Computer Vision: The Effectiveness of Deep Learning for Emotion Detection in Marketing Campaigns

Naidoo, Shan; Naicker, Nalindren; Patel, Sulaiman Saleem; Govender, Prinavin

doi:10.14569/ijacsa.2022.01305100

Cited by 3 publications

(2 citation statements)

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“…At home, baby cribs are equipped with a multitude of wireless sensors. Social media posts are closely monitored to detect and predict the emotional triggers of users based on their cognitive bias [25]. Even the success of romantic relationships is produced through an algorithm that is fed a growing volume of private information about individuals.…”

Section: Impact Of the Technological Developmentmentioning

confidence: 99%

Where has privacy gone?

Aïmeur,

Brassard

2024

Preprint

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Alice is reading a novel on her computer in the comfort of her home drinking a piña colada. Her husband John, on the other hand, is grabbing his morning cup of coffee at his favorite café while looking at his Facebook account. Alice’s webcam is on and John feels at ease using the café’s public WiFi. Then, suddenly, Alice gets an advertisement about Caribbean drinks. She is a bit perplexed. As for John, he receives an email from his boss asking why he is not at work and another from Facebook alerting him of suspicious activities on his account. He is irritated. They both thought their privacy was safe. There are a multitude of threats looming on the horizon from profiling, identity theft, and mass surveillance to depriving babies of their right to privacy before being born. Before diving into the dangers and how to eliminate or at least mitigate threats, let us understand the concept of privacy. To do so, we go back to the origins in order to comprehend its evolution throughout history. At each period, privacy practices adapt to the time-specific context. Not only that, but the historical context can have long-lasting implications for centuries to come. We then explain the situation today with the impacts on the individual and society. Finally, we draw some conclusions about the future of privacy.

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Section: Impact Of the Technological Developmentmentioning

confidence: 99%

Where has privacy gone?

Aïmeur,

Brassard

2024

Preprint

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“…Diverse issues in bioinformatics and cheminformatics applications [13], and more specifically, drug development and discovery [14], have been successfully solved using deep learning (DL) techniques [15,16]. By comparing DL techniques to traditional machine learning (ML) algorithms, DL algorithms designed to predict drug-target binding affinities (DTBA) occasionally do better [17].…”

Section: Introductionmentioning

confidence: 99%

Drug Repositioning for Coronavirus (COVID-19) using Different Deep Learning Architectures

Morad¹,

Aborizka²,

Maghraby³

2023

IJACSA

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In December 2019, the COVID-19 epidemic was found in Wuhan, China, and soon hundreds of millions were infected. Therefore, several efforts were made to identify commercially available drugs to repurpose them against COVID-19. Inferring potential drug indications through computational drug repositioning is an efficient method. The drug repositioning problem is a top-K recommendation function that presents the most likely drugs for specific diseases based on drug and diseaserelated data. The accurate prediction of drug-target interactions (DTI) is very important for drug repositioning. Deep learning (DL) models were recently exploited for promising DTI prediction performance. To build deep learning models for DTI prediction, encoder-decoder architectures can be utilized. In this paper, a deep learning-based drug repositioning approach is proposed, which is composed of two experimental phases. Firstly, training and evaluating different deep learning encoder-decoder architecture models using the benchmark DAVIS Dataset. The trained deep learning models have been evaluated using two evaluation metrics; mean square error and the concordance index. Secondly, predicting antiviral drugs for Covid-19 using the trained deep learning models created during the first phase. In this phase, these models have been experimented to predict different antiviral drug lists, which then have been compared with a recently published antiviral drug list for Covid-19 using the concordance index metric. The overall experimental results of both phases showed that the most accurate three deep learning compound-encoder/protein-encoder architectures are Morgan/AAC, CNN/AAC, and CNN/CNN with best values for the mean square error, the first phase concordance index, and the second phase concordance index.

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