Browsing by Author "Riajuliislam, Md"

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Forecasting of Inflation Rate Contingent on Consumer Price Index
(Scopus, 2021) Momo, Shampa Islam; Riajuliislam, Md; Hafiz, Rubaiya
Variations of inflation rate possess a diverse influence on the economic growth of any country. Inflation rate control can be accommodated to stabilize the financial aspect’s condition, including the political area. The way to restrain the inflation rate is the prediction of the inflation rate. This paper proposes forecasting the inflation rate by applying machine learning algorithms: support vector regression (SVR), random forest regressor (RFR), decision tree, AdaBoosting, gradient boosting, and XGBoost. These algorithms are employed since the predicting value is nonlinear and complex. Moreover, the regression and boosting algorithms confer good accuracy, as inflation is a frequent dynamic variable that depends on several factors. The models show decent accuracy using the elements consumer price index (CPI), food, non-food, clothing-footwear, and transportation. Among the models, AdaBoost retrospectives the most desirable outcome with the lowest MSE value of 0.041.
Predicting High Volatility Cryptocurrency Prices using Deep Learning
(Elsevier, 2024-01-15) Ito, Tsutomu; Hasebe, Kodai; Hamakawa, Fumito; Biki, Bidesh Biswas; Ikeda, Satoshi; Takei, Amane; Sakamoto, Makoto; Riajuliislam, Md; Shital, Sabrina Bari; Ito, Takao
Even if you want to make a profit from cryptocurrency, you are worried that you will lose money, and it is difficult to afford it. There are a vast number of papers that study such unpredictable price fluctuations of cryptocurrency. Currently, it is mainstream to use learning deep to predict the price of cryptocurrency. The goal of this research is to predict the price of cryptocurrency over the long-term using deep learning. The algorithms used are LSTM, GRU, and Bi-LSTM. The targeted cryptocurrencies are Bitcoin, Ethereum, Litecoin, and Cardano. Finally, we will compare it with previous research and verify the performance of our model.
Predicting High Volatility Cryptocurrency Prices using Deep Learning
(2024-02-12) Ito, Tsutomu; Ikeda, Satoshi; Takei, Amane; Sakamoto, Makoto; Riajuliislam, Md; Shital, Sabrina Bari; Ito, Takao
Even if you want to make a profit from cryptocurrency, you are worried that you will lose money, and it is difficult to afford it. There are a vast number of papers that study such unpredictable price fluctuations of cryptocurrency. Currently, it is mainstream to use learning deep to predict the price of cryptocurrency. The goal of this research is to predict the price of cryptocurrency over the long-term using deep learning. The algorithms used are LSTM, GRU, and Bi-LSTM. The targeted cryptocurrencies are Bitcoin, Ethereum, Litecoin, and Cardano. Finally, we will compare it with previous research and verify the performance of our model.
Prediction of Pneumonia Disease of Newborn Baby Based on Statistical Analysis of Maternal Condition Using Machine Learning Approach
(2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), IEEE, 2021-03-15) Hasan, Md. Mehedi; Faruk, Md. Omar; Biki, Bidesh Biswas; Riajuliislam, Md; Alam, Khairul; Shetu, Syeda Farjana
Pneumonia is one of the common diseases amongst children in Bangladesh. Many children die from pneumonia in Bangladesh. Pneumonia is an infection that infects the air sacs in one or both lungs. In Bangladesh, nearly 50,000 children die of pneumonia every year. For diseases forecasting, Machine learning algorithms are popular and used extensively. Machine Learning allows us to fulfill such a task with much consequence. We established our dataset from the particular obtainable from our survey. For prognosticating pneumonia, we employed six traditional Machine Learning algorithms. They are K- Nearest Neighbor (KNN), Naive Bayes classifier, Decision Tree, Support Vector Machine (SVM), Neural Network algorithm, and Random Forest. For implementing these algorithms, we applied Scikit-leam, Pandas, NumPy, and for visualizing our data, we have used Matplotlib and seaborn. By proper interpretation, we considered the best performing algorithm for the prediction of pneumonia. We have measured to classify whether pneumonia declines under pneumonia (Positive) and pneumonia (Negative) class. Among all the algorithms, we have chosen the best algorithm which is provided us best accuracy and F1-score. By the best accomplishing algorithm, our model can predict pneumonia quite well.
Prediction of Thyroid Disease (Hypothyroid) in Early Stage Using Feature Selection and Classification Techniques
(International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), 2021-04-12) Riajuliislam, Md; Rahim, Khandakar Zahidur; Mahmud, Antara
Thyroid disease is one of the most common diseases among the female mass in Bangladesh. Hypothyroid is a common variation of thyroid disease. It is clearly visible that hypothyroid disease is mostly seen in female patients. Most people are not aware of that disease as a result of which, it is rapidly turning into a critical disease. It is very much important to detect it in the primary stage so that doctors can provide better medication to keep itself turning into a serious matter. Predicting disease in machine learning is a difficult task. Machine learning plays an important role in predicting diseases. Again distinct feature selection techniques have facilitated this process prediction and assumption of diseases. There are two types of thyroid diseases namely 1. Hyperthyroid and 2.Hypothyroid. Here, in this paper, we have attempted to predict hypothyroid in the primary stage. To do so, we have mainly used three feature selection techniques along with diverse classification techniques. Feature selection techniques used by us are Recursive Feature Selection (RFE), Univar ate Feature Selection (UFS) and Principal Component Analysis (PCA) along with classification algorithms named Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Naive Bayes (NB). By observing the results, we could extrapolate that the RFE feature selection technique helps us to provide constant 99.35% accuracy for all four classification algorithms. Thus it's deduced from our research that RFE helps each classifier to attain better accuracy than all the other feature selection methods used.