Browsing by Author "Rahman, Mokhlesur"

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Analysing the ML and DL-based Models for Predicting Malarial Fever Prior to Clinical Trial
(IEEE, 2023-05-24) Islam, Md Robiul; Jeba, Tanjina Nur; Zulfiker, Md. Sabab; Rahman, Mirza Shahriyar; Rahman, Mokhlesur
Malaria is a deadly disease caused by unicellular protozoan parasites of the Plasmodium genus. This disease is widespread throughout the world. Confirming the presence of parasites early on in all cases of malaria allows for the administration of species-specific antimalarial medication, which reduces mortality and points to other illnesses when the diagnosis is negative. Nonetheless, light microscopy of thin and thick PB films stained with May-Grünwald-Giemsa (MGG) remains the gold standard. Because this is a labor-intensive process that relies on the expertise of a pathologist, medical professionals in areas of the world where malaria is not common may have difficulty diagnosing cases of the disease. This study used thirteen different machine-learning models to predict malaria fever. The Gaussian NB, Logistic Regression, XGB, Bagging Classifier, Random Forest Classifier, Extra Trees Classifier, Gradient Boosting Classifier, Hist Gradient Boosting Classifier, LGBM Classifier, Decision Tree Classifier, Ada Boost Classifier, SGD Classifier, and K Nearest Neighbors (KNN) Classifier were among the models used. This study classifies malaria cases using data obtained from chest X-ray images for this study. A dataset with 1079 patient records and 23 attributes was created. These characteristics were obtained from the Kaggle repository. Out of these 23 attributes, 80% of the data were used to train the model, and the remaining 20% were used to assess the validation accuracy. It has been shown that the Gaussian NB models were the most accurate, with a 97.66% accuracy rate.
Rose Plant Disease Detection using Deep Learning
(IEEE, 2023-05-24) Alvy, Md. Ali- Al; Khan, Golam Kibria; Alam, Mohammad Jahangir; Islam, Saiful; Rahman, Mokhlesur; Rahman, Mirza Shahriyar
The detection and identification of rose plant disease is the focus of this investigation. Identification and detection are essential components of contemporary agro technology. In this case, AI technology was utilized to identify a disease in rose plants, although plant disease detection is difficult for sustainable agriculture. There are several instances of rose plant disease, and as a result, fascinating decoration is being lost. Due to this situation, which is getting worse every day in Bangladesh, the economy of agricultural sector is suffering. Bangladesh's population relies heavily on agriculture industry for their revenue. This study includes some disease detection of rose plants, albeit not all plants are affected equally by the illness. The plant leaf provides the plant with vital sustenance. When a leaf is ill, the plant is at its most vulnerable. Due to the accessibility of the sick leaf, disease identification is difficult. Agriculture field must be properly assessed to see significant improvements in proposed work. The best resource for creating this kind of disease detection model is deep learning technology. Image pre-processing and model analysis are steps in the disease detection construction process. Few CNN architectures are used in this study, including ResNet50, VGG-16 (Visual Geometry Group), MobileNetV2, and Inception V3. Four diseases have been identified in rose plant leaves. Here, image processing is investigated using a discovered approach and obtain a MobileNetV2 model accuracy of 96.11%.
Visual Assessment and Genetic Distance Analysis of Gamma- Irradiated Lemon Leaf Mutants Developed Using the Leaf-Cut Method
(Research and Development Wing, MIST, 2025-12-30) Afnan, Jawata; Azad, Moriom; Rahman, Mokhlesur; Sarkar, Md Shaheen
Ionizing radiation, particularly gamma rays, is widely used to induce genetic mutations for improving morphological traits and enhancing genetic diversity in plants. In citrus plants, such mutations can be achieved by irradiating leaves and propagating the resulting mutants through vegetative methods. This study was conducted at the BINA research farm with the objective of developing Citrus limon mutants using gamma radiation and analyzing their morphological characteristics through visual inspection, alongside genetic diversity assessment using hierarchical clustering. A total of 400 fresh leaves were collected from the mother plant (BINA Lebu-1) and exposed to different doses of gamma radiation (0, 60, 80, and 100 Gy), divided into four treatment batches. The irradiated samples were planted in unit plots following a Randomized Complete Block Design (RCBD). Root and shoot development were monitored visually after 3 to 4 months. Maximum root length was observed at 100 Gy, the highest root quantity at 60 Gy, and the highest success rate at 80 Gy. After 7–8 months, mature plants were further assessed, and leaves were collected for genomic analysis. Genetic relationships among the different treatment groups were evaluated using a Dendrogram generated through simple hierarchical clustering. The results indicated clear genetic dissimilarities between irradiated (60, 80, and 100 Gy) and non-irradiated (0 Gy) plants, with the 100 Gy group showing the greatest genetic distance from the others. Due to time constraints, fruit production could not be observed. Nonetheless, the study demonstrates the effectiveness of combining visual inspection and genetic distance analysis in evaluating gamma-induced citrus mutants developed via the leaf-cut method.