76 20 11858 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Efficient andOptimized Convolution Neural Network forBrain Tumour Detection Subject The topic of the resource Acceleration; Brain Tumour; CNN; Compression Description An account of the resource Brain tumour is a life threatening disease and can affect children and adults. This study focuses on classifying MRI scan images of brain into one of 4 classes namely: glioma tumour, meningioma tumour, pituitary tumour and normal brain. Person affected with brain tumours will need treatments such as surgery, radiation therapy or chemotherapy. Pretrained Convolution Neural Networks such as VGG19, MobileNet, and AlexNet which have been widely used for image classification using transfer learning. However due to huge storage space requirements these are not effectively deployed on edge devices for creation of robotic devices. Hence a compressed version of these models have been created using Genetic Algorithm algorithm which occupies nearly 3040% of space and also a reduced inference time which is less by around 50% of original model. The accuracy provided by VGG19, AlexNet, MobileNet and Proposed CNN before compression was 92.18%, 89.45%, 93.75% and 96.85% respectively. Similarly the accuracy after compression for VGG19, AlexNet, MobileNet and Proposed CNN was 91.34%, 88.92%, 94.40% and 95.29%. 2023, Springer Nature Switzerland AG. Creator An entity primarily responsible for making the resource Agarwal M.; Sharma L.K.; Gupta S.K.; Garg D.; Jindal M. Source A related resource from which the described resource is derived Communications in Computer and Information Science, Vol-1781 CCIS, pp. 459-474. 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/10.1007/978-3-031-35641-4_38" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-35641-4_38</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169045456&doi=10.1007%2F978-3-031-35641-4_38&partnerID=40&md5=0b4ea2dae63e22522d25f9a277f455e0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169045456&doi=10.1007%2f978-3-031-35641-4_38&partnerID=40&md5=0b4ea2dae63e22522d25f9a277f455e0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18650929; ISBN: 978-303135640-7 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 Agarwal M., Bennett University, Greater Noida, 201310, India; Sharma L.K., Bennett University, Greater Noida, 201310, India; Gupta S.K., Bennett University, Greater Noida, 201310, India; Garg D., Bennett University, Greater Noida, 201310, India; Jindal M., Christ Deemed to be University Delhi NCR, Mariam Nagar, Ghaziabad, 201003, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An efficient approach for fractional nonlinear chaotic model with Mittag-Leffler law Subject The topic of the resource AB derivative; Chaotic system; Fixed point theorem; Homotopy analysis method; Laplace transform Description An account of the resource In this work, we exemplify the behaviour of the nonlinear model of arbitrary order differential equations by adopting q-homotopy analysis transform method (q-HATM). In the present study, the illustrated scheme is a graceful amalgamation of Laplace transform with q-homotopy analysis algorithm and we considered arbitrary order derivative using Atangana-Baleanu (AB) operator. The suggested nonlinear system exhibits chaotic behaviour in nature with respect to considered initial conditions. Fixed point hypothesis heard present the existence and uniqueness for the attained solution. We exemplified suggested arbitrary order system with to illustrate and confirm the efficiency of the projected solution procedure. Further, the numerical simulation is illustrated and also the chaotic behaviour of the obtained result captured with respect to arbitrary order in terms of plots. The obtained results confirm the projected scheme is highly methodical, easy to implement and very powerful to exemplify the nature of the dynamical system of arbitrary order. 2021 The Author(s) Creator An entity primarily responsible for making the resource Veeresha P.; Prakasha D.G.; Abdel-Aty A.-H.; Singh H.; Mahmoud E.E.; Kumar S. Source A related resource from which the described resource is derived Journal of King Saud University - Science, Vol-33, No. 2 Publisher An entity responsible for making the resource available Elsevier B.V. Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.jksus.2021.101347" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jksus.2021.101347</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100250207&doi=10.1016%2Fj.jksus.2021.101347&partnerID=40&md5=98c594b7db3a907de0f06626a5343891" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100250207&doi=10.1016%2fj.jksus.2021.101347&partnerID=40&md5=98c594b7db3a907de0f06626a5343891</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 10183647 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 Veeresha P., Department of Mathematics, CHRIST University, Bengaluru, 560029, India; Prakasha D.G., Department of Mathematics, Faculty of Science, Davangere University, Shivagangothri, Davangere, 577007, Karnataka, India; Abdel-Aty A.-H., Department of Physics, College of Sciences, University of Bisha, P.O. Box 344, Bisha, 61922, Saudi Arabia, Physics Department, Faculty of Science, Al-Azhar University, Assiut, 71524, Egypt; Singh H., Department of Mathematics, Post-Graduate College Ghazipur, 233001, Uttar Pradesh, India; Mahmoud E.E., Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia, Department of Mathematics, Faculty of Science, Sohag University, Sohag, 82524, Egypt; Kumar S., Department of Mathematics, National Institute of Technology, Jamshedpur, 831014, Jharkhand, 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 Efficient Approach for Obstacle Avoidance and Navigation in Robots Subject The topic of the resource Bayesian algorithms; DQN algorithm; dynamic policy; extended dynamic policy; physical environment; real-world transfer.; reinforcement learning; reward signal; robot obstacle avoidance; simulation; static algorithms Description An account of the resource Reinforcement learning has emerged as a prominent technique for enhancing robot obstacle avoidance capabilities in recent years. This research provides a comprehensive overview of reinforcement learning methods, focusing on Bayesian, static, dynamic policy, Deep Q-Learning (DQN) and extended dynamic policy algorithms. In the context of robot obstacle avoidance, these algorithms enable an agent to interact with its physical environment, learns effective operating strategies, and optimize actions to maximize a reward signal. The environment typically consists of a physical space that the robot must navigate without encountering obstacles. The reward signal serves as an objective measure of the robot's performance towards accomplishing specific goals, such as reaching designated positions or completing tasks. Furthermore, successful obstacle avoidance strategies acquired in simulation environments can be seamlessly transferred to real-world scenarios. The promising results achieved thus far indicate the potential of reinforcement learning as a powerful tool for enhancing robot obstacle avoidance. This research concludes with insights into the future prospects of reward learning, high-lighting its ongoing importance in the development of intelligent robotics systems. The proposed algorithm DQN outperforms well among all the other algorithms with an accuracy of 81%, Through this research, we aim to provide valuable insights and directions for further advancements in the field of robot obstacle avoidance using reinforcement learning techniques. 2023 IEEE. Creator An entity primarily responsible for making the resource Phani Shanmukh M.; Natarajan B.; Kannan C.; Tamilselvi M.; Vigneshwaran T.; Husain S.S. Source A related resource from which the described resource is derived International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICIICS59993.2023.10421219" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICIICS59993.2023.10421219</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186068026&doi=10.1109%2FICIICS59993.2023.10421219&partnerID=40&md5=e672abc91203236c75d435f19e01d893" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186068026&doi=10.1109%2fICIICS59993.2023.10421219&partnerID=40&md5=e672abc91203236c75d435f19e01d893</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835031545-5 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 Phani Shanmukh M., Amrita Vishwa Vidyapeetham, Amrita School of Computing, Department of Computer Science and Engineering (AIE), Chennai, India; Natarajan B., Vit University, School of Computer Science and Engineering, Chennai, India; Kannan C., Cmr Institute of Technology, Department of Computer Science and Engineering, Hyderabad, India; Tamilselvi M., Roever Engineering, Department of Computer Science and Engineering, Perambalur, India; Vigneshwaran T., Christ (Deemed to Be University), School of Engineering and Technology, Department of Cse, Bangalore, India; Husain S.S., K Ramakrishnan College of Engineering, Department of Electronics and Communication Engineering, Tamilnadu, Tiruchirapalli, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An efficient approach towards clustering using K-means algorithm Subject The topic of the resource Distance measure; Elbow method; Gap-statistic method; Hopkins index; Partition clustering; Standard K-means process Description An account of the resource Cluster analysis is one of the major knowledge mining methods in the field of data analytics; the approach used for clustering will influence the accuracy of the results and quality of the obtained clusters. A good clustering process or algorithm is one which increases the fit of the data points in each cluster and which satisfies the clustering criteria, if these measures are not met adequately the desired pattern will not be seen and the patterns obtained for analysis may turn out to be inaccurate or insufficient. This paper discusses the standard k-means clustering algorithm and provides an efficient approach towards clustering using the standard global K-means algorithm; the process eliminates the need for initializing random number of clusters multiple times which is followed as the standard process in the field. The effectiveness of the proposed approach was analyzed using the benchmark dataset and the implementation was performed using the well-known analytic tool R Studio and supporting packages. IAEME Publication. Creator An entity primarily responsible for making the resource Aiyappa S.N.; Ramamurthy B. Source A related resource from which the described resource is derived International Journal of Civil Engineering and Technology, Vol-9, No. 2, pp. 705-714. Publisher An entity responsible for making the resource available IAEME Publication 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="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042923571&partnerID=40&md5=f7d89e778c57439631dd5457aa75fade" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042923571&partnerID=40&md5=f7d89e778c57439631dd5457aa75fade</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9766308 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 Aiyappa S.N., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Ramamurthy B., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An efficient classification of cirrhosis liver disease using hybrid convolutional neural network-capsule network Subject The topic of the resource Adaptive emperor penguin optimization; Deep learning; Imaging modalities; Liver cirrhosis Description An account of the resource Liver cirrhosis is the diffuse and advanced phase of liver disease. Several morphological methods are used for imaging modalities. But, these modalities are biased and lack in higher detection accuracy. Hence, this work introduces automated cirrhosis liver disease classification using an optimized hybrid deep learning model. In this work, Magnetic Resonance Image (MRI) is considered for the process. Initially, an Extended Guided Filter (EGF) is used for eliminating the noise from input MRI images. Binomial thresholding is used to segment the tumor from the image. Then, Feature Extraction (FE) phase is carried out by Grey Level Co-occurrence Matrix (GLCM) and Gray level Run-length Matrix (GRLM). Finally, a hybrid of two Deep Learning (DL) algorithms Convolutional Neural Network and Capsule Network (HCNN-CN) are integrated to classify the Cirrhosis liver disease. Moreover, for fine tuning the parameters of the neural network, an optimization approach Adaptive Emperor Penguin Optimization (AEPO) is used. The proposed HCNN-CN-AEPO is compared over several approaches and depicted accuracy and sensitivity value of 0.993 and 0.986 on the real time dataset. The experimental results proved that the proposed HCNN-CN-AEPO can exactly diagnose the tumour. 2022 Elsevier Ltd Creator An entity primarily responsible for making the resource Shaheen H.; Ravikumar K.; Lakshmipathi Anantha N.; Uma Shankar Kumar A.; Jayapandian N.; Kirubakaran S. Source A related resource from which the described resource is derived Biomedical Signal Processing and Control, Vol-80 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.bspc.2022.104152" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.bspc.2022.104152</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140137549&doi=10.1016%2Fj.bspc.2022.104152&partnerID=40&md5=e2e8f46a450dfafb8f36b3f824c8e37f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140137549&doi=10.1016%2fj.bspc.2022.104152&partnerID=40&md5=e2e8f46a450dfafb8f36b3f824c8e37f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 17468094 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 Shaheen H., Department of Computer Science and Engineering, MVJ College of Engineering, Bangalore, India; Ravikumar K., Dept. Of Cse, Rrase College Of Engg, Chennai Padappai, India; Lakshmipathi Anantha N., CSE Department, Malla Reddy Engineering College, Hyderabad, India; Uma Shankar Kumar A., K.S.R.M. College of Engineering, Andhra Pradesh, Kadapa, India; Jayapandian N., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India; Kirubakaran S., Department of Computer Science and Engineering, CMR College of Engineering and Technology, Hyderabad, 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 efficient cloud based architecture for integrating content management systems Subject The topic of the resource Cloud; CMS; MYSQL; Open Source; PhP; REST; Web Services Description An account of the resource The use of digital content is increasing day after day and now it is an essential element of our day today life. The amount of stored information is so huge that it is highly difficult to manage the content especially in a distributed cloud environment. There are many open source software solutions available in cloud to handle huge amount of digital data. However none of these solutions addresses all the requirements needed to manage the content spread out in multiple systems effectively. The user has to relay on multiple content management systems to do the work. This turns into ever more unwieldy, time consuming and leads to loss of data. Using robust and integrated content management systems, these issues could be solved effectively. In this paper we have identified various challenges of using the content management system in the cloud after surveying many Content Management System related article and proposed an integrated solution named Cloud based Architecture integrating Content Management System which is capable of interfacing with various unique features available at different content management system installations in the cloud. This maximizes the functionality and performance of any Content management systems. The Representational State Transfer (REST) protocol is used to integrate the best features of various open source content management systems. REST provides higher level of security compared to existing systems as it does not store the user sessions. The users can interact with the system with the help of an interface which abstracts the complexities of multiple content management systems running in the cloud. 2017 IEEE. Creator An entity primarily responsible for making the resource Thrivani J.; Venugopal K.R.; Thomas B. Source A related resource from which the described resource is derived IEEE International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2017 - Proceedings, pp. 337-342. 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 2017-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICIMIA.2017.7975631" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICIMIA.2017.7975631</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027452395&doi=10.1109%2FICIMIA.2017.7975631&partnerID=40&md5=7ba25a89a532b05824c7d2ca13c6badc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027452395&doi=10.1109%2fICIMIA.2017.7975631&partnerID=40&md5=7ba25a89a532b05824c7d2ca13c6badc</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-150905960-7 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 Thrivani J., University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India; Venugopal K.R., University Visvesvaraya College of Engineering, Bangalore University, Bangalore, India; Thomas B., Department of Computer Science, Christ University, 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 An efficient clustering approach for optimized path selection and route maintenance in mobile ad hoc networks Subject The topic of the resource Clustering; Mobile ad hoc network; Network lifetime; Route maintenance; Routing protocol; Star topology Description An account of the resource Mobile ad hoc network (MANET) is arranged with multiple nodes that communicate wirelessly. However, MANET communication suffers from various issues such as inadequate security, low stability, high power consumption, and a lack of specific infrastructure of the network. Moreover, the route failure happened in the network due to the unrestricted node movement, which has increased energy utilization, delay, and reduced lifetime of the nodes. To overcome these issues, the novel Eagle Based Density Clustering (EBDC) approach is developed in this research that predicts the link failure and increased the lifetime of the nodes. Here, the developed EBDC approach is utilized for clustering and route maintenance in MANET for that it creates the nodes using the star topology. Initially, the developed approach selects the Cluster Head and transmits the message through the created path. Subsequently, the link failure is detected by the EBDC model, and it creates a new reference layer to replace the exhausted layer. Hence, the developed EBDC model has enhanced the network lifetime and reduced energy utilization. Furthermore, this model is implemented using Network Simulator 2, and the parameters like accuracy, energy consumption, Packet Delivery Ratio, network lifetime, end-to-end delay, and throughput are calculated. Additionally, the attained outcomes are compared with prevailing methods for evaluating the efficiency of the developed approach. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. Creator An entity primarily responsible for making the resource Vatambeti R.; Sanshi S.; Krishna D.P. Source A related resource from which the described resource is derived Journal of Ambient Intelligence and Humanized Computing, Vol-14, No. 1, pp. 305-319. 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/s12652-021-03298-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s12652-021-03298-3</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105447125&doi=10.1007%2Fs12652-021-03298-3&partnerID=40&md5=80fefcc73425c56f297c4d60643b7eb0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105447125&doi=10.1007%2fs12652-021-03298-3&partnerID=40&md5=80fefcc73425c56f297c4d60643b7eb0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18685137 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 Vatambeti R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Sanshi S., Department of Information Science and Engineering, BMS Institute of Technology & Management, Bangalore, India; Krishna D.P., Department of Computer Science and Engineering, Narayana Engineering College, Andhra Pradesh, Guduru, 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 Comparison on Machine Learning and Deep Neural Networks in Epileptic Seizure Prediction Subject The topic of the resource Area under curve (AUC); BONN dataset; Deep neural networks (DNN); Electroencephalogram (EEG); Epilepsy Description An account of the resource Electroencephalography signals have been widely used in cognitive neuroscience to identify the brains activity and behavior. These signals retrieved from the brain are most commonly used in detecting neurological disorders. Epilepsy is a neurological impairment in which the brains activity becomes abnormal, causing seizures or unusual behavior. Methods: The benchmark BONN dataset is used to compare and assess the models. The investigations were conducted using the traditional algorithms in machine learning algorithms such as KNN, naive Bayes, decision tree, random forest, and deep neural networks to exhibit the DNN models efficiency in epileptic seizure detection. Findings: Experiments and results prove that deep neural network model performs more than traditional machine learning algorithms, especially with the accuracy value of 97% and area under curve value of 0.994. Novelty: This research aims to focus on the efficiency of deep neural network techniques compared with traditional machine learning algorithms to make intelligent decisions by the clinicians to predict if the patient is affected by epileptic seizures or not. So, the focus of this paper helps the research community dive into the opportunities of innovations in deep neural networks. This research work compares the machine learning and deep neural network model, which supports the clinical practitioners in diagnosis and early treatment in epileptic seizure patients. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Roseline Mary R.; Zoraida B.S.E.; Ramamurthy B. Source A related resource from which the described resource is derived Lecture Notes on Data Engineering and Communications Technologies, Vol-114, pp. 677-687. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-16-9416-5_49" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-9416-5_49</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133696917&doi=10.1007%2F978-981-16-9416-5_49&partnerID=40&md5=233cf38562a73c2f6650ab9e70d97beb" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133696917&doi=10.1007%2f978-981-16-9416-5_49&partnerID=40&md5=233cf38562a73c2f6650ab9e70d97beb</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23674512 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 Roseline Mary R., Bharathidasan University, Tamil N?du, Tiruchirappalli, India, CHRIST (Deemed To Be University), Karnataka, Bangalore, India; Zoraida B.S.E., Bharathidasan University, Tamil N?du, Tiruchirappalli, India; Ramamurthy B., 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 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 Efficient Compressive Data Collection Scheme for Wireless Sensor Networks Subject The topic of the resource Compressive data collection; covariance function; Gaussian regression; margin-free estimator; maximum likelihood estimator; wireless sensor network Description An account of the resource The Compressive Data Collection (CDC) scheme is an efficient data-acquiring method that uses compressive sensing to decrease the bulk of data transmitted. Most existing schemes are modeled as Non-Uniform Sparse Random Projection (NSRP), and an NSRP-based estimator is used. These models cannot deal with anomaly readings that deviate from their standards and norms. Therefore, we provide a new CDC strategy in this study that uses an opportunistic estimator and routing. Initially, neighbor nodes are identified using the covariance function following the Gaussian process regression, and the data transfer to the neighbor node is done using the compressive sensing technique. Compressed data are then projected by using conventional random projection. Finally, the sample required to retrieve data is estimated using margin-free and maximum likelihood estimators. Results show that the sample needed to retrieve the data is less in the proposed scheme. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. Creator An entity primarily responsible for making the resource Mary Anita E.A.; Jenefa J.; Vinodha D.; Lapina M. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-1207 LNNS, pp. 31-47. 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/10.1007/978-3-031-77229-0_5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-77229-0_5</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210173679&doi=10.1007%2F978-3-031-77229-0_5&partnerID=40&md5=8aa125f0b9b6b0d1c51627fed1d64429" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210173679&doi=10.1007%2f978-3-031-77229-0_5&partnerID=40&md5=8aa125f0b9b6b0d1c51627fed1d64429</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-303177228-3 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Mary Anita E.A., CHRIST (Deemed to be University), Bangalore, India; Jenefa J., CHRIST (Deemed to be University), Bangalore, India; Vinodha D., CHRIST (Deemed to be University), Bangalore, India; Lapina M., North Caucasus Federal University, Stavropol, 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 Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An Efficient Copper-Catalyzed Regioselective One-Pot Synthesis of Pyrido[1,2-a]benzimidazole and Its Derivatives Subject The topic of the resource benzimidazole; C-N bond formation; Cu(I)Br; intermolecular cyclization; one-pot synthesis; Ullman coupling Description An account of the resource A facile and effectual regioselective one-pot synthesis protocol has been developed for the construction of pyrido[1,2-a]benzimidazole and its derivatives using Copper(I) bromide as the catalyst, 1,10-phenanthroline as ligand, and K3PO4 (Tripotassium phosphate) as the base in Dimethyl sulfoxide as solvent at 110 C for 12 h. The reaction conditions were optimized by screening various copper catalysts, ligands, solvents, and bases. The substrate scope of the reaction was also carried out with electron-withdrawing and donating functional groups to prepare novel functionalized regioselective benzimidazole compounds in good to excellent yields. All the isolated compounds were characterized by 1H, 13C, and 19F NMR. 2023 Wiley-VCH GmbH. Creator An entity primarily responsible for making the resource Kommuri V.C.; Vijayan A.; Pawar L.; Arunachalampillai A. Source A related resource from which the described resource is derived ChemistrySelect, Vol-8, No. 39 Publisher An entity responsible for making the resource available John Wiley and Sons 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/10.1002/slct.202303000" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/slct.202303000</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174463868&doi=10.1002%2Fslct.202303000&partnerID=40&md5=e18234f8523f5befbdf0577ffa27f5b5" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174463868&doi=10.1002%2fslct.202303000&partnerID=40&md5=e18234f8523f5befbdf0577ffa27f5b5</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23656549 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 Kommuri V.C., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bangalore, 560029, India, Syngene International Limited, Bommasandra Industrial Area, Bengaluru, 560099, India; Vijayan A., Department of Chemistry, CHRIST (Deemed to be University), Hosur Road, Bangalore, 560029, India; Pawar L., Syngene International Limited, Bommasandra Industrial Area, Bengaluru, 560099, India; Arunachalampillai A., Syngene International Limited, Bommasandra Industrial Area, Bengaluru, 560099, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An efficient deep learning approach for identifying interstitial lung diseases using HRCT images Subject The topic of the resource deep learning; DL; high-resolution computed tomography; HRCT; ILD; interstitial lung disease; multi-label classification; transfer learning Description An account of the resource Interstitial lung disease (ILD) encompasses over 200 fatal lung disorders affecting the interstitium, leading to significant mortality rates. We propose an AI-driven approach to diagnose and classify ILD from high-resolution computed tomography (HRCT) images. The research utilises a dataset of 3,045 HRCT images and employs a two-tier ensemble method that combines various machine learning (ML) models, convolutional neural networks (CNNs), and transfer learning. Initially, ML models achieve high accuracy, with the J48 model at 93.08% accuracy, mainly highlighting the importance of diagonal-wise standard deviation. Deep learning techniques are then applied, with three CNN models achieving test accuracies of 94.08%, 92.04%, and 93.72%. Transfer learning models also show promise, with InceptionV3 at 92.48% accuracy. Ensembling these models further boosts accuracy, with the ensemble of three CNN models reaching 97.42%. This research has the potential to advance ILD diagnosis, offering a robust computational framework that enhances accuracy and ultimately improves patient outcomes. Copyright 2024 Inderscience Enterprises Ltd. Creator An entity primarily responsible for making the resource Raju N.; Augustine D.P. Source A related resource from which the described resource is derived International Journal of Computational Science and Engineering, Vol-27, No. 3, pp. 286-301. Publisher An entity responsible for making the resource available Inderscience Publishers Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1504/IJCSE.2024.138427" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJCSE.2024.138427</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192870037&doi=10.1504%2FIJCSE.2024.138427&partnerID=40&md5=b3c6ded0e2833e6e04cba94656d8187a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192870037&doi=10.1504%2fIJCSE.2024.138427&partnerID=40&md5=b3c6ded0e2833e6e04cba94656d8187a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 17427185 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Raju N., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Augustine D.P., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An efficient deep learning based stress monitoring model through wearable devices for health care applications Subject The topic of the resource Deep learning (DL); Electrodermal activity (EDA) Signal; Grey wolf optimization (GWO); Random forest (RF); Stress detection Description An account of the resource Due to the mental stress of the human, the negative effects are known to be recent decades. Early detections of high level stresses are necessary to stop harmful consequences. Studies have proposed on wearable technologies which detect human stress. This study proposes stress detection systems which use physiological signals of people collected by wearable technologies and attached to human bodies. They can carry it during their daily routine. This works proposed system includes removal of artifacts in bio signals and feature extractions from these cleaned signals. Since, DL (deep learning) based models are proven to be the best for these analyses, this article uses a random differential GWO (Grey wolf optimization) algorithm for feature extraction and a ML (machine learning) algorithm called RF (random forest) has been used for classification of the human body parameters like activities of the heart, conductance in skins and corresponding accelerometer signals. The proposed stress detection system is implemented with the real time data gathered from 21 participants. This approach can detect the stress of a human and prevent it from early stages with necessary lectures to avoid the negative effects. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. Creator An entity primarily responsible for making the resource Prakash P.; Prabu P.; Sakthivel V.; Sivakumar R. Source A related resource from which the described resource is derived Optical and Quantum Electronics, Vol-56, No. 2 Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11082-023-05801-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11082-023-05801-w</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179624767&doi=10.1007%2Fs11082-023-05801-w&partnerID=40&md5=3b65fffe4273dc22d26de3f175e80a74" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179624767&doi=10.1007%2fs11082-023-05801-w&partnerID=40&md5=3b65fffe4273dc22d26de3f175e80a74</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 3068919; CODEN: OQELD 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 Prakash P., Vellore Institute of Technology, Tamilnadu, Chennai, India; Prabu P., CHRIST (Deemed to be University), Bengaluru, 560029, India; Sakthivel V., Vellore Institute of Technology, Tamilnadu, Chennai, India; Sivakumar R., CHRIST (Deemed to be University), Bengaluru, 560029, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An Efficient Deep Learning Framework FPR Detecting and Classifying Depression Using Electroencephalogram Signals Subject The topic of the resource Autoencoder; Classification; Convolutional Neural Network; Depression; Electroencephalogram Description An account of the resource Depression is a common and real clinical disease that has a negative impact on how you feel, how you think, and how you behave. It is a significant burdensome problem. Fortunately, it can also be treated. Feelings of self-pity and a lack of interest in activities you once enjoyed are symptoms of depression. It can cause a variety of serious problems that are real, and it can make it harder for you to work both at home and at work. The main causes include family history, illness, medications, and personality, all of which are linked to electroencephalogram (EEG) signals, which are thought of as the most reliable tools for diagnosing depression because they reflect the state of the human cerebrum's functioning. Deep learning (DL), which has been extensively used in this field, is one of the new emerging technologies that is revolutionizing it. In order to classify depression using EEG signals, this paper presents an efficient deep learning model that allows for the following steps: (a) acquisition of data from the psychiatry department at the Government Medical College in Kozhikode, Kerala, India, totaling 4200 files; (b) preprocessing of these raw EEG signals to avoid line noise without committing filtering; (c) feature extraction using Stacked Denoising Autoevolution; and (d) reference of the signal to estimate true and all. According to experimental findings, The proposed model outperforms other cutting-edge models in a number of ways (accuracy: 0.96, sensitivity: 0.97, specificity: 0.97, detection rate: 0.94). 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. Creator An entity primarily responsible for making the resource Aswathy S.U.; Vincent B.; Jacob P.M.; Aniyan N.; Daniel D.; Thomas J. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-647 LNNS, pp. 1179-1188. 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/10.1007/978-3-031-27409-1_108" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-27409-1_108</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163295237&doi=10.1007%2F978-3-031-27409-1_108&partnerID=40&md5=cb46eaf136448ba367c406bff0a336a7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163295237&doi=10.1007%2f978-3-031-27409-1_108&partnerID=40&md5=cb46eaf136448ba367c406bff0a336a7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-303127408-4 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 Aswathy S.U., Department of Computer Science and Engineering, Marian Engineering College, Kerala, Thiruvananthapuram, India; Vincent B., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Jacob P.M., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Aniyan N., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Daniel D., Department of Computer Science and Engineering, Providence College of Engineering, Kerala, Alappuzha, India; Thomas J., Department of Computer Science and Engineering, Christ University, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An Efficient Deep Learning Model Using Harris-Hawk Optimizer for Prognostication of Mental Health Disorders Subject The topic of the resource Adaptive Harris Hawk Optimizer; Adolescence; Emotion; Mental Health; Sparse Autoencoder Description An account of the resource Mental health disorders are primarily life style driven disorders, which are mostly unidentifiable by clinical or direct observations, but act as a silent killer for the impacted individuals. Using machine learning (ML), the prediction of mental ailments has taken significant interest in medical informatics community especially when clinical indicators are not there. But, majority studies now focus on usual machine learning methods used to predict mental disorders with few organized health data, this may give wrong signals. To overcome the drawbacks of the conventional ML prediction models, this work presents Deep Learning (DL) trained prediction model for automated feature extraction to realistically predict mental health disorders from the online textual posts of individuals indi cating suicidal and depressive contents. The proposed model encompasses three phases named pre-processing, feature extraction and optimal prediction phase. The developed model utilizes a novel Sparse Auto-Encoder based Optimal Bi-LSTM (SAE-O-Bi-LSTM) model, which integrates Bi-LSTM and Adaptive Harris-Hawk Optimizer (AHHO) for extracting the most relevant mental illness indicating features from the textual content in the dataset. The dataset utilized for training consist of 232074 unique posts from the "SuicideWatch" and "Depression" subreddits of the Reddit platform during December 2009 to Jan 2021 downloaded from Kaggle. In-depth comparative analysis of the testing results is conducted using accuracy, precisions, F1 score, specificity, and Recall and ROC curve. The results depict considerable improvement for our developed approach with an accuracy of 98.8% and precision of 98.7% respectively, which supports the efficacy of our proposed model. The Author(s) 2024. Creator An entity primarily responsible for making the resource Vandana; Srivastava S.; Arora N.; Gupta V. Source A related resource from which the described resource is derived International Research Journal of Multidisciplinary Technovation, Vol-6, No. 4, pp. 106-123. Publisher An entity responsible for making the resource available Asian Research Association 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/10.54392/irjmt2449" target="_blank" rel="noreferrer noopener">https://doi.org/10.54392/irjmt2449</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201567119&doi=10.54392%2Firjmt2449&partnerID=40&md5=7d50209ab7a98ebfced765bdf534c6fe" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201567119&doi=10.54392%2firjmt2449&partnerID=40&md5=7d50209ab7a98ebfced765bdf534c6fe</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 25821040 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 Vandana, School of Sciences, Christ University, Bengaluru, India; Srivastava S., School of Sciences, Christ University, Bengaluru, India; Arora N., Department of Computer Science, Kalindi College, University of Delhi, Delhi, India; Gupta V., School of Sciences, 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 An Efficient Deep Learning-Based Hybrid Architecture for Hate Speech Detection in Social Media Subject The topic of the resource CNN; Detection; Hate speech; Hybrid model; LSTM Description An account of the resource Social media has become an integral part of life as users are spending a significant amount of time networking online. Two primary reasons for its increasing popularity are ease of access and freedom of speech. People can express themselves without worrying about consequences. Due to lack of restriction, however, cases of cyberbullying and hate speeches are increasing on social media. Twitter and Facebook receive over a million posts daily, and manual filtration of this enormous number is a tedious task. This paper proposes a deep learning-based hybrid architecture (CNN + LSTM) to identify hate speeches by using Stanfords GloVe, which is a pre-trained word embedding. The model has been tested under different parameters and compared with several state-of-the-art models. The proposed framework has outperformed existing models and has also achieved the best accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Nath N.; George J.P.; Kesan A.; Rodrigues A. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-462, pp. 347-355. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-19-2211-4_30" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-2211-4_30</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135007967&doi=10.1007%2F978-981-19-2211-4_30&partnerID=40&md5=13972f303d79c8e87bb7e04c29d9b447" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135007967&doi=10.1007%2f978-981-19-2211-4_30&partnerID=40&md5=13972f303d79c8e87bb7e04c29d9b447</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981192210-7 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 Nath N., CHRIST (Deemed to be University), Bengaluru, India; George J.P., CHRIST (Deemed to be University), Bengaluru, India; Kesan A., CHRIST (Deemed to be University), Bengaluru, India; Rodrigues A., CHRIST (Deemed to be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An efficient design and comparison of machine learning model for diagnosis of cardiovascular disease Subject The topic of the resource Bayes; Evaluation Metrics; Pre-process; Prediction; Supervised Description An account of the resource Cardiovascular disease has a significant global impact. Cardiovascular disease is the primary cause of disability and mortality in most developed countries. Cardiovascular disease is a condition that disturbs the structures and functionality of the heart and can also be called heart disease. Cardiovascular diseases require more precise, accurate, and reliable detection and forecasting because even a small inaccuracy might lead to fatigue or mortality. There are very few death occurrences related to cardio sickness, and the amount is expanding rapidly. Predicting this disease at its early stage can be done by employing Machine Learning (ML) algorithms, which may help reduce the number of deaths. Data pre-processing can be employed here to eliminate randomness in data, replace missing data, fill in default values if appropriate, and categorize features for forecasting and making decisions at various levels. This research investigates various parameters that are related to the cause of heart disease. Several models discussed here will come under the supervised learning type of algorithms like Support Vector Machine (SVM), K-nearest neighbor (KNN), and Nae Bayes (NB) algorithm. The existing dataset of heart disease patients from the Kaggle has been used for the analysis. The dataset includes 300 instances and 13 parameters and 1 label are used for prediction and testing the performance of various algorithms. Predicting the likelihood that a given patient will be affected by the cardiac disease is the goal of this research. The most important purpose of the study is to make better efficiency and precision for the detection of cardiovascular disease in which the target output ultimately matters whether or not a person has heart disease. 2023, Bentham Books imprint. All rights reserved. Creator An entity primarily responsible for making the resource Sahu D.N.; Sudhakar G.; Raju C.G.; Joshi H.; Kumbhkar M. Source A related resource from which the described resource is derived AI and IoT-based intelligent Health Care & Sanitation, pp. 191-206. 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.2174/9789815136531123010015" target="_blank" rel="noreferrer noopener">https://doi.org/10.2174/9789815136531123010015</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205281009&doi=10.2174%2F9789815136531123010015&partnerID=40&md5=afc84bc1b0735c9326e0bf66c4187d05" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205281009&doi=10.2174%2f9789815136531123010015&partnerID=40&md5=afc84bc1b0735c9326e0bf66c4187d05</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-981513653-1; 978-981513654-8 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 Sahu D.N., Department of MCA, School of Computer Science, Gangadhar Meher University, Sambalpur, 768001, India; Sudhakar G., School of Information Technology, Jawaharlal Nehru Technological University, Hyderabad, 500085, India; Raju C.G., BMS College of Engineering, Bangalore, 560019, India; Joshi H., Department of Statistics, CHRIST (Deemed to be University), Bangalore, 560029, India; Kumbhkar M., Christian Eminent College, Indore, 452001, 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 Efficient Detection and Prediction of Intrusion in Smart Grids Using Artificial Neural Networks Subject The topic of the resource Artificial neural network (ANN); Conventional neural network (CNN); Internet of things (IoT); Intrusion detection; Root mean square error (RMSE) Description An account of the resource In recent years, fraud identification on Internet of Things (IoT) devices has been essential to obtaining better results in all fields, such as smart cities, smart grids, etc. As a result, there are more IoT devices in the smart grid's power management sectors, and apart from these identifications, intrusion into the smart grid is very difficult. Hence, to overcome this, a proposed intrusion detection system in a smart grid using an artificial neural network (ANN) has been used to detect the intrusion and improve the prediction rate, and it has been very effective on various faults injected into the smart grids in ranges and seasons. As per the simulation result, the proposed method shows better results as compared to a conventional neural network (CNN) with respect to the root mean square error in terms of weekly, monthly, and seasonal terms of 0.25%, 0.15%, and 0.26%, respectively. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. Creator An entity primarily responsible for making the resource Kumar S.M.; Velluri R.; Dayananda P.; Nagaraj S.; Srikantaswamy M.; Chandrappa K.Y. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 505-515. 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/10.1007/978-981-97-0975-5_45" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-0975-5_45</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197209163&doi=10.1007%2F978-981-97-0975-5_45&partnerID=40&md5=06ac5a10ebc1d20659c1cd84e7727873" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197209163&doi=10.1007%2f978-981-97-0975-5_45&partnerID=40&md5=06ac5a10ebc1d20659c1cd84e7727873</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981970974-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar S.M., Department of Information Science and Engineering, Bangalore Institute of Technology, Karnataka, Bengaluru, India; Velluri R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India; Dayananda P., Department of Electrical and Electronics Engineering, SJB Institute of Technology, Karnataka, Bengaluru, India; Nagaraj S., Electrical and Electronics Engineering, JSS Science and Technology University, Karnataka, Mysore, India; Srikantaswamy M., Electronics and Communication Engineering, JSS Academy of Technical Education, Karnataka, Bengaluru, India; Chandrappa K.Y., Information Science Engineering, Global Academy of Technology, Karnataka, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An Efficient Face Recognition System using Deep Transfer Learning Subject The topic of the resource Artificial neural networks; machine learning; Transfer learning Description An account of the resource Face recognition is an AI-based innovation used to find and recognize human appearances in videos and images. Organizations can apply face recognition to many different kinds of fields which may include biometrics, regulation of law, security and individual wellbeing; so as to take observation of individuals in any scenario. Face recognition has advanced from simple vision methods to progress in ML; and further to progressively refined neural networks (ANN) and related advances. It currently assumes an indispensable part as the initial phase in numerous basic applications, including the task of tracking a face. Face recognition is utilized to focus cameras or count the number of individuals present in a particular region. The innovation likewise has showcasing applications, for instance, showing recommended promotions when a specific user is detected. 2022 IEEE. Creator An entity primarily responsible for making the resource Ragsania R.; Vijayalakshmi S. Source A related resource from which the described resource is derived 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 2199-2203. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICACITE53722.2022.9823797" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACITE53722.2022.9823797</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135496522&doi=10.1109%2FICACITE53722.2022.9823797&partnerID=40&md5=d5638336639a173e1adede28f4944ae4" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135496522&doi=10.1109%2fICACITE53722.2022.9823797&partnerID=40&md5=d5638336639a173e1adede28f4944ae4</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166543789-9 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 Ragsania R., Christ University, Department of Data Science, India; Vijayalakshmi S., Christ University, Department of Data Science, 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 PHD PhD PhD 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/. Relation A related resource No Thesis Title A name given to the resource An efficient framework for scientific article recommendation system Subject The topic of the resource Computer Science Description An account of the resource Excess data makes it challenging to extract information that is relevant to a domain of study or research. Existing state-of-the-art systems focus majorly on the selection of highly connected, prestigious and cited articles, regardless of the relevance of papers. To improve quality of findings, recommender systems which are a subclass of information filtration systems are used. They filter out relevant information over prestigious data from an existing repository of information. There are various sub-domains under recommender systems. This study focuses on citation recommendation. Citations are an integral part of any scientific paper, academic dissertation or projects. Finding appropriate citations for any work is a scholar's most time-consuming task. Thus, a well-defined citation recommendation system provides fulfillment and completeness for citing the giants works. The thesis aims to study existing frameworks for citation recommendation systems and identify the best dataset to work on graph- based recommender systems. A framework that recommends the most similar and relevant article to the user rather than prestigious authors or papers is here by proposed. The study explores various machine learning and deep learning techniques and methods which can be used effectively in recommending loosely connected yet highly relevant articles. Creator An entity primarily responsible for making the resource Nair, Akhil M. Source A related resource from which the described resource is derived Author's Submission Publisher An entity responsible for making the resource available Christ(Deemed to be University) Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Contributor An entity responsible for making contributions to the resource George, Jossy P. Rights Information about rights held in and over the resource Open Access Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource PhD Identifier An unambiguous reference to the resource within a given context <a href="http://hdl.handle.net/10603/426631" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/426631</a> https://archives.christuniversity.in/files/original/14c4e789ae1124572d2de799217eddf4.pdf fdf89a9912e134d9eabcb101b64233a4 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 Thesis PhD PhD 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 An efficient framework for scientific article recommendation system / Subject The topic of the resource Computer Science Creator An entity primarily responsible for making the resource Nair, Akhil M. - 1840072 Publisher An entity responsible for making the resource available CHRIST (Deemed to be University) Date A point or period of time associated with an event in the lifecycle of the resource 2022-12-23 Language A language of the resource English Type The nature or genre of the resource PhD