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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal of Electrical Engineering and Computer Science
Arselan Ashraf
International Islamic University Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Development of video-based emotion recognition using deep learning with Google Colab Teddy Surya Gunawan; Arselan Ashraf; Bob Subhan Riza; Edy Victor Haryanto; Rika Rosnelly; Mira Kartiwi; Zuriati Janin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i5.16717

Abstract

Emotion recognition using images, videos, or speech as input is considered as a hot topic in the field of research over some years. With the introduction of deep learning techniques, e.g., convolutional neural networks (CNN), applied in emotion recognition, has produced promising results. Human facial expressions are considered as critical components in understanding one's emotions. This paper sheds light on recognizing the emotions using deep learning techniques from the videos. The methodology of the recognition process, along with its description, is provided in this paper. Some of the video-based datasets used in many scholarly works are also examined. Results obtained from different emotion recognition models are presented along with their performance parameters. An experiment was carried out on the fer2013 dataset in Google Colab for depression detection, which came out to be 97% accurate on the training set and 57.4% accurate on the testing set.
On the review of image and video-based depression detection using machine learning Arselan Ashraf; Teddy Surya Gunawan; Bob Subhan Riza; Edy Victor Haryanto; Zuriati Janin
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i3.pp1677-1684

Abstract

Machine learning has been introduced in the sphere of the medical field to enhance the accuracy, precision, and analysis of diagnostics while reducing laborious jobs. With the mounting evidence, machine learning has the capability to detect mental distress like depression. Since depression is the most prevalent mental disorder in our society at present, and almost the majority of the population suffers from this issue. Hence there is an extreme need for the depression detection models, which will provide a support system and early detection of depression. This review is based on the image and video-based depression detection model using machine learning techniques. This paper analyses the data acquisition techniques along with their databases. The indicators of depression are also reviewed in this paper. The evaluation of different researches, along with their performance parameters, is summarized. The paper concludes with remarks about the techniques used and the future scope of using the image and video-based depression prediction. 
Efficient Pavement Crack Detection and Classification Using Custom YOLOv7 Model Arselan Ashraf; Ali Sophian; Amir Akramin Shafie; Teddy Surya Gunawan; Norfarah Nadia Ismail; Ali Aryo Bawono
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4362

Abstract

It is crucial to detect and classify pavement cracks as part of maintaining road safety. The inspection process for identifying and classifying cracks manually is tedious, time-consuming, and potentially dangerous for inspectors. As a result, an efficient automated approach for detecting road cracks is essential for this development. Numerous issues, such as variations in intensity, uneven data availability, the inefficacy of traditional approaches, and others, make it challenging to accomplish. This research has been carried out to contribute towards developing an efficient pavement crack detection and classification system. This study uses state of the art deep learning algorithm, customized YOLOv7 model. Data from two sources, RDD2022, a publicly available online dataset, and the second set of data gathered from the roads of Malaysia have been used in this investigation. In order to have balanced data for training, many image preprocessing techniques have been applied to the data, such as augmentations, scaling, blurring, etc. Experimental results demonstrate that the detection accuracy of the YOLOv7 model is significant, 92% on the RDD2022 dataset and 88% on our custom dataset. This study reports the outcomes of experiments conducted on both datasets. RDD2022 achieved a precision of 0.9523 and a recall of 0.9545. On the custom dataset, the resulting values for precision and recall were 0.93 and 0.9158, respectively. The results of this study were compared to those of other recent studies in the same field in order toestablish a benchmark. Results from the proposed system were more encouraging and surpassed the benchmarking ones. 
Assessing the performance of YOLOv5, YOLOv6, and YOLOv7 in road defect detection and classification: a comparative study Najiha 'Izzaty Mohd Yusof; Ali Sophian; Hasan Firdaus Mohd Zaki; Ali Aryo Bawono; Abd Halim Embong; Arselan Ashraf
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6317

Abstract

Road defect inspection is a crucial task in maintaining a good transportation infrastructure as road surface distress can impact user’s comfortability, reduce the lifetime of vehicles’ parts, and cause road casualties. In recent years, machine learning has been adapted widely in various fields, including object detection, thanks to its superior performance and the availability of high computing power which is generally needed for its model training. Many works have reported using machine-learning-based object detection algorithms to detect defects, such as cracks in buildings and roads. In this work, YOLOv5, YOLOv6 and YOLOv7 models have been implemented and trained using a custom dataset of road cracks and potholes and their performances have been evaluated and compared. Experiments on the dataset show that YOLOv7 has the highest performance with mAP@0.5 score of 79.0% and an inference speed of 0.47 m for 255 test images.
Machine learning-based pavement crack detection, classification, and characterization: a review Arselan Ashraf; Ali Sophian; Amir Akramin Shafie; Teddy Surya Gunawan; Norfarah Nadia Ismail
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5345

Abstract

The detection, classification, and characterization of pavement cracks are critical for maintaining safe road conditions. However, traditional manual inspection methods are slow, costly, and pose risks to inspectors. To address these issues, this article provides a comprehensive overview of state-of-the-art machine vision and machine learning-based techniques for pavement crack detection, classification, and characterization. The paper explores the process flow of these systems, including both machine learning and traditional methodologies. The paper focuses on popular artificial intelligence (AI) techniques like support vector machines (SVM) and neural networks. It underscores the significance of utilizing image processing methods for feature extraction in order to detect cracks. The paper also discusses significant advancements made through deep learning strategies. The main objectives of this research are to improve efficiency and effectiveness in pavement crack detection, reduce inspection costs, and enhance safety. Additionally, the article presents data gathering approaches, various datasets for developing road crack detection models, and compares different models to demonstrate their advantages and limitations. Finally, the paper identifies open challenges in the field and provides valuable insights for future research and development efforts. Overall, this paper highlights the potential of AI-based techniques to revolutionize pavement maintenance practices and significantly improve road safety.
Co-Authors Abd Halim Embong Ali Aryo Bawono Ali Aryo Bawono Ali Sophian Amir Akramin Shafie Bob Subhan Riza Edy Victor Haryanto Edy Victor Haryanto, Edy Victor Hasan Firdaus Mohd Zaki Mira Kartiwi Najiha 'Izzaty Mohd Yusof Norfarah Nadia Ismail Norfarah Nadia Ismail Rika Rosnelly Teddy Surya Gunawan Zuriati Janin