370 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 Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Lalrengpuii; Pullot, Lijeesh Title A name given to the resource Attributes of virtual influencers: Science mapping and future agenda Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Multidisciplinary Reviews;Volume;9;Issue;7;Article No.;e2026370; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.31893/multirev.2026370" target="_blank" rel="noreferrer noopener">https://doi.org/10.31893/multirev.2026370</a> <a href="https://www.scopus.com/pages/publications/105027516289?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027516289?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 Lalrengpuii, Christ University, Karnataka, Bengaluru, India; Pullot L., Christ University, Karnataka, Bengaluru, India Description An account of the resource Virtual influencers, constructed using advanced computer-generated imagery and artificial intelligence, have emerged as disruptive forces in the realm of influencer marketing. In contrast to their human counterparts, virtual influencers provide brands with enhanced control, adaptability, and scalability, effectively engaging audiences on a global scale across various digital platforms. However, despite their increasing prevalence and commercial uptake, scholarly exploration into their attributes and impacts remains sporadic and underdeveloped. This study conducts a systematic literature review and bibliometric analysis of 172 peer-reviewed articles indexed in Scopus, employing the SPAR-4-SLR methodology to delineate the knowledge framework surrounding virtual influencers. By utilizing Biblioshiny from the bibliometrix package and VOSviewer for analytical and visualization purposes, this research reveals that attributes such as trustworthiness, expertise, attractiveness, similarity, and anthropomorphism dominate the current discourse. Conversely, attributes such as authenticity, source realness, emotional expression, novelty, autonomy, and interactivity have been identified as largely underexplored. The bibliometric analysis illustrates a marked increase in academic output since 2020, primarily concentrated in journals focused on marketing, social sciences, and human?computer interaction. Thematic mapping further highlights key areas of focus, with motor themes identified as anthropomorphism, virtual reality, and consumer engagement, indicating their foundational importance in the current landscape of virtual influencer research. Several research gaps persist, particularly in the areas of cross-cultural consumer responses to virtual influencers, ethical considerations, parasocial interactions, and comparative effectiveness analyses between virtual and human influencers. Overall, our study synthesizes the literature, systematically categorizes the key attributes of virtual influencers, and proposes a future research agenda aimed at advancing theoretical frameworks and managerial effectiveness. The insights gained here will guide researchers in identifying pertinent variables for empirical investigation and assisting practitioners in crafting strategies that enhance consumer trust, engagement, and purchase intention, ultimately navigating the evolving terrain of digital marketing more effectively. 2026, Malque Publishing. All rights reserved. Subject The topic of the resource anthropomorphism; bibliometric analysis; influencer marketing; SPAR-4-SLR Publisher An entity responsible for making the resource available Malque Publishing Relation A related resource ISSN: 25953982; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Trends in virtual influencers (VIs): A bibliometric analysis and SPAR-4-SLR protocol Subject The topic of the resource bibliometrix package; biblioshiny; virtual reality; VOSviewer Description An account of the resource This study aims to comprehensively understand qualitative and quantitative information about the current trends in VIs. It examines 106 articles published in Scopus-indexed journals between 2020 and 2024. The analysis was done with the help of Biblioshiny, an R-developed online application from the Bibliometrix package, and VOSviewer software for analytical and visualization purposes. This study was conducted using the SPAR-4-SLR protocol. The findings showed that recent years have been more productive, and many authors have demonstrated their interest in studying the VIs. Recent trends are social media, virtual reality, marketing, social networking, etc. The study employs a systematic review and bibliometric analysis to extract valuable insights from the extensive body of literature. These insights suggested several areas for future research, providing a roadmap for future researchers to proceed with their research in this area. The comprehensive scientific cartography of the area has yet to be presented; therefore, this study aims to synthesize the current knowledge frameworks within the field and determine the dominant research patterns in the specific area of investigation. 2024, Malque Publishing. All rights reserved. Creator An entity primarily responsible for making the resource Lalrengpuii; Srivastava M.K.; Belavandran V. Source A related resource from which the described resource is derived Multidisciplinary Reviews, Vol-7, No. 12 Publisher An entity responsible for making the resource available Malque Publishing 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/10.31893/multirev.2024285" target="_blank" rel="noreferrer noopener">https://doi.org/10.31893/multirev.2024285</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202560885&doi=10.31893%2Fmultirev.2024285&partnerID=40&md5=f5bc3e0847f0b28a1ace872c80f50a9e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85202560885&doi=10.31893%2fmultirev.2024285&partnerID=40&md5=f5bc3e0847f0b28a1ace872c80f50a9e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 25953982 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 Lalrengpuii, School of Commerce, Finance and Accountancy, CHRIST (Deemed to be University), Bengaluru, India; Srivastava M.K., School of Business and Management, CHRIST (Deemed to be University), Bengaluru, India; Belavandran V., School of Commerce, Finance and Accountancy, 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 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 Lalrengpuii; Srivastava, Manish Kumar; Dhaundiyal, Pankaj Title A name given to the resource Virtual Influencers (VIs): A Bibliometric Analysis and Future Agenda Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1572 LNNS;pp.186-199 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-00441-3_18" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-00441-3_18</a> <a href="https://www.scopus.com/pages/publications/105013616120?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013616120?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 Lalrengpuii, School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Bengaluru, India; Srivastava M.K., School of Business and Management, CHRIST (Deemed to Be University), Bengaluru, India; Dhaundiyal P., School of Business and Management, CHRIST (Deemed to Be University), Ghaziabad, India Description An account of the resource This study aims to comprehensively understand qualitative and quantitative information about the emerging trends in VIs. It examines 106 articles published in Scopus-indexed journals between 2020 and 2024. The analysis was done with the help of Biblioshiny, an R-developed online application from the Bibliometrix package, and VOSviewer software for analytical and visualization purposes. This study was conducted using the SPAR-4-SLR protocol. The findings showed that recent years have been more productive, and many authors have demonstrated their interest in studying the VIs. Recent trends are social media, virtual reality, marketing, social networking, etc. The study employs a systematic review and bibliometric analysis to extract valuable insights from the extensive body of literature. These insights suggested several areas for future research, providing a roadmap for future researchers to proceed with their research in this area. The comprehensive scientific cartography of the area has yet to be presented; therefore, this study aims to synthesize the current knowledge frameworks within the field and determine the dominant research patterns in the specific area of investigation. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource Bibliometrix package; Biblioshiny; SPAR-4-SLR protocol; Virtual Influencers (VIs); VOSviewer Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303200440-6; 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 Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Lalrinhlua, Benjamin; Saeed, Abdulkafi Mohammed; Ganie, Abdul Hamid; Seema; Tiwari, Rakhi; Das, Soumik; Mofarreh, Fatemah; Singhal, Abhinav Title A name given to the resource Study of vibrations in smart materials semiconductor under differential imperfect contact mechanism and nanoscale effect with electromechanical coupling effect 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 Acta Mechanica;Volume;236;Issue;4;Article No.;203509;pp.2383-2403 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s00707-025-04279-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s00707-025-04279-9</a> <a href="https://www.scopus.com/pages/publications/105000024075?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000024075?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 Lalrinhlua B., Christ University, Bengaluru, 560029, India; Saeed A.M., Department of Mathematics, College of Science, Qassim University, Buraydah, 51452, Saudi Arabia; Ganie A.H., Basic Science Department, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh, Saudi Arabia; Seema, Christ University, Bengaluru, 560029, India; Tiwari R., Department of Mathematics, Nitishwar College, A Constituent Unit of Babasaheb Bhimrao Ambedkar Bihar University, Bihar, Muzaffarpur, India; Das S., School of Physical Sciences, Amrita Vishwa Vidyapeetham, Mysuru Campus, Karnataka, Mysuru, 570026, India; Mofarreh F., Department of Mathematical Sciences, College of Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 11546, Saudi Arabia; Singhal A., Christ University, Bengaluru, 560029, India Description An account of the resource The work focuses on the transference of Love-type waves which are surface seismic waves that cause horizontal displacement perpendicular to the direction of propagation, in a multiferroic solid cylindrical structure, where the interface is assumed to be imperfect and made of a magneto-electro-elastic (MEE) structure. The analytical solution for the layer is obtained using the spatially variable quasi-classical technique which approximates complicated differential equations while maintaining their key physical properties. The coefficients of waves phase velocities and attenuation are greatly affected by different parameters as shown in the numerical example. In addition, a graphical comparison of electrical, magnetic, mechanical, magneto-mechanical, electromechanical, and magneto-electrical imperfections in electrically and magnetically open and short cases is presented. The phase velocity is higher in the electrically and magnetically open case as compared to the short case as shown in the results. Some major outcomes are summarized here: the bonding parameter is highly proportional to the phase velocity and inversely proportional to the attenuation coefficient, and imperfection parameters have a serious influence on the curve of phase velocity and attenuation coefficient. This theoretical study leads to the understanding of piezoelectric and piezomagnetic coupling and its potential application and design to sensors, actuators, energy harvesters, and nano-electronics. The novelty lies in the adoption of the quasi-classical method to approach solving differential equations using a polar coordinate system for the first time. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025. Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 15970; CODEN: AMHCA Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Lalrinmawii, R.; Lalramhluna, Robert; Gunavathi, R. Title A name given to the resource Detecting Abusive Comments in Mizo: A Machine Learning Approach for a Low-Resource Language 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 Proceedings - IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS;Issue;2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AVSS65446.2025.11149909" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AVSS65446.2025.11149909</a> <a href="https://www.scopus.com/pages/publications/105017422795?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017422795?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 Lalrinmawii R., Christ University, Department of Data Science, India; Lalramhluna R., National Institute of Technology, Department of Computer Science & Engineering, India; Gunavathi R., Christ University, Department of Data Science, India Description An account of the resource The detection of abusive language in online spaces is crucial for ensuring a safe digital environment, particularly for low-resource languages like Mizo. Mizo, a tonal Tibeto-Burman language spoken primarily in Mizoram, India, poses significant computational challenges due to its phonetic complexity and limited linguistic resources. This research presents a method based on machine learning for abusive comment detection in Mizo, addressing the lack of annotated datasets and specialized NLP tools. A structured pipeline involving data collection, preprocessing, feature engineering, and model evaluation was implemented. Our study compares the effectiveness of several conventional machine learning methods, such as Random Forest, Support Vector Machines (SVM), Logistic Regression, and XGBoost, against transformer-based models such as Multilingual BERT(mBERT) and MizBERT. According to experimental data, MizBERT achieves the highest accuracy and F1-score, outperforming all other models by a substantial margin. This work contributes to the development of computational tools for Mizo NLP, laying a foundation for automated moderation systems and fostering digital inclusivity for Mizo-Speaking communities. 2025 IEEE. Subject The topic of the resource abusive comment detection; content moderation; digital safety; Logistic Regression; low-resource NLP; machine learning; MizBERT; Mizo language; multilingual BERT (mBERT); n-grams; Random Forest; Support Vector Machines (SVM); TF-IDF vectorization; tonal language processing; transformer models; XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISSN: 26436213; 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 https://archives.christuniversity.in/files/original/8ee69615a1f91d10b0b579d97d4132ae.pdf 7dcd604e6a3601200b1cfbf8ccdeeec3 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 MPHIL Mphil Mphil Thesis 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 SELF CONCEPT OF VIII STANDARD STUDENTS OF CBSE SCHOOLS IN SOUTH BANGALORE IN RELATION TO THEIR ACADEMIC ACHIEVEMENT Creator An entity primarily responsible for making the resource Lama Priyanka Date A point or period of time associated with an event in the lifecycle of the resource 2015 Source A related resource from which the described resource is derived M A Education https://archives.christuniversity.in/files/original/ce0bc3dad57f079e863b4cdca93f18f6.pdf 1a7bae976d39a14233e4344b01de1a0e 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 MPHIL Mphil Mphil Thesis 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 EFFECT OF MAGNETIC FIELD ON THE ONSET OF RAYLEIGH-B??NARD CONVECTION IN A MICROPOLAR FLUID WITH INTERNAL HEAT GENERATION Creator An entity primarily responsible for making the resource LAMA SANJOK Date A point or period of time associated with an event in the lifecycle of the resource 2010 Source A related resource from which the described resource is derived Mathematics Description An account of the resource The effects of through flow, internal heat generation and magnetic field on the onset of Rayleigh-B??nard convection in electrically conducting Micropolar fluid are studied using the Galerkin technique. The eigenvalue is obtained for rigid-free velocity boundary combinations with isothermal and adiabatic on the spin-vanishing boundaries. A linear stability analysis is performed. The influence of various parameters on the onset of convection has been analyzed. The microrotation is assumed to vanish at the boundaries. A linear stability analysis is performed. The influence of various parameters on the onset of convection has been analyzed and their comparative influence on onset is discussed. The problem suggests an elegant method of external control of internal convection. 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 Automatic Diagnosis of Autism Spectrum Disorder Detection Using a Hybrid Feature Selection Model with Graph Convolution Network Subject The topic of the resource ABFO; Autism spectrum disorder; Graph convolutional networks; mRMR; SVM-RFE Description An account of the resource A neurodevelopmental disorder is called an autism spectrum disorder (ASD) that influences a persons assertion, interaction, and learning abilities. The consequences and severity of symptoms of ASD will vary from person to person; the disorder is mainly diagnosed in children aged 15years and older, and its symptoms may include unusual behaviors, interests, and social challenges. If it is not resolved at this stage, it will become severe in the coming days. So, in this manuscript, we propose a way to automatically tell if someone has ASD that works well by using a combination of feature selection and deep learning. Four phases comprise the proposed model: preprocessing, feature extraction, feature selection, and prediction. At first, the collected images are given to the preprocessing stage to remove the noise. Then, for each image, three types of features are extracted: the shape feature, texture feature, and histogram feature. Then, optimal features are selected to minimize computational complexity and time consumption using a new technique based on a combination of adaptive bacterial foraging optimization (ABFO), support vector machines-recursive feature elimination (SVM-RFE), minimum redundancy and maximum relevance (mRMR). Then, the graph convolutional network (GCN) classifier uses the selected features to identify an image as normal or autistic. According to the research observations, our models accuracy is enhanced to 97.512%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Lamani M.R.; Benadit P.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/s42979-023-02439-z" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-023-02439-z</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180210216&doi=10.1007%2Fs42979-023-02439-z&partnerID=40&md5=b01c9cae8e1de2938bc2945e56cff620" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180210216&doi=10.1007%2fs42979-023-02439-z&partnerID=40&md5=b01c9cae8e1de2938bc2945e56cff620</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 Lamani M.R., CHRIST (Deemed to be University), Computer Science and Engineering, Karnataka, Bangalore, 560074, India; Benadit P.J., CHRIST (Deemed to be University), Computer Science and Engineering, Karnataka, Bangalore, 560074, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Multi-atlas Graph Convolutional Networks and Convolutional Recurrent Neural Networks-Based Ensemble Learning for Classification of Autism Spectrum Disorders Subject The topic of the resource Autism spectrum disorder; Automatic diagnosis; Deep learning; Ensemble learning; Graph convolution network Description An account of the resource Autism spectrum disorder (ASD) has an influence on social conversation and interaction, as well as encouraging people to engage in repetitive behaviors. The complication begins in childhood and persists through adolescence and maturity. Autism spectrum disorder has become the most common kind of childhood development worldwide. ASD hinders the capacity to interact, socialize, and build connections with individuals of all ages, and thus its early intervention is critical. This paper discusses some of the most recent approaches to diagnostics using convolutional networks and multi-atlas graphs for autism spectrum disorders. Also, several pre-processing approaches are elaborated. Graph convolutional neural networks (GCNs) to diagnose autism spectrum disorder (ASD) because of their remarkable effectiveness in illness prediction using multi-site data. Convolutional neural network (CNN) and recurrent neural networks (RNN) infrastructure studies functional connection patterns between various brain regions to find particular patterns to diagnose ASD. In our research, we implemented the GCN + CRNN ensemble method and achieved 89.01% accuracy based on resting-state data from the fMRI (ABIDE-II), a novel framework for detecting early signs of autism spectrum disorders is presented and discussed. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Lamani M.R.; Benadit P.J.; Vaithinathan K. Source A related resource from which the described resource is derived SN Computer Science, Vol-4, No. 3 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/s42979-022-01617-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42979-022-01617-9</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148413670&doi=10.1007%2Fs42979-022-01617-9&partnerID=40&md5=96c448a9976c631411ea1c2339c34478" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148413670&doi=10.1007%2fs42979-022-01617-9&partnerID=40&md5=96c448a9976c631411ea1c2339c34478</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 Lamani M.R., Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Benadit P.J., Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Vaithinathan K., Computer Engineering, Karaikal Polytechnic College, Varichikudy, Karaikal, 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 An Efficient Security-Enabled Routing Protocol for Data Transmission in VANET Using Blockchain Ripple Protocol Consensus Algorithm Subject The topic of the resource ANN; Blockchain; GA; IoT; IoV; RPCA; RSU; VANET Description An account of the resource The security quality in Vehicular Ad-hoc NETworks (VANET) has improved as a result of recent developments in Intelligent Transportation Systems (ITS). However, within the current VANET system, providing a cheap computational cost with a high serving capability is a significant necessity. When a vehicle user goes between one Roadside Unit (RSU) to another RSU region in the current scenario, the current RSU periodically needs re-authentication of the vehicle user. This increases the computational complexity of the system. The gathering and broadcast of existing traffic event information by automobiles are critical in Vehicular Ad-hoc Networks (VANET). Traditional VANETs, on the other hand, have several security concerns. This work develops a blockchain-based authentication protocol to address the aforementioned difficulty. To address critical message propagation issues in the VANET, we invent a novel type of blockchain. We develop a local blockchain for exchanging real-world event messages among cars within a countrys borders, which is a novel sort of blockchain ideal for the VANET. We describe a public blockchain RPCA that records the trustworthiness of nodes and messages in such a distributed ledger suitable for secure message distribution. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024. Creator An entity primarily responsible for making the resource Lamani M.R.; Benadit P.J.; Vaithinathan K.; Parthiban L. Source A related resource from which the described resource is derived EAI/Springer Innovations in Communication and Computing, Vol-Part F1496, pp. 253-264. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-35751-0_18" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-35751-0_18</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174850567&doi=10.1007%2F978-3-031-35751-0_18&partnerID=40&md5=ea58943d2df932868f15d9fcc10e1cde" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174850567&doi=10.1007%2f978-3-031-35751-0_18&partnerID=40&md5=ea58943d2df932868f15d9fcc10e1cde</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 25228595 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 Lamani M.R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, India; Benadit P.J., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bangalore, India; Vaithinathan K., Department of Computer Engineering, Karaikal Polytechnic College, Puducherry, Karaikal, India; Parthiban L., Department of Computer Science, Pondicherry University, Tamil Nadu, Pondicherry, 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 Review on Deep Learning Algorithms in the Detection of Autism Spectrum Disorder Subject The topic of the resource ASD; CNN; CRNN algorithms; Deep learning; LSTM; RNN Description An account of the resource Autism spectrum disorder (ASD) is a neurodisorder that has an impact on how people interact and communicate with each other for the rest of their lives. Most autistic symptoms appear throughout the first two years of a child's life. This is why autism is called a behavioral disease. If you have a child with ASD, the problem starts in childhood and keeps going through adolescence and adulthood. Deep learning techniques are becoming more common in research on medical diagnosis. In this paper, there is an effort to see if convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory network (LSTM), and a fusion technique known as convolutional recurrent neural network (CRNN) can be used to detect ASD problems in a child, adolescents, and adults. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Creator An entity primarily responsible for making the resource Lamani M.R.; Julian Benadit P. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-865, pp. 283-297. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-99-9043-6_24" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-9043-6_24</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190363447&doi=10.1007%2F978-981-99-9043-6_24&partnerID=40&md5=c4f96946849d73c9d061c1e5f736be60" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190363447&doi=10.1007%2f978-981-99-9043-6_24&partnerID=40&md5=c4f96946849d73c9d061c1e5f736be60</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981999042-9 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 Lamani M.R., CHRIST (Deemed to Be University), Kanmanike, Kumbalgudu, Mysor Road, Karnataka, Bangalore, 560074, India; Julian Benadit P., CHRIST (Deemed to Be University), Kanmanike, Kumbalgudu, Mysor Road, Karnataka, Bangalore, 560074, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An Early Detection of Autism Spectrum Disorder Using PDNN and ABIDE I&II Dataset Subject The topic of the resource ABIDE I; ABIDE II; ASD; DL; fMRI; ML; rsfMRI Description An account of the resource The current study's objective was to use deep learning methods to separate valetudinarians amidst autism spectrum disorders (ASDs) from controls employing just the patients brain activation patterns from a dataset of large brain images. We examined brain imaging data from ASD patients from the global, multi-site ABIDE dataset (Autism Brain Imaging Data Exchange). Social impairments and repetitive behaviors are hallmarks of the brain condition known as autism spectrum disorder (ASD). ASD affects one in every 68 kids in the USA, as of the most recent data from the Disease Control Centers. To understand the neurological patterns that arose from the categorization, we looked into functional connectivity patterns that can be used to diagnose ASD participants precisely. The outcomes raised the state of the art by correctly identifying 72.10% of ASD patients in the sample vs. control patients. The classification patterns revealed an anti-correlation between the function of the brain's anterior and posterior regions; this anti-correlation supports the empirical data currently showing achingly ASD impedes communication between the livid brain's anterior and posterior areas. We found and pinpointed brain regions damn frolic, distinguishing ASD among typically developing reign according to our deep learning model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. Creator An entity primarily responsible for making the resource Lamani M.R.; Julian Benadit P. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-844, pp. 295-310. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-99-8479-4_22" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-8479-4_22</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181985509&doi=10.1007%2F978-981-99-8479-4_22&partnerID=40&md5=604bbec6b761e3ad6356f75166904f90" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181985509&doi=10.1007%2f978-981-99-8479-4_22&partnerID=40&md5=604bbec6b761e3ad6356f75166904f90</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981998478-7 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 Lamani M.R., CHRIST (Deemed to be University), Kanmanike, Kumbalgudu, Mysor Road, Karnataka, Bangalore, 560074, India; Julian Benadit P., CHRIST (Deemed to be University), Kanmanike, Kumbalgudu, Mysor Road, Karnataka, Bangalore, 560074, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource AdvanDNN: Deep Neural Network Analysis of Neuroimaging for Identifying Vulnerable Brain Regions in Autism Spectrum Disorder Subject The topic of the resource AAL116 atlas; AdvanDNN; Autism spectrum disorder; Brain connectivity; Deep learning; Neuroimaging analysis Description An account of the resource Exploring the neurological framework of autism spectrum disorder (ASD) presents a significant challenge due to its diverse manifestations and cognitive impacts. This study introduces an innovative deep learning approach, employing an advanced deep neural network (AdvanDNN) model to identify and analyze brain regions vulnerable to ASD. Utilizing the AAL116 brain atlas for anatomical standardization, our model processes a comprehensive set of neuroimaging data, including structural and functional MRI scans, to discern distinct neural patterns associated with ASD. The AdvanDNN model, with its robust deep learning architecture, was meticulously trained and validated, demonstrating a notable accuracy of 91.17% in distinguishing between ASD-affected individuals and controls. This marks an improvement over the state of the art, contributing a significant advance to the diagnostic processes. Notably, the model identified a pronounced anticorrelation in brain function between anterior and posterior regions, corroborating existing empirical evidence of disrupted connectivity within ASD neurology. The analysis further pinpointed critical regions, such as the prefrontal cortex, amygdala, and temporal lobes, that exhibit significant deviations from typical developmental patterns. These findings illustrate the potential of deep learning in enhancing early detection and providing pathways for intervention. The application of the AdvanDNN model offers a promising direction for personalized treatment strategies and underscores the value of precision medicine in addressing neurodevelopmental disorders. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Creator An entity primarily responsible for making the resource Lamani M.R.; Julian Benadit P.; Guruprasad C. Source A related resource from which the described resource is derived Lecture Notes in Electrical Engineering, Vol-1247 LNEE, pp. 497-510. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-6714-4_39" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-6714-4_39</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212955325&doi=10.1007%2F978-981-97-6714-4_39&partnerID=40&md5=a36f9a1118d6c754b691d588c044d7f9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212955325&doi=10.1007%2f978-981-97-6714-4_39&partnerID=40&md5=a36f9a1118d6c754b691d588c044d7f9</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18761100; ISBN: 978-981976713-7 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 Lamani M.R., CHRIST (Deemed to Be University), Kanmanike, Kumbalgodu, Mysore Road, Karnataka, Bangalore, 560074, India; Julian Benadit P., CHRIST (Deemed to Be University), Kanmanike, Kumbalgodu, Mysore Road, Karnataka, Bangalore, 560074, India; Guruprasad C., Departmentof Psychiatry, Bangalore Medical College & Research Institute (BMCRI), Karnataka, Bangalore, 560002, 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 Autism Spectrum Disorder: Automated Detection based on rs-fMRI images using CNN Subject The topic of the resource AAL116; ABIDE; ASD; CC200; CC400; CNN; resting-state fMRI Description An account of the resource Autism spectrum disorder (ASD) impacts approximately 1 in every 160 children globally and is classified as a neurodevelopmental condition. Image classification in neuroscience has advanced primarily due to convolutional neural networks (CNNs) and their capacity to provide better algorithms, more computing resources, and data. This study used a brain scan dataset to test the feasibility of utilizing CNN to detect ASD. Using functional connectivity patterns, the Autism Brain Imaging Exchange (ABIDE) data repository, which includes recordings of rest-state functional magnetic resonance imaging (rs-fMRI), the aim of using it was to distinguish between individuals who have Autism Spectrum Disorder (ASD) and those who are healthy controls. The proposed method effectively classified the two groups. According to the test findings, the suggested model has the ability to accurately detect ASD with a reliability rate of 92.22% when implemented on the ABIDE dataset using the CC200, CC400, and AAL116 brain atlases. The CNN model is computationally more efficient since it uses fewer parameters than other cutting-edge methods. 2023 IEEE. Creator An entity primarily responsible for making the resource Lamani M.R.; Julian Benadit P.; Vaithinathan K. Source A related resource from which the described resource is derived Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications 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/InC457730.2023.10262873" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC457730.2023.10262873</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174709944&doi=10.1109%2FInC457730.2023.10262873&partnerID=40&md5=2cdf24d81f619091568074a4ecf839d1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174709944&doi=10.1109%2fInC457730.2023.10262873&partnerID=40&md5=2cdf24d81f619091568074a4ecf839d1</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835033577-4 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 Lamani M.R., CHRIST (Deemed to Be University) Kengeri, Computer Science and Engineering, Bangalore, 560074, India; Julian Benadit P., CHRIST (Deemed to Be University) Kengeri, Computer Science and Engineering, Bangalore, 560074, India; Vaithinathan K., Karaikal Polytechnic College Computer Engineering, Puducherry, Karaikal, 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 Enhanced Lumpy Cattle Skin Disease Prognosis via Deep Learning Methods Subject The topic of the resource Deep Learning; ECLAT Algorithm; Lesk Algorithm; Lumpy skin disease; Prediction Description An account of the resource Animal illness is growing in importance. Identification of the illness is important since various diseases may affect different animals, and immediate guidance will be provided. Cows with lumpy skin issues are caused by the Neethling infection. The affection of these diseases causes lasting injury to the cattle's skin. Reduced Poor growth, reversal, milk production, gravidity, and, in severe cases, mortality are the most common adverse consequences of the illness. We developed a deep learning-based architecture that can predict or recognize disease. A deep literacy system is required to identify the microorganism causing the lumpy skin disease. This system collects diverse cattle electronic medical records and uses data analysis to create an intelligent diagnosis system for cattle diseases. It involves text preprocessing to enhance data quality, and the ECLAT algorithm correlates disease names with probabilities, providing tailored treatment plans. The system ensures timely disease treatment, reducing herders' losses and promoting scientific intelligence in animal husbandry. 2024 IEEE. Creator An entity primarily responsible for making the resource Lamani M.R.; Padmaja K.; Shilpa B.L.; Asha V. Source A related resource from which the described resource is derived 2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 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/NMITCON62075.2024.10699233" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NMITCON62075.2024.10699233</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207421713&doi=10.1109%2FNMITCON62075.2024.10699233&partnerID=40&md5=47605afd71b949cca44a50a97f997dda" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207421713&doi=10.1109%2fNMITCON62075.2024.10699233&partnerID=40&md5=47605afd71b949cca44a50a97f997dda</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835037289-2 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 Lamani M.R., CHRIST(Deemed to be University), Dept of Cse, Bangalore, India; Padmaja K., Gsssietw, Dept of Ise, Mysore, India; Shilpa B.L., Atmece, Dept of Cse, Mysore, India; Asha V., Rns Institute of Technology, Dept of Cse, 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 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 A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis Subject The topic of the resource ABIDE dataset; ASGCN (adaptive spatiotemporal; atlases; autism spectrum disorder (ASD); comprehensive review; Deep learning; electroencephalography (EEG); graph convolution network); LeNet; neuroimaging modalities; preprocessing tools; resting-state functional magnetic resonance imaging (rsfMRI); structural MRI (sMRI); task-based fMRI (tfMRI) Description An account of the resource Background: Autism Spectrum Disorder (ASD) is a multifaceted neurodevelop-mental condition with significant heterogeneity in its clinical presentation. Timely and precise identification of ASD is crucial for effective intervention and assistance. Recent advances in deep learning techniques have shown promise in enhancing the accuracy of ASD detection. Objective: This comprehensive review aims to provide an overview of various deep learning methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitiv-ity, specificity, and computational efficiency. Methods: We systematically review studies investigating ASD detection using deep learning across different neuroimaging modalities. These studies utilize various preprocessing tools, at-lases, feature extraction techniques, and classification algorithms. The performance metrics of interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the curve (AUC). Results: The review covers a wide range of studies, each with its own dataset and methodolo-gy. Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an im-pressive accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%. Conclusion: Deep learning-based approaches for ASD detection have demonstrated significant potential across multiple neuroimaging modalities. These methods offer a more objective and data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical evaluations. However, challenges remain, including the need for larger and more diverse da-tasets, model interpretability, and clinical validation. The field of deep learning in ASD diagnosis continues to evolve, holding promise for early and accurate identification of individuals with ASD, which is crucial for timely intervention and support. 2024 Bentham Science Publishers. Creator An entity primarily responsible for making the resource Lamani M.R.; Pernabas J.B. Source A related resource from which the described resource is derived Recent Advances in Computer Science and Communications, Vol-17, No. 8, pp. 73-91. 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/10.2174/0126662558284886240130154414" target="_blank" rel="noreferrer noopener">https://doi.org/10.2174/0126662558284886240130154414</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200203016&doi=10.2174%2F0126662558284886240130154414&partnerID=40&md5=07dc0ddc06e0de6f558286a4ad752df1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200203016&doi=10.2174%2f0126662558284886240130154414&partnerID=40&md5=07dc0ddc06e0de6f558286a4ad752df1</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 26662558 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 Lamani M.R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India; Pernabas J.B., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560074, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Lamani, Manjunath Ramanna; Asha, V.; Padmaja, K. Title A name given to the resource Leveraging Deep Learning for Early Detection of Autism Spectrum Disorder in Augmented and Virtual Reality Mental Healthcare Environments 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 Augmented and Virtual Reality in Immersive Healthcare;pp.151-173 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394302864.ch6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394302864.ch6</a> <a href="https://www.scopus.com/pages/publications/105032078917?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032078917?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 Lamani M.R., Department of CSE, CHRIST University, Kanmanike, Kumbalgudu, Karnataka, Bangalore, India; Asha V., Dept of CSE, RNS Institute of Technology, Karnataka, Bengaluru, India; Padmaja K., Dept of ISE, GSSSIETW, Karnataka, Mysore, India Description An account of the resource The advent of digital health interventions offers new vistas for the early detection and management of autism spectrum disorder (ASD). This paper explores the innovative application of deep learning algorithms within augmented reality (AR) and virtual reality (VR) environments to enhance the early detection of ASD. Integrating AR and VR in mental healthcare provides immersive, controlled settings in which individuals behaviors and responses can be observed and analyzed in real time. We propose a novel framework that utilizes deep learning techniques to process and analyze the data collected from these digital environments. Our approach leverages facial expression analysis, eye tracking, and behavioral pattern recognition to identify early signs of ASD. By capturing subtle nuances in behavior often overlooked in traditional diagnostic settings, our method offers a non-invasive, engaging, and efficient alternative for early ASD detection. The potential of this technology extends beyond diagnosis, offering possibilities for personalized intervention strategies that can be adapted to each individual's unique needs. Our findings indicate that deep learning in AR and VR environments could significantly enhance the accuracy and timeliness of ASD diagnosis, paving the way for earlier and more effective interventions. This study underscores the importance of interdisciplinary collaboration in developing innovative healthcare solutions, combining the fields of artificial intelligence, psychology, and digital media to improve outcomes for individuals with ASD. 2026 Scrivener Publishing LLC. Subject The topic of the resource augmented reality; Autism spectrum disorder; deep learning; early detection; mental healthcare; virtual reality Publisher An entity responsible for making the resource available wiley Relation A related resource ISBN: 978-139430286-4; 978-139430284-0; 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 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 Lamani, Manjunath Ramanna; Julian Benadit, P.; Guruprasad, C. Title A name given to the resource Enhancing Diagnostic Accuracy for Autism with BRCNet: A Novel Approach for Brain Region Segmentation and Classification Using Deep Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1144;pp.373-385 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-7839-3_26" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7839-3_26</a> <a href="https://www.scopus.com/pages/publications/85218470965?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218470965?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 Lamani M.R., CHRIST (Deemed to Be University), Kanmanike, Kumbalgudu, Mysore Road, Karnataka, Bangalore, 560074, India; Julian Benadit P., CHRIST (Deemed to Be University), Kanmanike, Kumbalgudu, Mysore Road, Karnataka, Bangalore, 560074, India; Guruprasad C., Department of Psychiatry, Bangalore Medical College and Research Institute (BMCRI), Karnataka, Bangalore, 560002, India Description An account of the resource In the quest to enhance the diagnostic accuracy of neural disorders, particularly autism, this paper presents a novel approach for brain region classification using advanced machine learning techniques. The study utilizes the ABIDE and AAL116 atlas datasets, focusing on segmenting and classifying brain regions from resting-state functional MRI (rs-fMRI) images. We propose a three-stage process. In the first stage, data collection and preprocessing are conducted, where rs-fMRI images are preprocessed into SPM12-NIfTI format. The second stage involves the segmentation of brain regions using a Regularized VNet, resulting in the extraction of AAL116 brain region images, which are then split into training, testing, and validation sets. In the third stage, we introduce a custom-designed BRCNet (Brain Region Classification Network), which discriminates between Autism and Normal classes. Our segmentation methods are rigorously evaluated using metrics such as Dice Score, Recall, and Precision, with the proposed method achieving a Dice Score of 0.985, Recall of 0.962, and Precision of 0.991, surpassing other tested methods like UNet, Active Contour, and Binary Unit. For classification, various methods, including Support Vector Machines (SVM), Decision Trees (DT), and Neural Networks like ResNet, are compared. Our findings demonstrate that ResNet achieves an exemplary performance with an Accuracy of 97.5%, Sensitivity of 96.2%, Specificity of 97.1%, Precision of 97%, and an F-Measure of 97.93%. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Autism diagnosis; Brain region classification; BRCNet performance metrics; Machine learning in neuroimaging; Regularized VNet segmentation; Resting-state fMRI Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981977838-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Lamani, Manjunath Ramanna; Mukku, Lalasa; Asha, V.; Padmaja, K. Title A name given to the resource Enhancing Early Detection of Alzheimers Disease Through Integrated Deep Learning Models: A Multimodal Diagnostic Approach Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1278;pp.1-11 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-2703-5_1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-2703-5_1</a> <a href="https://www.scopus.com/pages/publications/105006889129?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105006889129?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 Lamani M.R., Department of CSE, CHRIST (Deemed to be University), Kanmanike, Kumbalgudu, Mysore Road, Karnataka, Bangalore, 560074, India; Mukku L., Department of CSE, CHRIST (Deemed to be University), Kanmanike, Kumbalgudu, Mysore Road, Karnataka, Bangalore, 560074, India; Asha V., Department of CSE, RNS Institute of Technology, Bangalore, India; Padmaja K., Department of ISE, GSSSIETW, Mysore, India Description An account of the resource Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and functional impairment. Early detection is crucial for effective management and intervention. This study explores the effectiveness of an integrated deep learning approach combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to enhance the early detection of Alzheimer's disease using multimodal data. A novel deep learning model was developed and validated, integrating neuroimaging data (MRI and PET scans) with clinical data using a decision-level fusion strategy. The study utilized a dataset comprising 1000 anonymized patient records from the Alzheimers Disease Neuroimaging Initiative (ADNI). Models were assessed based on accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). The integrated model demonstrated superior performance with an accuracy of 95%, precision of 94%, recall of 93%, and an F1-score of 93.5%. The model's AUC was 0.97, indicating excellent diagnostic capability. The proposed deep learning approach significantly improves the early detection of Alzheimers disease by effectively analyzing complex, multimodal data. This model holds considerable potential for clinical applications, providing a robust tool for healthcare professionals to diagnose AD in its early stages. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Alzheimers disease; Clinical data integration; Convolutional neural networks; Deep learning; Early detection; Multimodal data; Neuroimaging; Recurrent neural networks Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981962702-8; 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 Lamani, Shrinivas; Murthy, Hosakatte Niranjana Title A name given to the resource Phytochemicals and Biological Activities of Ceriops tagal (Perr.). C. B. Rob. 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 Reference Series in Phytochemistry;Volume;2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-63920-3_10-1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-63920-3_10-1</a> <a href="https://www.scopus.com/pages/publications/105015347146?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015347146?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 Lamani S., Department of Botany, DVS College of Arts and Science, Shimoga, India, Department of Botany, Karnatak University, Dharwad, India; Murthy H.N., Department of Botany, Karnatak University, Dharwad, India, Department of Biotechnology, KLE Technological University, Hubballi, India, Department of Horticultural Science, Chungbuk National University, Cheongju, South Korea, Department of Life Sciences, CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource Plants have been used for medicines since ancient times as they serve critical needs and are easily accessible. In recent years, various nations have seen a major increase in the use of plant-based treatments, resulting in a significant rise in the global demand for herbal products. This chapter describes Ceriops tagal, a mangrove species with excellent potential for bioactive components and biological activity. The majority of the distinctive secondary metabolites and their analogs reported in this plant are di-, tri-, and tetra-terpenoids (dolabrane, lupane, oleanane, dammarane, and pimarane), phenolics, and steroids from the hypocotyls, roots, and aerial parts. Various studies reported 97 terpenoids and 14 other metabolites. Many biological activities have already been identified from various extracts, including anticancer, antidiabetic, antioxidant, anti-inflammatory, antibacterial, and neurotrophic activities. In this chapter, we explored the biological potential of C. tagal, particularly its anticancer and neuroprotective activities, and it may be valuable for young researchers looking into the potential drug for chemotherapeutic and neurotrophic properties for the treatment and prevention of cancerous and neurological disorders. Springer Nature Switzerland AG 2025. Subject The topic of the resource Bioactivity; Ceriops tagal; Mangroves; Phytochemicals Publisher An entity responsible for making the resource available Springer Science and Business Media B.V. Relation A related resource ISSN: 2511834X; 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