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Background and aim: Many sophisticated data mining and machine learning algorithms have been used for software defect prediction (SDP) to enhance the quality of software. However, real‐world SDP data sets suffer from class imbalance, which leads to a biased classifier and reduces the performance of existing classification algorithms resulting in an inaccurate classification and prediction. This work...
The success of Deep Neural Networks (DNNs) for various applications like language processing (NLP), image processing, character recognition inspired to use machine learning (ML) and Evolutionary Computation (EC) techniques for improving learning process. Using evolutionary algorithms to improve the efficiency of deep learning attained some success. However, these techniques are unable to reduce the...
Renewable energy offers alternative sources of energy which is in general pollution free, climate friendly, sustainable and unlimited. Therefore in the starting of 21st century, Government, utilities and research communities are working together to develop an intelligent power system that has potential to better integrate renewable energy sources with the grid. However, there are a number of potential...
Intrusion detection is one of the most challenging problems in network security. Detection of attacks on a particular network is not an easy task. Since recently, several machine learning, pattern classification and evolutionary techniques have been used on KDD99Cup dataset for detecting different kinds of intrusions that exist in the dataset. In this paper, we present a genetic algorithm (GA)-based...
This research is concentrated on using unsupervised learning technique and digital image processing to cluster mineral materials, Electrofused Magnesia Oxide specifically, for industry automation. We proposed a technique to construct an image database by generating data from images using a digital image process. This is based on a simple histogram mode and intensity deviation. A group of two popular...
Learning typical motion patterns or activities from videos of crowded scenes is an important visual surveillance problem. To detect typical motion patterns in crowded scenarios, we propose a new method which utilizes the instantaneous motions of a video, i.e, the motion flow field, instead of long-term motion tracks. The motion flow field is a union of independent flow vectors computed in different...
Boosting is a general approach for improving classifier performances. In this research we investigated these issues with the latest Boosting algorithm AdaBoostMl. A trial and error classifier feeding with the AdaBoostMl algorithm is a regular practice for classification tasks in the research community. We provide a novel statistical information- based rule method for unique classifier selection with...
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