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Abdullah-Al Nahid
Researcher at Khulna University
Publications - 69
Citations - 1315
Abdullah-Al Nahid is an academic researcher from Khulna University. The author has contributed to research in topics: Convolutional neural network & Computer science. The author has an hindex of 12, co-authored 59 publications receiving 598 citations. Previous affiliations of Abdullah-Al Nahid include Macquarie University.
Papers
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Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach
TL;DR: An electricity theft detection system is proposed based on a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) architecture that can classify both the majority class and the minority class with good accuracy.
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Effective Intrusion Detection System Using XGBoost
TL;DR: This paper reflects a model designed to measure the various parameters of data in a network such as accuracy, precision, confusion matrix, and others, and XGBoost is employed on the NSL-KDD (network socket layer-knowledge discovery in databases) dataset to get the desired results.
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Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering
TL;DR: A Convolutional Neural Network, a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification using novel DNN techniques guided by structural and statistical information derived from the images.
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Involvement of Machine Learning for Breast Cancer Image Classification: A Survey
Abdullah-Al Nahid,Yinan Kong +1 more
TL;DR: This work has put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification, and described the involvement of the conventional Neural Network, Logic Based classifiers such as the Random Forest algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods.
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Histopathological breast-image classification using local and frequency domains by convolutional neural network
Abdullah-Al Nahid,Yinan Kong +1 more
TL;DR: This paper has classified a set of Histopathological Breast-Cancer images utilizing a state-of-the-art CNN model containing a residual block and examined the performance of the novel CNN model as Histopathology image classifier.