Browsing by Author "Rahman, Shadikur"

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An Optimized CNN Model Architecture for Detecting Coronavirus (COVID-19) with X-Ray Images
(Springer, 2022-04-15) Basalamah, Anas; Rahman, Shadikur
This paper demonstrates empirical research on using convolutional neural networks (CNN) of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction. Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge. In this study, CNN architectures such as VGG-16, VGG-19, RestNet50, RestNet18 are compared, and an optimized model for feature extraction in X-ray images from various domains involving several classes is proposed. An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for classifying COVID-19 (Negative or Positive). Then, 2,134 X-rays of normal patients and COVID-19 patients generated by an existing open-source online dataset were labeled to train the optimized models. Among those, the optimized model architecture classifier technique achieves higher accuracy (0.97) than four other models, specifically VGG-16, VGG-19, RestNet18, and RestNet50 (0.96, 0.72, 0.91, and 0.93, respectively). Therefore, this study will enable radiologists to more efficiently and effectively classify a patient’s coronavirus disease.
Assessing the Effectiveness of Topic Modeling Algorithms in Discovering Generic Label with Description
(Springer, 2020-02-13) Rahman, Shadikur; Hossain, Syeda Sumbul; Arman, Md. Shohel; Rawshan, Lamisha; Toma, Tapushe Rabaya; Rafiq, Fatama Binta; Md. Badruzzaman, Khalid Been
Analyzing short text or documents using topic modeling becomes a popular solutions for the increasing number of documents produced in everyday life. For handling the large amount of documents, many topic modeling algorithms are used e.g. LDA, LSI, pLSI, NMF. In this study, we have used LDA, LSI, NMF and also lexical database wordNet synset for candidate labels in our topics labeling. And finally compare the effectiveness of topic modeling algorithms for short documents. Among those LDA gives the better result in terms of WUP similarity. This study will help to select the proper algorithm for labeling topics and can easily identify the meaning of topics.
Context-based News Headlines Analysis Using Machine Learning Approach
(Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2019-08-09) Rahman, Shadikur; Hossain, Syeda Sumbul; Islam, Saiful; Chowdhury, Mazharul Islam; Rafiq, Fatama Binta; Badruzzaman, Khalid Been Md.
An increasing number of people are changing their way of thinking by reading news headlines. The interactivity and sincerity present in online news headlines are becoming influential to society. Apart from that, news websites build efficient policies to catch people’s awareness and attract their clicks. In that case, it is a must to identify the sentiment polarity of the news headlines for avoiding misconception. In this paper, we analyze 3383 news headlines generated by five major global newspapers during a minimum of four consecutive months. In order to identify the sentiment polarity (or sentiment orientation) of news headlines, we use 7 machine learning algorithms and compare those results to find the better ones. Among those Bernoulli Naïve Bayes technique achieves higher accuracy than others. This study will help the public to make any decision based on news headlines by avoiding misconception against any leader or governance and will help to identify the most neutral newspaper or news blogs.
Customer Feedback Prioritization Technique
(Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2019-06-29) Hossain, Syeda Sumbul; Jubayer, S. A. M.; Rahman, Shadikur; Bhuiyan, Touhid; Rawshan, Lamisha; Islam, Saiful
Nowadays, a startup is being very popular and entrepreneurs are increasing day by day. Though we are watching many successful startups e.g. Dropbox, Amazon, Viber and so on, the list of unsuccessful startups is very long. Who is being successful they must have their own strategy, which they apply in their startup and get success. In lean startup strategy, the customers give feedbacks about the startup and the owner understands the demand of customers by collecting feedback from customers and provides service according to the feedback. On the other hand, all the feedbacks from the customers are not important for a startup project. So it is needed to separate or prioritize feedbacks which are needed to execute the startup project. But there are not sufficient techniques for prioritizing the feedbacks collected from customers. By conducting a systematic mapping study and a case study (interview and observation is used), we propose a technique which will be used to prioritize customer feedback in lean startup. This technique will be helpful for the startup projects to become successful.
Polynomial topic distribution with topic modeling for generic labeling
(Daffodil International University, 2018-12-13) Hassan, Md. Rezwan Ul-; Rahman, Shadikur
First of all, we are grateful to the Almighty Allah for giving us the ability to complete the final thesis. We would like to express our gratitude to our supervisor Ms. Syeda Sumbul Hossain for the consistent help of my thesis and research work, through his understanding, inspiration, energy, and knowledge sharing. Her direction helped us to finding the solutions of research work and reach to our final theory. We would like to express my extreme sincere gratitude and appreciation to all of our teachers of Software Engineering department for their kind help, generous advice and support during the study. We are also express our gratitude to all of our friend‘s, senior, junior who, directly or indirectly, have lent their helping hand in this venture. Last but not the least, we would like to thank our family for giving birth to us at the first place and supporting me spiritually throughout my life.
Polynomial Topic Distribution with Topic Modeling for Generic Labeling
(Communications in Computer and Information Science, Springer, 2019-07-19) Hossain, Syeda Sumbul; Ul-Hassan, Md. Rezwan; Rahman, Shadikur
Topics generated by topic models are typically reproduced as a list of words. To decrease the cognitional overhead of understanding these topics for end-users, we have proposed labeling topics with a noun phrase that summarizes its theme or idea. Using the WordNet lexical database as candidate labels, we estimate natural labeling for documents with words to select the most relevant labels for topics. Compared to WUP similarity topic labeling system, our methodology is simpler, more effective, and obtains better topic labels.