Facial Micro Emotion Detection and Classification Using Swarm Intelligence based Modified Convolutional Network

Arun, A.N.; Maheswaravenkatesh, P.; Jayasankar, T.

doi:10.1016/j.eswa.2023.120947

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…IV. LIMITATIONS First and foremost, the heart of these issues is the FER datasets containing emotion images which are unbalanced, causing problems to methods for detecting emotions and thus hampering progress on these issues [1], [14], [15]. Some investigations are too narrow by focusing only on particular muscles that are used for the expression; these neglect the important role played by other crucial facial movements [5].…”

Section: Techniques Usedmentioning

confidence: 99%

“…Therefore, a trained classifier is used to generate emotion labels for new, unseen facial expressions so that recognizing and categorizing emotions is possible. 1 Student, Information Technology Department, M.H. Saboo Siddik College of Engineering, Mumbai, India, husnas2933@gmail.com Human experiences are always accompanied by emotions that are complex and multifaceted.…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Facial Emotion Recognition and Fake Emotion Detection Techniques

Husna Shaikh

2024

jes

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Facial expressions play a crucial role in depicting the thoughts and feelings of any human. Moreover, they might even serve as the distinguishing factor in categorizing whether a person is genuine or not. In this survey, the authors have carried out a systematic literature review of the past work done on facial emotion recognition and discrimination of genuine and fake emotions. Various datasets that are previously and currently being used in this field have been surveyed. The main objective of this paper is to study the state-of-the-art techniques that are being used to analyze and identify facial emotions and to distinguish between their genuineness. For this purpose, papers from distinguished publications have been researched and a comprehensive review of the literature has been fulfilled.

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Section: Techniques Usedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Facial Emotion Recognition and Fake Emotion Detection Techniques

Husna Shaikh

2024

jes

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“…IoT becomes a combination of cloud-linked embedding mechanisms utilized by the user for accessing IT-based services using the mixture of internet protocol and electronics-based objects (Dat-Thinh et al, 2022;Doriguzzi-Corin et al, 2020;Le et al, 2022;Ullah & Mahmoud, 2021). In IoT, protocols utilized might have security vulnerability that has an effect on the mechanism (Arun et al, 2023;Karthick, Dawood, et al, 2023;Shajin et al, 2023). IoT gadgets were susceptible targets for attackers and cybercriminals due to their lack of basic security protocols (Shafiq et al, 2020;Rajesh, Shajin, & Kannayeram, 2022).…”

Section: Introductionmentioning

confidence: 99%

Intrusion detection system using metaheuristic fireworks optimization based feature selection with deep learning on Internet of Things environment

Jayasankar,

Kiruba Buri,

Maheswaravenkatesh

2023

Journal of Forecasting

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Internet of Things (IoT), cloud computing, and other significant advancements in communication have created new security challenges. Due to these advancements and the ineffectiveness of the current security measures, cyber‐attacks are also increasing quickly. Recently, several artificial intelligence (AI)–based solutions have been presented for various secure applications, such as intrusion detection. This article proposes an intrusion detection system using dynamic search fireworks optimization–based feature selection with optimal deep recurrent neural network (DFWAFS‐ODRNN) model in IoT environment. The presented DFWAFS‐ODRNN model follows a two‐stage process, namely, feature selection and intrusion classification. In the first phase, the DFWAFS‐ODRNN model elects an optimal subset of features using the dynamic search fireworks optimization algorithm (DFWAFS) technique. Next, in the second stage, the intrusions are identified and categorized using the DRNN model. At last, the hyperparameters of the DRNN are optimally chosen by the Nadam optimizer. A detailed simulation analysis of the DFWAFS‐ODRNN model is validated on benchmark intrusion detection system (IDS) dataset, and the outcomes show the efficacy of intrusion detection. The proposed model efficiently detects the intrusion detection with an accuracy of 96.11%.

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