The reliability of software depends on its ability to function without error. Unfortunately, errors can be generated during any phase of software development. In the field of software engineering, the prediction of software defects during the initial stages of development has therefore become a top priority. Scientific data are used to predict the software's future release. Study shows that machine learning and hybrid algorithms are change benchmarks in the prediction of defects. During the past two decades, various approaches to software defect prediction that rely on software metrics have been proposed. This paper explores and compares well-known supervised machine learning and hybrid ensemble classifiers in eight PROMISE datasets. The experimental results showed that AdaBoost support vector machines and bagging support vector machines were the best performing classifiers in Accuracy, AUC, recall and F-measure.

This paper presents a way to improve physical experiments at the engineering university level using a graphical programming environment for data acquisition. As a case study it is presented the experimental verification of the law of the magnetic circuit. Such a working method for experimentation opens the way for the future engineer to study physical phenomena using the computer.

Production of high quality software at lower cost can be possible by detecting defect prone software modules before the testing process. With this approach, less time and resources are required to produce a high quality software as only those modules are thoroughly tested which are predicted as defective. This paper presents a classification framework which uses Multi-Filter feature selection technique and Multi-Layer Perceptron (MLP) to predict defect prone software modules. The proposed framework works in two dimensions: 1) with oversampling technique, 2) without oversampling technique. Oversampling is introduced in the framework to analyze the effect of class imbalance issue on the performance of classification techniques. The framework is implemented by using twelve cleaned NASA MDP datasets and performance is evaluated by using: F-measure, Accuracy, MCC and ROC. According to results the proposed framework with class balancing technique performed well in all of the used datasets.

In a higher education such as universities, final project are under supervision of one or more supervisors with a similar research interest or topic. The determination of the final project supervisor is an important factor in the work of the student's final project. However, the lack of information about the supervisor can hamper students in making the determination of the supervisor. Thus, a system is needed that can facilitate students in determining the final project or thesis advisors in accordance with the research topic. This problem is the basis of this research. The study is conducted by developing a web-based system and applying the TF-IDF word weighting and cosine similarity method. TF-IDF method is a way to give the weight of the relationship of a word to the document. The cosine similarity is a method for calculating the similarity between two objects expressed in two vectors by using keywords from a document as a measure. The results of the advisor recommendation system can provide recommendations to students regarding the final assignment advisor who has conducted research in accordance with the topic of the student's final assignment written in Indonesian. In 20 testing, the accuracy of the comparison of the results of the system recommendations with the actual data obtained an average of 75% by comparing system recommendation with actual assigned supervisor.

The government of Bangladesh (GoB) recently have introduced the concept of Digital Bangladesh. Education is one of the most vital sectors to make the digital nation. For that reason, the GoB has started to convert primary to e-primary schools. The main objective of this study is to investigate the current Information and Communication Technology (ICT) implementation status in e-primary schools by the GoB. The study is quantitative in nature. The study also develops an ICT implementation status model from the e-primary school systems in Bangladesh. This model has identified the ICT equipment, analyzed the ICT support & equipment, given weighted to each factor and investigated the current state of ICT implementation of e-primary schools in Bangladesh. The study has taken 800 sample schools from 8 divisions to investigate the current ICT implementation status. The study suggested that before implementing the ICT they will make sure all the infrastructure of ICT is available in those primary schools.