http://dspace.ewubd.edu:8080/handle/123456789/3366 Sun, 05 Apr 2026 23:18:37 GMT 2026-04-05T23:18:37Z http://dspace.ewubd.edu:8080/handle/123456789/3729 Analyzing Effect of Feature Selection in Software Fault Detection Cynthia, Shamse Tasnim; Rasul, Md. Golam The quality of software is enormously affected by the faults associated with it. Detection of faults at a proper stage in software development is a challenging task and plays a vital role in the quality of the software. Machine learning is now a days a commonly used technique for fault detection and prediction. However, the effectiveness of the fault detection mechanism is impacted by the number of attributes presented in the dataset. This paper thoroughly gives the importance to compare between different machine learning approaches and by observing their performances we can conclude which models perform better to detect fault in the selected software modules and investigates the effect of various feature selection techniques on software fault classification by using NASA’s some benchmark publicly available datasets. Various metrics are used to analyze the performance of the feature selection and classification techniques. The experiment discovers that some particular classifiers can detect the presence of the faults more effectively and by selecting the best features and solving the class imbalance problem can ensure better quality of the software. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh. Tue, 24 Dec 2019 00:00:00 GMT http://dspace.ewubd.edu:8080/handle/123456789/3729 2019-12-24T00:00:00Z http://dspace.ewubd.edu:8080/handle/123456789/3728 A Comparative Study of Hybridized Neural Networks in Estimating Traffic Accident Severity Anik, Md. Mydul Islam; Akram, Wasim; Md. Ashikuzzaman The increasing number of populations causing increase of vehicles which leads to traffic accident. As transportation system expands, it needs to be monitored to assure safety to citizen. Cities are trying to adopt technological advancement in order to minimize traffic accident. Traffic accidents have become one of the largest national health issues and many factors like weather condition, road condition, light condition, etcetera is related to it. In the current paper, several hybridize machine learning models are used on dataset of city Leeds, UK to estimate traffic accident severity. Hybridize Machine learning models are Artificial Neural Network (ANN) with Gradient decent, Principle Component Analysis (PCA) with ANN, Genetic Algorithm with ANN, Particle Swarm Optimization with ANN. These models are also compared with other machine learning models such as Support Vector Machine (SVM), Naïve Bayes, Nearest Centroid, Logistic Regression, K Nearest Neighbor Classification and Random Forest. Comparison was done considering performance evaluation of each model’s accuracy result. Genetic Algorithm with ANN showed promising result of 86.63% accuracy which is the highest score of all model results. Whereas, Nearest Centroid Method gave 55% of accuracy resulting lowest of all. The Results and findings obtained in this study are significant which can provide invaluable information on reducing traffic accident. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh. Tue, 24 Dec 2019 00:00:00 GMT http://dspace.ewubd.edu:8080/handle/123456789/3728 2019-12-24T00:00:00Z http://dspace.ewubd.edu:8080/handle/123456789/3727 Bangladeshi Vehicle License Plate Detection and Recognition Hasan, Md. Mehedi; Das, Anik Kumar; Alam, Md. Zahid recognition (OCR) to identify vehicles using their license plate. In this project, an algorithm has been proposed to detect and read Bengali license plate. Nowadays automatic vehicle monitoring system has drawn the importance due to maintain numerous traffic that are increasing rapidly in Bangladesh. Every vehicle has unique identification (license plate).So it is very easy and simple task to track and monitor any particular vehicle using their license plate. Because it is an automatic system that save a lot of time and workers instead of tracking and monitoring manually. For these purpose Automatic Number Plate Recognition (ANPR) is designed and developed. Automatic Number plate Recognition plays a vital part for controlling and managing vehicle and vehicle related works. This automatic detection system can be efficient for toll collection system, vehicle parking system, traffic control system, vehicle-tracking system, finding lost vehicle as well as detecting guilty vehicle involved in crime for police etc. There are mainly two types license plate exist in Bangladeshi Vehicle. The green background license plate is for commercial use and the white background license plate is for personal used vehicle. These license plates have four major part ordering in two line. First line describes the area name and type of vehicle and the second line describes vehicle class and registration number. Many approaches are used for ANPR but it needs a simple method with low complexity for detecting vehicle as fast as possible. This algorithm has three major steps- detection, segmentation and recognition. For detection firstly, it uses the feature of Haar Cascade, which is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is used to detect objects in other images. It is well known for being able to detect faces and body parts in an image, but can be trained to identify almost any object. Then boundary box approach is use to segment these characters separately for area name, type, class and registration number and convert to text file from image. Finally, with the help of Template matching algorithm it recognizes the word and number used in Bengali license plate. This system has less complexity and high efficiency for number plate detection and recognition. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh. Tue, 24 Dec 2019 00:00:00 GMT http://dspace.ewubd.edu:8080/handle/123456789/3727 2019-12-24T00:00:00Z http://dspace.ewubd.edu:8080/handle/123456789/3726 Paraphrased Question Answering Using Recurrent Neural Network in Bangla Language Patwary, Nazmus Sakib; Hasan, Md. Mohaiminul; Rahman, Tanvir Recent studies on QA (Question Answering) system in English language have been emerged extensively with the composition of NLP (Natural Language Processing) and IR (Information Retrieval) by amplifying miniature sub tasks to accomplish a whole AI-system having capability of answering and reasoning complicated and long questions through understating paragraph. In our proposed study, we present a general heuristic framework, an end-to-end model used for paraphrased question answering using single supporting line which is the initial appearance ever in Bangla language. Corpus dataset was scrapped from Bangla wiki and then questions were generated corresponding context have been used to learn the model. Translated bAbI dataset (1 supporting fact) [5][6] in Bangla language has been also incorporated with to experiment the proposed model manually. To predict appropriate answer, model is trained with question-answer pair and a supporting line. For comparing our task applying variation of basic RNN (Recurrent Neural Network): LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) different accuracy has been found. For further accomplishment, synthetic and semantic word relevance in high dimension vector space: Bangla Word2vec (word embedding system) is added to the system as sentence representation along with PE (Positioning Encoding) and which outperforms both memory network GRU and LSTM precisely. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering of East West University, Dhaka, Bangladesh. Tue, 24 Dec 2019 00:00:00 GMT http://dspace.ewubd.edu:8080/handle/123456789/3726 2019-12-24T00:00:00Z