Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.

Abstract-Recently manufacturing industries have experienced a significant improvement. In manufacturing industries, PCB (Printed Circuit Board) manufacturing is known as the most complex to maintain the quality by producing non-defective printed circuit boards for further process. In order to manufacture the printed circuit boards, various electronic components need to be manufactured which urge for manufacturing accuracy and quality assurance. In order to maintain the quality of these products, various schemes have been introduced which are based on the computer vision and data mining techniques. During PCB image acquisition, original image gets contaminated due to occlusions and orientation or camera quality etc. which degrades the manufacturing quality and leads to the defective product manufacture. To cater this issue, we develop a novel approach for defect detection using computer vision scheme where we apply various pre-processing techniques and classification technique. In this work, a new classifier is developed based on the image background, foreground and defect shadow analysis. This article aims on producing an automated process for PCB defect detection using computer vision application with lower complexities. In order to carry out this work, PCB image is transformed into symbols and various features are extracted from the image by dividing image into sub-regions i.e. background, foreground and shadow of defective region. Finally, a binary classifier is constructed with the help of symbolic dynamics to provide improved classification performance. Experimental study shows improved performance of proposed model when compared with existing state of art algorithms. Keywords-PCB defect; computer vision; symbolic pattern; finite automata; classification I. INTRODUCTIONBare printed circuit boards are the type of electronic components which are used for placing other electronic components for any electronic component manufacturing process [1]. PCB is an important part for producing any electronic component. Now a day, technology has grown rapidly which results in higher production of electronic components and devices. During this mass manufacturing because of various issues some electronic components are produced defective which are not acceptable by consumers or doesn't meet industry manufacturing criteria. These issues increase the cost of manufacturing because a manual inspection is required for better quality assurance. Since electronic components are produced in a massive number where manual inspection is not feasible to implement. To adapt up to this issue, different procedures have been produced as of late for robotized PCB imperfection discovery and characterization. Ercal et al. [2] talked about this issue of PCB imperfection recognition where three classes of PCB assessment methodologies are examined which are given as: (a) reference based strategy (b) non-reference based review system and (c) half and half procedure for PCB deformity investigation. Reference based assessment approaches uses correl...

Excavating the prior literature shows that there has been an abundant prior studies in the area of breast cancer detection but, very little work has been put towards 'Early Detection of Breast Cancer.' In the country like US, where majority of the women has higher vulnerabilities of becoming a victim of breast cancer, as evident from history, early detection of breast cancer can play a boon in medical science. This paper therefore makes an attempt where the system is designed considering a dataset of mammogram from DDSM where feature extraction is performed using Discrete Wavelet Transform (DWT), and the feature vectors are then efficiently trained by Artificial Neural Network (ANN). The final trained results are stored in matrix and validation is performed using real time mammogram image to exhibit that the proposed model has successfully accomplished more than 90% in accuracy, sensitivity, and specificity.