Intelligent Machine Learning Techniques Research Articles

Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm

Computer-aided detection/diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. The objective of this paper is to review the recent published segmentation and classification techniques and their state-of-the-art for the human brain magnetic resonance images (MRI). The review reveals the CAD systems of human brain MRI images are still an open problem. In the light of this review we proposed a hybrid intelligent machine learning technique for computer-aided detection system for automatic detection of brain tumor through magnetic resonance images. The proposed technique is based on the following computational methods; the feedback pulse-coupled neural network for image segmentation, the discrete wavelet transform for features extraction, the principal component analysis for reducing the dimensionality of the wavelet coefficients, and the feed forward back-propagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 101 images consisting of 14 normal and 87 abnormal (malignant and benign tumors) from a real human brain MRI dataset. The classification accuracy on both training and test images is 99% which was significantly good. Moreover, the proposed technique demonstrates its effectiveness compared with the other machine learning recently published techniques. The results revealed that the proposed hybrid approach is accurate and fast and robust. Finally, possible future directions are suggested.

A study on software reliability prediction models using soft computing techniques

Accurate software reliability prediction can not only enable developers to improve the quality of software but also provide useful information to help them for planning valuable resources. In this paper, we examine an analytical perspective of software reliability prediction using soft computing techniques with specific focus on methods, metrics and datasets. Based on the investigated results, usage percentage of datasets of public domain and soft computing techniques has increased significantly in last ten years. However, measurements using metrics are still the most dominant methods for predicting software reliability. In practice, intelligent machine learning techniques have shown remarkable improvements for reliability prediction. Therefore, software practitioners working on software reliability prediction should continue to use public datasets and other machine learning algorithms to build better prediction models. The significant findings of our study, in conjunction with previous research, could be used as guidelines for practitioners to make predictions in more realistic operating-context.

A Model-Free Reinforcement Learning Approach Using Monte Carlo Method for Production Ramp-Up Policy Improvement - A Copy Exactly Test Case

Abstract Production Ramp-up is a phase in the production timeline which has gained interest from the industry in the literature in order to decrease time-to-market. Intelligent systems and machine learning (ML) techniques have been applied in manufacturing lines and have demonstrated their potential to support knowledge capturing which can aid decision making. However, they mostly focus on supervised learning techniques which require prior knowledge and data pairs are classified without a systematic framework. This work approaches ramp-up as an episodic problem with a clear final target. Ramp-up is formalised as a decision process and a reinforcement learning approach is followed for deriving a policy, for a copy-exactly test case. Finally, a test case of an assembly station ramp-up by, different users is presented. A Monte Carlo approach is used to apply Reinforcement Learning (RL) and an improved policy is generated and evaluated.

Intelligent RFID tag detection using support vector machine

RFID Tag detection/recognition is one of the most critical issues for successful deployment of RFID systems in diverse applications. The main factors influencing tag detection by RFID reader antenna include tag position, relative position of reader, read field length, etc. In this paper, we analyze the characteristics of tag detection for a carton box object on a wooden pallet by an experimental approach based on tag signal strength, and we propose a method for predicting detection related directly to the strength of tag signal using an intelligent machine learning technique called support vector machine (SVM). The use of the proposed method is able to save time and cost by quick prediction of tag detection. Extensive experiments showed that the proposed approach can predict tag recognition for a carton box object with an accuracy at 95% for various reader heights and read field lengths. The proposed approach is effective for determining the best tag detection influencing factor conditioned on the target object with the help of detectability prediction.