Informatics Publishing Limited

S. Vijayarangam ¹, Kavita V Wagh ², Vishal Gangadhar Puranik ³, S. Selvakanmani ⁴

Affiliations
1 Department of Computer Science Engineering, Sri Indu College of Engineering and Technology, India., IN
2 Department of Electronics and Telecommunication Engineering, Vasantdada Patil Pratishthan’s College of Engineering and Visual Arts, India., IN
3 Department of Electronics and Telecommunication Engineering, JSPM’s Bhivarabai Sawant Institute of Technology and Research, India., IN
4 Department of Information Technology, RMK Engineering College, India., IN

Abstract

Wireless sensor networks (WSNs) have become one of the most popular wireless communication systems in the world. Various attacks have been launched against the WSN individual nodes over the past few years, and the overall security of the network has gradually deteriorated. In this research, we propose a secure uneven clustering method for WSN. The proposed method is based on a trust score evaluation model with the objective of identifying malevolent users in WSNs. The trust score is used to determine whether or not a node is malicious. The results show that the proposed Trust Algorithm outperforms the currently used LEACH algorithm, the TASRP algorithm and the FLSO algorithm in terms of the percentage of packets that are successfully delivered across all use cases and density of nodes.

Keywords

Security, Routing Protocol, Wireless Sensor Network.

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D.K. Mohanty ¹, G.R. Thippeswamy ², G. Erappa ³, Vishal Gangadhar Puranik ⁴

Affiliations
1 Government B.Ed. Training College Kalinga, India., IN
2 Department of Computer Science and Engineering, Don Bosco Institute of Technology, India., IN
3 Department of Information Science and Engineering, RR Institute of Technology, India., IN
4 Department of Electronics and Telecommunication Engineering, JSPM’s Bhivarabai Sawant Institute of Technology and Research, India., IN

Abstract

In this paper, we propose a novel method that makes use of artificial intelligence to determine in a quick and accurate manner which bitrate ladder is best suited to each specific video scenario. This method is included as part of our overall contribution to this body of research. To accomplish fast entropy-based scene recognition using the artificial intelligence technique, a CNN model is utilised as part of the overall strategy. We were able to significantly reduce the amount of processing time necessary to recognise the scenes because we were dealing with versions of the video sequences that had both a lower quality and a lower bitrate. This allowed us to work more quickly. We first generated a training dataset that was large enough to train a convolutional neural network utilising the x264 video codec, and then we used that dataset to generate multiple encodings with varying bitrates, presets, and resolutions. The training dataset was created using the x264 video codec. As a result of the research that we carried out, we concluded that a particular collection of input features for the CNN model can be used to acquire a more accurate prediction of the level of video quality that will be produced. By predicting the PSNR quality measure for the segments, the suggested CNN model brings down the MAE and MSE to 0.2 and 0.05, respectively. This is accomplished by reducing the number of segments. This serves to reduce the amount of error overall.

Keywords

Artificial Intelligence, Convolutional Neural Network, Video Quality Enhancement, Video Rendering.

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Vishal Gangadhar Puranik ¹, V. Saravanan ², P. Selvaraju ³, R. Janaki ⁴

Affiliations
1 Department of Electronics and Telecommunications Engineering, MIT Academy of Engineering, IN
2 Department of Computer Science, Johns Hopkins University, US
3 Department of Artificial Intelligence and Machine Learning, Excel Engineering College, IN
4 Department of Electronics and Communication Engineering, Roever Engineering College, IN

Abstract

Social media platforms generate vast amounts of data, necessitating efficient content classification and community detection methods. This study addresses this challenge through the utilization of VGGNet analytics, a powerful deep learning architecture. We employed a two-step approach, beginning with VGGNet-based content classification to categorize social media posts. Subsequently, a community detection algorithm was applied to identify distinct user groups based on their interactions and content preferences. This research contributes an novel framework that seamlessly integrates VGGNet for content analysis and community detection, enhancing the understanding of user behavior in social media platforms. The proposed method aims to provide more accurate and insightful results compared to traditional approaches. Our experiments on diverse social media datasets demonstrate the effectiveness of the VGGNet-based approach. The content classification accurately assigns posts to relevant categories, while the community detection algorithm identifies cohesive user groups. The results highlight the potential for improved content recommendation systems and targeted marketing strategies.

Keywords

Community Detection, Content Classification, Social Media, VGGNet Analytics, Deep Learning.

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