Browsing by Author "Hossen, Md Shakil"

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    Emotion That Speaks:
    (2024-07-13) Shimul, Nazmul Islam; Hossen, Md Shakil
    Human emotions are spontaneous mental states produced by changes in facial muscles, leading to expressions. In various human-computer interaction applications, techniques for nonverbal communication like facial expressions, eye movements, and gestures are employed. Facial emotion, in particular, is widely utilized for conveying an individual's emotional states and feelings. However, emotion recognition is challenging due to the need for a clear distinction between facial expressions and the complexity and variability of emotions. Conventional machine learning algorithms frequently have difficulties in accurately recognizing emotions since they heavily depend on humangenerated elements. To address this issue, we explored the use of deep learning models for emotion detection based on facial expressions. Specifically, we evaluated Vision Transformer (ViT), VGG19, InceptionV3, EfficientNet, and ResNet50 models. The findings of our study demonstrated that Vision Transformer (ViT) achieved the highest accuracy rate of 82.96%, followed by Efficient-Net at 82.36%, ResNet50 at 80.87%, InceptionV3 at 79%, and VGG19 at 78.22%. Based on its excellent accuracy and robustness, we propose using the Vision Transformer (ViT) for the identification of six distinct emotions: anger, neutrality, happiness, sadness, disgust, and surprise.
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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%.