Impact of ratings of content on OTT platforms and prediction of its success rate

Singh, Krishna Kumar; Makhania, Jeroz; Mahapatra, Madhumita

doi:10.1007/s11042-023-15887-9

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…A wormhole attack is a complicated problem that records packets from a specific network region and tunnels them to another part of the network to disrupt wireless network performance and interact with most routing protocols [15]. Attacks on the IoT are not just directed at a few isolated devices; they provide a way to intrusion on numerous additional connected services [16]. The Internet of Things (IoT) is a collection of wireless and wired gadgets that may be controlled externally [19].…”

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confidence: 99%

IoT networks attack detection using CFD learning framework

Singh,

Mahapatra

2024

Preprint

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This research proposes a new Federated Deep Learning Framework specifically designed to safeguard Internet of Things networks by effectively identifying Wormhole Attacks. The framework embarks on a comprehensive pre-processing journey that includes various strategies for handling missing data, implementing Winsorizing for outlier management, and utilising the Min-Max scaling technique for normalisation. As authors delve into the framework's core, an advanced feature extraction phase unfolds. This phase leverages a wide range of categories, such as Temporal Features extracted through a combination of Long Short-Term Memory networks and Feedforward Neural Networks, Statistical Features consisting of Median Absolute Deviation, mean, variance, skewness, and kurtosis, Database-based Features encompassing Packet Rate and Packet Size Distribution, Correlation-based Features obtained through Cross-Correlation analysis, and behavioral Features that reveal communication patterns, frequency of interactions, and deviations from everyday routines. The framework embarks on a comprehensive pre-processing journey that includes various strategies for handling missing data, implementing Winsorizing for outlier management, and utilising the Min-Max scaling technique for normalisation. As we delve into the framework's core, an advanced feature extraction phase unfolds. This phase leverages a wide range of categories, such as Temporal Features extracted through a combination of Long Short-Term Memory networks and Feedforward Neural Networks, Statistical Features consisting of Median Absolute Deviation, mean, variance, skewness, and kurtosis, Database-based Features encompassing Packet Rate and Packet Size Distribution, Correlation-based Features obtained through Cross-Correlation analysis, and Behavioral Features that reveal communication patterns, frequency of interactions, and deviations from everyday routines.

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mentioning

confidence: 99%

IoT networks attack detection using CFD learning framework

Singh,

Mahapatra

2024

Preprint

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Defect Detection in Metal Surfaces Using Computer Vision

Singh,

Ghosh

2024

Lecture Notes in Networks and Systems

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TV shows popularity prediction of genre-independent TV series through machine learning-based approaches

Cammarano,

Guarino,

Malandrino

et al. 2024

Multimed Tools Appl

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The use of social media has grown exponentially in recent years up to become a reflection of human social attitudes and to represent today the main channel for conducting discussions and sharing opinions. For this reason, the vast amount of information generated is often used for predicting outcomes of real-world events in different fields, including business, politics, and health, as well as in the entertainment industry. In this paper, we focus on how data from Twitter can be used to predict ratings of a large set of TV shows regardless of their specific genre. Given a show, the idea is to exploit features concerning the pre-release hype on Twitter for rating predictions. We propose a novel machine learning-based approach to the genre-independent TV show popularity prediction problem. We compared the performance of several well-known predictive methods, and as a result, we discovered that LSTM and Random Forest can predict the ratings in the USA entertainment market, with a low mean squared error of 0.058. Furthermore, we tested our model by using data of “never seen” shows, by deriving interesting results in terms of error rates. Finally, we compared performance against relevant solutions available in the literature, with discussions about challenges arousing from the analysis of shows in different languages.

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Impact of ratings of content on OTT platforms and prediction of its success rate

Cited by 4 publications

References 14 publications

IoT networks attack detection using CFD learning framework

IoT networks attack detection using CFD learning framework

Defect Detection in Metal Surfaces Using Computer Vision

TV shows popularity prediction of genre-independent TV series through machine learning-based approaches

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