المؤلفون: Islam, Taminul, Sadik, Md Rezwane, Islam, Md. Fajle Rabbi, Mona, Tanzina Rahman, Rahman, Tanjila, Foysal, Md. Musfiqur Rahman

المصدر: 2023 2nd International Conference on Futuristic Technologies (INCOFT) Futuristic Technologies (INCOFT), 2023 2nd International Conference on. :1-7 Nov, 2023

Relation: 2023 2nd International Conference on Futuristic Technologies (INCOFT)

المؤلفون: Islam, Taminul, Islam, Monjurul, Rudra, Rahul, Pranto, Ismail Hossain, Mahmud, Md.Tanvir, Rahman Foysal, Md. Musfiqur

المصدر: 2023 2nd International Conference on Futuristic Technologies (INCOFT) Futuristic Technologies (INCOFT), 2023 2nd International Conference on. :1-7 Nov, 2023

Relation: 2023 2nd International Conference on Futuristic Technologies (INCOFT)

المؤلفون: Islam, Taminul, Sheakh, Md. Alif, Tahosin, Mst. Sazia, Hena, Most. Hasna, Akash, Shopnil, Jardan, Yousef A. Bin, Wondmie, Gezahign Fentahun, Nafidi, Hiba-Allah, Bourhia, Mohammed

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition

الوصف: Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires significant time and expertise. Since detecting breast cancer is a time-consuming process, preventing its further spread can be aided by creating machine-based forecasts. Machine learning and Explainable AI are crucial in classification as they not only provide accurate predictions but also offer insights into how the model arrives at its decisions, aiding in the understanding and trustworthiness of the classification results. In this study, we evaluate and compare the classification accuracy, precision, recall, and F-1 scores of five different machine learning methods using a primary dataset (500 patients from Dhaka Medical College Hospital). Five different supervised machine learning techniques, including decision tree, random forest, logistic regression, naive bayes, and XGBoost, have been used to achieve optimal results on our dataset. Additionally, this study applied SHAP analysis to the XGBoost model to interpret the model's predictions and understand the impact of each feature on the model's output. We compared the accuracy with which several algorithms classified the data, as well as contrasted with other literature in this field. After final evaluation, this study found that XGBoost achieved the best model accuracy, which is 97%.
Comment: Accepted for the Scientific Reports (Nature) journal. 32 pages, 12 figures

URL الوصول: http://arxiv.org/abs/2404.04686

المؤلفون: Roy, Utsha, Tahosin, Mst. Sazia, Hassan, Md. Mahedi, Islam, Taminul, Imtiaz, Fahim, Sadik, Md Rezwane, Maleh, Yassine, Sulaiman, Rejwan Bin, Talukder, Md. Simul Hasan

مصطلحات موضوعية: Computer Science - Computation and Language, Computer Science - Machine Learning

الوصف: The rise of fake news has made the need for effective detection methods, including in languages other than English, increasingly important. The study aims to address the challenges of Bangla which is considered a less important language. To this end, a complete dataset containing about 50,000 news items is proposed. Several deep learning models have been tested on this dataset, including the bidirectional gated recurrent unit (GRU), the long short-term memory (LSTM), the 1D convolutional neural network (CNN), and hybrid architectures. For this research, we assessed the efficacy of the model utilizing a range of useful measures, including recall, precision, F1 score, and accuracy. This was done by employing a big application. We carry out comprehensive trials to show the effectiveness of these models in identifying bogus news in Bangla, with the Bidirectional GRU model having a stunning accuracy of 99.16%. Our analysis highlights the importance of dataset balance and the need for continual improvement efforts to a substantial degree. This study makes a major contribution to the creation of Bangla fake news detecting systems with limited resources, thereby setting the stage for future improvements in the detection process.
Comment: Accepted for publication in the 7th International Conference on Networking, Intelligent Systems & Security. The conference Proceedings will be published by ACM International Conference Proceeding Series (ICPS) ISBN N{\deg}: 979-8-4007-0019-4. 8 pages, 11 figures

URL الوصول: http://arxiv.org/abs/2404.01345

المؤلفون: Sarker, Toqi Tahamid, Islam, Taminul, Ahmed, Khaled R

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition

الوصف: Analyzing and detecting cannabis seed variants is crucial for the agriculture industry. It enables precision breeding, allowing cultivators to selectively enhance desirable traits. Accurate identification of seed variants also ensures regulatory compliance, facilitating the cultivation of specific cannabis strains with defined characteristics, ultimately improving agricultural productivity and meeting diverse market demands. This paper presents a study on cannabis seed variant detection by employing a state-of-the-art object detection model Faster R-CNN. This study implemented the model on a locally sourced cannabis seed dataset in Thailand, comprising 17 distinct classes. We evaluate six Faster R-CNN models by comparing performance on various metrics and achieving a mAP score of 94.08\% and an F1 score of 95.66\%. This paper presents the first known application of deep neural network object detection models to the novel task of visually identifying cannabis seed types.
Comment: 6 pages, 2 figures, this has been submitted and accepted for publication at IEEE - ICACCS 2024

URL الوصول: http://arxiv.org/abs/2403.10722

المؤلفون: Sheakh, Md. Alif, Sazia Tahosin, Mst., Hasan, Md Maruf, Islam, Taminul, Islam, Onamika, Rana, Md Masum

المصدر: 2023 11th International Symposium on Digital Forensics and Security (ISDFS) Digital Forensics and Security (ISDFS), 2023 11th International Symposium on. :1-6 May, 2023

Relation: 2023 11th International Symposium on Digital Forensics and Security (ISDFS)

المؤلفون: Islam, Taminul, Vuyia, Adifa, Hasan, Mahadi, Rana, Md Masum

المصدر: 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES) Computational Intelligence and Sustainable Engineering Solutions (CISES), 2023 International Conference on. :813-819 Apr, 2023

Relation: 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES)

المؤلفون: Rahman, Md Habibur, Islam, Taminul, Rana, Md Masum, Tasnim, Rehnuma, Mona, Tanzina Rahman, Sakib, Md. Mamun

المصدر: 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES) Computational Intelligence and Sustainable Engineering Solutions (CISES), 2023 International Conference on. :358-364 Apr, 2023

Relation: 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES)

المؤلفون: Talukder, Md. Simul Hasan, Sulaiman, Rejwan Bin, Chowdhury, Mohammad Raziuddin, Nipun, Musarrat Saberin, Islam, Taminul

مصطلحات موضوعية: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning

الوصف: Potatoes are the third-largest food crop globally, but their production frequently encounters difficulties because of aggressive pest infestations. The aim of this study is to investigate the various types and characteristics of these pests and propose an efficient PotatoPestNet AI-based automatic potato pest identification system. To accomplish this, we curated a reliable dataset consisting of eight types of potato pests. We leveraged the power of transfer learning by employing five customized, pre-trained transfer learning models: CMobileNetV2, CNASLargeNet, CXception, CDenseNet201, and CInceptionV3, in proposing a robust PotatoPestNet model to accurately classify potato pests. To improve the models' performance, we applied various augmentation techniques, incorporated a global average pooling layer, and implemented proper regularization methods. To further enhance the performance of the models, we utilized random search (RS) optimization for hyperparameter tuning. This optimization technique played a significant role in fine-tuning the models and achieving improved performance. We evaluated the models both visually and quantitatively, utilizing different evaluation metrics. The robustness of the models in handling imbalanced datasets was assessed using the Receiver Operating Characteristic (ROC) curve. Among the models, the Customized Tuned Inception V3 (CTInceptionV3) model, optimized through random search, demonstrated outstanding performance. It achieved the highest accuracy (91%), precision (91%), recall (91%), and F1-score (91%), showcasing its superior ability to accurately identify and classify potato pests.

URL الوصول: http://arxiv.org/abs/2306.06206

المؤلفون: Islam, Taminul, Kundu, Arindom, Ahmed, Tanzim, Khan, Nazmul Islam

المصدر: 2022 IEEE 7th International conference for Convergence in Technology (I2CT) Convergence in Technology (I2CT), 2022 IEEE 7th International conference for. :1-6 Apr, 2022

Relation: 2022 IEEE 7th International conference for Convergence in Technology (I2CT)