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All Journal International Journal of Electrical and Computer Engineering 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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Computer Science & IT Electrical & Electronics Engineering Environmental Science

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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.
Fiber Optic Breakthrough: Terahertz Detection of Illegal Drugs Noor, Khalid Sifulla; Bani, Most. Momtahina; Islam, Md. Safiul; Ferdous, A.H.M. Iftekharul; Hossen, Md. Jakir; Al-Mamun, Abdullah; Badhon, Nasir Uddin
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-019

Abstract

The article presents an illegal drug detector that utilizes photonic crystal fiber (PCF). The fiber structure of the H-PCF comprises a dodecagonal core and circular air gaps in cladding areas. We have analyzed the designed terahertz (THz) frequency range utilizing the Finite Element Method (FEM) and the COMSOL Multiphysics application. The revised design has a high sensitivity in detecting amphetamine (n = 1.518) and cocaine (n = 1.5022) at a frequency of 3 THz, via detection rates of 99.43% and 99.20%, correspondingly. Furthermore, the suggested fiber, which operates at a frequency of 3 THz, has a relatively tiny confinement loss of 4.93×10-08 dB/m and 6.16×10-09 dB/m and a minimal effective material loss of construction of 0.0032 cm-1. In conclusion, it may be stated that drug misuse not only leads to immediate repercussions but also has severe and enduring impacts on human health, potentially resulting in fatality. Hence, it is imperative to accurately and effectively detect illicit substances. H-PCF architecture we offered is well-suited to detect illegal drugs. Doi: 10.28991/ESJ-2024-08-06-019 Full Text: PDF
Student Major Subject Prediction Model for Real-Application Using Neural Network Islam, Aminul; Hoque, Jesmeen Mohd Zebaral; Hossen, Md. Jakir; Basiron, Halizah; Tawsif Khan, Chy. Mohammed
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1490

Abstract

The university admission test is an arena for students in Bangladesh. Millions of students have passed the higher secondary school every year, and only limited government medical, engineering, and public universities are available to pursue their further study. It is challenging for a student to prepare all these three categories simultaneously within a short period in such a competitive environment. Selecting the correct category according to the student's capability became important rather than following the trend. This study developed a preliminary system to predict a suitable admission test category by evaluating students' early academic performance through data collecting, data preprocessing, data modelling, model selection, and finally, integrating the trained model into the real system. Eventually, the Neural Network was selected with the maximum 97.13% prediction accuracy through a systematic process of comparing with three other machine learning models using the RapidMiner data modeling tool. Finally, the trained Neural Network model has been implemented by the Python programming language for opinionating the possible option to focus as a major for admission test candidates in Bangladesh.
Incremental learning based fuzzy reasoning approach for diagnosis of thyroid disease Ramanath, Thirumalaimuthu Thirumalaiappan; Hossen, Md. Jakir
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9140

Abstract

This paper presents a novel hybrid fuzzy logic approach for the classification of thyroid disease. Hybrid fuzzy logic approaches have brought many benefits to the medical data classification problems such as reasoning on uncertain or incomplete data. The machine learning algorithms had been used with the fuzzy expert systems to define the fuzzy rule base. The optimization techniques had been used in the fuzzy expert systems for optimizing the fuzzy membership functions and fuzzy rules. Enhancing the machine learning algorithms and optimization techniques that are integrated with the fuzzy logic method can improve the overall performance of the fuzzy expert system. To deal with the curse of dimensionality problem and to enhance the integration of machine learning algorithm and fuzzy logic method, this paper presents an incremental learning based parallel fuzzy reasoning system (IL-PFRS) for medical diagnosis. In this research work, the decision tree classifier is used to extract the features from dataset. IL-PFRS is applied to classify the thyroid disease which is serious disease that needs attention and earlier detection. The thyroid disease dataset obtained from the UCI machine learning repository is used in this research work where the IL-PFRS showed the classification accuracy of 99% when testing using this dataset.
Enhancing Early Detection of Melanoma: A Deep Learning Approach for Skin Cancer Prediction al Huda, Md Sadi; Ali, Md. Asraf; Hossain, Ajran; Tuz Johora, Fatama; Liew, Tze Hui; Sadib, Ridwan Jamal; Hossen, Md. Jakir; Ahmed, Nasim
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3081

Abstract

Melanoma, a form of skin cancer, is a substantial global public health threat due to its rising prevalence and the potential for severe outcomes if not promptly identified and managed. Detecting skin cancer lesions in their first stages enhances patient outcomes and decreases mortality rates. The core issue investigated in this research paper is the enduring problem of early skin cancer prediction. In the past, individuals often lacked awareness of their skin cancer condition until it had reached late stages. Consequently, this resulted in delayed diagnoses, which restricted the available treatment options and perhaps led to worse outcomes.  This research focuses on finding key attributes and methods in a specialized dataset to effectively differentiate between benign and potentially malignant skin lesions, particularly the implementation of an early-stage skin cancer prediction model. It aims to accurately categorize skin mole pictures as benign or malignant using a Convolutional Neural Network (CNN) model built within the PyTorch framework. The primary aim of this study was to enhance the accuracy and effectiveness of diagnosing skin problems by implementing deep learning algorithms to automate the process of showing such conditions. The model underwent training using 3600 skin mole images sourced from the ISIC-Archive on a GPU RTX 3080. Its outstanding performance is shown by an F1 score of 0.8496 and an accuracy rate of 85%. This research aims to create a predictive model and offer a practical solution that healthcare professionals can readily use for early skin cancer prediction.
Ensemble recursive feature elimination-based ensemble classification for medical diagnosis Ramanathan, Thirumalaimuthu Thirumalaiappan; Hossen, Md. Jakir; Al Mamun, Abdullah; Raja, Joseph Emerson
Indonesian Journal of Electrical Engineering and Computer Science Vol 40, No 2: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v40.i2.pp758-771

Abstract

The application of data mining techniques for the extraction of patterns from medical datasets is useful in the prediction of various diseases from the data of patients. An appropriate feature selection method is required for the medical datasets to give better results for the medical data mining process. In data preprocessing, feature selection is an important process that finds the most relevant features from the dataset. Considering all features of the medical dataset without using any feature selection process may sometimes lead to inaccurate results. Most of the medical datasets contain meaningless data that are not relevant to the data mining process. These data can be eliminated through the feature selection process. This paper presents an integration of an ensemble feature selection approach and an ensemble classification approach through a classifier called the ensemble recursive feature elimination-based ensemble classifier (ERFE-EC) for the classification of medical data. Four different medical datasets were used for testing the ERFE-EC method, which showed promising results.
Co-Authors Ahmed, Nasim al Huda, Md Sadi Al Mamun, Abdullah Al-Mamun, Abdullah Ali, Md. Asraf Badhon, Nasir Uddin Bani, Most. Momtahina Basiron, Halizah Em, Poh Ping Ferdous, A.H.M. Iftekharul Hoque, Jesmeen Mohd Zebaral Hossain, Ajran Hossen, Busrat Islam, Aminul Islam, Md. Safiul Jahan, Busrat Liew, Tze Hui Musha, Ahmmad Noor, Khalid Sifulla Raja, Joseph Emerson Ramanath, Thirumalaimuthu Thirumalaiappan Ramanathan, Thirumalaimuthu Thirumalaiappan Ranjbari, Sima Sadib, Ridwan Jamal Tahabilder, Anik Tawsif Khan, Chy. Mohammed Tuz Johora, Fatama