236
20
16481
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Articles
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Faculty Publications -Articles
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Deep learning algorithms for intrusion detection systems in internet of things using CIC-IDS 2017 dataset
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CIC-IDS 2017 dataset; Convolution neural network; Deep neural network; Internet of things; Intrusion detection system
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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.
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Jose J.; Jose D.V.
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International Journal of Electrical and Computer Engineering, Vol-13, No. 1, pp. 1134-1141.
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Institute of Advanced Engineering and Science
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2023-01-01
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<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>
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All Open Access; Gold Open Access
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ISSN: 20888708
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Online
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English
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Article
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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
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Title
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Faculty Publications
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Kumar, Naveen; Somanathan, Chidambaram; Fowdur, Tulsi Pawan; Jogee, Deejaysing; Cowlessur, Maneeraj
Title
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Deep Learning Analysis of Satellite Images for UN SDG Monitoring in Mauritius' Black River District
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01-01-2025
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2025 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, ICARES 2025 - Conference Proceedings;
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<a href="https://doi.org/10.1109/ICARES67579.2025.11371462" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICARES67579.2025.11371462</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034855700?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034855700?origin=resultslist</a>
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Kumar N., ECE Deptt, CHRIST University, RF & Microwave Research Lab, Bangalore, India; Somanathan C., CHRIST University, ECE Department, Bangalore, India; Fowdur T.P., University of Mauritius, Faculty of Engineering, EEE Department, Reduit, Mauritius; Jogee D., University of Mauritius, Civil Engineering, Faculty of Engineering, Reduit, Mauritius; Cowlessur M., Government of Mauritius, Civil Engineering MNICD, Mauritius
Description
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This paper proposes an integrated strategy to analyse the progress of selected United Nations Sustainable Development Goals (SDGs 1, 2, and 13) using Earth Observation (EO) data and deep learning (DL) based classification. The research focuses on Mauritius's Black River district, which is facing growing urbanization, agricultural land demand, forest conservation needs, and land degradation. These challenges are closely related to the reduction of poverty (SDG 1) through settlement monitoring, food security (SDG 2) through green farmland analysis, and climate action (SDG 13) through forest cover and bare land tracking. High-resolution satellite images from the Satellogic constellation were pre-processed, classified, and mapped to the SDGs' key land cover categories. A convolutional neural network (CNN) model was trained to distinguish city structures, agriculture farmland, forest land, and barren land, with up to 99% overall precision. DL-based image analysis has the ability to monitor the UN SDGs in specific regions and provide actionable information for the sustainable development plans of small island governments. 2025 IEEE.
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Classification; Deep learning; Earth observation; Mauritius; UN SDGs
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833155707-2;
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English
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Conference paper
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Faculty Publications
Conference Paper
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Biju, Nandana; Ramasamy, Gobi
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Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
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01-01-2025
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Proceedings of 2025 International Conference on Emerging Technologies in Computing and Communication, ETCC 2025;
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<a href="https://doi.org/10.1109/ETCC65847.2025.11108423" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ETCC65847.2025.11108423</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015796996?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015796996?origin=resultslist</a>
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Biju N., Christ (Deemed To Be University), Bangalore, India; Ramasamy G., Department of Computer Science, Christ University, Bangalore, India
Description
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Microplastic pollution is a growing environmental concern, threatening aquatic ecosystems and human health. This study presents a dual deep learning approach for microplastic detection and classification using two datasets. For water microplastics, YOLOv8 and YOLOv11 were employed for object detection. InceptionV3, VGG19, ResNet50, ResNet152, DenseNet121, EfficientNetB0, and a custom CNN were applied for classification, classifying three distinct microplastic types in non-aquatic environments. Experimental findings display high accuracy, and indicate the potential of AI-enabled solutions for environmental monitoring. This research contributes to SDG 6 Clean Water and Sanitation, promoting sustainable management of water. 2025 IEEE.
Subject
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CNN; Deep Learning; Environmental Monitoring; Microplastic Detection; Object Detection; Water Pollution; YOLO
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833152476-0;
Language
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English
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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Title
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Conference Papers
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Faculty Publications- Conference Papers
Dublin Core
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Deep Learning Approaches for Environmental Monitoring in Smart Cities
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Air pollution; Deep learning; Environmental monitoring; Smart cities
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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.
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Latha Soundarraj P.; Jamadar V.M.; Rappai S.; Alkhafaji M.A.; Shareef A.M.; Zearah S.A.
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Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/ICONSTEM60960.2024.10568705" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONSTEM60960.2024.10568705</a>
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ISBN: 979-835036509-2
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Online
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English
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Conference paper
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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
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Title
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Book Chapter
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Faculty Publications- Book Chapter
Dublin Core
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Title
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Deep learning approaches to understanding psychological impacts on vulnerable populations
Description
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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.
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Mehta M.; Prasad R.S.; Giri M.; Rajendran R.K.
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The Climate Change Crisis and Its Impact on Mental Health, pp. 159-168.
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IGI Global
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2024-01-01
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<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>
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ISBN: 979-836933273-3; 979-836933272-6
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Online
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English
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Book chapter
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Mehta M., Christ University, India; Prasad R.S., Christ University, India; Giri M., Christ University, India; Rajendran R.K., Christ University, India
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Faculty Publications
Book Chapter
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Savithri, M.; Arul Selvan Gnanamonickam, A.
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Deep learning architectures for multimodal fusion
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01-01-2025
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Multimodal Learning Using Heterogeneous Data;pp.57-68
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<a href="https://doi.org/10.1016/B978-0-443-27528-9.00004-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-27528-9.00004-8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032924333?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032924333?origin=resultslist</a>
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Savithri M., Department of Data Science, Christ University, Karnataka, Bengaluru, India; Arul Selvan Gnanamonickam A., Department of Software System and Computer Science (PG), KG College of Arts and Scienc, Tamil Nadu, Coimbatore, India
Description
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The advancement in technology during the recent years has provided deep learning technology as an emerging and powerful paradigm which can be used for processing and understanding complex data across various domains. Multimodal fusion is integrating the information that is collected from various sources or modalities, which requires a comprehensive understanding of data like autonomous driving, medical diagnosis, etc. In this chapter, we will explore the various advanced deep learning architectures that have been specially designed based on the multimodal fusion. The various challenges that are being faced in multimodal data, which include heterogeneity, noise, reliability of data, etc. Various deep learning architectures that are built to address the various challenges, like convolutional neural networks, recurrent neural networks, are reviewed, and the suitability of the fusion strategies is highlighted. The various techniques that are used for combining the information from disparate modalities, like early fusion, late fusion, and hybrid approaches, are also discussed with their pros and cons. Various real-time applications in the field of healthcare, multimedia, robotics, etc., are demonstrated based on the impact of the architectures. Finally, the potential of deep learning architecture based on the revolutionary multimodal fusion will be discussed. 2026 Elsevier Inc. All rights reserved.
Subject
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convolutional neural networks; deep learning; explainable AI (XAI); Multimodal fusion; multimodal generative adversarial networks; recurrent neural networks
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Elsevier
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ISBN: 978-044327528-9; 978-044327529-6;
Language
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English
Type
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Book chapter
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Title
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Deep Learning Based Age Estimation Model
Subject
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Accuracy; convolutional neural network (CNN); Data Augmentation; Machine learning algorithm; Mean absolute error; Preprocessing; RELU
Description
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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.
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Reddy G.J.; Bendili S.; Vinodha D.; Jenefa J.; Sambandam R.K.; Vetriveeran D.
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TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
Publisher
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/TQCEBT59414.2024.10545269" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TQCEBT59414.2024.10545269</a>
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ISBN: 979-835038427-7
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Online
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English
Type
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Conference paper
Coverage
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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
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Faculty Publications
Article
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Harde, Neha; B, Tulasi
Title
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Deep learning based classification of microplastic in edible food using optical microscopy images
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01-01-2026
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Discover Artificial Intelligence;Volume;6;Issue;1;Article No.;478;
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<a href="https://doi.org/10.1007/s44163-026-01246-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s44163-026-01246-8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040515934?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040515934?origin=resultslist</a>
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Harde N., CHRIST (Deemed to be University), Karnataka, Bangalore, India; B T., CHRIST (Deemed to be University), Karnataka, Bangalore, India
Description
An account of the resource
Microplastics (MPs), a prevalent pollution in food, water, and ecosystems around the world, have become a serious environmental and health concern. The traditional detection and classification techniques are labor-intensive by nature and do not support extensive, large-scale monitoring. The main emphasis of this study is to generate a novel image dataset via a simple extraction method that will be useful for classification applications in high-consumption edible food by integrating with the deep-learning model. This study compares the efficacy of several Deep learning (DL) architectures, including MobileNetV2, ResNet101V2, ResNet50V2, InceptionV3, EfficientNetB0, and a baseline Convolutional Neural Network (CNN) in classification into three groups: threads, beads, and fragments. The best performance was recorded by MobileNetV2, ResNet101V2, and ResNet50 V2, all with 98 percent test accuracy and weighted F1-scores of 0.986 and 0.983, respectively, which is a strong and consistent MPs classification. The outcome indicates that the DL models, especially ResNet101V2 and MobileNetV2, outperform the baseline CNN in terms of classification accuracy (98%). The present study provides strong, scalable opportunities for Artificial Intelligence (AI) based solutions for the assessment and reduction of MPs contamination globally in edible food. The Author(s) 2026.
Subject
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Automated detection; Deep learning; Food contamination; Image classification; MP
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Springer Nature
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ISSN: 27310809;
Language
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English
Type
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Article
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All Open Access; Gold Open Access
Format
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online
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Deep Learning Based Face Recognized Attendance Management System using Convolutional Neural Network
Subject
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Convolutional Neural Network; Database Management; Deep Learning; Face Recognition; Generative Adversarial Networks
Description
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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.
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Joseph A.; Nair A.; Koshy A.B.; Nallaballi M.; Jayapandian N.
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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023
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Institute of Electrical and Electronics Engineers Inc.
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2023-01-01
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<a href="https://doi.org/10.1109/ICTBIG59752.2023.10456032" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG59752.2023.10456032</a>
<br /><br /><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>
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ISBN: 979-835034327-4
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Online
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English
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Conference paper
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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
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Book Chapter
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Title
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Deep learning based federated learning scheme for decentralized blockchain
Subject
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Blockchain; CNN model; Deep Learning; Federated Learning; LSTM model
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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.
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Ramkumar G.; Sivakumar S.; Soni M.; Muhammed Y.; Salman H.M.; Soomar A.M.
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Artificial Intelligence, Blockchain, Computing and Security: Volume 1, Vol-1, pp. 679-689.
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CRC Press
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2023-01-01
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<a href="https://doi.org/10.1201/9781003393580-101" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003393580-101</a>
<br /><br /><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>
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ISBN: 978-100384581-2; 978-103249393-0
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Online
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English
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Book chapter
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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
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Title
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Conference Papers
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Faculty Publications- Conference Papers
Dublin Core
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Title
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Deep learning based federated learning scheme for decentralized blockchain
Subject
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Blockchain; CNN model; Deep Learning; Federated Learning; LSTM model
Description
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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
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Ramkumar G.; Sivakumar S.; Soni M.; Muhammed Y.; Salman H.M.; Soomar A.M.
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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.
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CRC Press/Balkema
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2024-01-01
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<a href="https://doi.org/10.1201/9781003393580-101" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003393580-101</a>
<br /><br /><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>
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ISBN: 978-103266966-3
Format
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Online
Language
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English
Type
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Conference paper
Coverage
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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
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Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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Deep learning based model for computing percentage of fake in user reviews using topic modelling techniques
Subject
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Challenging task; Deep learning; Exhaustivity; Fake review detection; Topic Modelling
Description
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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)
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K S S.; Danti A.; Snehitha Reddy T.; Nath V.
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e-Prime - Advances in Electrical Engineering, Electronics and Energy, Vol-8
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Elsevier Ltd
Date
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2024-01-01
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<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>
<br /><br /><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>
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All Open Access; Gold Open Access
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ISSN: 27726711
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Online
Language
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English
Type
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Article
Coverage
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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
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Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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Deep learning based modeling of groundwater storage change
Subject
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Forecasting; Keras; LSTM; Modeling; Tensorflow; Time series
Description
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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.
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Haq M.A.; Jilani A.K.; Prabu P.
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Computers, Materials and Continua, Vol-70, No. 3, pp. 4599-4617.
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Tech Science Press
Date
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2022-01-01
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<a href="https://doi.org/10.32604/cmc.2022.020495" target="_blank" rel="noreferrer noopener">https://doi.org/10.32604/cmc.2022.020495</a>
<br /><br /><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>
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All Open Access; Gold Open Access
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ISSN: 15462218
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Online
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English
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Article
Coverage
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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
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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/.
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Deep Learning Based Multi Constraint Hybrid Optimization Algorithm for Transshipment-Based Inventory Routing with Dynamic Demands
Subject
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Dynamic demands; Inventory routing; Quantum neural network; Snake artificial ecosystem optimization; Transshipments
Description
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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
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Sivadevuni S.S.; J N.
Source
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International Journal of Networked and Distributed Computing, Vol-13, No. 1
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Springer Science and Business Media B.V.
Date
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2025-01-01
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<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>
<br /><br /><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>
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All Open Access; Gold Open Access
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ISSN: 22117938
Format
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Online
Language
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English
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Article
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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
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Title
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Faculty Publications
Article
Faculty Publications -Articles
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Sivadevuni, Sreeparnesh Sharma; J, Naveen
Title
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Deep Learning Based Multi Constraint Hybrid Optimization Algorithm for Transshipment-Based Inventory Routing with Dynamic Demands
Date
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01-01-2025
Source
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International Journal of Networked and Distributed Computing;Volume;13;Issue;1;Article No.;4;
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<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> <br /><br /><a href="https://www.scopus.com/pages/publications/85211185460?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85211185460?origin=resultslist</a>
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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
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.
Subject
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Dynamic demands; Inventory routing; Quantum neural network; Snake artificial ecosystem optimization; Transshipments
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Springer Science and Business Media B.V.
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ISSN: 22117938;
Language
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English
Type
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Article
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All Open Access; Gold Open Access
Format
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online
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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
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Govind, P Jai; Kumar, Naveen
Title
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Deep Learning Based Performance Prediction of Sustainable Microwave Absorbers
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01-01-2025
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2025 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2025;
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<a href="https://doi.org/10.1109/MAPCON65020.2025.11426455" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/MAPCON65020.2025.11426455</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036394036?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036394036?origin=resultslist</a>
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Govind P.J., CHRIST University, RF and Microwave Research Laboratory, ECE Department, Bangalore, India; Kumar N., CHRIST University, RF and Microwave Research Laboratory, ECE Department, Bangalore, India
Description
An account of the resource
This paper proposed a convolutional neural network (CNN) based deep learning (DL) approach to predict performance of sustainable microwave absorbers. This study explores the transformative potential of DL in predicting and optimizing microwave absorber performance, offering a datadriven alternative to traditional approaches. The absorber is a composite of tea and carbon powder considered as waste mixed in various composition percentages. The measured S21 data is used for training the proposed DL model. The prediction of absorber's S21 performance shows an accuracy of above 98 %. 2025 IEEE.
Subject
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absorber; CNN; deep learning; microwave; sustainable
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Relation
A related resource
ISBN: 979-833153722-7;
Language
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English
Type
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Conference paper
Rights
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Restricted Access; Hardcopy may be available in the library
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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.
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Sharma A.; Mithun B.N.
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Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
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Institute of Electrical and Electronics Engineers Inc.
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2023-01-01
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<a href="https://doi.org/10.1109/InC457730.2023.10263251" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC457730.2023.10263251</a>
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ISBN: 979-835033577-4
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Online
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English
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Conference paper
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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
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Book Chapter
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Deep Learning Decision Support Model for Police Investigation
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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.
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Devan A.M.; Raghul V.; Dhasmana U.; Rath A.; Jacob L.
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Artificial Intelligence for Cyber Defense and Smart Policing, pp. 76-90.
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CRC Press
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2024-01-01
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<a href="https://doi.org/10.1201/9781003251781-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003251781-6</a>
<br /><br /><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>
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ISBN: 978-100381553-2; 978-103217093-0
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Online
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English
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Book chapter
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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
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Kiruthika, D.; Fulgen, Maria; Nambiraj, L.
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Deep Learning Driven Predictive Analytics Framework for Assessing Customer Satisfaction in Health Insurance Services
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01-01-2025
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2nd International Conference on Intelligent Systems for Cybersecurity, ISCS 2025;
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<a href="https://doi.org/10.1109/ISCS69371.2025.11386199" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ISCS69371.2025.11386199</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034869226?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034869226?origin=resultslist</a>
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Kiruthika D., Dr. SNS Rajalakshmi College of Arts and Science (Autonomous), Department of Commerce with Computer Applications, Tamil Nadu, Coimbatore, India; Fulgen M., Christ Deemed to Be University, Department of Commerce, Bengaluru, India; Nambiraj L., Dr. SNS Rajalakshmi College of Arts and Science (Autonomous), Department of Commerce with Computer Applications, Tamil Nadu, Coimbatore, India
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The paper aims to design a predictive analytics platform based on deep learning that can measure the satisfaction of health insurance policyholders with accuracy. One of the major challenges in predicting customer satisfaction is the use of multiple sources of data. These sources also include unstructured consumer sentiments and official demographic and policy measures. The system that is suggested integrates a 1-D Convolutional Neural Network with TabNet, a deep attention-based neural network that is specifically good at handling tabular data. The two are combined to deal with inputs that are highly sentiment-loaded. To enable joint feature learning while maintaining interpretability, the dual-path architecture leverages feature attribution. The suggested method surpasses standard machine learning and isolated deep learning baselines accuracy more than 97% through experimental evaluation on a real-world LIC health insurance dataset. The findings provide the basis for an interpretable and scalable customer-oriented decision-making framework in health insurance. 2025 IEEE.
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Attention Mechanism; CNN; Customer Satisfaction; Deep Learning; Health Insurance; Hybrid Model; Interpretability; Predictive Analytics; Sentiment Analysis; TabNet
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833157243-3;
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English
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment
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convolutional neural network; image annotation; inception v2; Object detection; tracking
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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.
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Vijaya Kumar K.; Laxmi Lydia E.; Dutta A.K.; Parvathy V.S.; Ramasamy G.; Pustokhina I.V.; Pustokhin D.A.
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Computers, Materials and Continua, Vol-73, No. 2, pp. 2541-2554.
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Tech Science Press
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2022-01-01
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<a href="https://doi.org/10.32604/cmc.2022.028570" target="_blank" rel="noreferrer noopener">https://doi.org/10.32604/cmc.2022.028570</a>
<br /><br /><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>
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All Open Access; Gold Open Access
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ISSN: 15462218
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Online
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English
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Article
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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