Learning-Based Model to Fight against Fake Like Clicks on Instagram Posts

Thejas, G. S.; Soni, Jayesh; Chandna, Kshitij; Iyengar, S. Sitharama; Sunitha, N. R.; Prabakar, Nagarajan

doi:10.1109/southeastcon42311.2019.9020533

Cited by 10 publications

(1 citation statement)

References 19 publications

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“…For the UNM dataset, KSM had a higher detection rate than HMMs. Neural networks have been utilized to extract multiple features from one-dimensional data using various embeddings [11].…”

Section: Related Workmentioning

confidence: 99%

DYNA-B: An Enhanced And Dynamic Batch Size Tuning For LSTM Neural Network

Soni,

Prabakar,

Upadhyay

2024

Preprint

Self Cite

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Deep neural networks can be trained in various domains using increasingly large batch sizes without compromising the efficiency. However, this massive data parallelism may differ from domain to domain. It is computationally challenging to train large deep neural networks on large datasets. In response, there has been a surge of interest in utilizing large batch size values during the optimization process, as it enables faster training of these networks, thereby facilitating distributed processing. However, this approach also presents a well-known problem called the "generalization gap," which can result in a degraded performance across multiple datasets. Currently, there is limited understanding of how to determine the optimal batch size. To address this issue, we propose an adaptive tuning algorithm that dynamically adjusts the batch size. Our algorithm consists of four stages: gradient warm-up, loss derivation, calculation of weighted loss using historical batch size data, and batch size updating. We demonstrate the superior performance of our algorithm compared with the traditional constant-batch size approach by comparing it with multiple system-call datasets of varying sizes.

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“…For the UNM dataset, KSM had a higher detection rate than HMMs. Neural networks have been utilized to extract multiple features from one-dimensional data using various embeddings [11].…”

Section: Related Workmentioning

confidence: 99%

DYNA-B: An Enhanced And Dynamic Batch Size Tuning For LSTM Neural Network

Soni,

Prabakar,

Upadhyay

2024

Preprint

Self Cite

View full text Add to dashboard Cite

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A novel machine learning‐based framework for detecting fake Instagram profiles

Kaushik

Bhardwaj

Kumar

et al. 2022

Concurrency and Computation

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Recently, there has been a massive rise in the popularity of Instagram, which connects individuals globally and allows videos and images to be uploaded and exchanged, and communicated over social media. Instagram is also an online playground of deceit. The use of filters, lighting, and cunning angles transforms the mundane into something spectacular. Automated spam accounts and fake profiles use this to their malicious advantage for executing attacks targeting high-profile executives. Creating fake Instagram identities is easy to reproduce the idea of being accepted by many fans on social media. Fake accounts are used in the marketing of fake services and products. This research focused on designing and training a unique neural network model and proposed a new algorithm for detecting automated spam and fake Instagram account profiles. The precision and accuracy of the proposed method were achieved at 93% and 91%, respectively.

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