Nowadays, conventional security method of using passwords can be easily forged by unauthorized person. Hence, biometric cues such as fingerprints, voice, palm print, and face are more preferable for recognition but to preserve the liveliness, another one important biometric trait is vein pattern, which is formed by the subcutaneous blood vessels that contain all the achievable recognition properties. Accordingly, in this paper, we propose a multibiometric system using palm vein, hand vein, and finger vein. Here, Holoentropy-based thresholding mechanism is newly developed for extracting the vein patterns. Also, Fuzzy Brain Storm Optimization (FBSO) method is proposed for score level fusion to achieve the better recognition performance. These two contributions are effectively included in the biometric recognition system and the performance analysis of the proposed method is carried out using the benchmark datasets of palm vein image, finger vein image, and hand vein image. The quantitative results are analyzed with the help of FAR, FRR, and accuracy. From outcome, we proved that the proposed FBSO approach attained a higher accuracy of 81.3% than the existing methods.
The impact tumours in the brain in medical field cannot be ignored and may lead to a short life in their highest grade. Thus, conduction of proper diagnosis that too in its early stage to improve the quality of life of patients is a necessity. Normally, several image processing techniques including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) is a type of imaging that uses a (MRI) are be ingutilized to localize and calculate the size and tumor in a brain. But has limited performance for accurate quantitative measurements that too in a small number of sample images. In this project, a simple yet robust classification using Convolutional Neural Networks (CNN) for for brain cancer is proposed. The investigational outcomes with low complication is anticipated and potentially competes the relevant state of arts methods.
Data mining is the process of discovering actionable information from large sets of data. Data mining, or knowledge discovery, has become an indispensable technology for businesses and researchers in many fields. The data mining methods such as clustering, association rules, sequential pattern, statistics analysis, characteristics rules and so on can be used to find out the useful knowledge, enabling such data to become the real fortune for decisions and development. This paper introduces the significance of the application of data mining in different areas, challenges its future directions. Finally, it is pointed out that the data mining technology is becoming more and more powerful.
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