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Yuvaraj, B.
- Enhanced Analysis of Blockchain Based Security Systems In Financial Institutions
Abstract Views :90 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, Karpaga Vinayaga College of Engineering and Technology, India., IN
2 Department of Computer Science and Engineering, Sphoorthy Engineering College, India., IN
3 Department of Master of Computer Application, Thirumalai Engineering College, India., IN
4 Department of Computer Science and Engineering, Thirumalai Engineering College, India., IN
1 Department of Computer Science and Engineering, Karpaga Vinayaga College of Engineering and Technology, India., IN
2 Department of Computer Science and Engineering, Sphoorthy Engineering College, India., IN
3 Department of Master of Computer Application, Thirumalai Engineering College, India., IN
4 Department of Computer Science and Engineering, Thirumalai Engineering College, India., IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 1 (2023), Pagination: 2861-2867Abstract
The blockchain technology has provided a revolutionary way of secure data storage and transfer. It is a distributed ledger technology (DLT) that creates a secure and immutable record of transactions. This technology has been used to create secure and reliable systems for financial institutions. The blockchain based security system provides a secure platform for financial institutions. It ensures that the transactions are secure and immutable. This technology has been used to create digital assets that can be securely stored and transferred. It also ensures that the transactions are transparent and secure. The blockchain technology can be used to enable secure transactions between financial institutions. By using the blockchain, financial institutions can create a secure platform for transactions. This will ensure that the transactions are secure and immutable. It also ensures that the transactions are transparent and secure. The blockchain technology can also be used to create a secure system for digital identity management. This will ensure that the digital identities of the users are secure and immutable. It also ensures that the users’ identity is protected and secure. The blockchain technology can also be used to create a secure platform for digital payments. This will ensure that the payments are secure and immutable. It also ensures that the payments are transparent and secure.Keywords
Blockchain, Secure, Data Storage,Technology,Transactions, Financial Institutions.
References
- A.S. Hosen, S. Singh, P.K. Sharma and G.H. Cho, “Blockchain-Based Transaction Validation Protocol for a Secure Distributed IoT Network”, IEEE Access, Vol. 8, pp. 117266-117277, 2020.
- S.M.H. Bamakan, A. Motavali and A.B. Bondarti, “A Survey of Blockchain Consensus Algorithms Performance Evaluation Criteria”, Expert Systems with Applications, Vol. 154, pp. 1-19, 2020.
- S. Trivedi and R. Sharma, “Systematic Literature Review on Application of Blockchain Technology in E-Finance and Financial Services”, Journal of Technology Management and Innovation, Vol. 16, No. 3, pp. 89-102, 2021.
- S.B. Patel and N. Kumar, “Kirti: A Blockchain-Based Credit Recommender System for Financial Institutions”, IEEE Transactions on Network Science and Engineering, Vol. 8, No. 2, pp. 1044-1054, 2020.
- N. Kabra and S. Tyagi, “MudraChain: Blockchain-based Framework for Automated Cheque Clearance in Financial Institutions”, Future Generation Computer Systems, Vol. 102, pp. 574-587, 2020.
- K. Fanning and D.P. Centers, “Blockchain and its Coming Impact on Financial Services”, Journal of Corporate Accounting and Finance, Vol. 27, No. 5, pp. 53-57, 2016.
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- Y. Guo and C. Liang, “Blockchain Application and Outlook in the Banking Industry”, Financial Innovation, Vol. 2, pp. 1-12, 2016.
- H. Rathore, A. Mohamed and M. Guizani, “A Survey of Blockchain Enabled Cyber-Physical Systems”, Sensors, Vol. 20, No. 1, pp. 282-291, 2020.
- N. Jiwani and K. Gupta, “Exploring Business Intelligence Capabilities for Supply Chain: A Systematic Review”, Transactions on Latest Trends in IoT, Vol. 1, No. 1, pp. 1- 10, 2018.
- B. Gobinathan, M.A. Mukunthan, S. Surendran, and V.P. Sundramurthy, “A Novel Method to Solve Real Time Security Issues in Software Industry using Advanced Cryptographic Techniques”, Scientific Programming, Vol. 2021, pp. 1-7, 2021.
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- R. Chaudhary, A. Jindal, G.S. Aujla and K.K.R. Choo, “Best: Blockchain-Based Secure Energy Trading in SDNEnabled Intelligent Transportation System”, Computers and Security, Vol. 85, pp. 288-299, 2019.
- Design of Efficient Routing Paths Using Similarity Estimation Based Stochastic Gradient Descent in Wireless Sensor Network
Abstract Views :39 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Kings Engineering College, IN
1 Department of Computer Science and Engineering, Kings Engineering College, IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 3 (2023), Pagination: 2988-2991Abstract
Wireless Sensor Networks (WSNs) offer versatile deployment options, particularly in battery-powered scenarios, addressing energy consumption concerns among sensor nodes. However, the data-intensive nature of WSNs poses challenges in routing, particularly in maintaining balanced paths while accommodating rapid data acquisition. This paper presents an innovative approach called Similarity Estimation-Based Stochastic Gradient Descent (SESGD) routing for WSNs, designed to establish stable routing paths that align with the speed of data acquisition. Sensor nodes play a crucial role in data collection and acquisition, while WSNs facilitate data routing through multiple hops from source to sink nodes. SESGD effectively manages data routing, synchronizing it with data acquisition rates, thereby ensuring network stability. Simulation results assess key performance metrics, including average delay, throughput, and network energy efficiency. The findings demonstrate that the proposed machine learning method outperforms existing algorithms, achieving superior network throughput.Keywords
Machine Learning, WSN, Stochastic Gradient, Routing, Energy Efficiency.References
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- R. Sabitha and V. Saravanan, “Network Based Detection of IoT Attack using AIS-IDS Model”, Wireless Personal Communications, Vol. 128, No. 3, pp. 1543-1566, 2023.
- T. Karthikeyan and K. Praghash, “Improved Authentication in Secured Multicast Wireless Sensor Network (MWSN) using Opposition Frog Leaping Algorithm to Resist Man-in-Middle Attack”, Wireless Personal Communications, Vol. 123, No. 2, pp. 1715-1731, 2022.
- K. Praghash and A.A. Stonier, “An Artificial Intelligence Based Sustainable Approaches-IoT Systems for Smart Cities”, Springer, 2023
- H. AlAamri, M. Abolhasan and T. Wysocki, “On Optimizing Route Discovery in Absence of Previous Route Information in MANETs”, Proceedings of IEEE International Conference on Vehicular Technology, pp. 1-5, 2009.
- X.M. Zhang, E.B. Wang, J.J. Xia and D.K. Sung, “An Estimated Distance based Routing Protocol for Mobile Ad Hoc Networks”, IEEE Transactions on Vehicular Technology, Vol. 60, No. 7, pp. 3473-3484, 2011.
- Y. Liu, K. Ota, K. Zhang, M. Ma and N. Xiong, “QTSAC: An Energy-Efficient MAC Protocol for Delay Minimization in Wireless Sensor Networks”, IEEE Access, Vol. 6, pp. 8273-8291, 2018.
- X. Deng, Z. Tang, L.T. Yang, M. Lin and B. Wang, “Confident Information Coverage Hole Healing in Hybrid Industrial Wireless Sensor Networks”, IEEE Transactions on Industrial Informatics, Vol. 14, No. 5, pp. 2220-2229, 2017.
- L. Kong, J.S. Pan and V. Snasel, “An Energy-Aware Routing Protocol for Wireless Sensor Network based on Genetic Algorithm”, Telecommunication Systems, Vol. 67, No. 3, pp. 451-463, 2018.
- A. Agrawal, V. Singh, S. Jain and R.K. Gupta, “GCRP: Grid-Cycle Routing Protocol for Wireless Sensor Network with Mobile Sink”, AEU-International Journal of Electronics and Communications, Vol. 94, No. 2, pp. 1-11, 2018.
- T.E. Bogale and L. Vandendorpe, “Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty”, IEEE Transactions on Wireless Communications, Vol. 13, No. 1, pp. 280-290, 2014.
- D.M. Martinez and A.G. Andrade, “On the Reduction of the Noise Uncertainty Effects in Energy Detection for Spectrum Sensing in Cognitive Radios”, Proceedings of IEEE Annual International Symposium Personal, Indoor and Mobile Radio Communications, pp. 1975-1979, 2014.
- K.M. Thilina, K.W. Choi, N. Saquib and E. Hossain, “Pattern Classification Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks: SVM and W-KNN Approaches”, Proceedings of IEEE International Conference on Global Communications, pp. 1260-1265, 2012.
- Soumitra Das and Sanjeev Wagh, “Prolonging the Lifetime of the Wireless Sensor Network Based on Blending of Genetic Algorithm and Ant Colony Optimization”, Journal of Green Engineering, Vol. 4, No. 3, pp. 245-260, 2015.
- Mehr Moslem Afrashteh, “Design and Implementation a New Energy Efficient Clustering Algorithm using Genetic Algorithm for Wireless Sensor Networks”, World Academy of Science, Engineering and Technology, Vol. 5, No. 4, pp. 373-376, 2011.
- Image Representation and Rendering From Low-Resolution Surveillance Videos Using Densenet
Abstract Views :55 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Kings Engineering College, IN
2 Department of Computer Science and Engineering, Kings Engineering College
3 Department of Computer Science and Engineering, Kings Engineering College, IN
1 Department of Computer Science and Engineering, Kings Engineering College, IN
2 Department of Computer Science and Engineering, Kings Engineering College
3 Department of Computer Science and Engineering, Kings Engineering College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 2 (2023), Pagination: 3155-3160Abstract
In surveillance, the need for enhanced image representation and rendering from low-resolution videos is paramount for effective analysis and decision-making. This research addresses the limitations of conventional methods in extracting meaningful information from low-resolution footage. The prevalent challenge lies in the compromised clarity and detail inherent in surveillance videos, hindering accurate identification and analysis of critical events. The ubiquity of surveillance cameras has led to an influx of low-resolution videos, limiting the efficacy of traditional image processing techniques. This research aims to bridge this gap by leveraging DenseNet, a densely connected convolutional neural network (CNN) known for its ability to capture intricate features. The DenseNet seeks to enhance the representation and subsequent rendering of images, transcending the constraints imposed by low resolutions. The network ability to capture intricate details will be harnessed to enhance image representation. Subsequent rendering techniques will be employed to reconstruct high-quality images for improved analysis. The results showcase promising advancements in image representation and rendering using DenseNet. The enhanced visual quality of surveillance images allows for more precise identification and analysis of events, demonstrating the potential impact of the proposed methodology on improving surveillance systems.Keywords
Surveillance, Low-Resolution Videos, DenseNet, Image Representation, RenderingReferences
- X. Gao, J. Szep, S. Shao and S. Hariri, “Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery”, Remote Sensing, Vol. 14, No. 2, pp. 255-262, 2022.
- Z. Chen and X. Xie, “CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution”, Proceedings of IEEE/CVF International Conference on Computer Vision, pp. 21185-21195, 2023.
- Z.T. Jahromi, S.M.T. Hasheminejad and S.V. Shojaedini, “Deep Learning Semantic Image Synthesis: A Novel Method for Unlimited Capacity, High Noise Resistance Coverless Video Steganography”, Multimedia Tools and Applications, Vol. 89, pp. 1-19, 2023.
- Z. Zhao and W. Gao, “Lightweight Infrared and Visible Image Fusion via Adaptive DenseNet with Knowledge Distillation”, Electronics, Vol. 12, No. 13, pp. 2773-2779, 2023.
- Y. Zhang and C. Busch, “NTIRE 2023 Challenge on Image Super-Resolution (X4): Methods and Results”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1864-1883, 2023.
- C. Niu and D. Tarapore, “An Embarrassingly Simple Approach for Visual Navigation of Forest Environments”, Frontiers in Robotics and AI, Vol. 67, pp. 10-19, 2023.
- D.C. Lepcha and V. Goyal, “Image Super-Resolution: A Comprehensive Review, Recent Trends, Challenges and Applications”, Information Fusion, Vol. 91, pp. 230-260, 2023.
- K. Chauhan and R. Sharma, “Deep Learning-based Single-Image Super-resolution: A Comprehensive Review”, IEEE Access, Vol. 9, pp. 1-12, 2023.
- A. Greco, M. Vento and V. Vigilante, “Benchmarking Deep Networks for Facial Emotion Recognition in the Wild”, Multimedia Tools and Applications, Vol. 82, No. 8, pp. 11189-11220, 2023.
- T.A. Kadhim and D. Ben Aissa, “A Face Recognition Application for Alzheimer’s Patients using ESP32-CAM and Raspberry Pi”, Journal of Real-Time Image Processing, Vol. 20, No. 5, pp. 100-114, 2023.
- A Hybrid Machine Learning Approach for Early Detection of Paddy Blight Disease
Abstract Views :25 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science and Engineering, Kings Engineering College, IN
1 Department of Computer Science and Engineering, Kings Engineering College, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 4 (2023), Pagination: 3068-3074Abstract
Paddy blight is a widespread disease that affects various parts of the paddy plant, including leaves, bark, nodes, neck, part of rays, and leaves sheath. The symptoms of the disease manifest as pale yellow to pale green leaves with eye-shaped lesions, distorted margins, and gray or white centers. As the lesions expand, the leaves progressively wither and dry out, eventually leading to rot and death of the affected plant parts. In this study, we propose a machine learning algorithm for detecting paddy disease by analyzing changes in paddy leaves and correlating them with existing paddy images. The algorithm incorporates fuzzy logic and deep learning techniques to enhance disease detection accuracy and provide appropriate treatment recommendations. By leveraging the power of these advanced technologies, the proposed approach aims to facilitate early detection and effective management of paddy diseases, ultimately improving crop yield and ensuring food security.Keywords
Paddy Blight, Disease Detection, Machine Learning, Fuzzy Logic, Deep Learning, Treatment Recommendation.References
- V. Rajpoot and A.S. Jalal, “Automatic Early Detection of Rice Leaf Diseases using Hybrid Deep Learning and Machine Learning Methods”, Multimedia Tools and Applications, Vol. 34, pp. 1-27, 2023.
- A. Chug and D. Singh, “A Novel Framework for Image-Based Plant Disease Detection using Hybrid Deep Learning Approach”, Soft Computing, Vol. 74, No. 1, pp. 1-26, 2022.
- J. Gowrishankar and N. Narmadha, “Convolutional Neural Network
- Classification on 2D Craniofacial Images”, International Journal of Grid and Distributed Computing, Vol. 13, No. 1, pp. 1026-1032, 2020.
- R.D. Aruna and B. Debtera, “An Enhancement on Convolutional Artificial Intelligent Based Diagnosis for Skin Disease using Nanotechnology Sensors”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 1-12, 2022.
- K. Anandhan and A.S. Singh, “Detection of Paddy Crops Diseases and Early Diagnosis using Faster Regional Convolutional Neural Networks”, Proceedings of International Conference on Advance Computing and Innovative Technologies in Engineering, pp. 898-902, 2021.
- S. Lamba, S. Rani and S.H. Ahmed, “A Novel Hybrid Severity Prediction Model for Blast Paddy Disease using Machine Learning”, Sustainability, Vol. 15, No. 2, pp. 1502-1512, 2023.
- A. Sirohi and A. Malik, “A Hybrid Model for the Classification of Sunflower Diseases using Deep Learning”, Proceedings of International Conference on Intelligent Engineering and Management, pp. 58-62, 2021.
- S. Chidambaram and D. Shreecharan, “Hyperspectral Image Classification using Denoised Stacked Auto Encoder-Based Restricted Boltzmann Machine Classifier”, Proceedings of International Conference on Hybrid Intelligent Systems, pp. 213-221, 2022.
- R. Sharma, A. Bansal and A. Kaur, “Rice Leaf blight Disease Detection using Multi-Classification Deep Learning Model”, Proceedings of International Conference on Reliability, Infocom Technologies and Optimization Trends and Future Directions, pp. 1-5, 2022.
- Z. Liu, M. Tausif and Q. Umer, “Internet of Things (IoT) and Machine Learning Model of Plant Disease Prediction-
- Blister Blight for Tea Plant”, IEEE Access, Vol. 10, pp. 44934-44944, 2022.
- G. Dhiman and K. Srihari, “Multi-Modal Active Learning with Deep Reinforcement Learning for Target Feature Extraction in Multi-Media Image Processing Applications”, Multimedia Tools and Applications, Vol. 82, No. 4, pp. 5343-5367, 2023.
- R. Sangeetha and J. Lloret, “An Improved Agro Deep Learning Model for Detection of Panama Wilts Disease in Banana Leaves”, Agri Engineering, Vol. 5, No. 2, pp. 660-679, 2023.
- N. Jiwani and M. Alibakhshikenari, “Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) using Computational Deep Learning”, IEEE Access, Vol. 11, pp. 29541-29553, 2023.
- J. Zhang and Y. He, “Rice Bacterial Blight Resistant Cultivar Selection based on Visible/Near-Infrared Spectrum and Deep Learning”, Plant Methods, Vol. 18, No. 1, pp. 1-16, 2022.
- M.H. Tunio and I. Memon, “Identification and Classification of Rice Plant Disease using Hybrid Transfer Learning”, Proceedings of International Conference on Computer on Wavelet Active Media Technology and Information Processing, pp. 525-529, 2021.