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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Nusrat Jahan
Daffodil International University
Computer Science & IT Electrical & Electronics Engineering

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Depression prognosis using natural language processing and machine learning from social media status Md. Tazmim Hossain; Md. Arafat Rahman Talukder; Nusrat Jahan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2847-2855

Abstract

Depression is an acute problem throughout the world. Due to worst and prolong depression many people dies in every year. The problem is that most of the people are not concern of the fact that they are suffering from depression. In this research, our aim was to find out whether an individual is depressed or not by analyzing social media status. Therefore, we focused on real data. Our dataset consists of 2000 sentences, which was collected from different social media platforms Facebook, Twitter, and Instagram. Then, we have performed five data pre-processing approaches for natural language processing (NLP) such as tokenization, removal of stop words, removing empty string, removing punctuations, stemming and lemmatization. For our selected model, we considered that processed data as an input. Finally, we applied six machine learning (ML) classifiers multinomial Naive Bayes (NB), logistic regression, liner support vector classifier, random forest, K-nearest neighbour, and decision tree to achieve better accuracy over our dataset. Among six algorithms, multinomial NB and logistic regression performed well on our dataset and obtained 98% accuracy.
Modelling consumer’s intention to use IoT devices: role of technophilia Nusrat Jahan; Md. Abu Hosen Shawon; Farzana Sadia; Dilara Khanom Nitu; Md. Enam Kobir Ribon; Imran Mahmud
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp612-620

Abstract

The present study has been conducted to examine whether skills and general technology-related value (GTV) required to operate the internet of things (IoT). This study also investigates is there any effect of technophilia to adopt IoT. The research method we use in this quantitative study was the sample survey. For investigating results, 352 surveys were conducted where 26 surveys were led through online and 292 surveys were distributed to different age groups. The proposed model was examined using partial least square structural equation model where the results revealed that IoT skills and General knowledge on technology directly contribute to technophilia which covers behavioural, emotional, and cognitive aspects. That is if people have a fascination for new technologies then they are willing to use IoT.
Predicting fertilizer treatment of maize using decision tree algorithm Nusrat Jahan; Rezvi Shahariar
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i3.pp1427-1434

Abstract

Machine learning approaches are progressively successful in image based analysis such as different diseases prediction as well as level of risk assessment. In this paper, image based data analysis with machine learning technique was applied to fertilizer treatment of maize. We address this issue as our country depend on agricultural field rather than others. Maize has a bright future. To predict fertilizer treatment of maize dataset was comprised of ground coverage region which highlights the green pixels of a maize image. For calculating green pixels from an image we used “Can Eye” tool. The achievement of machine learning approaches is highly dependent on quality and quantity of the dataset which is used for training the machine for better classification result. For this perseverance, we have collected images from the maize field directly. Then processed those images and classified the data into four classes (Less Nitrogen=-N, Less Phosphorus=-P, Less Potassium=-K and NPK) to train our machine using decision tree algorithm to predict fertilizer treatment. We have got 93% classification accuracy for decision tree. Finally, the outcome of this paper is fertilizer treatment of a maize field based on the ground cover percentage, and we implemented this whole paper work using an android platform because of the availability of android mobile phone throughout the world.
Deep learning approach for detecting and localizing brain tumor from magnetic resonance imaging images Abu Shahed Shah. Md. Nazmul Arefin; Shah Mohd. Ishtiaque Ahammed Khan Ishti; Mst. Marium Akter; Nusrat Jahan
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 3: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i3.pp1729-1737

Abstract

Brain is the most important part of the nervous system. Brain tumor is mainly a mass or growth of abnormal tissues in a brain. Early detection of brain tumor can reduce complex treatment process. Magnetic resonance images (MRI) are used to detect brain tumor. In this paper, we have introduced a deep convolutional neural network (CNN) to automatic brain tumor segmentation using MRI medical images which can solve the vanishing gradient problem. Classifying the brain MRI images with Resnet-50 and InceptionV3 in order to identify whether there is tumor or not. After this step, we have compared the accuracy level of both of the CNN models. Thereafter, applied U-Net architecture individually with encoder Resnet-50 and InceptionV3 to avieved promising results. The publicly available low grade gliomas (LGG) segmentation dataset has been utilized to test the model. Before applying the model on the MRI images preprocessing and several augmentation techniques have been done to obtain quality a dataset. U-net architecture with InceptionV3 provided 99.55% accuracy. On the other hand, our proposed method U-net with encoder ResNet-50 showed 99.77% accuracy.
Social crisis detection using Twitter based text mining-a machine learning approach Shoaib Rahman; Nusrat Jahan; Farzana Sadia; Imran Mahmud
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.3957

Abstract

Social-media and blogs are increasingly used for social-communication, an idea and thought publishing platform. Public intentions, wisdom, problems, solutions, mental states are shared in social media. Text is being the best and the most common way to communicate over social networks. All kinds of data shared in social sites like Facebook, Twitter, and Microblogs. People from different pursuance uses these media to publish thoughts and convey messages through text. Consequently, occurrences in social life are rapidly discussed in social blogs in daily manner. This work aims at discovering ongoing social crisis from the Twitter data. Text mining technique and sentiment analysis were applied to detect the current social crisis from the social sites. Twitter data were collected to identify the recent social crisis. Furthermore, the identified crisis was compared to reputed newspapers. A hybrid method used to detect recent social issues resulted nicely. However, our proposed analysis shows identifying rate 89%, 95%, 83%, 53%, and 98% for the top 5 identified crisis accordingly in the date between 27 February and 11 March 2020. The strategy used in this study for the detection of recent social crisis will contribute to the social life and findings of crisis will be eliminated easily.
Bitcoin trading indicator: a machine learning driven real time bitcoin trading indicator for the crypto market Ashikur Rahaman; Abu Kowshir Bitto; Khalid Been Md. Badruzzaman Biplob; Md. Hasan Imam Bijoy; Nusrat Jahan; Imran Mahmud
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4486

Abstract

As opposed to other fiat currencies, bitcoin has no relationship with banks. Its price fluctuation is largely influenced by fresh blocks, news, mining information, support or resistance levels, and public opinion. Therefore, a machine-learning model will be fantastic if it learns from data and tells or indicates if we need to purchase or sell for a little period. In this study, we attempted to create a tool or indicator that can gather tweets in real-time using tweepy and the Twitter application programming interface (API) and report the sentiment at the time. Using the renowned Python module "FBProphet," we developed a model in the second phase that can gather historical price data for the bitcoin to US dollar (BTCUSD) pair and project the price of bitcoin. In order to provide guidance for an intelligent forex trader, we finally merged all of the models into one form. We traded with various models for a very little number of days to validate our bitcoin trading indicator (BTI), and we discovered that the combined version of this tool is more profitable. With the combined version of the instrument, we quickly and with little error root mean square error (RMSE: 1,480.58) generated a profit of $1,000.71 USD.
An effective deep learning network for detecting and classifying glaucomatous eye Md. Tanvir Ahmed; Imran Ahmed; Rubayed Ahmmad Rakin; Mst. Tuhin Akter; Nusrat Jahan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5305-5313

Abstract

Glaucoma is a well-known complex disease of the optic nerve that gradually damages eyesight due to the increase of intraocular pressure inside the eyes. Among two types of glaucoma, open-angle glaucoma is mostly happened by high intraocular pressure and can damage the eyes temporarily or sometimes permanently, another one is angle-closure glaucoma. Therefore, being diagnosed in the early stage is necessary to safe our vision. There are several ways to detect glaucomatous eyes like tonometry, perimetry, and gonioscopy but require time and expertise. Using deep learning approaches could be a better solution. This study focused on the recognition of open-angle affected eyes from the fundus images using deep learning techniques. The study evolved by applying VGG16, VGG19, and ResNet50 deep neural network architectures for classifying glaucoma positive and negative eyes. The experiment was executed on a public dataset collected from Kaggle; however, every model performed better after augmenting the dataset, and the accuracy was between 93% and 97.56%. Among the three models, VGG19 achieved the highest accuracy at 97.56%.
Co-Authors Abu Kowshir Bitto Abu Shahed Shah. Md. Nazmul Arefin Ashikur Rahaman Dilara Khanom Nitu Farzana Sadia Imran Ahmed Imran Mahmud Khalid Been Md. Badruzzaman Biplob Md. Abu Hosen Shawon Md. Arafat Rahman Talukder Md. Enam Kobir Ribon Md. Hasan Imam Bijoy Md. Tanvir Ahmed Md. Tazmim Hossain Mst. Marium Akter Mst. Tuhin Akter Rezvi Shahariar Rubayed Ahmmad Rakin Shah Mohd. Ishtiaque Ahammed Khan Ishti Shoaib Rahman