Neural network

characterize microstructures. For example, Albuquerque, Filho, Cavalcante, and Tavares [--] quantified the porosity of synthetic materials from optical microscopic images successfully, and the solution proposed, which was based on an artificial neuronal network (ANN), proved to be more reliable. Albuquerque, de Alexandria, Cortez, and Tavares [--] characterized the microstructures in images of nodular, grey, and malleable cast irons using a multilayer perceptron

This paper studies various techniques used for the diagnosis of breast cancer. Different methods are explored for their merits and de-merits for the diagnosis of breast lesion. It was found that the recent use of the combination of Artificial Neural Networks in most of the instances give accurate results for the diagnosis of breast cancer and their use can also be extended to other diseases.

estimating the class and location of objects contained within the images. With the improvements in object representations and machine learning models, it is possible to achieve much advancement in Object Recognition. For the last few years, Deep Neural Network has

Propagation for Classification (Research Essay) Linfeng Gu Introduction This research essay mainly discusses back propagation used in artificial neural networks based on three research papers. From the second section, each section contains the discussion of one research paper. In the first research paper, researchers apply feed forward neutral networks with back propagation in medical fields and present several statistical normalization methods. [4] In the second research paper, researchers focus

Emergence networks mimics biological nervous system unleash generations of inventions and discoveries in the artificial intelligent field. These networks have been introduced by McCulloch and Pitts and called neural networks. Neural network’s function is based on principle of extracting the uniqueness of patterns through trained machines to understand the extracted knowledge. Indeed, they gain their experiences from collected samples for known classes (patterns). Quick development of neural networks promotes

ISBN 048624864X,Courier Corporation (1985).‏ 5. SuzukiK.: Artificial neural networks: methodological advances and biomedical applications. InTech, ISBN-13: 9789533072432( 2011).‏ 6. Lingras P. J.: Rough neural network. In: Proc. of the 6th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU96). pp.1445-1450, Granada, Spain (1996). 7. ellaHassanien, A., &Ślzak, D. (2006). Rough neural intelligent approach for image classification: A case of patients with

Abstract There are numerous indications that the field of Artificial intelligence (AI) is now well established. There are several computer science departments with AI specialties and some important disciplines whose roots are in AI, such as Pattern Recognition and Symbolic Algebraic Manipulation, have already spun off as independent areas. Still, I identify this field of Artificial intelligence has the positivity beyond its aspect. It matters how we accept it and use it in a positive manner. Its

FPGA BASED IMPLEMENTATION OF DIGIT RECOGNITION Under Supervision of : Dr. Pavan Chakaraborty. Group members: IEC2012015 IEC2012028 IEC2012041 IEC2012089 IEC2012090 Table of Contents About platforms used: 4 Xilinx ISE: 4 Web Edition: 4 MATLAB: [matlab] 4 Feature extraction: 5 Algorithm speed up using FPGA implementation: 6 [parallization abitlity of NN] 6 Conclusion 7 Result: [Verilog outputs] 4 References 7 About platforms used: Xilinx ISE: “Xilinx ISE[xilinx] (Integrated Software

all the knowledge used by a human being. Common sense and learning, which are so natural in a human being, cannot be easily captured by a machine. This is where computing models triggered by biological neural networks hope to give solutions to problems that arise in natural tasks. A neural network could get the relevent features from the input and perform pattern recognition by learning from examples. It doesn’t need the explicit stating of rules for performing the task. Keywords: heuristics I. INTRODUCTION

Analysis of Attentiveness using Physiological and Environmental Factors Abstract—Wearable computing is picking up speed and devices like smart watches and fitness bands are increasingly equipped with heart rate sensors. Common applications for these devices include fitness and sleep tracking. Heart rate sensor data opens avenues for exploring newer applications. There is a close correlation between attentiveness and the variability in heart rate in adults. In this paper, we utilize this correlation