Daffodil International University

About: Daffodil International University is a education organization based out in Dhaka, Bangladesh. It is known for research contribution in the topics: Computer science & Photonic-crystal fiber. The organization has 1509 authors who have published 1622 publications receiving 8593 citations. The organization is also known as: DIU & Daffodil University.

NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison

Abstract: Digital world is growing very fast and become more complex in the volume (terabyte to petabyte), variety (structured and un-structured and hybrid), velocity (high speed in growth) in nature. This refers to as ‘Big Data’ that is a global phenomenon. This is typically considered to be a data collection that has grown so large it can’t be effectively managed or exploited using conventional data management tools: e.g., classic relational database management systems (RDBMS) or conventional search engines. To handle this problem, traditional RDBMS are complemented by specifically designed a rich set of alternative DBMS; such as - NoSQL, NewSQL and Search-based systems. This paper motivation is to provide - classification, characteristics and evaluation of NoSQL databases in Big Data Analytics. This report is intended to help users, especially to the organizations to obtain an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes of data.

Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques

Abstract: Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).

Design of D-shaped elliptical core photonic crystal fiber for blood plasma cell sensing application

Abstract: In this research, the proposed design presents the compact sensing performances while infiltrating the blood fluid in the central hole of the D-shaped elliptical dual-core photonic crystal fiber (PCF) configuration. The properties such as index difference, coupling length, and transmission spectrum pertains the sensing property of the blood plasma cell. The proposed sensor can detect plasma by finite element method (FEM) which can be utilized to detect the variation of plasmon of light using plasma materials.

Water Pollution and its Impact on the Human Health

Abstract: River pollution has been one of the main topics in the environmental issue of urban Dhaka, the capital city of Bangladesh. This study was conducted to find out the pollution situation of Turag river and the health problem of the surrounding residents. The results clearly determine that the water quality of Turag river may not be in a position to sustain the aquatic life and not suitable for using domestic purpose. This is indicated by the very low dissoloved oxygen (DO) levels and other measured parameters in the river. The maximum recorded values of pH, color, turbidity, biochemical oxygen demand (BOD5), hardness, total dissolved solids (TDS), chloride (Cl-), carbon-di-oxide (CO2) and chemical oxygen demand (COD) were 7.1 mg/L, 625 ptcu, 97.2, 4.65 mg/L, 1816 mg/L, 676mg/L, 5 mg/L, 15.5, and 78 mg/L, respectively. The maximum concentration of turbidity, BOD, hardness, TDS, and COD found in the Turag river is much higher than the standard permissible limit. The study also provides evidence that local communities are suffering from a variety of health problems including skin, diarrhea, dysentery, respiratory illnesses, anemia and complications in childbirth. Yellow fever, cholera, dengue, malaria and other epidemic diseases are also available in this area. Furthermore, the people are suffering by the odor pollution and respiratory problems.