In modern power constrained applications, as with most of those belonging to the Internet-of-Things world, custom hardware supports are ever more commonly adopted deploy artificial intelligence algorithms. these operating environments, limiting dissipation much possible is mandatory, even at expense reduced computational accuracy. this paper we propose a novel prediction method identify potenti...

Most of the deaths in world happen due to Cancer. It is a disease which cells our body organs or tissues grow an undisciplined way turn can harm normal and body. These very smartly trick immune system so that cancerous are kept alive not destroyed. In human body, tumors be classified into three types: cancerous, non-cancerous, pre-cancerous. Timely identification cancer helpful many ways. As it...

This paper presents a new method for optimizing tomato leaf disease classification using Modified Visual Geometry Group (VGG)-InceptionV3. Improved performance of VGG-16 model as base with InceptionV3 block reduced the number convolution layers from 16 to 10 layers, and added an that was improved by adding layer 3 4 increase accuracy reduce parameters computation time model. The experiments wer...

BACKGROUND Cell polarity genes including Crumbs (Crb) and Par complexes are essential for controlling photoreceptor morphogenesis. Among the Crb and Par complexes, Bazooka (Baz, Par-3 homolog) acts as a nodal component for other cell polarity proteins. Therefore, finding other genes interacting with Baz will help us to understand the cell polarity genes' role in photoreceptor morphogenesis. M...

By means of the delta operator, a new type of CNN, the (δ,c)-CNN, is introduced. It is a superclass of continuoustime (CT) and discrete-time (DT) CNNs with any saturation-, high-gain-, or hardlimiting sign-nonlinearity. It is shown that the (δ,c)-CNN allows continuous transition between different types of nonlinearities and between CTand DT-CNNs, providing a unifying framework for CNN theory. I...

Description: Statistical approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. With the Cellular Neural Networks [9] (CNN), a new image processing tool is coming into consideration. Its VLSI implementation takes place on a single analog chip containing several thousands (recently about 10,000 to ...

Description: Statistical approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. With the Cellular Neural Networks [9] (CNN), a new image processing tool is coming into consideration. Its VLSI implementation takes place on a single analog chip containing several thousands (recently about 10,000 to ...

Description: Statistical approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. With the Cellular Neural Networks [9] (CNN), a new image processing tool is coming into consideration. Its VLSI implementation takes place on a single analog chip containing several thousands (recently about 10,000 to ...

Description: Statistical approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. With the Cellular Neural Networks [9] (CNN), a new image processing tool is coming into consideration. Its VLSI implementation takes place on a single analog chip containing several thousands (recently about 10,000 to ...

Description: Statistical approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. With the Cellular Neural Networks [9] (CNN), a new image processing tool is coming into consideration. Its VLSI implementation takes place on a single analog chip containing several thousands (recently about 10,000 to ...