Browsing by Author "Siddika, Fatema"

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A Web Based Four-Tier Architecture using Reduced Feature Based Neural Network Approach for Prediction of Student Performance
(Scopus, 2021) Hossen, Md. Anwar; Alamgir, Rakib Bin; Alam, Arman Ul; Siddika, Fatema; Hossain, Shah Fahad; Arman, Md. Shohel
Enhancing student's performance is a significant part of developing quality education in any educational institute. It is very difficult to get promising student performance without student categorization according to their academic performance as there are different standardized students. In this paper, our aim is to determine the performance of the students. For this purpose, a survey has been conducted on students in our university in order to collect data and to analyze and predict the student category based on their performance. Apart from this, another purpose of this study is to examine the effect of the reduced features on the classification model using state-of-art machine learning algorithms. Here, we propose a workflow of web-based four-tier architecture for the student performance prediction that will define the student's category in order to help them exactly pinpoint their learning capabilities. Hence, we used multiple supervised learning-based machine learning techniques for the prediction of student performance. Each of the student category categorized by considering on the top features. The analysis results indicate that we got the highest performance that is 88.00% by using the Artificial Neural Network (ANN) among the classifiers by showing its superiority to the existing model.
A Web Based Four-Tier Architecture using Reduced Feature Based Neural Network Approach for Prediction of Student Performance
(IEEE, 2021-01) Hossen, Md. Anwar; Bin Alamgir, Rakib; Alam, Arman Ul; Siddika, Fatema; Hossain, Shah Fahad; Arman, Md. Shohel
Enhancing student's performance is a significant part of developing quality education in any educational institute. It is very difficult to get promising student performance without student categorization according to their academic performance as there are different standardized students. In this paper, our aim is to determine the performance of the students. For this purpose, a survey has been conducted on students in our university in order to collect data and to analyze and predict the student category based on their performance. Apart from this, another purpose of this study is to examine the effect of the reduced features on the classification model using state-of-art machine learning algorithms. Here, we propose a workflow of web-based four-tier architecture for the student performance prediction that will define the student's category in order to help them exactly pinpoint their learning capabilities. Hence, we used multiple supervised learning-based machine learning techniques for the prediction of student performance. Each of the student category categorized by considering on the top features. The analysis results indicate that we got the highest performance that is 88.00% by using the Artificial Neural Network (ANN) among the classifiers by showing its superiority to the existing model.
Ensemble Method Based Architecture Using Random Forest Importance to Predict Employee's Turn Over
(Journal of Physics: Conference Series, IOP, 2020) Hossen, Md. Anwar; Hossain, Emran; Abdul Khalib, Zahereel Ishwar; Siddika, Fatema
The departure of a skilled employee can create a problem for a company and this incident is increasing globally. Employee turnover has become an important issue these days due to the heavy workload, low pay, low job satisfaction, poor working environment. Companies face problems as their budget will increase, losing skilled manpower and employees’ trust. It’s taking time to adjust for a new employee and bring risk and increase the cost for the company. It is necessary to bring appropriate solutions to the problem. The main purpose of this paper is to predict the turnover of employees with the help of state of the art machine learning classifier. We have determined employee turnover selection factors using some prediction models. We first pre-processed the dataset by removing correlative attributes. Then, we have scaled the attributes. Secondly, a Sequential selection algorithm (SBS) has been using to reduce features from a high number to a relatively small signal-canton. Then use Chi-square and Random Forest important algorithms to determine the most significant shared key features. Then we get average_montly_hours, satisfaction_level, time_spend_company are responsible for the employee’s departure. Then, we have applied different state of the art machine learning algorithm to measure the accuracy. We have achieved the highest accuracy of 99.4% using the reduced feature with 10-Fold Cross-validation by applied the Random Forest classifier and which is higher than the mentioned reference work.
Ensemble Method Based Architecture Using Random Forest Importance to Predict Employee's Turn Over
(Journal of Physics: Conference Series, 2021) Hossen, Md. Anwar; Hossain, Emran; Khalib, Zahereel Ishwar Abdul; Siddika, Fatema
The departure of a skilled employee can create a problem for a company and this incident is increasing globally. Employee turnover has become an important issue these days due to the heavy workload, low pay, low job satisfaction, poor working environment. Companies face problems as their budget will increase, losing skilled manpower and employees' trust. It's taking time to adjust for a new employee and bring risk and increase the cost for the company. It is necessary to bring appropriate solutions to the problem. The main purpose of this paper is to predict the turnover of employees with the help of state of the art machine learning classifier. We have determined employee turnover selection factors using some prediction models. We first pre-processed the dataset by removing correlative attributes. Then, we have scaled the attributes. Secondly, a Sequential selection algorithm (SBS) has been using to reduce features from a high number to a relatively small signal-canton. Then use Chi-square and Random Forest important algorithms to determine the most significant shared key features. Then we get average_montly_hours, satisfaction_level, time_spend_company are responsible for the employee's departure. Then, we have applied different state of the art machine learning algorithm to measure the accuracy. We have achieved the highest accuracy of 99.4% using the reduced feature with 10-Fold Cross-validation by applied the Random Forest classifier and which is higher than the mentioned reference work.
Machine Learning Approach for Software Defect Prediction
(Lecture Notes in Electrical Engineering, Springer, 2020-03-24) Hossen, Md Anwar; Islam, Md. Shariful; Yusof, Nurhafizah Abu Talip; Rahman, Md. Sakib; Siddika, Fatema; Rahman, Mostafijur; Khatun, Sabira; Karim, Mohamad Shaiful Abdul; Mahmud, S. M. Hasan
The software has turn into an imperious part of human’s life. In the recent computing era, many large-scale complex network systems and millions of modern technological devices produce a huge amount of data every second. Among these data, the amount of imbalanced data is relatively excessive. The machine learning model is miss leaded by these imbalanced data. Software Defect Prediction (SDP) is a standout amongst the most helping exercises during the testing phase. The estimated cost of finding and fixing defects is approximately billions of pounds per year. To reduce this problem, software defect prediction has come forth but need fine tuning to have expected efficiency. In this chapter, we have proposed a new model based on machine learning approach to predict software defect and identify the key factors that may help the software engineer to identify the most defect-prone part of the system. The proposed model works as follows. First, need to remove highly correlated features and turn all the feature in the same scale using the scaling feature approach. Second, we have used Synthetic Minority Over-Sampling Technique (SMOTE), Adaptive Synthetic (ADASYN) and Hybrid sampling method to balance highly imbalanced datasets. Third, Random Forest Importance and Chi-square algorithms are chosen to find out the factors which have high effect on software defect. Cross validation is used to remove overriding problem. Scikit-learn library is used for machine learning algorithms. Pandas library is used for data processing. Matplotlib, and PyPlot are used for graph and data visualization respectively. The hybrid sampling method and Random Forest (RF) algorithms achieved the highest prediction accuracy about 93.26% by showing its superiority.