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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Jamal Ahmad Dargham
Universiti Malaysia Sabah
Computer Science & IT Electrical & Electronics Engineering

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Ensemble-based face expression recognition approach for image sentiment analysis Ervin Gubin Moung; Chai Chuan Wooi; Maisarah Mohd Sufian; Chin Kim On; Jamal Ahmad Dargham
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2588-2600

Abstract

Sentiment analysis based on images is an evolving area of study. Developing a reliable facial expression recognition (FER) device remains a difficult challenge as recognizing emotional feelings reflected in an image is dependent on a diverse set of factors. This paper presented an ensemble-based model for FER that incorporates multiple classification models: i) customized convolutional neural network (CNN), ii) ResNet50, and iii) InceptionV3. The model averaging ensemble classifier method is used to ensemble the predictions from the three models. Subsequently, the proposed FER model is trained and tested on a dataset with an uncontrolled environment (FER-2013 dataset). The experiment demonstrated that ensembling multiple classifiers outperformed all single classifiers in classifying positive and neutral expressions (91.7%, 81.7% and 76.5% accuracy rate for happy, surprise, and neutral, respectively). However, when classifying disgust, anger, and sadness, the ResNet50 model alone is the better choice. Although the Custom CNN performs the best in classifying fear expression (55.7% accuracy), the proposed FER model can still classify fear expression with comparable performance (52.8% accuracy). This paper demonstrated the potential of using the ensemble-based method to enhance the performance of FER. As a result, the proposed FER model has shown a 72.3% accuracy rate.
A review of hyperspectral imaging-based plastic waste detection state-of-the-arts Owen Tamin; Ervin Gubin Moung; Jamal Ahmad Dargham; Farashazillah Yahya; Sigeru Omatu
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3407-3419

Abstract

Plastic waste issues emerged from the build-up of plastics that negatively impacts the environment. As a result, plastic waste detection is proposed in many research studies to tackle the problems. Therefore, this paper aims to review hyperspectral imaging techniques and machine learning in plastic waste detection. Hyperspectral imaging techniques are found to be effective in detecting plastic waste and microplastics as they were able to capture plastic reflectance spectral by using the near-infrared sensor. However, the review also shows that hyperspectral imaging techniques were less efficient in capturing the electromagnetic spectrum of black plastics due to carbon-black absorption properties. Carbon-black strongly absorbs light in the ultraviolet and infrared spectral range of the electromagnetic spectrum, therefore not detected by the near-infrared sensor. This paper also reviews how machine learning can alternatively detect and sort all types of waste, including plastics. Multiple studies show that the machine learning model achieved good accuracy in detecting all types of plastics based on the waste dataset. Finally, it can be seen that the spectral information of plastic can be used as feature extraction for machine learning models for better plastic detection. It is hoped that this study will contribute to more systematic research on the same topic.
A review of solar drying technology for agricultural produce Mohd Khairulanwar Rizalman; Ervin Gubin Moung; Jamal Ahmad Dargham; Zuhair Jamain; Nurul’azah Mohd Yaakub; Ali Farzamnia
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1407-1419

Abstract

Agriculture contributes to large export earnings for many countries and provides food all over the world. However, most agricultural products need some post-harvest processing, such as drying, to extend their shelf life while still maintaining their respective nutrient quality. One popular post-harvest processing method is drying using solar energy. It is a type of renewable energy that is abundant and free. Conventional dryers use grid electricity and can be expensive to operate. Consequently, there is a growing need for cost-effective solar-powered agricultural dryers that is reasonable for smaller-scale farmers. Although current solar dryers are still not on par with modern electricity-powered dryers, solar dryers have lower running costs and are sustainable and able to generate electricity. They can also be used practically anywhere with abundant solar energy. As numerous solar drying technologies have been proposed over the past decade, it is necessary to assess the current state of solar drying technology in the agricultural sector to identify current advancements and potential research gaps. In this paper, a review of existing solar dryers mechanism and the state of the art of solar drying technology research for agricultural products is presented.
Co-Authors Ali Farzamnia Chai Chuan Wooi Chin Kim On Ervin Gubin Moung Farashazillah Yahya Maisarah Mohd Sufian Mohd Khairulanwar Rizalman Nurul’azah Mohd Yaakub Owen Tamin Sigeru Omatu Zuhair Jamain