Smiling and Non-smiling Emotion Recognition Based on Lower-half Face using Deep-Learning as Convolutional Neural Network

Malallah, Fahad Layth; Al-Jubouri, Ahmed; Sabaawi, Abdulbasit

doi:10.4108/eai.28-6-2020.2298175

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…Table 1 The seven categories of Iraqi paper currency The operation of recognizing Iraq paper currency is achieved using the supervised deep learning (DL) algo-rithm. The DL algorithm is principally based on the convolutional neural network (CNN), which has been wildly applied in different applications such as medical therapy, face recognition, voice classification [18][19][20], and so on. Therefore, the big data is collected aggregating all of the currency categories in both the front side and back side of Iraq paper currency.…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based iraqi banknotes classification system for blind people

Awad¹,

Sharef

Salih

et al. 2022

EEJET

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Modern systems have been focusing on improving the quality of life for people. Hence, new technologies and systems are currently utilized extensively in different sectors of our societies, such as education and medicine. One of the medical applications is using computer vision technology to help blind people in their daily endeavors and reduce their frequent dependence on their close people and also create a state of independence for visually impaired people in conducting daily financial operations. Motivated by this fact, the work concentrates on assisting the visually impaired to distinguish among Iraqi banknotes. In essence, we employ computer vision in conjunction with Deep Learning algorithms to build a multiclass classification model for classifying the banknotes. This system will produce specific vocal commands that are equivalent to the categorized banknote image, and then inform the visually impaired people of the denomination of each banknote. To classify the Iraqi banknotes, it is important to know that they have two sides: the Arabic side and the English side, which is considered one of the important issues for human-computer interaction (HCI) in constructing the classification model. In this paper, we use a database, which comprises 3,961 image samples of the seven Iraqi paper currency categories. Furthermore, a nineteen layers Convolutional Neural Network (CNN) is trained using this database in order to distinguish among the denominations of the banknotes. Finally, the developed system has exhibited an accuracy of 98.6 %, which proves the feasibility of the proposed model.

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Section: Methodsmentioning

confidence: 99%

Deep learning-based iraqi banknotes classification system for blind people

Awad¹,

Sharef

Salih

et al. 2022

EEJET

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show abstract

“…Deep learning. Usually, the deep learning (DL) [22] technique can be exploited for the prediction task by using the convolutional neural network [23]. To explain DL in terms of the proposed paper as depicted in Fig.…”

Section: Methodsmentioning

confidence: 99%

Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT

Alsaydia¹,

Saadallah²,

Malallah³

et al. 2021

EEJET

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During the current outbreak of the COVID-19 pandemic, controlling and decreasing the possibilities of infections are massively required. One of the most important solutions is to use Artificial Intelligence (AI), which combines both fields of deep learning (DL) and the Internet of Things (IoT). The former one is responsible for detecting any face, which is not wearing a mask. Whereas, the latter is exploited to manage the control for the entire building or a public area such as bus, train station, or airport by connecting a Closed-Circuit Television (CCTV) camera to the room of management. The work is implemented using a Core-i5 CPU workstation attached with a Webcam. Then, MATLAB software is programmed to instruct both Arduino and NodeMCU (Micro-Controller Unit) for remote control as IoT. In terms of deep learning, a 15-layer convolutional neural network is exploited to train 1,376 image samples to generate a reference model to use for comparison. Before deep learning, preprocessing operations for both image enhancement and scaling are applied to each image sample. For the training and testing of the proposed system, the Simulated Masked Face Recognition Dataset ( SMFRD) has been exploited. This dataset is published online. Then, the proposed deep learning system has an average accuracy of up to 98.98 %, where 80 % of the dataset was used for training and 20 % of the samples are dedicated to testing the proposed intelligent system. The IoT system is implemented using Arduino and NodeMCU_TX (for transmitter) and RX (for receiver) for the signal transferring through long distances. Several experiments have been conducted and showed that the results are reasonable and thus the model can be commercially applied

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“…During practical training, the agent can ensure accurate responses to inquiries posed by the environment. There are several supervised learning algorithms for DL, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Recurrent Neural Networks (RNNs), which use Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) (Fahad et al, 2020) (Nasim et al, 2019.…”

Section: Deep Supervised Learningmentioning

confidence: 99%

Analyzing Social Media Sentiment: Twitter as a Case Study

Jasim

Saeed

Raewf

2023

ADCAIJ

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This study examines the problem of Twitter sentimental analysis, which categorizes Tweets as positive or negative. Many applications require analyzing public mood, including organizations attempting to determine the market response to their products, political election forecasting, and macroeconomic phenomena such as stock exchange forecasting. Twitter is a social networking microblogging and digital platform that allows users to update their status in a maximum of 140 characters. It is a rapidly expanding platform with over 200 million registered users, 100 million active users, and half of the people log on every day, tweeting out over 250 million tweets. Public opinion analysis is critical for applications, including firms looking to understand market responses to their products, predict political choices, and forecast socio-economic phenomena like bonds. Through the deep learning methodologies, a recurrent neural network with convolutional neural network models was constructed to do Twitter sentiment analysis to predict if a tweet is positive or negative using a dataset of tweets. The applied methods were trained using a publicly available dataset of 1,600,000 tweets. Several model architectures were trained, with the best one achieving a (93.91%) success rate in recognizing the tweets' matching sentiment. The model's high success rate makes it a valuable advisor and a technique that might be improved to enable an integrated sentiment analyzer system that can work in real-world situations for political marketing.

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Smiling and Non-smiling Emotion Recognition Based on Lower-half Face using Deep-Learning as Convolutional Neural Network

Cited by 8 publications

References 23 publications

Deep learning-based iraqi banknotes classification system for blind people

Deep learning-based iraqi banknotes classification system for blind people

Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT

Analyzing Social Media Sentiment: Twitter as a Case Study

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