2023

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Subject: search.filters.subject.Deep Learning

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    Understanding the Dynamics of Dengue in Bangladesh: EDA, Climate Correlation, and Predictive Modeling
    (Independent University, Bangladesh, 2023-07) Meem, Sabrina Masum; Hossain, Mohammed Tahmid; Chowdhury, Jannat Khair; Miah, Md Saef Ullah; Monir, Md. Fahad
    Dengue, a mosquito-borne viral infection, poses a significant threat, especially in warm, tropical climate countries like Bangladesh, India, Thailand, Malaysia, Laos, etc. This study is solely focused on the dengue data of Bangladesh as it explores the historical dengue data spanning 23 years (2000 to 2022) for EDA purposes, with a focus on 9 years (2014-2022) divisional data for model performance analysis. Additionally, climate data was collected for the same period to examine the potential correlation between dengue cases and climate factors. Machine learning (ML) and Deep learning (DL) models, including Random Forest Regression (RFR), Long Short-Term Memory (LSTM), and LSTM with Artificial Neural Networks (ANN), were implemented and validated against ground truth data. The results reveal notable differences in performance between ML and DL models when handling imbalanced datasets with outliers, with RFR outperforming LSTM when compared to the ground truth data. The study uncovers significant correlations between dengue cases and climate factors like humidity, temperature, and precipitation. The insights gained from this research have practical implications for dengue prevention and control efforts in Bangladesh and beyond, paving the way for more effective strategies and interventions.
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    A Deep Learning Approach to Recognize Bangladeshi Shrimp Species
    (Independent University, Bangladesh, 2023-07) Hasan, Md. Mehedi; Nishi, Jubiria Subrin; Habib, Md. Tarek; Islam, Mohammad Monirul; Ahmed, Farruk
    Shrimp, the most popular shellfish in Bangladesh, is a good source of protein, minerals, vitamin D, and iodine that promote a healthy body and balanced nutrition. In Bangladesh shrimp is referred to as white gold. It consumes about 70% of exported agricultural food. In our country, about 56 species of shrimp are found. Most people do not know all of the species very well. Ordinary people even the fisherman are sometimes confused about different species because of looking like the same. To solve the problem in this work we introduced an intelligence mahine that can help people to concede Shrimp species accurately. We expect this work also help the export sector to differentiate the shrimp species monitoring. To achieve the goal, we build a custom CNN algorithm for image processing and feature extraction. We build three different CNN architectures and differentiate them by hyperparameter and number of convolutional layers. Model 1 and Model 3 both obtain an accuracy of 99.01%, however Model 3 was chosen as the final model for Computer Vision integration. Though both models generated the best accuracy why do we use model 3 as the final model? In this work, we will also describe with appropriate reason.
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    Artificial Intelligence in Software Testing: A Systematic Review
    (IEEE Region 10 Technical Conference (TENCON 2023), Thailand, 2023-07) Islam, Mahmudul; Khan, Farhan; Alam, Sabrina; Hasan, Mahady
    Software testing is a crucial component of software development. With the increasing complexity of software systems, traditional manual testing methods are becoming less feasible. Artificial Intelligence (AI) has emerged as a promising approach to software testing in recent years. This systematic review study aims to provide the recent trend and the current state of software testing using AI. This study examines different types of approaches, techniques, and tools used in this area and assesses their effectiveness. The selected articles for this study have been extracted from different research databases using a search string. Initially, 90 articles were extracted from different research libraries. After gradual filtering in three different phases, 20 articles were selected for final review. Around 50 articles were studied to explore the use of AI in software testing and get an in-depth overview of it. The findings of this study suggest that various testing tasks can be automated successfully using AI, including Machine Learning (ML) and Deep Learning (DL), such as Test Case Generation, Defect Prediction, Test Case Prioritization, Metamorphic Testing, Android Testing, Test Case Validation, and White Box Testing. This study concludes that the integration of AI in software testing is simplifying software testing activities while improving overall performance. This study offers a comprehensive analysis of the utilization of AI techniques in different software testing activities.
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    Late and Early Blight Diseases Identification of Potatoes with a Light Weight Hybrid Transfer Learning Model
    (Independent University, Bangladesh, 2023-05) Siddique, Abu Zobayer Bin; Das, Shoibal; Tabassum, Poonam; Tasir, All Moon; Roy, Shovon; Rahman, Md. Saifur; Mridha, M. F.; Islam, Ashraful
    Potatoes are one of the world’s most important commodities, and leaf maladies such as early and late blight can substantially reduce their yield and quality. Hence, both farmers and researchers must prioritize quick and precise illness diagnosis. In our research, we propose a strategy based on transfer learning for classifying toxic and diseased potato leaf tissue. We specifically used our dataset of potato leaf photos to fine-tune the Mobile-Net model, which was a pre-trained convolutional neural network. To enhance the model’s functionality, we also added a few more layers. Our study found that, in comparison to other state-of-the-art methods, our methodology outperformed them all by achieving a multi-class classification accuracy of 99%. Our method can be used to detect and monitor potato leaf maladies in real-world situations, which could eventually contribute to enhancing potato productivity and food security
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    Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques
    (Independent University, Bangladesh, 2023-05) Pasha, Syed Tangim; Islam, Ashraful; Rahman, Mohammed Masudur; Ahmed, Eshtiak; Foysal, Md. Ferdouse Ahmed; Alam, Md Zahangir
    The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%.