Informatics Publishing Limited

R Manikandan ¹, Muruganantham Ponnusamy ², K Raja ³, M Ramkumar ⁴

Affiliations
1 The Quaide Milleth College for Men, IN
2 Indian Institute of Information Technology Kalyani, IN
3 Ministry of Education, Maldives, IN
4 Gnanamani College of Technology, IN

Abstract

Energy conservation is a key element in MANET and so the architecture of the protocol demands particular consideration. Several MANET designs explored processes for the storage of renewable resources. Energy dissipation is a key coordinating factor among MANET nodes. Power utilization is ensured in monitoring applications that battery depletions are lowered to avoid regular substitution. MANET primarily aims to relay data by using energy-efficient routing protocols and an increasing lifespan of the network. The results of the simulation show that the approach suggested improves energy efficiency compared to other approaches. The findings are checked and provide decreased power usage, better packet distribution, lower packet error rate, higher network lifetime and lower end-to-end latency compared to conventional methods.

Keywords

Energy Efficiency, MANETs, Mobility, Routing.

Full Text

   

R. Manikandan ¹, Muruganantham Ponnusamy ², Jayasri Subramaniam ³

Affiliations
1 Department of Computer Science, The Quaide Milleth College for Men, IN
2 Indian Institute of Information Technology, Kalyani, IN
3 Department of Master of Science in Computing, University of Northampton, GB

Abstract

In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images. The Image processing as a subgroup or background of digital signal processing has many advantages over analog image processing. The Digital image processing allows the use of a wide range of algorithms for input data and avoids problems such as noise accumulation and signal distortion during the processing process. Because images are defined in two dimensions (perhaps more than two dimensions), image processing can be formatted into multi-dimensional systems. In this paper an effective Mathematical morphology model was proposed to enhance the quality of images. In this mode, the image is pre-processed and then the gradient is changed using a mathematical image system. Then, the edges are detected by the margin detection method based on the statistical data. This method removes the shadow contours caused by the lights, directly separates the boundaries of the objects and has an impact on the background noise suppression.

Keywords

Digital Image Processing, Computer Algorithms, Digital Images, Mathematical Morphology

Full Text

     

Muruganantham Ponnusamy ¹, T. Prabakaran ², Swapna Thouti ³, Saivarshini Ravichandran ⁴

Affiliations
1 Department of Mechanical Engineering, Indian Institute of Information Technology Kalyani, IN
2 Department of Computer Science and Engineering, Joginpally B. R. Engineering College, IN
3 Department of Electronics and Communication Engineering, CVR College of Engineering, IN
4 Business Analytics Department, University of Rochester - Simon Business School, US

Abstract

The rapid growth of multimedia data in various domains has necessitated the development of efficient techniques to enhance and optimize its quality. Traditional approaches often struggle to address the complexity and diversity of multimedia data, leading to suboptimal results. This paper presents a novel approach to tackle this challenge by leveraging the power of deep reinforcement learning (DRL). The proposed method utilizes DRL to learn and optimize multimedia data in an improvised manner. By employing a combination of convolutional neural networks and deep Q-networks, the model can effectively extract high-level features and make informed decisions to enhance the quality of multimedia data. The reinforcement learning framework enables the system to learn from its actions, continuously improving its performance through an iterative process. To evaluate the effectiveness of the proposed method, extensive experiments were conducted using a diverse set of multimedia datasets. The results demonstrate significant improvements in various quality metrics, including image resolution, video frame rate, and audio clarity. Additionally, the proposed approach exhibits robustness across different types of multimedia data, ensuring consistent enhancement performance across various domains. Furthermore, the computational efficiency of the proposed method is also highlighted, as it demonstrates faster convergence and lower computational overhead compared to traditional optimization methods. This makes the approach practical for real-time applications where multimedia data needs to be processed efficiently. Overall, this paper introduces an innovative framework that combines deep reinforcement learning with multimedia data optimization. The results indicate its potential for enhancing multimedia data quality, offering a promising solution to the challenges associated with traditional approaches. The proposed method not only improves the visual and auditory aspects of multimedia content but also provides a scalable and efficient solution for real-world applications in domains such as image processing, video streaming, and audio analysis.

Keywords

Deep Reinforcement Learning, Multimedia Data, Optimization, Enhancement, Convolutional Neural Networks, Deep Q-Networks, Quality Metrics, Computational Efficiency.

Full Text

     

Veeranan Arunprasad ¹, Brijendra Gupta ², T. Karthikeyan ³, Muruganantham Ponnusamy ⁴

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

Abstract

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.

Full Text