Browsing by Author "Moharram, Md. Shakil"

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A Transfer Learning Approach for Face Recognition Using Average Pooling and MobileNetV2
(Daffodil International University, 2022-07-01) Shamrat, F. M. Javed Mehedi; Chakraborty, Sovon; Moharram, Md. Shakil; Roy, Tonmoy; Rahman, Masudur; Aronya, Biraj Saha
Facial recognition is a fundamental method in facial-related science such as face detection, authentication, monitoring, and a crucial phase in computer vision and pattern recognition. Face recognition technology aids in crime prevention by storing the captured image in a database, which can then be used in various ways, including identifying a person. With just a few faces in the frame, most facial recognition systems function sufficiently when the techniques have been tested under artificial illumination, with accurate facial poses and non-blurry images. In our proposed system, a face recognition system is proposed using average pooling and MobileNetV2. The classifiers are implemented after a set of preprocessing steps on the retrieved image data. To compare the model is more effective, a performance test on the result is performed. It is observed from the study that MobileNetV2 triumphs over average pooling with an accuracy rate of 98.89% and 99.01% on training and test data, respectively.
Sports Events Classification Using Convolutional Neural Networks
(Daffodil International University, 2018-11) Shultana, Shahana; Moharram, Md. Shakil
Analysis of different sports data to get valuable insight has become immensely important now-a-days. Profuse application of Artificial Intelligence in different sectors has become a very popular trend as well. However, application of AI in sports analytics is still a new research domain left for exploration. With a view to applying AI in sports analytics, we have deployed Inception V3 and MobileNet which are Google's most popular Convolutional Neural Networks to successfully recognize 5 different sports events from a huge image dataset of these events. We also developed a Convolutional Neural Network model which name is SP-Net and we trained our proposed model with these 5 different sports events. SP-Net correctly predicted the class almost all images during the period of testing and gives a high performance. In terms of performance our proposed model SP Net surpass Inception v3 and MobileNet both of these models. Besides, Inception v3 and MobileNet also achieved a very high performance in terms of accuracy, precision, recall and f-measure while applied on the target dataset for successful classification.
Supervised Machine Learning Based Liver Disease Prediction Approach with LASSO Feature Selection
(Bulletin of Electrical Engineering and Informatics, 2021) Afrin, Saima; Shamrat, F. M. Javed Mehedi; Nibir, Tafsirul Islam; Muntasim, Mst. Fahmida; Moharram, Md. Shakil; Imran, M. M.; Abdulla, Md
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.