174 20 11858 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection Subject The topic of the resource Deep fake detection; Deep learning; Deep sparse auto encoder; Dnn; Face2face; Faceforensics++; Faceswap; Temporal convolutional neural network Description An account of the resource In the recent research era, artificial intelligence techniques have been used for computer vision, big data analysis, and detection systems. The development of these advanced technologies has also increased security and privacy issues. One kind of this issue is Deepfakes which is the combined word of deep learning and fake. DeepFake refers to the formation of a fake image or video using artificial intelligence approaches which are created for political abuse, fake data transfer, and pornography. This paper has developed a Deepfake detection method by examining the computer vision features of the digital content. The computer vision features based on the frame change are extracted using a proposed deep learning model called the Cascaded Deep Sparse Auto Encoder (CDSAE) trained by temporal CNN. The detection process is performed using a Deep Neural Network (DNN) to classify the deep fake image/video from the real image/video. The proposed model is implemented using Face2Face, FaceSwap, and DFDC datasets which have secured an improved detection rate when compared to the traditional deep fake detection approaches. 2022. Balasubramanian et al. Creator An entity primarily responsible for making the resource Balasubramanian S.B.; Kannan R.J.; Prabu P.; Venkatachalam K.; TrojovskP. Source A related resource from which the described resource is derived PeerJ Computer Science, Vol-8 Publisher An entity responsible for making the resource available PeerJ 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/10.7717/peerj-cs.1040" target="_blank" rel="noreferrer noopener">https://doi.org/10.7717/peerj-cs.1040</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134509137&doi=10.7717%2Fpeerj-cs.1040&partnerID=40&md5=2778015b0bf532431ca59ed8b77b1464" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134509137&doi=10.7717%2fpeerj-cs.1040&partnerID=40&md5=2778015b0bf532431ca59ed8b77b1464</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Relation A related resource ISSN: 23765992 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 Balasubramanian S.B., Department of Information Technology, Lebanese French University, Erbil, Iraq; Kannan R.J., School of Computer Science and Engineering, VIT Chennai, Tamilnadu, Chennai, India; Prabu P., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Venkatachalam K., Department of Applied Cybernetics, University of Hradec Krov Hradec Kralove, Czech Republic; TrojovskP., Department of Mathematics, University of Hradec Krov Hradec Kralove, Czech Republic 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 Deep Insights into 3D Face Reconstruction from Blurred 2D Inputs: A Comprehensive Framework Subject The topic of the resource Computer Vision; Convolutional Neural Network; Deep Learning; Generative Adversarial Network Description An account of the resource This framework outlines a multi-stage methodology for 3D face reconstruction driven by advancements in deep learning. The process involves image preprocessing with deblurring techniques and subsequent feature extraction using CNNs alongside traditional methods. Deep learning adapts to diverse image challenges, ensuring accuracy in 3D reconstructions. In medical imaging, the proficiency of 3D CNNs and GANs shines in extracting structures from MRI and CT scans. Post-processing steps encompass mesh smoothing and texture mapping for enhanced visual quality. Evaluation metrics (MAE, RMSE, IoU) guarantee the precision of depth estimations. Applications of deep learning span across CNNs, 3DMM, GANs, and networks for landmark detection and dense correspondence. Challenges include optimizing eye reconstruction, expanding applications, and addressing concerns related to data quality, privacy, and hardware requirements. 2024 IEEE. Creator An entity primarily responsible for making the resource Pastay S.; Joseph A.; Kumar R. Source A related resource from which the described resource is derived TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TQCEBT59414.2024.10545220" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545220</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204429282&doi=10.1109%2FTQCEBT59414.2024.10545220&partnerID=40&md5=a088e0a79848e46165f5bf7a73130c0a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204429282&doi=10.1109%2fTQCEBT59414.2024.10545220&partnerID=40&md5=a088e0a79848e46165f5bf7a73130c0a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038427-7 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 Pastay S., Christ Deemed to Be University, Department of Data Science, Lavasa, Pune, India; Joseph A., Christ Deemed to Be University, Department of Data Science, Lavasa, Pune, India; Kumar R., Christ Deemed to Be University, Department of Data Science, Lavasa, Pune, 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 Deep Learning Advancements in E-commerce Supply Chain Management in Forecasting and Optimization Strategies Subject The topic of the resource Deep learning; E-commerce; Forecasting; Optimization; Supply chain management Description An account of the resource In this study, the influence of deep learning technologies on the optimization of supply chain management in the context of the e-commerce industry is examined. Using a dataset of historical data of sales, inventories, market fluctuations, and customer and supplier details, I investigate the efficiency of different deep learning models to predict demand and facilitate the optimal balance of inventories. Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and a model proposed by the authors are defined and applied, considering their accuracy, precision, recall, and F-1 score. The results show that the proposed model outperforms traditional products, achieving 97.5% of accuracy. In the context of the comparative analysis, the specific features of CNN, LSTM, and RNN are revealed, helping to understand the benefits and drawbacks of each recommendation. As a result, the proposed model proves that deep learning technologies have the power to change the approach to predictive analytics and supply chain management, allowing practitioners to focus on strengths and overcome the weaknesses of their structures. The impact of data preprocessing and hyperparameters is also considered along with the necessity to choose the most appropriate model evaluation technique. In the future, it is possible to implement other complex deep learning models, integrate additional data, and address the problem of data scaling and heterogeneity. In the era of modern technologies, e-commerce organizations should take these findings into consideration to discover the potential of deep learning, improve supply chain performance, reduce costs, and attract clients. This research contributes to the topic of using deep learning technologies in supply chain management, promoting innovation, and changes that may affect the industry drastically. 2024 IEEE. Creator An entity primarily responsible for making the resource Singh J.; Shelke N.A.; Upreti K.; Saiyad F.B.J.; Divakaran P.; Madhukar K.S. Source A related resource from which the described resource is derived 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/SEFET61574.2024.10718068" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SEFET61574.2024.10718068</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208957887&doi=10.1109%2FSEFET61574.2024.10718068&partnerID=40&md5=e94520d4e9964fc26326f684de1e1c40" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208957887&doi=10.1109%2fSEFET61574.2024.10718068&partnerID=40&md5=e94520d4e9964fc26326f684de1e1c40</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038399-7 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 Singh J., School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India; Shelke N.A., School of Computer Science Engineering & Technology, Bennett University, Greater Noida, India; Upreti K., Christ (Deemed to be University), Department of Computer Science, Ghaziabad, India; Saiyad F.B.J., Bath spa university Rak, Department of Commerce, Ras Al-Khaimah, United Arab Emirates; Divakaran P., Himalayan University, Department of management, Itanagar, India; Madhukar K.S., KIT's Institute of Management, Education and Research, Kolhapur, 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 Deep Learning Algorithms Comparison forMultiple Biological Sequences Alignment Subject The topic of the resource A comparison study; Deep learning algorithms; q-gram; Sequence alignment; TC score Description An account of the resource In this paper, deep learning algorithms are compared for aligning multiple biological molecular sequences such as DNA, RNA, and protein. Efficient algorithms are necessary for sequence alignment to identify significant insights, but there is a trade-off between time and accuracy. This study compares deep learning algorithms for multiple sequence alignment with better accuracy, using a new similarity measure to choose the best resemblance sequences in a set. Using a benchmark dataset, the algorithms compared include CNN, VAE, MLPNN, DBNs, Deep Boltzmann Machine, and GAN. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Jayapriya J.; Vinay M. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-720 LNNS, pp. 563-575. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-99-3761-5_50" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-3761-5_50</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172729053&doi=10.1007%2F978-981-99-3761-5_50&partnerID=40&md5=eb065a69e1ae821d86e73d16b0e35882" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172729053&doi=10.1007%2f978-981-99-3761-5_50&partnerID=40&md5=eb065a69e1ae821d86e73d16b0e35882</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981993760-8 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 Jayapriya J., Department of Computer Science, Yeshwanthpur Campus, CHRIST (Deemed to be University), Karnataka, Bangalore, India; Vinay M., Department of Computer Science, Yeshwanthpur Campus, CHRIST (Deemed to be University), Karnataka, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset Subject The topic of the resource CIC-IDS 2017 dataset; Convolution neural network; Deep neural network; Internet of things; Intrusion detection system Description An account of the resource Due to technological advancements in recent years, the availability and usage of smart electronic gadgets have drastically increased. Adoption of these smart devices for a variety of applications in our day-to-day life has become a new normal. As these devices collect and store data, which is of prime importance, securing is a mandatory requirement by being vigilant against intruders. Many traditional techniques are prevailing for the same, but they may not be a good solution for the devices with resource constraints. The impact of artificial intelligence is not negligible in this concern. This study is an attempt to understand and analyze the performance of deep learning algorithms in intrusion detection. A comparative analysis of the performance of deep neural network, convolutional neural network, and long short-term memory using the CIC-IDS 2017 dataset. 2023 Institute of Advanced Engineering and Science. All rights reserved. Creator An entity primarily responsible for making the resource Jose J.; Jose D.V. Source A related resource from which the described resource is derived International Journal of Electrical and Computer Engineering, Vol-13, No. 1, pp. 1134-1141. Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijece.v13i1.pp1134-1141" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijece.v13i1.pp1134-1141</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143871730&doi=10.11591%2Fijece.v13i1.pp1134-1141&partnerID=40&md5=12a48d09a9dcc0f7ae42d4c546f58484" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143871730&doi=10.11591%2fijece.v13i1.pp1134-1141&partnerID=40&md5=12a48d09a9dcc0f7ae42d4c546f58484</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 20888708 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jose J., Department of Computer Science, CHRIST University, Bangalore, India, Department of Computer Science, Rajagiri College of Social Sciences, Cochin, India; Jose D.V., Department of Computer Science, CHRIST University, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Deep Learning Approaches for Environmental Monitoring in Smart Cities Subject The topic of the resource Air pollution; Deep learning; Environmental monitoring; Smart cities Description An account of the resource It introduces a novel integrated environmental monitoring system capable of doing on-the-go measurements. In metropolitan settings, air pollution is one of the most serious environmental threats to human health. The widespread use of automobiles, emissions from manufacturing processes, and the use of fossil fuels for propulsion and power generation have all contributed to this issue. Air quality predictions in smart cities may now be made using deep learning methods, thanks to the widespread adoption of these tools and their continued rapid growth. Particulate Matter (PM) with a width of less than 2.5 m (PM2.5) is one of the most perilous kinds of air pollution. To anticipate the hourly gauge of PM2.5 focus in Delhi, India, we utilized verifiable information of poisons, meteorological information, and PM2.5 fixation in the adjoining stations to make a spatial-worldly element for our CNN-LSTM-based deep learning arrangement. According to our experiments, our 'hybrid CNN-LSTM multivariate' method outperforms all of the above conventional models and allows for more precise predictions. 2024 IEEE. Creator An entity primarily responsible for making the resource Latha Soundarraj P.; Jamadar V.M.; Rappai S.; Alkhafaji M.A.; Shareef A.M.; Zearah S.A. Source A related resource from which the described resource is derived Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICONSTEM60960.2024.10568705" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONSTEM60960.2024.10568705</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198827731&doi=10.1109%2FICONSTEM60960.2024.10568705&partnerID=40&md5=72fabe32bc6a15112448c445393af879" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198827731&doi=10.1109%2fICONSTEM60960.2024.10568705&partnerID=40&md5=72fabe32bc6a15112448c445393af879</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835036509-2 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Latha Soundarraj P., School of Business & Management - Mba, Christ University, Maharashtra, Pune, 412112, India; Jamadar V.M., Dr. DaulatraoAher College of Engineering, Department of Mechanical Engineering, Satara, Maharashtra, Karad, 415124, India; Rappai S., Kristu Jayanti College (Autonomous), Department of Computer Science, Kothanur, K. Narayanapura, Karnataka, Bangalore, 560077, India; Alkhafaji M.A., College of Engineering Technology, National University of Science And Technology, DhiQar, Iraq; Shareef A.M., National University of Science And Technology, DhiQar, Iraq; Zearah S.A., Al-ayen University, Scientific Research Center, Thi-Qar, Iraq 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 Deep learning approaches to understanding psychological impacts on vulnerable populations Description An account of the resource This chapter investigates the psychological effects on vulnerable groups, with a particular emphasis on the relationship between deep learning techniques and the impact of climate. Vulnerable groups confront particular problems, which might lead to negative psychological results. Investigating this complexity is critical to designing effective intervention techniques. Using sophisticated deep learning techniques, this study seeks to find subtle patterns and correlations in a variety of datasets, including psychological markers, socioeconomic characteristics, and climatic variables. The work employs a comprehensive technique that includes deep learning models, feature extraction, and interpretability analysis to untangle complicated relationships. Preliminary findings imply that deep learning approaches might uncover previously unknown links between climate change and psychological effects on vulnerable groups. This insight adds to a more comprehensive understanding of the difficulties. This understanding contributes to a more holistic grasp of the challenges faced by these groups. By including climate-related factors into the deep learning framework, this study hopes to close the gap between environmental impacts and psychological 2024, IGI Global. All rights reserved. Creator An entity primarily responsible for making the resource Mehta M.; Prasad R.S.; Giri M.; Rajendran R.K. Source A related resource from which the described resource is derived The Climate Change Crisis and Its Impact on Mental Health, pp. 159-168. Publisher An entity responsible for making the resource available IGI Global Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-3272-6.ch012" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-3272-6.ch012</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191841376&doi=10.4018%2F979-8-3693-3272-6.ch012&partnerID=40&md5=86dc97b936c59eecbf7e622e1d02e750" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191841376&doi=10.4018%2f979-8-3693-3272-6.ch012&partnerID=40&md5=86dc97b936c59eecbf7e622e1d02e750</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-836933273-3; 979-836933272-6 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 Mehta M., Christ University, India; Prasad R.S., Christ University, India; Giri M., Christ University, India; Rajendran R.K., Christ University, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Deep Learning Based Age Estimation Model Subject The topic of the resource Accuracy; convolutional neural network (CNN); Data Augmentation; Machine learning algorithm; Mean absolute error; Preprocessing; RELU Description An account of the resource To improve accuracy and resilience in demographic categorization, this research presents a novel use of Convolutional Neural Networks (CNNs) for age prediction. Deep learning is utilized to achieve this goal. Precise estimation of age has become essential in a variety of areas, including human-computer interaction, marketing, and healthcare. The ability of CNNs to handle the intricacies of facial features for accurate demographic forecasts is examined in this study. The research covers every step of the age prediction process, including dataset collection, prepossessing, model architecture, and assessment measures. The CNN is trained to automatically extract hierarchical characteristics from facial photos, which enables the model to recognize complex patterns related to age. The architecture's flexibility to different lighting conditions, facial expressions, and postures. In this research, we deal with deep learning-based perceived age estimation in still-face pictures. Our Convolution Neural Network models (CNNs) have been trained prior on Image Net for picture classification, as they use the VGG architecture. In addition, we analyze the effects of tailoring over Web photos having known age, considering a lack of apparent age-annotated annotated images. In addition, this work adds to the increasing library of studies on the use of deep learning methods for demographic data evaluation by showing the effectiveness of CNNs to predict age. The results show how, in practical situations, CNNs could significantly enhance the precision and dependability of age prediction systems. 2024 IEEE. Creator An entity primarily responsible for making the resource Reddy G.J.; Bendili S.; Vinodha D.; Jenefa J.; Sambandam R.K.; Vetriveeran D. Source A related resource from which the described resource is derived TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TQCEBT59414.2024.10545269" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545269</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204482593&doi=10.1109%2FTQCEBT59414.2024.10545269&partnerID=40&md5=13551662f0d49d361ac40ccbfeca5911" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204482593&doi=10.1109%2fTQCEBT59414.2024.10545269&partnerID=40&md5=13551662f0d49d361ac40ccbfeca5911</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038427-7 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 Reddy G.J., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Bendili S., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Vinodha D., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Jenefa J., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Sambandam R.K., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Vetriveeran D., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Deep Learning Based Face Recognized Attendance Management System using Convolutional Neural Network Subject The topic of the resource Convolutional Neural Network; Database Management; Deep Learning; Face Recognition; Generative Adversarial Networks Description An account of the resource In today's digital age, manual attendance tracking is plagued by inefficiency and the potential for inaccuracies, often leading to proxy attendance. The main aim of this research work is to manage and monitor the student's attendance by using face recognition technology. This proposed model is mainly categorized four major modules. First module is database creation. Second module is face detection. Then third module is face recognition and final module is automatic attendance updating process. Student images are compiled to create a comprehensive database, ensuring inclusivity across the class roster. The system utilizes the face recognition library, which relies on deep learning based algorithms for face detection and recognition during testing. This face recognition part Convolutional Neural Network algorithm is used. The system matches detected faces with the known database and marks attendance, ensuring a streamlined and accurate attendance tracking process. This innovative approach has the potential to revolutionize attendance management in educational settings, offering a contactless and efficient solution while mitigating proxy attendance concerns. The proposed model is to compare the accuracy level of face recognition. 2023 IEEE. Creator An entity primarily responsible for making the resource Joseph A.; Nair A.; Koshy A.B.; Nallaballi M.; Jayapandian N. Source A related resource from which the described resource is derived 3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 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/ICTBIG59752.2023.10456032" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG59752.2023.10456032</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189246538&doi=10.1109%2FICTBIG59752.2023.10456032&partnerID=40&md5=1bee201076f9399339c845e9ea1c6d81" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189246538&doi=10.1109%2fICTBIG59752.2023.10456032&partnerID=40&md5=1bee201076f9399339c845e9ea1c6d81</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835034327-4 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 Joseph A., (Deemed to Be University), Department of Cse Christ, India; Nair A., (Deemed to Be University), Department of Cse Christ, India; Koshy A.B., (Deemed to Be University), Department of Cse Christ, India; Nallaballi M., School of Engineering and Technology Christ (Deemed to Be University), India; Jayapandian N., School of Engineering and Technology Christ (Deemed to Be University), India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource 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 Deep learning based federated learning scheme for decentralized blockchain Subject The topic of the resource Blockchain; CNN model; Deep Learning; Federated Learning; LSTM model Description An account of the resource Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized, federated learning has the problem of performance degradation caused by non-independent and identical training data distribution. To solve this problem, a calculation method for model similarity is proposed, and then a decentralized, federated learning strategy based on the similarity of the model is designed and tested using five federated learning tasks: CNN model training fashion-mnist dataset, alexnet model training cifar10 dataset, TextRnn model training thusnews dataset, Resnet18 model training SVHN dataset and LSTM model training sentiment140 dataset. The experimental results show that the designed strategy performs decentralized, federated learning under the nonindependent and identically distributed data of five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23 percentage points, respectively. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors. Creator An entity primarily responsible for making the resource Ramkumar G.; Sivakumar S.; Soni M.; Muhammed Y.; Salman H.M.; Soomar A.M. Source A related resource from which the described resource is derived Artificial Intelligence, Blockchain, Computing and Security: Volume 1, Vol-1, pp. 679-689. Publisher An entity responsible for making the resource available CRC Press Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003393580-101" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003393580-101</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180047931&doi=10.1201%2F9781003393580-101&partnerID=40&md5=f996da7314446bfae0b1ca3724f999ae" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180047931&doi=10.1201%2f9781003393580-101&partnerID=40&md5=f996da7314446bfae0b1ca3724f999ae</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-100384581-2; 978-103249393-0 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 Ramkumar G., Department of Commerce, School of Commerce, Finance and Accountancy, Christ University, Bengaluru, India; Sivakumar S., Department of Electrical and Electronics Engineering, St. Josephs College of Engineering, OMR, Chennai, India; Soni M., Department of CSE, University Centre for Research & Development, Chandigarh University, Punjab, Mohali, India; Muhammed Y., College of Technical Engineering, Al-Farahidi University, Baghdad, Iraq; Salman H.M., Al-Turath Universiy College, Baghdad, Iraq; Soomar A.M., Faculty of Electrical and Control Engineering, Gda?sk University of Technology, Poland 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 Deep learning based federated learning scheme for decentralized blockchain Subject The topic of the resource Blockchain; CNN model; Deep Learning; Federated Learning; LSTM model Description An account of the resource Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized, federated learning has the problem of performance degradation caused by non-independent and identical training data distribution. To solve this problem, a calculation method for model similarity is proposed, and then a decentralized, federated learning strategy based on the similarity of the model is designed and tested using five federated learning tasks: CNN model training fashion-mnist dataset, alexnet model training cifar10 dataset, TextRnn model training thusnews dataset, Resnet18 model training SVHN dataset and LSTM model training sentiment140 dataset. The experimental results show that the designed strategy performs decentralized, federated learning under the nonindependent and identically distributed data of five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23 percentage points, respectively. 2024 The Author(s). Creator An entity primarily responsible for making the resource Ramkumar G.; Sivakumar S.; Soni M.; Muhammed Y.; Salman H.M.; Soomar A.M. Source A related resource from which the described resource is derived Artificial Intelligence, Blockchain, Computing and Security - Proceedings of the International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023, Vol-1, pp. 679-689. Publisher An entity responsible for making the resource available CRC Press/Balkema Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003393580-101" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003393580-101</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186650992&doi=10.1201%2F9781003393580-101&partnerID=40&md5=706b82b911f80b65626c131e267c1b90" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186650992&doi=10.1201%2f9781003393580-101&partnerID=40&md5=706b82b911f80b65626c131e267c1b90</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-103266966-3 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 Ramkumar G., Department of Commerce, School of Commerce Finance and Accountancy, Christ University, Bengaluru, India; Sivakumar S., Department of Electrical and Electronics Engineering, St. Josephs College of Engineering, OMR, Chennai, India; Soni M., Department of CSE, University Centre for Research and Development, Chandigarh University, Punjab, Mohali, India; Muhammed Y., College of Technical Engineering, Al-Farahidi University, Baghdad, Iraq; Salman H.M., Al-Turath Universiy College, Baghdad, Iraq; Soomar A.M., Faculty of Electrical and Control Engineering, Gda?sk University of Technology, Poland 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 Deep learning based model for computing percentage of fake in user reviews using topic modelling techniques Subject The topic of the resource Challenging task; Deep learning; Exhaustivity; Fake review detection; Topic Modelling Description An account of the resource Sentiment analysis plays a vital role in real time environment for knowing the history of a product or any other specific entity. Due to large number of users in the www, chances are there that many fake users may upload the fake reviews to damage the business for the sake of money. Identifying the fake reviews or percentage of fake content in the review is yet a challenging task. In this paper, an attempt has been made to find the percentage of fake in the review data. Two methodologies are combined to address this issue. Concept of spelling checking, topic modelling and deep learning for context extraction is extensively used to build the effective model. Proposed technique is exhaustively checked for efficiency with many trails of experiments. Also, the training and testing samples were shuffled for experimentation. The results of the models show its goodness. The details of the results can be found at experiments section. 2024 The Author(s) Creator An entity primarily responsible for making the resource K S S.; Danti A.; Snehitha Reddy T.; Nath V. Source A related resource from which the described resource is derived e-Prime - Advances in Electrical Engineering, Electronics and Energy, Vol-8 Publisher An entity responsible for making the resource available Elsevier Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.prime.2024.100506" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.prime.2024.100506</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189762741&doi=10.1016%2Fj.prime.2024.100506&partnerID=40&md5=01102abbca05bd564fd2f3868d66ef0b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189762741&doi=10.1016%2fj.prime.2024.100506&partnerID=40&md5=01102abbca05bd564fd2f3868d66ef0b</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 27726711 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 K S S., Dept of MCA, PESITM, SHIMOGA, India; Danti A., Christ university, Bangalore, India; Snehitha Reddy T., Research Scholar Department of Electronics & Communication Engineering Birla Institute of Technology, JH, Mesra Ranchi, 835215, India; Nath V., VLSI Design Group, Department of ECE, B.I.T. Mesra, (JH), Ranchi, 835215, 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 Deep learning based modeling of groundwater storage change Subject The topic of the resource Forecasting; Keras; LSTM; Modeling; Tensorflow; Time series Description An account of the resource The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning. Monitoring GWS change and future water resource availability are crucial, especially under changing climatic conditions. Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability. The present investigation utilized the Long Short Term Memory (LSTM) networks to monitor and forecast Terrestrial Water Storage Change (TWSC) and Ground Water Storage Change (GWSC) based on Gravity Recovery and Climate Experiment (GRACE) datasets from 20032025 for five basins of Saudi Arabia. An attempt has been made to assess the effects of rainfall, water used, and net budget modeling of groundwater. Analysis of GRACE-derived TWSC and GWSC estimates indicates that all five basins show depletion of water from 20032020 with a rate ranging from -5.88 1.2 mm/year to -14.12 1.2 mm/year and -3.5 1.5 to -10.7 1.5, respectively. Forecasting based on the developed LSTM model indicates that the investigated basins are likely to experience serious water depletion at rates ranging from -7.78 1.2 to -15.6 1.2 for TWSC and -4.97 1.5 to -12.21 1.5 for GWSC from 20202025. An interesting observation was a minor increase in rainfall during the study period for three basins. 2022 Tech Science Press. All rights reserved. Creator An entity primarily responsible for making the resource Haq M.A.; Jilani A.K.; Prabu P. Source A related resource from which the described resource is derived Computers, Materials and Continua, Vol-70, No. 3, pp. 4599-4617. Publisher An entity responsible for making the resource available Tech Science Press 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/10.32604/cmc.2022.020495" target="_blank" rel="noreferrer noopener">https://doi.org/10.32604/cmc.2022.020495</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116999970&doi=10.32604%2Fcmc.2022.020495&partnerID=40&md5=da28107901edec508b2a673c3e80cb32" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116999970&doi=10.32604%2fcmc.2022.020495&partnerID=40&md5=da28107901edec508b2a673c3e80cb32</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 15462218 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 Haq M.A., College of Computer and Information Sciences, Majmaah University, Almajmaah, 11952, Saudi Arabia; Jilani A.K., College of Computer and Information Sciences, Majmaah University, Almajmaah, 11952, Saudi Arabia; Prabu P., 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 Deep Learning Based Multi Constraint Hybrid Optimization Algorithm for Transshipment-Based Inventory Routing with Dynamic Demands Subject The topic of the resource Dynamic demands; Inventory routing; Quantum neural network; Snake artificial ecosystem optimization; Transshipments Description An account of the resource The Inventory-Routing Problem (IRP) is considered a major issue in supply chain management as it comprises two areas: vehicle routing and inventory control. The existing techniqueswere unable to incorporate location details for enhancing the decision-making and it failed to consider the uncertainty of the demands. Hence to solve this issue, a Snake Artificial Ecosystem Optimization (SAEO) algorithm is proposed in this paper. The SAEO algorithm is developed to address the transshipment IRP with dynamic demands by combining the AEO model and SO to enhance the optimizer's performance. Further, a penalty strategy is proposed, where Deep Quantum Neural Network (DQNN) is employed for calculating the penalty for verifying the feasibility of the solution generated in case of violations in model constraints. In addition, the efficiency of the proposed SAEO-DQNN technique is examined by considering metrics, like transportation cost, transshipment cost, and total cost, and it achieved improved values of 0.391, 0.518, and 1.012 when compared to existing techniques such as Genetic Algorithm with Deep Reinforcement Learning (GA + Deep RL) and Kernel Search Multi-vehicle IRP (KSMIRP). The Author(s) 2024. Creator An entity primarily responsible for making the resource Sivadevuni S.S.; J N. Source A related resource from which the described resource is derived International Journal of Networked and Distributed Computing, Vol-13, No. 1 Publisher An entity responsible for making the resource available Springer Science and Business Media B.V. Date A point or period of time associated with an event in the lifecycle of the resource 2025-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s44227-024-00048-7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s44227-024-00048-7</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211185460&doi=10.1007%2Fs44227-024-00048-7&partnerID=40&md5=0096cc354e41a5f51be1c5dcea2b1551" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211185460&doi=10.1007%2fs44227-024-00048-7&partnerID=40&md5=0096cc354e41a5f51be1c5dcea2b1551</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 22117938 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 Sivadevuni S.S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560001, India; J N., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, 560001, 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 Deep Learning Character Recognition of Handwritten Devanagari Script: A Complete Survey Subject The topic of the resource Character Recognition; Computer Vision; Deep Learning; Pattern Recognition Description An account of the resource Recognition of handwritten characters is a concept in which the single characters are classified, it is a facility of an electronic device to scan and decipher the handwritten input from a variety of sources, including written texts, images, and other digital touch-screen devices. This concept is being used in distinctive sectors such as the processing of bank checks, form data entry, and parcel posting and nowadays it is becoming a very important issue in the pattern recognition domain and a very challenging task to resolve it. Since deep learning is a crucial strategy in solving detection and pattern recognition problems, several algorithms are available to classify the characters with better prediction rates on different datasets, and ultimately, whichever algorithm gives the optimized results will be considered the best solution for the character recognition problem. As a result, various solutions proposed by the existing researchers are discussed using deep learning algorithms in this survey article. 2023 IEEE. Creator An entity primarily responsible for making the resource Sharma A.; Mithun B.N. Source A related resource from which the described resource is derived Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/InC457730.2023.10263251" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC457730.2023.10263251</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174745758&doi=10.1109%2FInC457730.2023.10263251&partnerID=40&md5=1b6c28316a0c99b76f0a19756c74a91e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174745758&doi=10.1109%2fInC457730.2023.10263251&partnerID=40&md5=1b6c28316a0c99b76f0a19756c74a91e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835033577-4 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sharma A., CHRIST(Deemed to Be University), Department of Computer Science, and Engineering, Bangalore, India; Mithun B.N., CHRIST(Deemed to Be University), Department of Computer Science, and Engineering, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Deep Learning Decision Support Model for Police Investigation Description An account of the resource A police investigation is an exciting task with many complicated processes that may or may not succeed. However, it is the sole duty of a police officer to understand the crime scene, reconstruct the event and predict the criminal with accuracy. There are various methods for interrogations, predictions, and confirmation after identifying a person as a criminal or upon concluding their actions as a criminal act. However, we can see massive growth in crime rates every day. This massive growth rate makes conventional prediction or analysis very strenuous. In such times we can use or take the help of deep learning and machine learning methods for crime analysis and suspect prediction by identifying the data points in a set. This prediction methodology is known as intelligence analysis which simulates the dataset to draw a connection or pattern collectively from millions of data points to identify the instigator and linkman. This chapter will summarize the uses of deep learning and artificial intelligence in a decision support model for police investigation. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. Creator An entity primarily responsible for making the resource Devan A.M.; Raghul V.; Dhasmana U.; Rath A.; Jacob L. Source A related resource from which the described resource is derived Artificial Intelligence for Cyber Defense and Smart Policing, pp. 76-90. Publisher An entity responsible for making the resource available CRC Press Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003251781-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003251781-6</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190214004&doi=10.1201%2F9781003251781-6&partnerID=40&md5=1ee6497875cf3c703b150bcb40860ee3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190214004&doi=10.1201%2f9781003251781-6&partnerID=40&md5=1ee6497875cf3c703b150bcb40860ee3</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-100381553-2; 978-103217093-0 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 Devan A.M., Department of Data Science, Christ University, Pune, Lavasa, India; Raghul V., Department of Data Science, Christ University, Pune, Lavasa, India; Dhasmana U., Department of Data Science, Christ University, Pune, Lavasa, India; Rath A., Department of Data Science, Christ University, Pune, Lavasa, India; Jacob L., Department of Data Science, Christ University, Pune, Lavasa, 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 Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment Subject The topic of the resource convolutional neural network; image annotation; inception v2; Object detection; tracking Description An account of the resource Recently, big data becomes evitable due to massive increase in the generation of data in real time application. Presently, object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation, augmented reality, surveillance, etc. This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN (AIA-IFRCNN) model in big data environment. The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR), named DCF-CSRT model. The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking, which comprises region proposal network (RPN) and Fast R-CNN. In addition, inception v2 model is applied as a shared convolution neural network (CNN) to generate the feature map. Lastly, softmax layer is applied to perform classification task. The effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%. 2022 Tech Science Press. All rights reserved. Creator An entity primarily responsible for making the resource Vijaya Kumar K.; Laxmi Lydia E.; Dutta A.K.; Parvathy V.S.; Ramasamy G.; Pustokhina I.V.; Pustokhin D.A. Source A related resource from which the described resource is derived Computers, Materials and Continua, Vol-73, No. 2, pp. 2541-2554. Publisher An entity responsible for making the resource available Tech Science Press 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/10.32604/cmc.2022.028570" target="_blank" rel="noreferrer noopener">https://doi.org/10.32604/cmc.2022.028570</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132113177&doi=10.32604%2Fcmc.2022.028570&partnerID=40&md5=755701ef1b5649ab84c659cbdbe93c0a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132113177&doi=10.32604%2fcmc.2022.028570&partnerID=40&md5=755701ef1b5649ab84c659cbdbe93c0a</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 15462218 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 Vijaya Kumar K., Department of Computer Science and Engineering, Vignan's Institute of Engineering for Women, Visakhapatnam, 530049, India; Laxmi Lydia E., Department of Computer Science and Engineering, Vignan's Institute of Information Technology, Visakhapatnam, 530049, India; Dutta A.K., Department of Computer Science and Information System, College of Applied Sciences, AlMaarefa University, Riyadh, 11597, Saudi Arabia; Parvathy V.S., Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Tamilnadu, Krishnankoil, 626126, India; Ramasamy G., Department of Computer Science, Christ University, Bangalore, 560029, India; Pustokhina I.V., Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow, 117997, Russian Federation; Pustokhin D.A., Department of Logistics, State University of Management, Moscow, 109542, 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 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 Deep Learning Enabled Parent Involvement and Its Influence on Student Academic Achievement Analysis Subject The topic of the resource Deep Learning; Ethical Considerations; Longitudinal Study; Neural Network; Parental Engagement; Parental Involvement; Student Academic Achievement; Technology in Education Description An account of the resource Studying the substantial effect that Deep Learning Enabled Parent Involvement (DLEPI) has on kid academic success. Using a made-up data set and a neural network model, we find that parents' level of involvement, as measured by the Parental Involvement Score (PIS), is positively correlated with their children's academic performance. DLEPI, driven by cutting-edge deep learning algorithms, equips parents with unique insights and suggestions regardless of where they live, therefore promoting educational equality and diversity. This study underlines the potential of technology to reduce performance inequalities and highlights its central role in increasing parental participation. Critical elements for future study include ethical issues, real-world validation, effect evaluations over time, and chances for personalization. This research lays the groundwork for reinventing education in a future where DLEPI improves student outcomes and offers a more inclusive and personalized educational environment. 2024 IEEE. Creator An entity primarily responsible for making the resource Baruah A.; Kaur A.; Venugopal P.R.; Raghunandan S.; Sreedevi C.; Atrayan L. Source A related resource from which the described resource is derived Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICONSTEM60960.2024.10568816" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONSTEM60960.2024.10568816</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198842543&doi=10.1109%2FICONSTEM60960.2024.10568816&partnerID=40&md5=99db5838b9dd3defd965e3b758d5ab90" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198842543&doi=10.1109%2fICONSTEM60960.2024.10568816&partnerID=40&md5=99db5838b9dd3defd965e3b758d5ab90</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835036509-2 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Baruah A., Christ University, Department of Psychology, Delhi-NCR, India; Kaur A., Amity Institute of Psychology And Allied Sciences (AIPS), Amity University, Delhi-NCR, India; Venugopal P.R., Bhavan's Vivekananda College of Science, Humanities And Commerce, Commerce & Business Management, Sainikpuri, Secunderabad, India; Raghunandan S., Bharatiya Vidya Bhavans Vivekananda College of Science, Humanities And Commerce, Department of Commerce, Sainikpuri, Telangana, Secunderabad, India; Sreedevi C., Bharatiya Vidya Bhavans Vivekananda College of Science, Humanities And Commerce, Department of Commerce, Sainikpuri, Telangana, Secunderabad, India; Atrayan L., Saveetha School of Engineering, Simats, Department of Mechanical Engineering, Tamil Nadu, Chennai, 602105, 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 Deep Learning for Arrhythmia Classification: A Comparative Study on Different Deep Learning Models Subject The topic of the resource Arrhythmia; Custom CNN; Deep Learning; Electrocardiogram; Inception V3; Machine Learning; VGG19 Description An account of the resource Arrhythmias, or irregular heart rhythms, are a major global health concern. Since arrhythmias can cause fatal conditions like cardiac failure and strokes, they must be rapidly identified and treated. Traditional arrhythmia diagnostic techniques include manual electrocardiogram (ECG) image interpretation, which is time consuming and frequently required for expertise. This research automates and improves the identification of heart problems, with a focus on arrhythmias, by utilizing the capabilities of deep learning, an advanced machine learning technique that performs well at recognizing patterns in data. Specifically, we implement and compare Custom CNN, VGG19, and Inception V3 deep learning models, which classify ECG images into six categories, including normal heart rhythms and various types of arrhythmias. The VGG19 model excelled, achieving a training accuracy of 95.7% and a testing accuracy of 93.8%, showing the effectiveness of deep learning in the comprehensive diagnosis of heart diseases. 2023 IEEE. Creator An entity primarily responsible for making the resource Teja N.B.; Ajay H.K.; Kumar R.S.; Deepa S.; Jayapriya J.; Vinay M. Source A related resource from which the described resource is derived IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 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/AIKIIE60097.2023.10390484" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIKIIE60097.2023.10390484</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187262480&doi=10.1109%2FAIKIIE60097.2023.10390484&partnerID=40&md5=49e0d53e75da98806b57774413dc3100" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187262480&doi=10.1109%2fAIKIIE60097.2023.10390484&partnerID=40&md5=49e0d53e75da98806b57774413dc3100</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835031646-9 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 Teja N.B., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Ajay H.K., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Kumar R.S., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Deepa S., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Jayapriya J., Christ Deemed to Be University, Department of Computer Science, Bangalore, India; Vinay M., Christ Deemed to Be University, Department of Computer Science, 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 Deep Learning for Early Detection of Tomato Leaf Diseases: A ResNet-18 Approach for Sustainable Agriculture Subject The topic of the resource binary classification; Convolution neural networks; deep learning; disease detection; tomato crop health Description An account of the resource The paper explores the application of Convolutional Neural Networks (CNNs), specifically ResNet-18, in revolutionizing the identification of diseases in tomato crops. Facing threats from pathogens like Phytophthora infestans, timely disease detection is crucial for mitigating economic losses and ensuring food security. Traditionally, manual inspection and labour-intensive tests posed limitations, prompting a shift to CNNs for more efficient solutions. The study uses a well-organized dataset, employing data preprocessing techniques and ResNet-18 architecture. The model achieves remarkable results, with a 91% F1 score, indicating its proficiency in distinguishing healthy and unhealthy tomato leaves. Metrics such as accuracy, sensitivity, specificity, and a high AUC score on the ROC curve underscore the model's exceptional performance. The significance of this work lies in its practical applications for early disease detection in agriculture. The ResNet-18 model, with its high precision and specificity, presents a powerful tool for crop management, contributing to sustainable agriculture and global food security. (2024), (Science and Information Organization). All Rights Reserved. Creator An entity primarily responsible for making the resource Asha M.S.; Yogish H.K. Source A related resource from which the described resource is derived International Journal of Advanced Computer Science and Applications, Vol-15, No. 1, pp. 876-883. Publisher An entity responsible for making the resource available Science and Information Organization 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/10.14569/IJACSA.2024.0150188" target="_blank" rel="noreferrer noopener">https://doi.org/10.14569/IJACSA.2024.0150188</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185005271&doi=10.14569%2FIJACSA.2024.0150188&partnerID=40&md5=2332fbaba90cd4dd808c8071e7d87e36" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185005271&doi=10.14569%2fIJACSA.2024.0150188&partnerID=40&md5=2332fbaba90cd4dd808c8071e7d87e36</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 2158107X 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 Asha M.S., Department of Computer Science and Engineering, Christ University (Deemed to be University), Karnataka, India; Yogish H.K., Department of Information Science and Engineering, M S Ramaiah Institute of Technology, Karnataka, India, Visvesvaraya Technological University, Belagavi, 590018, India