Browsing by Author "Ryan, Abdullah Al"

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    FinTech: Deep Learning-Based Sentiment Classification of User Reviews from Various Bangladeshi Mobile Financial Services
    (Springer Nature Limited, 2023-07-24) Ryan, Abdullah Al; Mahmud, Md. Shihab; Mahi, Hasibul Hasan Chowdhury; Hossen, Md Shakil; Shimul, Nazmul Islam; Noori, Sheak Rashed Haider
    Banking has become an integral part of our lives. Fintech (Financial Technology) skyrocketed the number of people willing to use Mobile Financial Services (MFS) for their daily financial transactions. The banks are providing their services via mobile applications, which can be found on the Google Play Store. These Mobile Financial Services (MFS) provide mobility and increase efficiency by 10-fold. With an astonishing number of users came an abundant number of reviews for these apps. User reviews are the backbone of an application’s success. They provide information about hands-on experience. This study mainly focuses on the reactions of the users of such apps. Sentiment analysis is being used to draw out emotions from the users based on their written reviews. The primary goal of this paper is to examine the points of view of such application users. A total of 5414 pieces of data were collected from the Google Play Store and classified as negative, neutral, or positive. The data model has been evaluated using CNN, LSTM, and BiLSTM algorithms. Compared to CNN and LSTM, the BiLSTM algorithm produced the best model with an accuracy of 97.07%.
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    Forecasting Tea Production in the Context of Bangladesh Utilizing Machine Learning
    (IEEE, 2023-07-15) Ryan, Abdullah Al; Shuvessa, Silvia Kh; Mamun, Shahriar; Arpita, Habiba Dewan; Ahamed, Mr. Md. Sazzadur
    One of the most popular drinks, second only to water, is tea. Bangladesh ranks as the world’s 10th-largest manufacturer of tea. Tea has a big impact on poverty reduction, rural development, and nutrition security. Over the past ten years, Bangladesh has increased its tea supply. According to the BTB figures, over 96.51 million kg were produced in 2021, an increase of almost 54% from that of 2012. As tea is a profitable crop from Bangladesh’s perspective, tea yield prediction can play a significant role in increasing the production of tea. In this paper, tea yield has been forecast using various machine learning algorithms based on area-based tea production and their weather data from 1968 to 2021. Necessary data has been collected from BBS, BARC, and BMD government organizations. Eight climate factors have been used for the research. The data model has been evaluated by using eight classification and regression algorithms, with the Random Forest classifier showing the best accuracy of 97%. With this approach, tea production can be increased while reducing food threats and managing it for other nations.