Automatic brain tumor detection in MR Images is one of the basic applications of machine vision in medical image processing, which, despite much research, still needs further development. Using multiple machine learning techniques as an ensemble system is one of the solutions that can be effective in achieving this goal. In this paper, a novel method for diagnosing brain tumors by combining data mining and machine learning techniques has been proposed. In the proposed method, each image is initially pre-processed to eliminate its background region and identify brain tissue. The Social Spider Optimization (SSO) algorithm is then utilized to segment the MRI Images. The MRI Images segmentation allows for a more precise identification of the tumor region in the image. In the next step, the distinctive features of the image are extracted using the SVD technique. In addition to removing redundant information, this strategy boosts the speed of the processing at the classification stage. Finally, a combination of the algorithms Naïve Bayes, Support vector machine and K-nearest neighbor is used to classify the extracted features and detect brain tumors. Each of the three algorithms performs feature classification individually, and the final output of the proposed model is created by integrating the three independent outputs and voting the results. The results indicate that the proposed method can diagnose brain tumors in the BRATS 2014 dataset with an average accuracy of 98.61%, sensitivity of 95.79% and specificity of 99.71%. Additionally, the proposed method could diagnose brain tumors in the BTD20 database with an average accuracy of 99.13%, sensitivity of 99% and specificity of 99.26%. These results show a significant improvement compared to previous efforts. The findings confirm that using the image segmentation technique, as well as the ensemble learning, is effective in improving the efficiency of the proposed method.

Rapid and precise reliability evaluation of electronic circuits plays a key role in the design stage of the electronic systems. The task becomes even more difficult when several major parameters contribute into the reliability evaluation. This paper proposes a neural network aided approach as a prediction tool for estimating the useful lifetime of the ball shaped solder joint as the most resistless part under accidental drops in the electronic devices. Several contributory factors including ball grid array (BGA) chip location, printed circuit board (PCB) thickness, solder alloy composition and solder ball volume are considered in our proposed rapid prediction model and their effects are investigated. 480 finite element simulations as well as 20 experimental tests are performed to obtain an enriched database for our neural network based prediction model. The accuracy of the proposed model is calculated as 97.55% in comparison with the finite element and the experimental results. Ability of considering multi contributory factors in the reliability evaluation of the BGA chip makes our proposed approach be a suitable candidate in design for reliability of the electronic systems.INDEX TERMS Solder joint, drop test, lifetime estimation, machine learning.