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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Emerging Science Journal Indonesian Journal of Electrical Engineering and Computer Science
Hossen, Md. Jakir
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Automotive Engineering Computer Science & IT Electrical & Electronics Engineering Environmental Science

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Journal : International Journal of Electrical and Computer Engineering

 1 
Efficient lane marking detection using deep learning technique with differential and cross-entropy loss Al Mamun, Abdullah; Em, Poh Ping; Hossen, Md. Jakir; Tahabilder, Anik; Jahan, Busrat
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4206-4216

Abstract

Nowadays, researchers are incorporating many modern and significant features on advanced driver assistance systems (ADAS). Lane marking detection is one of them, which allows the vehicle to maintain the perspective road lane. Conventionally, it is detected through handcrafted and very specialized features and goes through substantial post-processing, which leads to high computation, and less accuracy. Additionally, this conventional method is vulnerable to environmental conditions, making it an unreliable model. Consequently, this research work presents a deep learning-based model that is suitable for diverse environmental conditions, including multiple lanes, different daytime, different traffic conditions, good and medium weather conditions, and so forth. This approach has been derived from plain encode-decode E-Net architecture and has been trained by using the differential and cross-entropy losses for the backpropagation. The model has been trained and tested using 3,600 training and 2,700 testing images from TuSimple, a robust public dataset. Input images from very diverse environmental conditions have ensured better generalization of the model. This framework has reached a max accuracy of 96.61%, with an F1 score of 96.34%, a precision value of 98.91%, and a recall of 93.89%. Besides, this model has shown very small false positive and false negative values of 3.125% and 1.259%, which bits the performance of most of the existing state of art models.
A deep learning approach for COVID-19 and pneumonia detection from chest X-ray images Musha, Ahmmad; Al Mamun, Abdullah; Tahabilder, Anik; Hossen, Md. Jakir; Hossen, Busrat; Ranjbari, Sima
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3655-3664

Abstract

There has been a surge in biomedical imaging technologies with the recent advancement of deep learning. It is being used for diagnosis from X-ray, computed tomography (CT) scan, electrocardiogram (ECG), and electroencephalography (EEG) images. However, most of them are solely for particular disease detection. In this research, a computer-aided deep learning model named COVID-CXDNetV2 has been presented to detect two separate diseases, coronavirus disease 2019 (COVID-19) and pneumonia, from the X-ray images in real-time. The proposed model is made based on you only look once (YOLOv2) with residual neural network (ResNet) and trained by a vast X-ray images dataset containing 3788 samples of three classes named COVID-19 pneumonia and normal. The model has obtained the maximum overall classification accuracy of 97.9% with a loss of 0.052 for multiclass classification (COVID-19, pneumonia, and normal) and 99.8% accuracy, 99.52% sensitivity, 100% specificity with a loss of 0.001 for binary classification (COVID-19 and normal), which beats some current state-of-the-art results. Authors believe that this method will be applicable in the medical domain for the diagnosis and will significantly contribute to real life.
The ethics of AI technology in academic work: assessing the line between assistance and plagiarism Patwary, Md. Owafeeuzzaman; Islam, Md. Reazul; Islam, Abtahi; Khan, Nur-e Sarjina; Al–Jubair, Md. Abdullah; Hossen, Md. Jakir; Mridha, M. F.
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp924-944

Abstract

The integration of artificial intelligence (AI) into academia has transformed educational practices and enhanced personalized learning and problem-solving capabilities. However, this raises significant ethical concerns regarding the balance between legitimate assistance and plagiarism. This study investigated public perceptions of AI in academic settings, focusing on its impact on effectiveness, dependency, and ethical considerations of AI use. A survey of 498 respondents from various educational roles was conducted, and the data were analyzed using SPSS for descriptive statistics, chi-square tests, and regression analyses. The results identified a significant correlation between people’s educational roles and their interaction with AI tools (χ2(6) = 16.488, p = 0.036), reflecting the diverse patterns of interaction within the academic community. More frequent use of AI was linked to less dependency (β = −0.298, p < 0.001), contradicting the widespread belief of over-reliance on AI. Age and educational role had limited explanatory value in perception of AI dependency issues (R2 = 0.033). The findings indicate a strong correlation between AI usage frequency and dependency levels, with increased exposure to AI fostering a more critical approach rather than a dependent one. Concerns regarding the unethical use of AI, inaccuracies in AI-generated content, and the need for clear institutional policies were also highlighted. This study underscores the importance of responsible AI integration, advocating for ethical frameworks and educational interventions to ensure that AI enhances learning without compromising academic integrity.
Stress detection during job interview using physiological signal Mand, Ali Afzalian; Sayeed, Md. Shohel; Hossen, Md. Jakir; bin Zuber, Muhammad Amer Ridzuan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5531-5542

Abstract

A job interview can be challenging and stressful even when one has gone through it many times. Failure to handle the stress may lead to unsuccessful delivery of their best throughout the interview session. Therefore, an alternative method which is preparing a video resume and interview before the actual interview could reduce the level of stress. An intelligent stress detection is proposed to classify individuals with different stress levels by understanding the physiological signal through electrocardiogram (ECG) signals. The Augsburg biosignal toolbox (AUBT) dataset was used to obtain the state-of-art results. Only five selected features are significant to the stress level were fed into neural network multi-layer perceptron (MLP) as the optimum classifier. This stress detection achieved an accuracy of 92.93% when tested over the video interview dataset of 10 male subjects who were recording the video resume for the analysis purposes.
Co-Authors Ahmed, Nasim al Huda, Md Sadi Al Mamun, Abdullah Al-Mamun, Abdullah Ali, Md. Asraf Al–Jubair, Md. Abdullah Badhon, Nasir Uddin Bani, Most. Momtahina Basiron, Halizah bin Zuber, Muhammad Amer Ridzuan Em, Poh Ping Ferdous, A.H.M. Iftekharul Hishamuddin, Muhammad Hoque, Jesmeen Mohd Zebaral Hossain, Ajran Hossen, Busrat Husni Zai, Mohamed Ashraf Ibrahim, Azmi Islam, Abtahi Islam, Aminul Islam, Md. Reazul Islam, Md. Safiul Jahan, Busrat Khan, Nur-e Sarjina Liew, Tze Hui Man, Abdullah Mand, Ali Afzalian Mridha, M. F. Musha, Ahmmad Noor, Khalid Sifulla Patwary, Md. Owafeeuzzaman Raja, Joseph Emerson Ramanath, Thirumalaimuthu Thirumalaiappan Ramanathan, Thirumalaimuthu Thirumalaiappan Ranjbari, Sima Sadib, Ridwan Jamal Sayeed, Md. Shohel Tahabilder, Anik Tawsif Khan, Chy. Mohammed Tuz Johora, Fatama Yusof, Ashaari