishna kumar M., Department of Physics and Electronics, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560 029, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An Empirical and Statistical Analysis of Fetal Health Classification Using Different Machine Learning Algorithm Description An account of the resource The health of both the mother and the baby is affected by how well the fetus is doing during pregnancy, making it a matter of utmost importance. To achieve the best results possible, it is essential to regularly monitor and intervene when needed. While there are many ways to observe the wellbeing of the fetus in the mother's womb, using artificial intelligence (AI) has the potential to enhance accuracy, efficiency, and speed when it comes to diagnosing any issues. This study focuses on developing a machine learning-driven system for accurate fetal health classification. The dataset comprises detailed information on the signs and symptoms of pregnant individuals, particularly those at risk or with emerging fetal health issues. Employing a set of ten machine learning models namely Nae Bayes, Logistic Regression, Decision Tree, Random Forest, KNN, SVM, Gradient Boosting, Linear Discriminant Analysis, Quadratic Discriminant Analysis Light Gradient Boosting Machine (LGBM) along with ensemble-based processes, the Light Gradient Boosting Machine (LGBM) has been identified as a standout performer, accomplishing an accuracy of 96.9%. Furthermore, our exploration demonstrates overall performance like character fashions, signaling promising prospects for sturdy and correct fetal fitness class systems. This study highlights the power of machine learning that could revolutionize prenatal care by identifying fetal health problems early. 2024 IEEE. Creator An entity primarily responsible for making the resource Nagavibha R.; Satheesh M.M.; Chandy M.P.; Prathap B.R. Source A related resource from which the described resource is derived IEEE International Conference on Data Engineering and Communication Systems, ICDECS 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/ICDECS59733.2023.10503087" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDECS59733.2023.10503087</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192565405&doi=10.1109%2FICDECS59733.2023.10503087&partnerID=40&md5=aaaa0591508d2d27802c1ab77c37f36e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192565405&doi=10.1109%2fICDECS59733.2023.10503087&partnerID=40&md5=aaaa0591508d2d27802c1ab77c37f36e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835039335-4 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nagavibha R., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Satheesh M.M., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Chandy M.P., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Prathap B.R., Christ (Deemed to Be University), Department of Computer Science and Engineering, 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 Reviews Review Faculty Publications- Reviews 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 Production, Delivery, and Regulatory Aspects for Application of Plant-Based Anti-microbial Peptides: a Comprehensive Review Subject The topic of the resource Bioinformatics; Food spoilage; Nano delivery; Plant antimicrobial peptides; Synthetic peptides Description An account of the resource Antimicrobial peptides (AMPs) are small, positively charged biomolecules produced by various organisms such as animals, microbes, and plants. These AMPs play a significant role in defense mechanisms and protect from adverse conditions. The emerging problem of drug resistance in microbes poses a global health challenge in treating diseases. This plant-based antimicrobial peptide is a promising candidate for fighting against drug-resistant microbes. The PAMPs process specific key properties, proving their efficacy as antimicrobial agents against a broad spectrum of microbes such as Gram-positive, Gram-negative, and fungi. Extensive research on PAMPs has explored their potential as plant growth regulators and therapeutic agents. Their diverse mode of action on microbes encouraged their application in food industries. ThePAMPs are isolated and purified from various plant species organs such as roots, shoots, leaves, flowers, and seeds. These are bioactive molecules with significant stability, and low toxicity has encouraged their application as food additives. Furthermore, to meet the consumer demand, mass production of AMPs was possible with recombinant DNA technology. The advanced and nanotechnology-based delivery system has significantly improved the efficacy and bioavailability of PAMPs as food preservatives for improved shelf-life and prevent spoilage of food products. ThePAMPs are of green origin and can be used as natural bio preservatives that do not alter the sensory properties of food and are harmless to consumers. Plants being the rich resource of AMPs to support their quick identification, and retrieval for commercial applications there is a need to integrate the omics approach with databases. TheAMPs are small, positively charged biomolecules produced by various organisms such as animals, microbes, and plants. These AMPs play a significant role in defense mechanisms and protect from adverse conditions. The emerging problem of drug resistance in microbes poses a global health challenge in treating diseases. This plant-based antimicrobial peptide is a promising candidate for fighting against drug-resistant microbes. The PAMPs process specific key properties, proving their efficacy as antimicrobial agents against a broad spectrum of microbes such as Gram-positive, Gram-negative, and fungi. Extensive research on PAMPs has explored their potential as plant growth regulators and therapeutic agents. Their diverse mode of action on microbes encouraged their application in food industries. ThePAMPs are isolated and purified from various plant species organs such as roots, shoots, leaves, flowers, and seeds. These are bioactive molecules with significant stability, and low toxicity has encouraged their application as food additives. Furthermore, to meet the consumer demand, mass production of AMPs was possible with recombinant DNA technology. The advanced and nanotechnology-based delivery system has significantly improved the efficacy and bioavailability of PAMPs as food preservatives for improved shelf-life and prevent spoilage of food products. ThePAMPs are of green origin and can be used as natural bio preservatives that do not alter the sensory properties of food and are harmless to consumers. Plants being the rich resource of AMPs to support their quick identification, and retrieval for commercial applications there is a need to integrate the omics approach with databases. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Creator An entity primarily responsible for making the resource Nagella P.; Balasubramanian B.; Park S.; Singh U.; Jayan A.; Mukherjee S.; Nizam A.; Meyyazhagan A.; Pappuswamy M.; Sebastian J.K.; Lakshmaiah V.V.; Mousavi Khaneghah A. Source A related resource from which the described resource is derived Probiotics and Antimicrobial Proteins 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 2025-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s12602-024-10421-1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s12602-024-10421-1</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214136925&doi=10.1007%2Fs12602-024-10421-1&partnerID=40&md5=1b64893f932c21b2a5f24239be8aed33" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214136925&doi=10.1007%2fs12602-024-10421-1&partnerID=40&md5=1b64893f932c21b2a5f24239be8aed33</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18671306 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 Review Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nagella P., Department of Life Sciences, Christ University, Karnataka, Bangalore, 560 029, India; Balasubramanian B., Department of Food Science and Biotechnology, College of Life Science, Sejong University, Seoul, 05006, South Korea; Park S., Department of Food Science and Biotechnology, College of Life Science, Sejong University, Seoul, 05006, South Korea; Singh U., Department of Life Sciences, Christ University, Karnataka, Bangalore, 560 029, India; Jayan A., Department of Life Sciences, Christ University, Karnataka, Bangalore, 560 029, India; Mukherjee S., Department of Life Sciences, Christ University, Karnataka, Bangalore, 560 029, India; Nizam A., Department of Chemistry, Christ University, Karnataka, Bangalore, 560 029, India; Meyyazhagan A., Department of Life Sciences, Christ University, Karnataka, Bangalore, 560 029, India; Pappuswamy M., Department of Life Sciences, Christ University, Karnataka, Bangalore, 560 029, India; Sebastian J.K., Department of Life Sciences, Christ University, Karnataka, Bangalore, 560 029, India; Lakshmaiah V.V., Department of Life Sciences, Christ University, Karnataka, Bangalore, 560 029, India; Mousavi Khaneghah A., Faculty of Biotechnologies (BioTech), ITMO University, 9 Lomonosova Street, Saint Petersburg, 191002, Russian Federation 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 In vitro production of bacosides from Bacopa monnieri Subject The topic of the resource Adventitious shoot; Bacopa monnieri; Bacoside A; Biomass; Cell suspension; Medicinal plant; Secondary metabolites Description An account of the resource Bacopa monnieri (L.) Wettst. (Plantaginaceae) is an important Ayurvedic medicinal herb commonly known as brahmi, growing in the region of Indian subcontinent. Bacosides are the major chemical component having the major role in the biological and pharmacological field. Bacopa cultivation is time-consuming, requires labor team, and needs great efforts to maintain the quality of bacosides as growths are affected by environmental factors such as soil, water, temperature, climate, pests, and pathogens. To solve these problems, organ and cell cultures have been adopted for swift and efficient production of Bacopa biomass and bacosides. In the current chapter, various parameters, such as types of media, media composition, elicitors, salinity, drought, types of vessels used, and effect of heavy metals, were investigated against the in vitro production of bacosides from Bacopa monnieri. Springer Nature Singapore Pte Ltd. 2018. Creator An entity primarily responsible for making the resource Nagella P.; Naik P.M.; Al-Khayri J.M. Source A related resource from which the described resource is derived Biotechnological Approaches for Medicinal and Aromatic Plants: Conservation, Genetic Improvement and Utilization, pp. 289-301. Publisher An entity responsible for making the resource available Springer Singapore 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/10.1007/978-981-13-0535-1_13" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-13-0535-1_13</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063493465&doi=10.1007%2F978-981-13-0535-1_13&partnerID=40&md5=bb79beb4a9db1abb71b018031fc0513a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063493465&doi=10.1007%2f978-981-13-0535-1_13&partnerID=40&md5=bb79beb4a9db1abb71b018031fc0513a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-981130535-1; 978-981130534-4 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource 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 Nagella P., Department of Life Sciences, Christ University, Bengaluru, India; Naik P.M., Department of Agricultural Biotechnology, College of Agriculture and Food Sciences, King Faisal University, Al-Hassa, Saudi Arabia; Al-Khayri J.M., Department of Agricultural Biotechnology, College of Agriculture and Food Sciences, King Faisal University, Al-Hassa, Saudi Arabia 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 In vitro propagation and secondary metabolite production from Withania Somnifera (L.) dunal Subject The topic of the resource Agrobacterium rhizogenes; Ashwagandha; Callus culture; Withaferin-A; Withania somnifera; Withanolide-A Description An account of the resource Withania somnifera (L.) Dunal, commonly known as ashwagandha or Indian ginseng, is an important medicinal plant that belongs to the family Solanaceae. Ashwagandha has been used from time immemorial in different systems of medicine and extensively used in the Indian system of medicine, and there is discussion of this plant in different ayurvedic scripts like Charaka samhita, Ashtanga sangraha, etc. The plant is extensively used for anti-aging and general well-being, and also has anti-cancer potential. Ashwagandha is also known for its antioxidant, anti-inflammatory, and other therapeutic activities. In the recent days of Covid-19, the plant has been extensively used as an immunostimulant. The plant has great potential for its raw materials, especially for the extraction of bioactive molecules like withanolide-A, withaferin-A, withasomniferin, withanone, etc. The conventional mode of propagation could not meet the required commercial demand for either the pharmaceutical industries or the traditional practitioners. The conventional method of obtaining biomass is influenced by a large number of environmental factors, where biomass quality and quantity of bioactive molecules have shown variation. To overcome this, biotechnological approaches such as plant tissue culture techniques have been established for large-scale cultivation using micropropagation and also other techniques like a callus and cell suspension culture, shoot culture, adventitious root culture, and hairy root culture have been extensively used for in vitro production of bioactive molecules from ashwagandha. With the advent of metabolic engineering, biosynthetic pathway editing has made it possible to obtain higher yields of desired metabolites. The present chapter focuses on the in vitro propagation, biosynthesis of withanolides, and tissue culture strategies for obtaining high biomass and metabolites. The chapter also focuses on different elicitation strategies, metabolic engineering approaches, and the development of elite germplasms for improved metabolite content. The chapter also identifies research lacunas that need to be addressed for the sustainable production of important bioactive molecules from ashwagandha. 2024 Bentham Science Publishers. All rights reserved. Creator An entity primarily responsible for making the resource Nagella P.; Sudheer W.N.; Banadka A. Source A related resource from which the described resource is derived In Vitro Propagation and Secondary Metabolite Production from Medicinal Plants: Current Trends (Part 2), pp. 64-91. Publisher An entity responsible for making the resource available Bentham Science 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/10.2174/9789815196351124010005" target="_blank" rel="noreferrer noopener">https://doi.org/10.2174/9789815196351124010005</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203340914&doi=10.2174%2F9789815196351124010005&partnerID=40&md5=2b94ba158a2dd5c88089b0f673516f00" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203340914&doi=10.2174%2f9789815196351124010005&partnerID=40&md5=2b94ba158a2dd5c88089b0f673516f00</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-981519635-1; 978-981519636-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource 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 Nagella P., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, Karnataka, 560029, India; Sudheer W.N., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, Karnataka, 560029, India; Banadka A., Department of Life Sciences, CHRIST (Deemed to be University), Bangalore, Karnataka, 560029, India https://archives.christuniversity.in/files/original/75d62f73bf4a091fe6dd6be0c20a0fed.jpg e435738eb1bbefb7cb01947b9b2fdb9b 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 Production of gymnemic acid from cell suspension cultures of gymnema sylvestre / Subject The topic of the resource Botony Description An account of the resource Protocols For In Vitro Cultures And Secondary Metabolite Analysis Of Aromatic And Medicinal Plants, Part of the Methods in Molecular Biology book series (MIMB,volume 1391), pp.229–239; 2nd ed. Creator An entity primarily responsible for making the resource Nagella, Praveen., Dandin, Vijayalaxmi S., Murthy, Hosakatte Niranjana. Source A related resource from which the described resource is derived <a href="https://link.springer.com/protocol/10.1007/978-1-4939-3332-7_16" target="_blank" title="Production of gymnemic acid from cell suspension cultures of gymnema sylvestre" rel="noreferrer noopener">https://link.springer.com/protocol/10.1007/978-1-4939-3332-7_16</a> Publisher An entity responsible for making the resource available Human Press Date A point or period of time associated with an event in the lifecycle of the resource 2016-02-15 Language A language of the resource English Type The nature or genre of the resource Journal Article Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Financial Lexicon based Sentiment Prediction for Earnings Call Transcripts for Market Intelligence Subject The topic of the resource Classification; Earnings Call Transcripts (ECT); Inverse Effect; Lexicon; Sentiments; Stock Price Description An account of the resource Sentiment based stock price direction detection has been an exciting study in the field of finance which is drawing a lot of attention from the investor community. Sentiments are used to detect the changes in the stock price movements for the subsequent periods. Investor community uses these sentiments derived from news, celebrity speech and events to plan trading and investment strategies. Several studies have been done in the past with sentiments, but use of Earnings Call Transcripts (ECT) has not been explored for market intelligence hitherto. Standard dictionary based lexicons like Vader, AFINN and NRC have not performed well in finance as they are domain agnostic. There is a need to develop a financial lexicon based on the ECT corpora, which may provide a better lift over the standard lexicons. This study has observed that Vader has performed poorly as opposed to the newly developed financial lexicon. Machine learning based generative lexicon engine using Bayesian approach, which is termed as FNB Lex was developed in this research study to overcome the limitations of standard domain agnostic lexicons. The lexicon development was performed on quarterly Earning Call Transcripts (ECT) of sixteen IT companies spanning over ten years. The study also investigates the detection of inverse effect in stock price movements based on the sentiments of the previous period. Machine Learning (ML) models like Naive Bayes, FNB Lex, SVM and biLSTM were developed and their discriminatory powers were assessed. NB Lex provided much better lift in detecting the inverse effect as opposed to other models. 2024 IEEE. Creator An entity primarily responsible for making the resource Nagendra B.V.; Kumar Chandar S.; Simha J.B.; John Bazil J.A. Source A related resource from which the described resource is derived Proceedings - 2024 5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024, pp. 595-603. 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/ICIPCN63822.2024.00103" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICIPCN63822.2024.00103</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204783457&doi=10.1109%2FICIPCN63822.2024.00103&partnerID=40&md5=4a4d6e405690728209abb81feec7b6e7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204783457&doi=10.1109%2fICIPCN63822.2024.00103&partnerID=40&md5=4a4d6e405690728209abb81feec7b6e7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835036717-1 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 Nagendra B.V., Christ University, Bengaluru, India; Kumar Chandar S., Christ University, Bengaluru, India; Simha J.B., REVA University, REVA Academy for Corporate Excellence, India; John Bazil J.A., Christ University, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Deploying NLP Techniques for Earnings Call Transcripts for Financial Analysis: A Reverse Phenomenon Paradigm Subject The topic of the resource 'Inverse Effect'; Behavioral Finance; Earnings Call Transcripts; Earnings per Share; Efficient Market Hypothesis; Lexicon Based Sentiments Analysis; Market Performance; Polarity; Stock Price; Weak Market Hypothesis Description An account of the resource This study analyses the influence of quarterly board room discussions conducted in the form of "Earnings Call Transcripts"and company's stock price changes in the subsequent periods. In this study, sentiments were extracted from the "textual quarterly transcripts"of three major software companies for the last ten years. The extracted sentiments were statistically analyzed for patterns and types. The study led to the development of a new response variable called the 'Inverse Effect'. The 'Inverse Effect' simply refers to the discordance between the sentiment in the boardroom discussions available in the document form and changes in the stock price movements. If the sentiment for the current quarter is positive and the changes in the stock price movements is also positive in the subsequent quarter, it is considered as "concordance"and if the changes in the stock price movements is opposite to the sentiments it will be called as "discordance"which is the inverse effect. The study basically looks at the areas where the Weak Market Hypothesis (WMH) is not valid.The findings emerged from the study suggest a possible causality between the sentiments in the transcripts and the stock price changes. It was also found that sentiment polarity, three-quarter average stock price and the previous quarter stock price are the key determinants of the 'Inverse Effect'. Based on the findings from the study, appropriate machine learning models were developed and evaluated to predict the 'Inverse Effect' on the performance of individual stocks of a few select companies. 2023 IEEE. Creator An entity primarily responsible for making the resource Nagendra B.V.; Simha J.B.; Abhi S.; Kumar Chandar S. Source A related resource from which the described resource is derived 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023 - Proceedings, pp. 368-375. 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/I-SMAC58438.2023.10290494" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/I-SMAC58438.2023.10290494</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177617890&doi=10.1109%2FI-SMAC58438.2023.10290494&partnerID=40&md5=755f50ad1b49b04b1e9d9a7925199572" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177617890&doi=10.1109%2fI-SMAC58438.2023.10290494&partnerID=40&md5=755f50ad1b49b04b1e9d9a7925199572</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835034148-5 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 Nagendra B.V., Christ (Deemed to Be University), Bengaluru, India; Simha J.B., Reva University, Reva Academy for Corporate Excellence, Bangalore, India; Abhi S., Reva University, Reva Academy for Corporate Excellence, Bangalore, India; Kumar Chandar S., Christ (Deemed to Be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Sentence Classification Using Attention Model for E-Commerce Product Review Subject The topic of the resource Deep Learning; Linear Support Vector Machine (LSVC); Logistic Regression (LR); Machine Learning; Multinomial Nae Bayes (MNB); N-Gram; Natural Language Processing (NLP); Neural Networks; Random Forest (RF); Text Classification; TF-IDF Description An account of the resource The importance of aspect extraction in text classification, particularly in the e-commerce sector. E-commerce platforms generate vast amounts of textual data, such as comments, product descriptions, and customer reviews, which contain valuable information about various aspects of products or services. Aspect extraction involves identifying and classifying individual traits or aspects mentioned in textual reviews to understand customer opinions, improve products, and enhance the customer experience. The role of product reviews in e-commerce is discussed, emphasizing their value in aiding customers' purchase decisions and guiding businesses in product stocking and marketing strategies. Reviews are essential for boosting sales potential, maintaining a good reputation, and promoting brand recognition. Customers extensively research product reviews from different sources before purchasing, making them vital user-generated content for e-commerce businesses. The current work provided an efficient and novel classification model for sentence classification using the ABNAM model. The automated text classification models available cannot categorize the data into sixteen distinct classes. The technologies applied for the mentioned work contain TF-IDF, N-gram, CNN, linear SVM, random forest, Nae bays, and ABNAM with significant results. The best-performing ML method for the successful classification of a given sentence into one of the sixteen categories is achieved with the proposed model named the based Neural Attention Model (ABNAM), which has the highest accuracy at 97%. The research acclaimed ABNAM as a novel classification model with the highest-class categorizations. 2024 Nagendra N and Chandra J. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Creator An entity primarily responsible for making the resource Nagendra N.; Chandra J. Source A related resource from which the described resource is derived Journal of Computer Science, Vol-20, No. 5, pp. 535-547. Publisher An entity responsible for making the resource available Science Publications 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/10.3844/jcssp.2024.535.547" target="_blank" rel="noreferrer noopener">https://doi.org/10.3844/jcssp.2024.535.547</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188239085&doi=10.3844%2Fjcssp.2024.535.547&partnerID=40&md5=b28053f96307a12a68fb5e458856566a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188239085&doi=10.3844%2fjcssp.2024.535.547&partnerID=40&md5=b28053f96307a12a68fb5e458856566a</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 15493636 | CS; 2023-2024; Vol-1; 00093-00106 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 Nagendra N., 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 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 Hybrid Approach for Multi-Classification of News Documents Using Artificial Intelligence Subject The topic of the resource BERT; Deep Learning; Machine Learning; Natural Language Processing; Text Classification Description An account of the resource In the context of news articles, text classification is essential for organizing and retrieving useful information from massive amounts of textual data. Effectively categorizing news titles has gotten more challenging due to the development of online news outlets and the ongoing production of news. A multi-text classification technique primarily targeted at news titles is shown. The suggested approach automates the classification of news titles into predetermined classes or subjects by combining deep learning approaches and natural language processing (NLP) algorithms. Data preprocessing, which includes text normalization, tokenization, and feature extraction, is the first step in the procedure. This prepares the raw news titles for deep learning models. 2024 IEEE. Creator An entity primarily responsible for making the resource Nagendra N.; Chandra J. Source A related resource from which the described resource is derived Proceedings - 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024, pp. 466-473. 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/ICICV62344.2024.00079" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICICV62344.2024.00079</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193255348&doi=10.1109%2FICICV62344.2024.00079&partnerID=40&md5=b7e7cd69387a5bf8266796b9fd4c193d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193255348&doi=10.1109%2fICICV62344.2024.00079&partnerID=40&md5=b7e7cd69387a5bf8266796b9fd4c193d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038564-9 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 Nagendra N., Christ (Deemed to Be) University, Department of Computer Science., Bengaluru, India; Chandra J., Christ (Deemed to Be) University, Department of Computer Science., 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 Systematic Review on Features Extraction Techniques for Aspect Based Text Classification using Artificial Intelligence Description An account of the resource Aspect Extraction is an important, challenging, and meaningful task in aspect-based text classification analysis. To apply variants of topic models on task, while reasonably successful, these methods usually do not produce highly coherent aspects. This review presents a novel neural/cognitive approach to discover coherent methods. They exploited the distribution of word co-occurrences through neural/cognitive word embeddings. Unlike topics that typically assume independently generated words, word embedding models encourage words that appear in similar factors close to each other in the embedding space. Also, use an attention mechanism to de-emphasize irrelevant words during training, improving aspects coherence. Methods results on datasets demonstrate that the approach discovers more meaningful and coherent aspects and substantially outperforms baseline. Aspect-based text analysis aims to determine people's attitudes towards different aspects in a review. The Electrochemical Society Creator An entity primarily responsible for making the resource Nagendra N.; Chandra J. Source A related resource from which the described resource is derived ECS Transactions, Vol-107, No. 1, pp. 2503-2514. Publisher An entity responsible for making the resource available Institute of Physics 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/10.1149/10701.2503ecst" target="_blank" rel="noreferrer noopener">https://doi.org/10.1149/10701.2503ecst</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130545628&doi=10.1149%2F10701.2503ecst&partnerID=40&md5=9ac389b5a788b4b9a27946763972f6cb" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130545628&doi=10.1149%2f10701.2503ecst&partnerID=40&md5=9ac389b5a788b4b9a27946763972f6cb</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 19386737; ISBN: 978-160768539-5 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 Nagendra N., Department of Computer Science, Christ University, Karnataka, Bangalore, 560029, India; Chandra J., Department of Computer Science, Christ University, Karnataka, 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 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 Nagendra, B.V.; Kumar Chandar, S.; Simha, J.B.; Manu, K.S.; Kirubanand, K.V.; Kaushal, Yash Title A name given to the resource Cross Domain Lexicon Transfer -A Case Specific to Application in Banking Domain 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 6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings;pp.851-860 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICMCSI64620.2025.10883579" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICMCSI64620.2025.10883579</a> <a href="https://www.scopus.com/pages/publications/105000017716?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000017716?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 Nagendra B.V., Christ University, Bengaluru, India; Kumar Chandar S., Christ University, Bengaluru, India; Simha J.B., REVA Academy for Corporate Excellence, REVA University, India; Manu K.S., Christ University, Bengaluru, India; Kirubanand K.V., Christ University, Bengaluru, India; Kaushal Y., Christ University, Bengaluru, India Description An account of the resource This study demonstrates the development of the financial domain lexicon and the implementation of the same in the banking sector. The study compared the working of Financial Nae Bayes Lexicon (FNB Lex) developed with 533 quarterly Earnings Call Transcript (ECT) of 16 software companies, with standard available dictionaries like VADER and Loughran-McDonald. The study showed VADERs poor discriminatory power and Loughran-McDonald with satisfactory performance. FNB Lex lexicon performed better and provided better lift over VADER and Loughran-McDonald with improved precision, recall and F1-score. 2025 IEEE. Subject The topic of the resource Classification; Cross domain lexicon transfer; Earnings Call Transcripts (ECT); Lexicon; Sentiments; Stock Price Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152266-7; 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