Browsing by Author "Islam, Md. Jihadul"

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Effect of climate change on electricity demand and power generation of Bangladesh
(BRAC University, 2018-03) Wares, S.M. Abrar Fahim; Hasan, Md. Mehedi; Islam, Md. Jihadul; Saleh, Syed Taha; Khan, Dr. Shahidul Islam
Twenty first century has faced a lot of challenges among them climate change is one of the major concerning issue. Climate change has threatened to the human settlement industrial sectors specially the power sector. Frequent study has been conducted to identify to impact of climate variation on electricity demand and power generation in Bangladesh. For maintain the minimum living standard and economic growth, a sustainable power generation and electricity market is necessary. The objective of this study is to detect the effect of climate change on electricity demand and power generation in developed country like Bangladesh. The study consists of two cases (a) Electricity demand and power generation in Bangladesh (b) Electricity market in Bangladesh. By conducting this research, some direct and indirect parameters affecting the electricity demand, power generation and electricity market due to environment change. The parameters will shake the efficiency of the power generation and can create physical threat on infrastructure. Electricity demand also affected by climate change for instant heat island. Consequently the electricity market of Bangladesh has been changed according to global market. To investigate the impact on electricity demand and power generation present and future power capacitated has been noted till 2100. This study identified the effect of climate change power generation capacity which is followed by electricity demand and electricity market. Due to climate change in Bangladesh 30% of total power generation especially in coastal area will face severe risk. Additionally electricity demand will be affected by heat island due to 2.4℃ temperature rise.
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.