Browsing by Author "Islam, Md. Arafat"

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Machine Learning Classifier Algorithms for Predicting Malnutrition Among Under Five Children in Asia
(Daffodil International University, 2025-01-13) Islam, Md. Arafat
Malnourished children may have serious health issues. Furthermore, doctors often struggle to pinpoint the underlying causes of their patients' ailments, leading them to perform surgeries that may not be appropriate for all children. This is a frequent reason why children die. As a result, undernourished children are put in grave danger. Therefore, the primary goal of our research is to use AI to forecast the starvation status of children aged 0 to 5 in Asia. We looked for active research papers from 2010 to 2020 that accepted our point of view, consolidated the data, and attempted to identify benefits and downsides. Like I said before, we used an acceptable open-source dataset for this. They also studied several articles to gain an understanding of the benefits and drawbacks of ML techniques. Eight common ML classifiers Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, Bernolli Naive Bayes, Complement Naive Bayes, Decision Tree, and Gradient Boosting predict malnutrition in children under 5 with excellent accuracy. Finally, they searched for algorithms with the highest accuracy scores. Logistic Regression and K-Nearest Neighbors performed best, with train accuracy of 1.000 and 0.98 and success rates of 95.34% and 93.02%, respectively. Furthermore, the application of logistic regression classification indicated a very significant capacity to detect differences. They looked at eight machine learning algorithms to discover which one was the most successful. Among them, Logistic Regression and K-Nearest Neighbors do very well. My aim is to alleviate the future suffering of malnourished children. My next research will focus on Bangladesh's highland and coastal areas, which have poor educational levels and a high risk of child marriage.
Marketing and Sales Activities of an Uprising Real Estate Company in Bangladesh
(Daffodil International University, 2023-12-07) Islam, Md. Arafat
In this report, I would like to emphasize that real estate encompasses both tangible and intangible assets. Tangible assets in real estate include land, buildings, and vehicles, while intangible assets encompass various forms of documentation, such as contractual agreements. ASSK Developers Ltd. stands as an environmentally conscious response to the rapid urbanization in Bangladesh. As a requirement of the BRE program, I completed a three-month internship at ASSK Developers Ltd. within the marketing department, with my primary goal being to gain insights into the organization's marketing activities. The purpose of this internship report is to analyze the current marketing situation within the organization, encompassing its objectives, mission, vision, and departments. This internship report offers an in-depth analysis of ASSK Developers Ltd.'s marketing activities, including the selection of a target market, product offering, pricing strategies, and policies for target customers. It contains comprehensive information about the overall marketing efforts of ASSK Developers Ltd. The concluding section of the report provides important findings related to ASSK Developers Ltd, along with suggestions from my own perspective. Ultimately, this report serves to elucidate the marketing endeavors of ASSK Developers Limited.
Predicting Potato Leaf Diseases with Convolutional Neural Networks
(Daffodil International University, 2023-09-12) Islam, Md. Arafat
Early disease diagnosis in potato leaves is complicated by the wide range of crop types, agricultural disease signs, and environmental factors involved. These problems make it difficult to detect diseases in potato leaves at an early stage. For the purpose of identifying diseases in potato leaves, a number of machine-learning techniques have been developed. The models used to detect crop species and agricultural illnesses are only tested on photographs of plant leaves from a specific geographical area, limiting the effectiveness of existing methodologies. The farmer can prevent severe financial losses by promptly detecting and controlling such outbreaks. The results of this study contribute to a unique approach that makes use of image processing to accurately detect illnesses in potato leaf populations. There are several machine learning methods for spotting symptoms of disease in potato leaves pictures; here, we employ the Convolutional Neural Network (CNN) model. The goal of this research is to develop a convolutional neural network (CNN)-based sequential model for disease prediction in potato leaves. This study's model accuracy was 92.58%. The presented model was tested on both typical and deformed potato leaves, with mixed results. Next, the algorithm is applied to the images, and the potato tree's leaves are classified as healthy or unhealthy.