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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Uddin, Jia
Woosong University
Computer Science & IT Electrical & Electronics Engineering

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Feature Optimization for Machine Learning Based Bearing Fault Classification Mohiuddin, Mohammad; Islam, Md Saiful; Uddin, Jia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 3: September 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i3.5671

Abstract

The most critical and essential parts of rotating machinery are bearings. The main problem of the bearing fault classification is to select the fault features effectively because all extracted features are not useful, and the high-dimensional features give poor performances and slow down the training process. Due to the effective feature selection problem, the bearing fault diagnosis method does not achieve a satisfactory result. The main goal of this paper is to extract the effective fault features with an optimization technique to classify the bearing faults using machine learning algorithms. Since wavelet entropy can determine complexity and degree of order of a vibration signal, this research uses it in features optimization.  The proposed wavelet entropy-based optimization technique reduces the dimensionality of input, elapsed time and raises the learning process. Four Machine learning algorithms (naïve Bayes, support vector machine, artificial neural network and KNN) are applied to classify the bearing faults using the optimized features.    To evaluate the proposed method, Case Western Reserve University’s (CWRU’s) bearing dataset is used which consists of three types of bearing faults. The accuracy and robustness of the bearing fault classification are tested by adding noise to the vibration raw signals at various levels of Signal-to-Noise Ratio (SNR). Experimental results show that the proposed method is very highly reliable in detecting bearing faults compared to the conventional methods.
Transfer Learning for Detecting Alzheimer’s Disease in Brain Using Magnetic Resonance Images Islam, Md. Monirul; Uddin, Jia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 1: March 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i1.5069

Abstract

Alzheimer’s Disease (AD) is one of the most concerning diseases because the patients show very few symptoms at the earlier stages. Dementia is very common in patients who have suffered brain damage or those who have suffered from psychotic trauma. Patients who have a lot of age suffer the most from it. Magnetic resonance imaging (MRI) is widely used to clinically treat patients with Alzheimer’s. Currently, there is no known remedy for the disease. We can only identify and try to give the proper medications to give some relief to patients. In this study, we have collected MRI data from patients with 4 different stages of Alzheimer’s. The purpose of this paper is to build a model to securely detect these stages for the betterment of medical science. We implemented a transfer learning method with state-of-the-art models such as ResNet50, DenseNet121, and VGG19. We proposed our method with these models which have pre-trained weights of “ImageNet”. The layers that we added are our novelty. We were able to achieve 97.70% accuracy on our best pre-trained model with an F1 score of 97% and a precision of 97% on our test data.
Bengali Word Detection from Lip Movements Using Mask RCNN and Generalized Linear Model Bhuiyan, Abul Bashar; Uddin, Jia
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 12, No 2: June 2024
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v12i2.5088

Abstract

Speech processing with the help of lip detection and lip reading is an advancing field. For this, we need proper algorithms and techniques to detect lips and movements of lips perfectly. Lip detection and configuration are the most important parts of speech recognition. In this paper, we focus on detecting the lip segment properly. Mask R-CNN (Regional Convolutional Neural Network) performs object detection and instance segmentation per video frame to detect the lip segment. The process of mask R-CNN adds only a small overhead to Faster R-CNN and is quite simple to train, running at 5 frames per second. The Mask R-CNN involves keypoint detection which helps to extract the location of the lip landmarks pixel by pixel. Once the lip region is extracted and the landmarks are highlighted, we observe how the lip landmarks change as the object's lips move over time to each Bengali word. The keypoint changes that are observed during each millisecond are then the landmarks used to train the GLM (Generalized Linear Model). In addition, we compare the performance of GLM with Naive Bayes, Logistic Regression, and Decision Tree. The GLM has exhibited the highest 91.8% accuracy, whereas the Naive Bayes, Logistic Regression, and Decision Tree show the accuracy of 87.1%, 38.3%, and 82.2%, respectively.
Co-Authors Bhuiyan, Abul Bashar Islam, Md Saiful Islam, Md. Monirul Mohiuddin, Mohammad