Browsing by Author "Imran, Md. Al Mahmud"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Performance Analysis of Chronic Kidney Disease through Machine Learning Approaches
    (Daffodil International University, 2021-01-28) Emon, Minhaz Uddin; Imran, Md. Al Mahmud; Islam, Rakibul
    Machine learning and data mining play a vital role in health care and also medical information and detection, Now a day machine learning techniques use awareness of some major health risks such as diabetic prediction, brain tumor detection, covid 19 detections, and many more. The kidney is the most important organ of our body and if it has any problem then the impact is more dangerous to our body. Chronic kidney disease (CKD), otherwise referred to as renal disease. Chronic kidney disease requires disorders that damage and reduce the capacity of our kidneys to keep us healthy. So, we need to be concerned about kidney disease to our very primary stage. We take a few attributes to measure our analysis about chronic kidney disease and this attribute is one of the major occurrences of chronic kidney disease. Therefore 8 machine learning classifier are used to measure analysis using weka tools namely: Naive Bayes(NB), Logistic Regression(LG), Multilayer Perceptron(MLP), Stochastic Gradient Descent(SGD), Adaptive Boosting(Adaboost), Bagging, Decision Tree(DT), Random Forest(RF) classifier are used. We feature extraction of all attributes using principal component analysis(PCA). We gain the highest accuracy from the Random Forest(RF) and it is 99% and ROC(receiver operating characteristic) curve value is also highest from other algorithms
  • No Thumbnail Available
    Item
    Predicting Performance Analysis of Garments Women Working Status in Bangladesh Using Machine Learning Approaches
    (2021 6th International Conference on Inventive Computation Technologies (ICICT), IEEE, 2021-02-26) Keya, Maria Sultana; Emon, Minhaz Uddin; Akter, Himu; Imran, Md. Al Mahmud; Hassan, Md. Kamrul; Mojumdar, Mayen Uddin
    Maria Sultana Keya, Minhaz Uddin Emon, Himu Akter, Md. Al Mahmud Imran, Md. Kamrul Hassan, Mayen Uddin Mojumdar Abstract: In Bangladesh, the garment industry has played an important role in economically uplifting a diverse community of poor and marginalized people. There are now 4,825 garment factories that employ more than three million people. Completely 85% of these employees are female. But most of the female workers work to support their family and also contribute his family to lead a minimum life. In this paper, we try to find out relation between their health status, their family earning, their family member information, their working time or how many year they work in this sector and how many time they want to work. The dataset is collected from the Ashulia and Gazipur area garments of Bangladesh. This research work has observed that most of the female workers work at finishing, swing, helper, and cleaner sector. In this sector they cannot get huge salary that's why their income is limited and the range of their salaries is very low. It has also been found that, some women manage their whole family with their own income. Besides they are feeling bored with the same work. Nowadays machine learning and data mining tools play a vital role in finding the measurement of some important factors. This paper analyses the women working performance based on their previous activity and use some machine learning algorithms likely: Decision Tree Classifier (DTC), Logistic Regression (LR), Random Forest Classifier (RFC), and Stochastic Gradient Descent (SGD) we get the best result from Logistic Regression (LR) and it is 69%.