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Gnanamurthy, R. K.
- Concert of Broadcasting Techniques in MANET
Abstract Views :193 |
PDF Views:3
Authors
Affiliations
1 Audisankara Institute of Engineering and Technology, Gudur, IN
2 Vivekanandha Engineering College for Women, Sankari, IN
3 Department of CSE, Vivekanandha Engineering College for Women, Sankari, IN
1 Audisankara Institute of Engineering and Technology, Gudur, IN
2 Vivekanandha Engineering College for Women, Sankari, IN
3 Department of CSE, Vivekanandha Engineering College for Women, Sankari, IN
Source
Wireless Communication, Vol 5, No 1 (2013), Pagination: 1-4Abstract
Broadcasting is the process in which a source node sends a message to all other nodes in MANET. Network wide broadcasting in Mobile Ad Hoc Networks provides important control and route establishment functionality for a number of unicast and multicast protocols. This paper presents an overview of Flooding; Probability based broadcast forwarding, Location based and Neighbor Knowledge method broadcasting techniques in mobile ad hoc networks. The simulations are designed to pinpoint, in each category, specific failures to network conditions that are relevant to MANETs. For instance, protocols such as dynamic source routing (DSR), ad hoc on demand distance vector (AODV), use broadcasting to establish routes. Broadcasting MANET poses more challenges than in wired networks due to node mobility and scarce system resources. Because of the mobility there is no single optimal scheme for all scenarios.Keywords
AODV-Adhoc on Demand Distance Vector Protocol, DSR-Dynamic Source Routing Protocol, MANET–Mobile Adhoc Network.
- C4.5 Algorithm Based Adversarial Learning-Based ADA Based Color and Multispectral Processing for Enhanced Image Analysis
Abstract Views :70 |
PDF Views:1
Authors
Affiliations
1 Department of Information Technology, Easwari Engineering College, IN
2 Department of Computer Science and Engineering, Manipur Institute of Technology, IN
3 Geneva Business Center, Swiss School of Business and Management, CH
4 Department of Electronics and Communication Engineering, VSB College of Engineering Technical Campus, IN
1 Department of Information Technology, Easwari Engineering College, IN
2 Department of Computer Science and Engineering, Manipur Institute of Technology, IN
3 Geneva Business Center, Swiss School of Business and Management, CH
4 Department of Electronics and Communication Engineering, VSB College of Engineering Technical Campus, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 1 (2023), Pagination: 3049-3054Abstract
This research presents a novel approach that combines the C4.5 algorithm with Adversarial Learning-based Adaptive Data Augmentation (ADA) for Color and Multispectral Processing, leading to a significant enhancement in Image Analysis. The C4.5 algorithm, known for its decision tree construction, is integrated with ADA, which employs adversarial learning principles to generate diverse and realistic training samples. This integration enables the augmentation of both color and multispectral images, effectively boosting the robustness and accuracy of image analysis tasks. The proposed method showcases improved performance in various applications such as object recognition, classification, and scene understanding. Experimental results demonstrate the superiority of the proposed approach compared to traditional methods, substantiating its potential for advancing image analysis techniques.Keywords
C4.5 algorithm, Adversarial Learning, Adaptive Data Augmentation (ADA), Color Processing, Multispectral Processing, Image AnalysisReferences
- X. Li and S. Qiu, “Cloud Contaminated Multispectral Remote Sensing Image Enhancement Algorithm based on MobileNet”, Remote Sensing, Vol. 14, No. 19, pp. 4815-4823, 2022.
- S. Hussain and M. Aslam, “Spatiotemporal Variation in Land use Land Cover in the Response to Local Climate change using Multispectral Remote Sensing Data”, Land, Vol. 11, No. 5, pp. 595-605, 2022.
- M. Naghdyzadegan Jahromi and S. Jamshidi, “Enhancing Vegetation Indices from Sentinel-2 using Multispectral UAV Data, Google Earth Engine and Machine Learning”, Proceedings of International Conference on Computational Intelligence for Water and Environmental Sciences, pp. 507-523, 2022.
- I. Moretti, L. Aimar and A. Rabaute, “Natural Hydrogen Emanations in Namibia: Field Acquisition and Vegetation Indexes from Multispectral Satellite Image Analysis”, International Journal of Hydrogen Energy, Vol. 47, No. 84, pp. 35588-35607, 2022.
- S.D. Jawak and K. Balakrishna, “Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping using Variable Processing Routines”, Remote Sensing, Vol. 14, No. 24, pp. 6311-6319, 2022.
- W. Diao, K. Zhang and L. Bruzzone, “ZeRGAN: Zero-Reference GAN for Fusion of Multispectral and Panchromatic Images”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 72, No. 2, pp. 1-13, 2022.
- C. Yoon and C. Kim, “Motion Compensation for 3D Multispectral Handheld Photoacoustic Imaging”, Biosensors, Vol. 12, No. 12, pp. 1092-1098, 2022.
- D. Irfan and V. Saravanan, “Prediction of Quality Food Sale in Mart using the AI-Based TOR Method”, Journal of Food Quality, Vol. 2022, pp. 1-12, 2022.
- K.L. Narayanan, S. Vimal and M. Kaliappan, “Banana Plant Disease Classification using Hybrid Convolutional Neural Network”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 1-9, 2022.
- B. Subramanian, T. Gunasekaran and S. Hariprasath, “Diabetic Retinopathy-Feature Extraction and Classification using Adaptive Super Pixel Algorithm”, International Journal on Engineering Advanced Technology, Vol. 9, pp. 618-627, 2019.
- M. Bhende, S. Shinde and V. Saravanan, “Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection”, BioMed Research International, Vol. 2022, pp. 1-14, 2022.
- K. Praghash, 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.
- C. Sivakumar and A. Shankar, “The Speech-Language Processing Model for Managing the Neuro-Muscle Disorder Patients by using Deep Learning”, NeuroQuantology, Vol. 20, No. 8, pp. 918-925, 2022.
- H. Zhang and X. Guan, “Multispectral and SAR Image Fusion based on Laplacian Pyramid and Sparse Representation”, Remote Sensing, Vol. 14, No. 4, pp. 870-881, 2022.
- S. Zheng and Q. Lu, “In-Situ Measurements of Temperature and Emissivity during MSW Combustion using Spectral Analysis and Multispectral Imaging Processing”, Fuel, Vol. 323, pp. 124328-124335, 2022.
- AI-Based Video Summarization for Efficient Content Retrieval
Abstract Views :128 |
PDF Views:1
Authors
Affiliations
1 Department of Computational Intelligence, SRM Institute of Science and Engineering, Kattankulathur Campus, IN
2 Department of MCA, Jyoti Nivas College, IN
3 Department of Electronics and Communication Engineering, ACE Engineering College, IN
4 Department of Electronics and Communication Engineering, VSB College of Engineering Technical Campus, IN
5 Department of Electrical and Electronics Engineering, MAI-NEFHI College of Engineering and Technology Asmara, ER
1 Department of Computational Intelligence, SRM Institute of Science and Engineering, Kattankulathur Campus, IN
2 Department of MCA, Jyoti Nivas College, IN
3 Department of Electronics and Communication Engineering, ACE Engineering College, IN
4 Department of Electronics and Communication Engineering, VSB College of Engineering Technical Campus, IN
5 Department of Electrical and Electronics Engineering, MAI-NEFHI College of Engineering and Technology Asmara, ER
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 2 (2023), Pagination: 3137-3142Abstract
The explosive growth of video data poses a significant challenge in retrieving relevant content swiftly. Existing methods often fall short in providing concise yet informative summaries and efficient retrieval mechanisms. The primary issue lies in the overwhelming volume of video data, making it cumbersome for users to identify and access pertinent information efficiently. Traditional summarization techniques lack the sophistication to capture the nuances of video content, leading to a gap in effective content retrieval. Our approach involves training a Deep Belief Network (DBN) to autonomously generate concise yet comprehensive video summaries. Simultaneously, the Radial Basis Function (RBF) is employed to develop an efficient content retrieval system, leveraging the learned features from the video summarization process. The integration of these two methods promises a novel and effective solution to the challenges posed by the burgeoning volume of video content. Preliminary results demonstrate a significant improvement in the efficiency of content retrieval, with the integrated DBN and RBF approach outperforming traditional methods. The video summaries generated by the DBN exhibit enhanced informativeness, contributing to more accurate and rapid content retrieval.Keywords
Video Summarization, DBN, Content Retrieval, RBF, Multimedia ContentReferences
- B.S. Tung and N.H. Thinh, “AI-Based Video Analysis for Traffic Monitoring”, Proceedings of Asia-Pacific Conference on Signal and Information Processing, pp. 2035-2040, 2022.
- P. Narwal and K.K. Bhatia, “A Comprehensive Survey and Mathematical Insights Towards Video Summarization”, Journal of Visual Communication and Image Representation, Vol. 89, pp. 1-11, 2022.
- A. Sabha and A. Selwal, “Data-Driven Enabled Approaches for Criteria-Based Video Summarization: A Comprehensive Survey, Taxonomy, and Future Directions”, Multimedia Tools and Applications, Vol. 78, pp. 61-75, 2023.
- M. Tahir, B. Lee and M.N. Asghar, “Privacy Preserved Video Summarization of Road Traffic Events for IoT Smart Cities”, Cryptography, Vol. 7, No. 1, pp. 1-7, 2023.
- L.J. Nixon, B. Philipp and R. Bocyte, “Content Wizard: Demo of a Trans-Vector Digital Video Publication Tool”, Proceedings of ACM International Conference on Interactive Media Experiences, pp. 296-298, 2021.
- P.Y. Ingle and Y.G. Kim, “Multiview Abnormal Video Synopsis in Real-Time”, Engineering Applications of Artificial Intelligence, Vol. 123, pp. 1-14, 2023.
- S. Selvi and V. Saravanan, “Mapping and Classification of Soil Properties from Text Dataset using Recurrent Convolutional Neural Network”, ICTACT Journal on Soft Computing, Vol. 11, No. 4, pp. 2438-2443, 2021.
- K. Muhammad and V.H.C. De Albuquerque, “Human Action Recognition using Attention based LSTM Network with Dilated CNN Features”, Future Generation Computer Systems, Vol. 125, pp. 820-830, 2021.
- K. Asha, D. Anuradha and M. Rizvana, “Human Vision System Region of Interest Based Video Coding”, Compusoft, Vol 2, No. 5, pp. 127-134, 2013.
- A. Sabha and A, Selwal, “Towards Machine Vision-Based Video Analysis in Smart Cities: A Survey, Framework, Applications and Open Issues”, Multimedia Tools and Applications, Vol. 87, 1-52, 2023.
- L. Nixon and V. Mezaris, “Data-Driven Personalisation of Television Content: A Survey”, Multimedia Systems, Vol. 28, No. 6, pp. 2193-2225, 2022.
- S. Gupta and K.S. Babu, “Supervised Computer-Aided Diagnosis (CAD) Methods for Classifying Alzheimer Disease-Based Neurodegenerative Disorders”, Computational and Mathematical Methods in Medicine, Vol. 2022, pp. 1-11, 2022.
- A.A. Khan, W. Ali and S. Tumrani, “Content-Aware Summarization of Broadcast Sports Videos: An Audio-Visual Feature Extraction Approach”, Neural Processing Letters, Vol. 52, pp. 1945-1968, 2020.
- R.K. Nayak and D.K. Anguraj, “A Novel Strategy for Prediction of Cellular Cholesterol Signature Motif from G Protein-Coupled Receptors based on Rough Set and FCM Algorithm”, Proceedings of International Conference on Computing Methodologies and Communication, pp. 285-289, 2020.
- W.E.N. Zheng and S.A.T.O. Takuro, “Content-Oriented Common IoT Platform for Emergency Management Scenarios”, Proceedings of International Symposium on Wireless Personal Multimedia Communications, pp. 1-6, 2019.
- Optimizing Plant Disease Prediction: A Neuro-fuzzy-genetic Algorithm Approach
Abstract Views :100 |
PDF Views:1
Authors
Sachin Vasant Chaudhari
1,
T. S. Sasikala
2,
R. K. Gnanamurthy
3,
Vijay Kumar Dwivedi
4,
Davinder Kumar
5
Affiliations
1 Department of Electronics and Computer Engineering, Sanjivani College of Engineering, IN
2 Department of Computer Science and Engineering, Amrita College of Engineering and Technology, IN
3 Department of Electronics and Communication Engineering, VSB College of Engineering Technical Campus, IN
4 Department of Mathematics, Vishwavidyalaya Engineering College, IN
5 Micron Technology, US
1 Department of Electronics and Computer Engineering, Sanjivani College of Engineering, IN
2 Department of Computer Science and Engineering, Amrita College of Engineering and Technology, IN
3 Department of Electronics and Communication Engineering, VSB College of Engineering Technical Campus, IN
4 Department of Mathematics, Vishwavidyalaya Engineering College, IN
5 Micron Technology, US
Source
ICTACT Journal on Soft Computing, Vol 14, No 2 (2023), Pagination: 3200-3205Abstract
In this essay, the idea of improving plant disease prediction using a Neuro-Fuzzy-Genetic algorithm (NFGA) technique is explored. The concept of a Neuro-Fuzzy-Genetic algorithm is first described. The advantages of the NFGA technique for predicting plant disorders are next addressed. An example is then given to demonstrate how this technique has been successfully used and the advantages it offers you. A hybrid artificial intelligence technique known as a Neuro-Fuzzy-Genetic set of rules (NFGA) combines the genetic algorithms, fuzzy logic, and neural network algorithms. It entails using a method of organizing fuzzy rules for statistics type, developing a network of neurons to anticipate the level of the group of data points to positive fuzzy training, adjusting the weights of fuzzy classes using a genetic algorithm-based totally optimization method to better fit the data factors, and ultimately identifying and predicting patterns of statistics points. The main benefits of this method for predicting plant diseases are its abilities to analyze various plant characteristics, extract complex relationships between statistics points, identify correlations between various environmental factors and illnesses, choose the best combinations of fuzzy rules for accurate classification, and finally adapt to changing data over timeKeywords
Plant Disease Prediction, Neuro-Fuzzy-Genetic Algorithm, Optimization, Machine Learning, Classification, Feature Extraction.References
- C.F. Jisieike and E. Betiku, “Crude Rubber Seed Oil Esterification using a Solid Catalyst: Optimization by Hybrid Adaptive Neuro-Fuzzy Inference System and Response Surface Methodology”, Energy, Vol. 2663, pp. 1-13, 2023.
- A. Thakare and H.N. Patel, “Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer”, Biomed Research International, Vol. 2022, pp. 1-13, 2022.
- R. Shesayar, S. Rustagi, S. Bharti and S. Sivakumar, “Nanoscale Molecular Reactions in Microbiological Medicines in Modern Medical Applications”, Green Processing and Synthesis, Vol. 12, No. 1, pp. 1-13, 2023.
- G.G. Tiruneh and A.R. Fayek, “Hybrid GA-MANFIS Model for Organizational Competencies and Performance in Construction”, Journal of Construction Engineering and Management, Vol. 148, No. 4, pp. 1-16, 2022
- 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.
- V.C. Anadebe and R.C. Barik, “Multidimensional Insight into the Corrosion Inhibition of Salbutamol Drug Molecule on Mild Steel in Oilfield Acidizing Fluid: Experimental and Computer Aided Modeling Approach”, Journal of Molecular Liquids, Vol. 349, pp. 118482-118488, 2022.
- K.L. Narayanan and M. Kaliappan, “Banana Plant Disease Classification using Hybrid Convolutional Neural Network”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 1-10, 2022.
- I.B. Ali, J. Belhadj and X. Roboam, “Fuzzy Logic for Solving the Water-Energy Management Problem in Standalone Water Desalination Systems: Water-Energy Nexus and Fuzzy System Design”, International Journal of Fuzzy System Applications, Vol. 12, No. 1, pp. 1-28, 2023.
- C. Sivakumar and A. Shankar, “The Speech-Language Processing Model for Managing the Neuro-Muscle Disorder Patients by using Deep Learning”, NeuroQuantology, Vol. 20, No. 8, pp. 918-925, 2022.
- M. Bhende, A. Thakare and M. Pant “Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection”, BioMed Research International, Vol. 2022, pp. 1-9, 2022.
- S. Thota and M.B. Syed, “Analysis of Feature Selection Techniques for Prediction of Boiler Efficiency in Case of Coal based Power Plant using Real Time Data”, International Journal of System Assurance Engineering and Management, Vol. 2022, pp. 1-14, 2022.
- G. Dhiman, A.V. Kumar and S. Sujitha, “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.
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