Browsing by Author "Akter, Flora"

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A Convolutional Neural Network Based Potato Leaf Diseases Detection Using Sequential Model
(IEEE, 2023-04-03) Bonik, Choyon Chandra; Akter, Flora; Rashid, Md. Harunur; Sattar, Abdus
One of Bangladesh’s primary agricultural products is the potato. In recent decades, Bangladesh has seen a surge in the popularity of potato farms. Nonetheless, farmer’s expenses in potato production are rising as a result of a number of illnesses. Nonetheless, the high cost of potato production is mostly attributable to a number of illnesses that are affecting the crop. Which is wreaking havoc on the farmer’s schedule. In order to modernize the potato industry and speed up disease diagnosis, automation has been implemented. In spite of the claims to the contrary, potato leaf disease is a serious problem that can severely reduce crop yields. The leaves of diseased potato plants will show symptoms of early blight, Septoria blight, late blight, and other diseases. If such outbreaks are discovered at the initial level and enough intervention is done, the farmer will not be at risk of incurring significant economic losses. Based on the results of this study, a new model is presented for accurately identifying and detecting illnesses in potato leaf stands using image processing. While there are several methods that may be utilized in machine learning, the Convolutional Neural Network (CNN) model is what’s being employed here to identify the disease in potato leaf photos. This work implements a CNN based sequential model to predict the disease of potato leaves. This research achieved 94.2% model accuracy on this model. The presented model was tested on both typical and disordered potato leaves in an effort to distinguish between the two. Next, the algorithm is applied to the images, and the potato tree’s leaf is classified as either healthy or unhealthy.
A Convolutional Neural Network Based Potato Leaf Diseases Detection Using Sequential Model
(23-01-29) Bonik, Choyon Chandra; Akter, Flora; Rashid, Md. Harunur
One of Bangladesh's primary agricultural products is the potato. In recent decades, Bangladesh has seen a surge in the popularity of potato farms. Nonetheless, farmer’s expenses in potato production are rising as a result of a number of illnesses. Nonetheless, the high cost of potato production is mostly attributable to a number of illnesses that are affecting the crop. Which is wreaking havoc on the farmer's schedule. In order to modernize the potato industry and speed up disease diagnosis, automation has been implemented. In spite of the claims to the contrary, potato leaf disease is a serious problem that can severely reduce crop yields. The leaves of diseased potato plants will show symptoms of early blight, Septoria blight, late blight, and other diseases. If such outbreaks are discovered at the initial level and enough intervention is done, the farmer will not be at risk of incurring significant economic losses. Based on the results of this study, a new model is presented for accurately identifying and detecting illnesses in potato leaf stands using image processing. While there are several methods that may be utilized in machine learning, the Convolutional Neural Network (CNN) model is what's being employed here to identify the disease in potato leaf photos. This work implements a CNN based sequential model to predict the disease of potato leaves. This research achieved 94.2% model accuracy on this model. The presented model was tested on both typical and disordered potato leaves in an effort to distinguish between the two. Next, the algorithm is applied to the images, and the potato tree’s leaf is classified as either healthy or unhealthy.
Machine Learning Approaches to Predict Breast Cancer
(Daffodil International University, 2022-06-20) Islam, Taminul; Kundu, Arindom; Khan, Nazmul Islam; Bonik, Choyon Chandra; Akter, Flora; Islam, Md. Jihadul
Nowadays, Breast cancer has risen to become one of the most prominent causes of death in recent years. Among all malignancies, this is the most frequent and the major cause of death for women globally. Manually diagnosing this disease requires a good amount of time and expertise. Breast cancer detection is time-consuming, and the spread of the disease can be reduced by developing machine-based breast cancer predictions. In Machine learning, the system can learn from prior instances and find hard-to-detect patterns from noisy or complicated data sets using various statistical, probabilistic, and optimization approaches. This work compares several machine learning algorithms' classification accuracy, precision, sensitivity, and specificity on a newly collected dataset. In this work Decision tree, Random Forest, Logistic Regression, Naïve Bayes, and XGBoost, these five machine learning approaches have been implemented to get the best performance on our dataset. This study focuses on finding the best algorithm that can forecast breast cancer with maximum accuracy in terms of its classes. This work evaluated the quality of each algorithm's data classification in terms of efficiency and effectiveness. And also compared with other published work on this domain. After implementing the model, this study achieved the best model accuracy, 94% on Random Forest and XGBoost.