99 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 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 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 Poonia, Ramesh Chandra; Upreti, Kamal; Singh, Nidhi; Kesarwani, Jyoti; Alam, Mohammad Shabbir Title A name given to the resource An Efficient Detection of Suspicious Objects from Dynamic Video Surveillance by Fusion-based Multiview Deep Learning Techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Mobile Multimedia;Volume;21;Issue;1;pp.1-26 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.13052/jmm1550-4646.2111" target="_blank" rel="noreferrer noopener">https://doi.org/10.13052/jmm1550-4646.2111</a> <a href="https://www.scopus.com/pages/publications/105003927796?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003927796?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 Poonia R.C., Department of Computer Science, Christ University, NCR, Delhi, India; Upreti K., Department of Computer Science, Christ University, NCR, Delhi, India; Singh N., G. D. Goenka University, Haryana, Gurgaon, India; Kesarwani J., United College of Engineering and Research, Uttar Pradesh, Prayagraj, India; Alam M.S., Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia Description An account of the resource Real-time detections of suspicious objects are needed to identify for finding criminal activities and are used in immediate alert systems for public safety applications. Video surveillance systems use live, closed-circuit televisions (live CCTVs) for dynamic video capturing of objects. Finding criminal activities over the dynamic video data is an emerging surveillance problem. The deep learning techniques are tedious for detecting suspicious movable objects and criminal activities. YOLO (You Only Look Once) gives more prominent movable video object detection accuracy than conventional deep models, like Convolutional Neural Network (CNN), 3D CNN, and Convolutional LSTM. State-of-the-art YOLO models, YOLOv8n, YOLOv8s, and YOLOv8l, are emphasized for extracting and detecting object motion detection from the dynamic video. YOLO models use single-view deep learning to classify or detect objects. These models limit the accuracy of the detection of complex and dynamic objects of dynamic video data. This paper presents the Fusion-based Multiview deep learning techniques to overcome this issue. The experimental study demonstrates that the proposed methodology efficiently detects suspicious data objects more than the single-view deep models. 2025 River Publishers. Subject The topic of the resource Deep learning; fusion; object detection; video surveillance; YOLO Publisher An entity responsible for making the resource available River Publishers Relation A related resource ISSN: 15504646; 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 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 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 Fuzzy Logic Cluster Formation Protocol for Data Aggregation and Data Reporting in Cluster-Based Mobile Crowdsourcing Subject The topic of the resource Cluster cranium; Data aggregation; Data reporting; Mobile crowdsourcing; Query optimization; Security Description An account of the resource Crowdsourcing is a procedure of outsourcing the data to an abundant range of individual workers rather than considering an exclusive entity or a company. It has made various types of chances for some difficult issues by utilizing human knowledge. To acquire a worldwide optimal task assignment scheme, the platform usually needs to collect location information of all workers. During this procedure, there is a major security concern; i.e., the platform may not be trustworthy, and so, it brings about a threat to workers location privacy. Recently, many distinguished research papers are published to address the security and privacy issues in mobile crowdsourcing. According to our knowledge, the security issues that occur in terms of data reporting were not addressed. Secure and efficient data aggregation and data reporting are the critical issue in Mobile Crowdsourcing (MCS). Cluster-based mobile crowdsourcing (CMCS) is the efficient way for data aggregation and data reporting. In this paper, we propose a novel procedure, the efficient fuzzy logic cluster formation protocol (EFLCFP) for cluster formation, and use cluster cranium (CC) for data aggregation and data reporting. We recommend a couple of secure and efficient data transmission (SET) protocols for CMCS, (i) SET-IBE uses additively homomorphic identity-based encryption system and (ii) SET-IBOOS uses the identity-based online/offline digital signature system, respectively. Then, we have widen the features of cluster cranium by increasing the propensity to achieve aggregation and reporting on the data yielded by the requesters without scarifying their privacy. Also, considering query optimization using cost and latency. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Renukadevi M.; Mary Anita E.A.; Mohana Geetha D. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-351, pp. 427-446. 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/10.1007/978-981-16-7657-4_35" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-7657-4_35</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126228633&doi=10.1007%2F978-981-16-7657-4_35&partnerID=40&md5=a37e6bb7d6d2bed05075a37849e9c4f2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126228633&doi=10.1007%2f978-981-16-7657-4_35&partnerID=40&md5=a37e6bb7d6d2bed05075a37849e9c4f2</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981167656-7 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 Renukadevi M., Department of CSE, S.A. Engineering College, Chennai, India; Mary Anita E.A., Department of CSE, School of Engineering and Technology, CHIRST University, Bengaluru, India; Mohana Geetha D., Department of CSE, Sri Krishna College of Engineering and Technology, Coimbatore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Saranya, R.; Rajagopal, Manikandan; Ramprasath, J.; ThamaraiSelvi, K.; Leelavathy, S. Title A name given to the resource An Efficient Fuzzy Logic-Integrated Hybrid Deep Learning Framework for Medical Diagnosis 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 Fuzzy Information and Engineering;Volume;18;Issue;1;pp.1-18 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.26599/FIE.2025.9270072" target="_blank" rel="noreferrer noopener">https://doi.org/10.26599/FIE.2025.9270072</a> <a href="https://www.scopus.com/pages/publications/105038223936?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038223936?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 Saranya R., Dr. SNS Rajalakshmi College of Arts and Science, Department of Computer Science with Cyber Security, Coimbatore, 600049, India; Rajagopal M., School of Business and Management, Christ University, Bangalore, 600871, India; Ramprasath J., Dr. Mahalingam College of Engineering and Technology, Department of Information Technology, Pollachi, 642003, India; ThamaraiSelvi K., New Horizon College of Engineering, Department of Information Science and Engineering, Bangalore, 600671, India; Leelavathy S., Panimalar Engineering College, Department of AI&DS, Chennai, 600123, India Description An account of the resource Medical diagnosis involves analyzing symptoms, test results, and patient histories, but uncertainty from vague symptoms and incomplete records complicates the process. Fuzzy logic-based systems address this issue but often depend on manual rule creation, which is time-consuming. This research proposes a hybrid approach integrating fuzzy logic with deep learning techniques (FL-DLT) for intelligent diagnosis. The framework combines adaptive neuro-fuzzy inference system (ANFIS) for handling uncertainty with convolutional neural networks (CNNs) for extracting features from medical images like X-rays and MRIs. ANFIS models relationships between symptoms, results, and diagnoses, while CNNs analyze medical images. Experimental results show high accuracy and reliability, even with noisy or incomplete data. The proposed approach can improve diagnostic accuracy and efficiency, supporting clinicians in decision-making. Key contributions include the development of the FL-DLT framework and its evaluation using a large dataset of patient records and medical images. Additionally, the research offers insights into the application of fuzzy logic and deep learning in medical diagnosis, highlighting their potential to enhance diagnostic outcomes and efficiency in clinical practice. 2009 Tsinghua University Press. Subject The topic of the resource adaptive neuro-fuzzy inference system (ANFIS); decision support systems; deep learning; fuzzy logic; intelligent healthcare systems; medical diagnosis Publisher An entity responsible for making the resource available Tsinghua University Press Relation A related resource ISSN: 16168658; 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 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 HOG-Centroid Descriptor for Human Gait Recognition Subject The topic of the resource Centroid; Descriptor; Feature fusion; Gait; HOG; Silhouette Description An account of the resource Automatic recognition of human gait have gained much attention nowadays. Histogram of Oriented Gradient (HOG) is a widely adopted descriptor for object's shape analysis. In this paper, combination of HOG descriptor with silhouette centroid for human gait recognition is proposed. The resultant descriptor, namely HOG-Centroid, achieves better recognition performance on comparison with HOG descriptor individually as well as other existing gait recognition methods. Experiments are carried out with CASIA gait dataset B and cumulative matching scores of 95.3%, 98.1% and 99.2% are obtained for rank 1, rank 5 and rank 10 respectively. 2019 IEEE. Creator An entity primarily responsible for making the resource Sugandhi K.; Raju G. Source A related resource from which the described resource is derived Proceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019, pp. 355-360. 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 2019-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AICAI.2019.8701366" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AICAI.2019.8701366</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065618469&doi=10.1109%2FAICAI.2019.8701366&partnerID=40&md5=eb92a946574c1129c67cb7be7ed985f0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065618469&doi=10.1109%2fAICAI.2019.8701366&partnerID=40&md5=eb92a946574c1129c67cb7be7ed985f0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-153869346-9 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 Sugandhi K., Department of Information Technology, Kannur University, Kannur, Kerala, 670567, India; Raju G., Department of CSE, Christ (Deemed to Be University), Bengaluru, Karnataka, 560764, 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 hybrid approach for numerical study of two-dimensional time-fractional Cattaneo model with Riesz distributed-order space-fractional operator along with stability analysis Subject The topic of the resource Cattaneo model; Distributed-order derivative; Finite element method; Legendre spectral method; Stability analysis Description An account of the resource In this article, we study and analyze the two-dimensional time-fractional Cattaneo model with Riesz space distributed-order. To obtain approximate solutions of this type of fractional model the combined and effective numerical approach based on the ADI Galerkin method and the Legendre spectral method used the ADI Galerkin numerical method uses the finite difference approach. The ADI Galerkin numerical method is used to approximate the proposed model in terms of the time variable, and the Legendre spectral method is applied to discretize the fractional model with respect to the space variable. Also, the convergence analysis and stability of the proposed method are discussed and reviewed in this manuscript. In the end, some numerical examples are tested for the effectiveness and accuracy of the proposed method. As well as, in the numerical examples section, the presented numerical approach is compared with two numerical methods and the results are reported in a table. 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. Creator An entity primarily responsible for making the resource Derakhshan M.H.; Mortazavifar S.L.; Veeresha P.; Gez-Aguilar J.F. Source A related resource from which the described resource is derived Physica Scripta, Vol-99, No. 9 Publisher An entity responsible for making the resource available Institute of Physics Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1088/1402-4896/ad6d02" target="_blank" rel="noreferrer noopener">https://doi.org/10.1088/1402-4896/ad6d02</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201868007&doi=10.1088%2F1402-4896%2Fad6d02&partnerID=40&md5=7c45678867b3e81e70191af8d351c71c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201868007&doi=10.1088%2f1402-4896%2fad6d02&partnerID=40&md5=7c45678867b3e81e70191af8d351c71c</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 318949; CODEN: PHSTB 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 Derakhshan M.H., Department of Industrial Engineering, Apadana Institute of Higher Education, Shiraz, Iran, Zand Institute of Higher Education, Faculty of Engineering, Shiraz, 71887-73489, Iran; Mortazavifar S.L., Zand Institute of Higher Education, Faculty of Engineering, Shiraz, 71887-73489, Iran; Veeresha P., Department of Mathematics, CHRIST, Deemed to be University, Bengaluru, 560029, India; Gez-Aguilar J.F., Centro de Investigaci en Ingenier y Ciencias Aplicadas (CIICAp-IICBA), UAEM, Universidad Automa del Estado de Morelos, Cuernavaca, Mexico 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 hybrid digital architecture for space vector PWM method for multilevel VSI Subject The topic of the resource Hybrid digital structure; Multilevel inverter; Space vector pulse width modulation (SVPWM); Voltage source inverter (VSI) Description An account of the resource This paper presents an efficient, cost effective design implementation of a hybrid digital architecture for space vector pulse width modulation (SVPWM) method for multilevel inverters (MLIs). The SVPWM method is one of the most popular real time PWM method for three phase voltage source inverter (VSI). The implementation of SVPWM method becomes complex with an increase in the number of levels in a multilevel inverter. The SVPWM method for multilevel inverter is a multitask system. The main constraint when it comes to implementing SVPWM for multilevel inverters is the processing of dwell time computation and the generation of PWM gate signals for all of the switches with an accurate delay. A hybrid hardware structure consisting of a simple low-cost, low-power dsPIC micro controller (dsPIC 30F4011) and a state of the art Field Programmable Gate Array (FPGA) (Cyclone V 5CGXFC5C6F27C7N) is used to implement SVPWM. The proposed hybrid digital architecture utilizes the advantages and resources of the dsPIC and FPGA. The hybrid digital architecture meets the timing constraints of multitasking through synchronization and parallelism. A communication interface between the dsPIC and the FPGA reduces the design complexity. The software overhead for the communication interface remains fixed for any number of levels. The hybrid structure of the digital architecture provides scalability for the SVPWM method with more number of levels in multilevel inverter. The operation of the proposed hybrid digital architecture is experimentally validated with an optimized SVPWM method for a five level VSI. An optimized region identification algorithm and simple dwell time expressions are described for a five level SVPWM. The input DC of the five level VSI is obtained from a differential power processing (DPP) based PV system. Experimental results under different operating conditions are presented. 2020, The Korean Institute of Power Electronics. Creator An entity primarily responsible for making the resource Anjana K.G.; Kumar M.A.; Biswas J.; Barai M. Source A related resource from which the described resource is derived Journal of Power Electronics, Vol-20, No. 5, pp. 1328-1341. 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 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s43236-020-00120-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s43236-020-00120-9</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087813967&doi=10.1007%2Fs43236-020-00120-9&partnerID=40&md5=fd7841f7fcc84e3e3b1679e3ddab9a47" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087813967&doi=10.1007%2fs43236-020-00120-9&partnerID=40&md5=fd7841f7fcc84e3e3b1679e3ddab9a47</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 15982092 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 Anjana K.G., Department of Electrical and Electronics Engineering, National Institute of Technology, Calicut, India; Kumar M.A., Department of Electrical and Electronics Engineering, National Institute of Technology, Calicut, India; Biswas J., Department of Computer Science, Christ University, Bangalore, India; Barai M., Department of Electrical and Electronics Engineering, National Institute of Technology, Calicut, 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 image denoising method based on bilateral filter model and neighshrink SURE Subject The topic of the resource Bilateral filter; Image denoising; Neighshrink SURE; Wavelet thresholding Description An account of the resource In all the instances of image acquisition, transmission and storage, the unwanted noise gets into the information content of the image and thereby introduces an unpleasant visual quality to the observer. So the field of image processing has produced a lot of image denoising algorithms and techniques to improve the visual quality of the image. Since noise cannot be reduced to zero practically, the need for faithful and efficient denoising techniques to produce almost noiseless images demands a systematic research work in the field of denoising methods. The denoising process using a bilateral filter even though produces improvement in the image quality, it does not show consistency when the noise level is high and also the peak signal to noise ratio (PSNR) and Image quality Index (IQI) do not show any improvement. This paper proposes an improved algorithm that incorporates the function of bilateral filter model and wavelet thresholding using Neighshrink SURE method. The results show significant improvement in both PSNR and IQI values with respect to the four standard test images under various noise conditions. BEIESP. Creator An entity primarily responsible for making the resource Naragund M.N.; Jagadale B.N.; Priya B.S.; Panchaxri; Hegde V. Source A related resource from which the described resource is derived International Journal of Recent Technology and Engineering, Vol-8, No. 3, pp. 8470-8475. Publisher An entity responsible for making the resource available Blue Eyes Intelligence Engineering and Sciences Publication Date A point or period of time associated with an event in the lifecycle of the resource 2019-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.35940/ijrte.C6629.098319" target="_blank" rel="noreferrer noopener">https://doi.org/10.35940/ijrte.C6629.098319</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073549674&doi=10.35940%2Fijrte.C6629.098319&partnerID=40&md5=cfb0bedcb77d7c770336d8b994b8963d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073549674&doi=10.35940%2fijrte.C6629.098319&partnerID=40&md5=cfb0bedcb77d7c770336d8b994b8963d</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 22773878 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 Naragund M.N., Department of Physics and Electronics, CHRIST (Deemed to be University), Bengaluru, India; Jagadale B.N., Department of PG studies and research in Electronics, Kuvempu University, Shimoga, India; Priya B.S., Department of Electronics, Kuvempu University, Shimoga, India; Panchaxri, Department of Electronics, SSA Govt. First grade College, Ballari, India; Hegde V., Department of Electronics, MESMM Arts and Science College, Sirsi, 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 Inclusion Complex Based Fluorescent Sensor for Mercury (II) and its Application in Live-Cell Imaging Subject The topic of the resource Chemosensor; Colorimetric; Fluorescence enhancement; Hg2+; Hydroxypropyl-beta-cyclodextrin (H-CD); Inclusion complex Description An account of the resource The formation of an inclusion complex between hydroxypropyl-?-cyclodextrin (H-CD) and 4-acetylphenyl-4-(((6-chlorobenzo[d]thiazol-2-yl)-imino)-methyl)-benzoate (L) was investigated by FT-IR, 1H-NMR, X-ray diffraction (XRD), FT-Raman, scanning electron microscope (SEM) techniques in the solid-state, absorption and emission spectroscopy in the liquid state and the virtual state as molecular docking technique. The binding properties of the inclusion complex (H-CD: L) with cations in deionized water was observed via absorbance and photoluminescence (PL) emission spectroscopy. The fluorescence probe (H-CD: L) inclusion complex (IC) was examined for several heavy metal cations, and identified that the PL emission wavelength of the complex displayed a continuous rise in the fluorescence intensity for Hg2+. A linearity range of 1 108 11 108M and limit of detection value of 2.71 1010M was found to be achieved for the detection of Hg2+. This outcome proves that the inclusion complex H-CD: L would be a promising material for the development a solid-state fluorescence probe for detecting Hg2+. It also shows application in real sample analysis and cell imaging. 2022, 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 Keerthana P.; Abbo H.; Cherian A.R.; Titinchi S.; Varghese A. Source A related resource from which the described resource is derived Journal of Fluorescence, Vol-32, No. 3, pp. 1109-1124. 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 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s10895-022-02931-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s10895-022-02931-4</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126786566&doi=10.1007%2Fs10895-022-02931-4&partnerID=40&md5=955b1de3bc687cbea3dd4771ad4c0ffa" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126786566&doi=10.1007%2fs10895-022-02931-4&partnerID=40&md5=955b1de3bc687cbea3dd4771ad4c0ffa</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 10530509; PubMed ID: 35305207; CODEN: JOFLE 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 Keerthana P., Department of Chemistry, CHRIST (Deemed To Be University), Hosur Road, Bengaluru, 560029, India; Abbo H., Department of Chemistry, University of the Western Cape, Cape Town, South Africa, Department of Chemistry, College of Science, University of Basrah, Basrah, Iraq; Cherian A.R., Department of Chemistry, CHRIST (Deemed To Be University), Hosur Road, Bengaluru, 560029, India; Titinchi S., Department of Chemistry, University of the Western Cape, Cape Town, South Africa; Varghese A., Department of Chemistry, CHRIST (Deemed To Be University), Hosur Road, Bengaluru, 560029, India https://archives.christuniversity.in/files/original/180dd6ca48aa38b60306174514347365.jpg cab2e5ea026c11da557600ba85ba4e1e 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 inclusion complex based fluorescent sensor for mercury (II) and its application in live-cell imaging / Subject The topic of the resource Chemistry Description An account of the resource Journal of Fluorescence, Vol.32, pp.1109–1124, ISSN No: 1573-4994. The formation of an inclusion complex between hydroxypropyl-β-cyclodextrin (H-CD) and 4-acetylphenyl-4-(((6-chlorobenzo[d]thiazol-2-yl)-imino)-methyl)-benzoate (L) was investigated by FT-IR, 1H-NMR, X-ray diffraction (XRD), FT-Raman, scanning electron microscope (SEM) techniques in the solid-state, absorption and emission spectroscopy in the liquid state and the virtual state as molecular docking technique. The binding properties of the inclusion complex (H-CD: L) with cations in deionized water was observed via absorbance and photoluminescence (PL) emission spectroscopy. Creator An entity primarily responsible for making the resource Abbo, Hanna., Cherian, Anila Rose., Titinchi, Salam., Varghese, Anitha. Source A related resource from which the described resource is derived <a href="https://link.springer.com/article/10.1007/s10895-022-02931-4" target="_blank" title="" An efficient inclusion complex based fluorescent sensor for mercury (II) and its application in live-cell imaging"" rel="noreferrer noopener">https://link.springer.com/article/10.1007/s10895-022-02931-4</a> Publisher An entity responsible for making the resource available SpringerLink Date A point or period of time associated with an event in the lifecycle of the resource 2022-05-13 Language A language of the resource English Type The nature or genre of the resource Research Publication