Browsing by Author "Hasan, Md Maruf"

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A Harmful Disorder: Predictive and Comparative Analysis for fetal Anemia Disease by Using Different Machine Learning Approaches
(IEEE, 2023) Hasan, Mahadi; Tahosin, Mst. Sazia; Farjana, Afia; Sheakh, Md. Alif; Hasan, Md Maruf
Anemia is a major issue for public health with significant implications for national development, it remains a largely neglected health problem in many developing countries. Iron deficiency is responsible for at least 50% of all cases of anemia and kills nearly 1 million people each year. Africa and Southeast Asia account for three quarters of these deaths. Surprisingly, one of the top ten risk factors that contributes to the global burden of disease is iron deficiency anemia (IDA). This study investigated the use of machine learning models to predict anemia. The study compared the performance of five different machine learning models: K-Nearest Neighbors, Logistic Regression, Support Vector Machines, Gaussian Naive Bayes, and Light Gradient Boosting Machines. These models are combined using a voting classifier method to improve prediction accuracy. The study highlights the importance of accurately predicting diseases in the medical field. The ability to predict anemia at the right time is essential for effective prevention and treatment. This study demonstrates the potential of machine learning models to predict anemia and improve disease prevention and treatment. Using advanced algorithms and data processing techniques can help doctors make accurate predictions and make decisions, leading to better patient outcomes. This research result shows that the voting classifier achieved 99.95% accuracy.
A study of forecasting stocks price by using deep Reinforcement Learning
(Independent University, Bangladesh, 2023-06) Khan, Razib Hayat; Miah, Jonayet; Rahman, Md Minhazur; Hasan, Md Maruf; Mamun, Muntasir
Financial investors are so concerned now about the future of the stock market and how the market will behave next decade because the world economy is now in an alarming condition which leads to losses in the stock market. That is why traders want to know a little bit about the future forecast of the stock market. So, in this paper, we approach a bit to predict the stock market using the deep reinforcement learning method. Traditional methods for stock price prediction often rely on statistical models or technical indicators, which may struggle to capture the non-linear patterns and sudden shifts in stock prices. In recent years, deep reinforcement learning (DRL) has emerged as a promising approach for predicting stock prices, as it can learn complex patterns from raw data and make decisions based on sequential actions. n this study, we propose a novel framework for stock price prediction using DRL. The framework incorporates a deep neural network as a function approximator, which is trained using the Q-learning algorithm to learn optimal actions for buying, selling, or holding stocks. The neural network takes historical stock price data as input and outputs Qvalues, which represent the expected rewards for different actions at each time step. The best course of action to pursue in each market state is then determined using the Q-values. We performed a sensitivity analysis to investigate the effects of various network designs and hyperparameters on the effectiveness of our DRL-based strategy. We found that the choice of hyperparameters, such as learning rate and exploration rate, had a significant impact on the performance, and tuning these hyperparameters could further improve the prediction accuracy. Our experimental results showed that our DRLbased approach outperformed the traditional methods in terms of predicting stock prices, with higher accuracy and lower prediction errors.
Child and Maternal Mortality Risk Factor Analysis Using Machine Learning Approaches
(IEEE, 2023-05-26) Sheakh, Md. Alif; Tahosin, Mst. Sazia; Hasan, Md Maruf; Islam, Taminul
Global attention is now being paid to maternal and child mortality. The incidence of maternal mortality is high in low and middle-income countries, particularly among adolescents and young adults. Healthcare professionals can monitor the mother's heartbeat during pregnancy to determine fetal viability using CTGs to prevent these deaths. To reduce child and maternal mortality, this work presented a risk factor analysis using machine learning approaches. As part of this study, this work evaluated seven machine learning algorithms. To assess the performance of different categorization algorithms, accuracy, precision, and recall were used. The random forest has achieved the highest 99.98% accuracy among the other algorithms. Initially, the dataset was imbalanced, after applying undersampling and oversampling methods, all algorithms performed excellently. A major focus of the present study was to predict the risk factor of child and maternal mortality using clinical data. Sending an ultrasound pulse and reading the response is how ultrasound devices work. To prevent child and maternal mortality, this analysis is an effective and cost-effective option for healthcare professionals.