Informatics Publishing Limited

Logeshwari Dhavamani ¹, A. Kaliappan ², M. Sakthivel ³, V. Balaji ⁴

Affiliations
1 Department of Information Technology, St. Joseph’s College of Engineering, IN
2 Department of Computer Science and Engineering, P.A. College of Engineering and Technology, IN
3 Department of Computer Science and Engineering - Artificial Intelligence and Machine Learning, Malla Reddy College of Engineering, IN
4 Department of Electrical and Electronics Engineering, Mai Nefhi College of Engineering and Technology, ER

Abstract

In today’s rapidly evolving digital landscape, interactive communication networks play a pivotal role in facilitating real-time interactions among users. One of the critical challenges in these networks is ensuring the seamless delivery of multimedia content that caters to the diverse needs and preferences of individual users. This research endeavors to address this challenge by introducing a novel approach, where it places user satisfaction at its core, leveraging adaptive streaming techniques to dynamically adjust multimedia content delivery. By considering parameters such as network conditions, device capabilities, and user preferences, it optimizes the streaming experience in real-time. A key innovation lies in the integration of Shannon-Fano coding principles and genetic algorithms. Shannon-Fano coding enhances data compression efficiency, reducing bandwidth consumption, while genetic algorithms fine-tune the adaptive streaming parameters for each user. Our experimentation and evaluations demonstrate the effectiveness of this approach, showcasing improved multimedia streaming quality, reduced latency, and efficient bandwidth utilization. The synergy of user-centricity, adaptive streaming, Shannon-Fano coding, and genetic algorithms presents a promising avenue for enhancing multimedia communication in interactive networks.

Keywords

User-Centric, Adaptive Multimedia Streaming, Interactive Communication Networks, Shannon-Fano Coding, Genetic Algorithm.

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Logeshwari Dhavamani ¹, A. Rajavel ², Subhadra Mishra ³, Komal B. Umare ⁴

Affiliations
1 Department of Information Technology, St. Joseph College of Engineering, IN
2 Department of Electronics and Instrumentation Engineering, Kamaraj College of Engineering and Technology, IN
3 Department of Computer Science and Application, Odisha University of Agriculture and Technology, IN
4 Department of Artificial Intelligence and Machine Learning, Shri Ramdeobaba College of Engineering and Management, IN

Abstract

In this study, we address the problem of unsupervised transductive transfer learning for image feature extraction and representation. While transfer learning has shown promising results in various domains, its application to image feature extraction in an unsupervised transductive setting remains relatively unexplored. The research gap lies in the scarcity of methods that can effectively learn meaningful image representations without access to labeled data in the target domain, hindering the broader applicability of transfer learning in computer vision. Our research seeks to bridge this gap by proposing a novel framework that leverages unsupervised feature learning to enhance the adaptability of models across different image domains, thus contributing to the advancement of transfer learning in the field of computer vision. Experimental results demonstrate the effectiveness of our method in addressing this critical research gap and its potential for real-world applications.

Keywords

Unsupervised, Transductive Transfer Learning, Image Feature Extraction, Representation, Deep Neural Networks

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