What are neural nets? The biological model: The human brain The components of a neural net What they can and where they fail The learning process What means "learning" refering to neural nets? Supervised and unsupervised learning Forwardpropagation Backpropagation Selforganization

Using face detection to secure places is an important application merging with machine vision. This paper reposed a system do and recognition using existing architecture google-net transfer learning let the network learn images based on pre-trained architecture, The design of leads that maximizing accuracy accurately detecting faces are saved database specifying effect weights used within nodes...

Abstract Cancer is one of the main reason for loss human life across world. All medical practitioners and researchers are dealing with demanding situations to fight against cancer. Based on report in 2019 from American Society, 96,480 deaths anticipated due skin cancers, 142,670 lung 42,260 breast 31,620 prostate 17,760 mind cancers. Initial detection most cancers has pinnacle precedence saving...

Neural networks are applied to the system identification problems using adaptive algorithms for either parameter or functional estimation of dynamic systems. In this paper the neural networks' sensitivity to input values and connections' weights, is studied. The Reduction-Sigmoid-Amplification (RSA) neurons are introduced and four different models of neural network architecture are proposed and...

Deep learning has recently been extensively used for crack detection in structural health monitoring settings. However, detecting cracks levee systems have yet to receive considerable critical attention. Thus, this study presents a novel encoder-decoder-based fully convolutional neural network detect from images at pixel level automatically. We propose that the feature be strengthened using dec...

This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end convolutional neural (CNN) model encoder–decoder architecture. operations are concatenated max-pooling to combine the sparse and features. addresses limitation of contour conglutination objects while counting. evaluation...

Cracks affect the robustness and adaptability of various infrastructures, including buildings, bridge piers, pavement, pipelines. Therefore, reliability automated crack detection are essential. In this study, we conducted image segmentation using datasets by applying advanced architecture U-Net. First, collected integrated from prior studies, cracks in buildings pavements. For effective localiz...