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Gupta, Brijendra
- Identification and recognition of Leaf Disease Using Enhanced Segmentation Techniques
Abstract Views :91 |
PDF Views:1
Authors
Affiliations
1 Department of Information Technology, Siddhant College of Engineering, IN
2 Department of Electrical and Electronics Engineering, St. Peter’s Institute of Higher Education and Research, IN
3 Department of Electronics and Communication Engineering, CMR Institute of Technology, IN
4 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN
1 Department of Information Technology, Siddhant College of Engineering, IN
2 Department of Electrical and Electronics Engineering, St. Peter’s Institute of Higher Education and Research, IN
3 Department of Electronics and Communication Engineering, CMR Institute of Technology, IN
4 Department of Electronics and Communication Engineering, Vivekanandha College of Engineering for Women, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 2 (2022), Pagination: 2825-2830Abstract
Segmenting refers to the technique of breaking up an image into its component parts one by one. When it comes to the process of segmenting photos, there is a plethora of choice available at current point in time. These options range from the easy thresholding approach to the complicated color image segmentation techniques. The bulk of the time, the parts that go into making up these sub-assemblies are items that individuals are able to easily identify and categorize as being distinct from one another. As a result of the limitation of computer lack of intelligence to differentiate between distinct items, a wide variety of techniques have been devised and utilized in the process of segmenting photographs. In order to complete its tasks, the image segmentation algorithm requires a wide range of image characteristics to be provided as input. This could be referring to the colors that are contained within an image, the borders that are included within the image, or a particular region that is contained within the image. In order to break down color images into their component elements, we make use of an algorithm that is inspired by natural selection. The research uses enhanced segmentation techniques to identify and recognize the leaf disease in plants. The study conducts extensive simulation to test the efficacy of the model. The results show that the proposed method achieves higher segmentation accuracy than other methods.Keywords
No Keywords.References
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- Hybrid Neuro-fuzzy-genetic Algorithms for Optimal Control of Autonomous Systems
Abstract Views :23 |
PDF Views:2
Authors
Affiliations
1 Department of Mechanical Engineering, Theni Kammavar Sangam College of Technology, IN
2 Department of Information Technology, Siddhant College of Engineering, IN
3 Department of Information Technology, University College of Technology and Applied Sciences - Salalah, OM
4 Indian Institute of Information Technology Kalyani, IN
1 Department of Mechanical Engineering, Theni Kammavar Sangam College of Technology, IN
2 Department of Information Technology, Siddhant College of Engineering, IN
3 Department of Information Technology, University College of Technology and Applied Sciences - Salalah, OM
4 Indian Institute of Information Technology Kalyani, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 4 (2023), Pagination: 3015-3020Abstract
In recent years, there has been an increasing demand for efficient and robust control algorithms to optimize the performance of autonomous systems. Traditional control techniques often struggle to handle the complexity and uncertainty associated with such systems. To address these challenges, hybrid neuro-fuzzy-genetic algorithms have emerged as a promising approach. This paper presents a comprehensive review of the application of hybrid neuro-fuzzy-genetic algorithms for optimal control of autonomous systems. The proposed algorithms combine the strengths of neural networks, fuzzy logic, and genetic algorithms to achieve adaptive and optimal control in real-time scenarios. The neuro-fuzzy component provides the ability to model and handle complex and uncertain systems, while the genetic algorithm component facilitates the optimization of control parameters. The combination of these techniques enables autonomous systems to adapt and optimize their control strategies based on changing environments and objectives. The paper discusses the underlying principles of hybrid neuro-fuzzy-genetic algorithms, their advantages, and challenges. It also provides a review of the state-of-the-art research in this field, highlighting successful applications and potential future directions. Overall, the integration of neuro-fuzzy-genetic algorithms in autonomous systems holds great promise for achieving optimal control in various domains, including robotics, aerospace, and autonomous vehicles.Keywords
Hybrid Algorithms, Neuro-Fuzzy-Genetic Algorithms, Optimal Control, Autonomous Systems, Neural Networks, Fuzzy Logic, Genetic Algorithms, Real-Time Control, Adaptive Control, Uncertainty, Robotics, Aerospace, Autonomous Vehicles.References
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- R. Tripathy and P. Das, “Spectral Clustering based Fuzzy C-means Algorithm for Prediction of Membrane Cholesterol from ATP-Binding Cassette Transporters”, Proceedings of International Conference on Intelligent and Cloud Computing, Vol. 2, pp. 439-448, 2021.
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- Swarm Intelligence Optimization for Resource Allocation in Cloud Computing Environments
Abstract Views :21 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, IN
2 Department of Information Technology, Siddhant College of Engineering, IN
3 Department of Computer Engineering, Government Polytechnic College, Namakkal, IN
4 School of Computing Science and Engineering, VIT Bhopal University, IN
1 Department of Computer Science and Engineering, St. Joseph’s Institute of Technology, IN
2 Department of Information Technology, Siddhant College of Engineering, IN
3 Department of Computer Engineering, Government Polytechnic College, Namakkal, IN
4 School of Computing Science and Engineering, VIT Bhopal University, IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 4 (2023), Pagination: 3048-3054Abstract
Cloud computing has emerged as a powerful paradigm for resource allocation due to its scalability and flexibility. Efficient resource allocation is critical for optimizing the performance and utilization of cloud resources. In this context, swarm intelligence optimization algorithms, such as Salp Swarm Optimization (SSO), have shown promising results in solving complex optimization problems. This paper presents a novel approach that utilizes SSO for resource allocation in cloud computing environments. The proposed approach aims to maximize resource utilization, minimize response time, and improve overall system performance. The SSO algorithm is used to dynamically allocate virtual machines (VMs) to physical hosts based on their resource demands and availability. Experimental results demonstrate that the proposed approach outperforms existing methods in terms of resource utilization and response time, thereby enhancing the efficiency of cloud computing environments.Keywords
Swarm Intelligence Optimization, Salp Swarm Optimization, Resource Allocation, Cloud Computing, Virtual Machines, Resource Utilization, Response Time, Performance Optimization.References
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