Classification techniques’ performance evaluation for facial expression recognition

Mahmood, Mayyadah Ramiz; Abdulrazaq, Maiwan B.; Zeebaree, Subhi R. M.; Ibrahim, Abbas; Zebari, Rizgar R.; Dino, Hivi Ismat

doi:10.11591/ijeecs.v21.i2.pp1176-1184

Cited by 22 publications

(9 citation statements)

References 32 publications

(39 reference statements)

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“…Pooling is a key phase in lowering the dimensions of the map of activation, retaining just the necessary elements while minimizing spatial invariance. As a result, the model's learnable features are reduced [38]. This contributes to the resolution of the overfitting issue.…”

Section: Poolingmentioning

confidence: 99%

COVID-19 detection based on convolution neural networks from CT-scan images: a review

Ibrahim

Mahmood²

2023

IJEECS

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<span lang="EN-US">The COVID-19 outbreak has been affecting the health of people all around the world. With the number of confirmed cases and deaths still rising daily, so the main aim is to detect positive cases as soon as and provide them with the necessary treatment. The utilization of imaging data including chest x-rays and computed tomography (CT) was proven that is would be beneficial for quickly diagnosing COVID-19. Since Computerized Tomography provides a huge number of images, recognizing these visual traits would be difficult and take enormous amounts of time for radiologists so automated diagnosis technologies including deep learning (DL) models are recently for COVID-19 screening in CT scans. This review paper presents different researches which used deep learning approaches including various models of convolutional neural networks (CNN) used in image classification tasks well, and large training, like ResNet, VGG, AlexNet, LeNet, GoogleNet, and others for COVID-19 diagnosing and severity assessments using chest CT images. As a result, automated COVID-19 analysis on CT images is essential to save medical personnel and essential time for disease prevention.</span>

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Section: Poolingmentioning

confidence: 99%

COVID-19 detection based on convolution neural networks from CT-scan images: a review

Ibrahim

Mahmood²

2023

IJEECS

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“…Performance of DRL techniques can be improved by adopting neural networks to approximate the function efficiently, thereby, enhancing its capability to handle both, large state and action spaces [15]. Particularly, multilayer concept (MLP) is chosen as a predictive tool for this study showed promising results in applications of financial forecasting [8], [15], power forecasting [42], attack detection [43]- [45], facial expression recognition [46] and wind speed prediction [47]. Here, MLP is used to describe time arrangement information that attempts to predict the near distant future information dependent on its past information.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Application of multilayer perceptron to deep reinforcement learning for stock market trading and analysis

Sagiraju

Shashi

2021

IJEECS

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Trading strategies to maximize profits by tracking and responding to dynamic stock market variations is a complex task. This paper proposes to use a multilayer perceptron method (a part of artificial neural networks (ANNs)), that can be used to deploy deep reinforcement strategies to learn the process of predicting and analyzing the stock market products with the aim to maximize profit making. We trained a deep reinforcement agent using the four algorithms: proximal policy optimization (PPO), deep Q-learning (DQN), deep deterministic policy gradient (DDPG) method, and advantage actor critic (A2C). The proposed system, comprising these algorithms, is tested using real time stock data of two products: Dow Jones (DJIA-index), and Qualcomm (shares). The performance of the agent linked to the individual algorithms was evaluated, compared and analyzed using Sharpe ratio, Sortino ratio, Skew and Kurtosis, thus leading to the most effective algorithm being chosen. Based on the parameter values, the algorithm that maximizes profit making for the respective financial product was determined. We also extended the same approach to study and ascertain the predictive performance of the algorithms on trading under highly volatile scenario, such as the pandemic coronavirus disease 2019 (COVID-19).

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“…The Internet of Things makes use of the Internet as a global networking environment for a broad range of physical things, which enables it to promote the wireless transmission of data and interaction between those physical devices. The information and data we have are very important to our day-to-day existence [40], [41]. There is a pervasive information-driven perspective on today's corporate systems, as seen by the proliferation of IoT and service-oriented enterprise structures, social networks, smart portable devices, and intelligent automobiles.…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Impact on Web Technology and Enterprise Systems

Yazdeen¹,

Qashi²,

Malallah³

et al. 2023

JASTT

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It's been clearer over the last two decades that computer science plays a crucial role in the growth of every company. With each passing cycle, the IT industry blossoms a brand-new subfield. The Internet of Things (IoT) refers to the idea of connecting any device to the Internet in order to interact with the consumer and convert it into an intelligent device; this is a relatively new concept in the realm of information technology (IT) and the Internet, but it is gaining popularity and importance in people's daily lives. The Internet of Things is predicted to play a pivotal role in the development of business IT infrastructure. With the help of IoT, we can link billions of smart gadgets that can talk to one another in an automated fashion. One of the most rapidly expanding fields in computer history, with an expected 50 billion devices by the end of 2020. Security techniques, such as encryption, authentication, access control, network security, and application security, are rendered useless when applied to Internet of Things (IoT) devices because of these inherent weaknesses. The Internet of Things (IoT) has attracted the attention of many academics and industry leaders, who have published studies and made statements on the significance of IoT architectural layers for corporate systems (ESs). This literature study included a wide variety of studies that implemented and developed IoT technology in several human domains, including healthcare, smart homes and cities, assisted charities, and monitoring systems. The article also detailed the computers and programs that facilitated each study. Plus, the reliable algorithm used in the research articles' tests and analyses of data. Lastly, drawing parallels between these publications helps to provide insight on the difficulties and possibilities facing potential IoT impact researchers in the industrial sector.

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Classification techniques’ performance evaluation for facial expression recognition

Cited by 22 publications

References 32 publications

COVID-19 detection based on convolution neural networks from CT-scan images: a review

COVID-19 detection based on convolution neural networks from CT-scan images: a review

Application of multilayer perceptron to deep reinforcement learning for stock market trading and analysis

Internet of Things Impact on Web Technology and Enterprise Systems

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