Search · four archives
Search · four archives
27 papers · ranked by Valyu relevance
Keiron O’Shea, Ryan Nash
The field of machine learning has taken a dramatic twist in recent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models are able to far exceed the performance of previous forms of artificial intelligence in common machine learning tasks. One of the most…
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, Ayad Al-Dujaili + 6 more
In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or…
Savita Ahlawat, Amit Choudhary, Anand Nayyar, Saurabh Singh + 1 more
'Byungun Yoon'] Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep…
Ladislav Karrach, Elena Pivarčiová, Hocine Cherifi
Artificial neural networks can solve various tasks in computer vision, such as image classification, object detection, and general recognition. Our comparative study deals with four types of artificial neural networks-multilayer perceptrons, probabilistic neural networks, radial basis function neural networks, and…
Ravi Raj, Andrzej Kos, Stefanos Kollias
Convolutional neural networks (CNNs), a type of artificial neural network (ANN) in the deep learning (DL) domain, have gained popularity in several computer vision applications and are attracting research in other fields, including robotic perception. CNNs are developed to autonomously and effectively acquire spatial…
Rahib Abiyev, Murat Arslan
In the paper, a vision-based vehicle identification system is proposed for autonomous intelligent car driving. The accurate detection of obstacles (vehicles) during intelligent car driving allows avoiding crashes, preventing accidents, saving people’s lives and reducing harm. The vehicle detection system, which uses…
Tianyi Liu, Shuangsang Fang, Yuehui Zhao, Peng Wang + 1 more
Deep learning refers to the shining branch of machine learning that is based on learning levels of representations. Convolutional Neural Networks (CNN) is one kind of deep neural network. It can study concurrently. In this article, we gave a detailed analysis of the process of CNN algorithm both the forward process and…
Guta Tesema Tufa, Fitsum Assamnew Andargie, Anchit Bijalwan
Convolutional neural network (CNN) training often necessitates a considerable amount of computational resources. In recent years, several studies have proposed for CNN inference and training accelerators in which the FPGAs have previously demonstrated good performance and energy efficiency. To speed up the processing…
Na Yao, Fuchuan Ni, Ziyan Wang, Jun Luo + 3 more
Background Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to…
Fabian Amherd, E. Rodríguez
| 1 | Introduction | | 1 | | --- | --- | --- | --- | | | 1.1 | What is Artificial Intelligence? | 1 | | | 1.1.1 | Definition of Artificial Intelligence (AI) | 1 | | | 1.1.2 | Different types of AI | 1 | | | 1.1.3 | A closer look at the field of AI | 2 | | | 1.2 History | | 3 | | | 1.2.1 | Earlier Epochs | 3 | | | 1.2.2…
Kedar Potdar, Chinmay D. Pai, Sukrut Akolkar
—This paper introduces a live object recognition system that serves as a blind aid. Visually impaired people heavily rely on their other senses such as touch and auditory signals for understanding the environment around them. The act of knowing what object is in front of the blind person without touching it (by hands…
Rajani Raman, Haruo Hosoya
Recent computational studies have emphasized layer-wise quantitative similarity between convolutional neural networks (CNNs) and the primate visual ventral stream. However, whether such similarity holds for the face-selective areas, a subsystem of the higher visual cortex, is not clear. Here, we extensively investigate…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Rishabh Malhotra, Dhron Joshi, Ku Young Shin
—Malaria is a deadly disease which claims the lives of hundreds of thousands of people every year. Computational methods have been proven to be useful in the medical industry by providing effective means of classification of diagnostic imaging and disease identification. This paper examines different machine learning…
Ljubiša Stanković, Danilo P. Mandic
Deep Neural Networks (DNN) and especially Convolutional Neural Networks (CNN) are a de-facto standard for the analysis of large volumes of signals and images. Yet, their development and underlying principles have been largely performed in an ad-hoc and black box fashion. To help demystify CNNs, we revisit their…
Alexander Muacevic, John R Adler, Shreya Roy, Prachi Nagrale
This review aims to assess the anatomy of the human eye with a focus on exploring opportunities to mimic certain functionalities of photoreceptors in the optical system. This can help restore vision issues in people who had normal vision earlier, but their vision was impaired due to reasons that damaged parts of the…
Pushparaja Murugan
Convolution Neural Networks (CNN), known as ConvNets are widely used in many visual imagery application, object classification, speech recognition. After the implementation and demonstration of the deep convolution neural network in Imagenet classification in 2012 by krizhevsky, the architecture of deep Convolution…
Umut Özkaya, Şaban Öztürk, Mücahid Barstuğan
Coronavirus (COVID-19) emerged towards the end of 2019. World Health Organization (WHO) was identified it as a global epidemic. Consensus occurred in the opinion that using Computerized Tomography (CT) techniques for early diagnosis of pandemic disease gives both fast and accurate results. It was stated by expert…
Danny da Costa, Rainer Goebel, Mario Senden
The distribution of retinal ganglion cells in primate visual systems portrays a densely distributed central region, with an incrementally decreasing cell density as the angle of visual eccentricity increases. This results in a non-uniform sampling of the retinal image that resembles a wheelbarrow distortion. We propose…
Qi Yan, Zhaofei Yu, Feng Chen, Jian K. Liu
Deep convolutional neural networks (CNNs) have demonstrated impressive performance on visual object classification tasks. In addition, it is a useful model for predication of neuronal responses recorded in visual system. However, there is still no clear understanding of what CNNs learn in terms of visual neuronal…
Jianghong Shi, Bryan Tripp, Eric Shea-Brown, Stefan Mihalas + 1 more
Convolutional neural networks trained on object recognition derive inspiration from the neural architecture of the visual system in primates, and have been used as models of the feedforward computation performed in the primate ventral stream. In contrast to the deep hierarchical organization of primates, the visual…
Amr Farahat, Martin Vinck
Convolutional neural networks (CNNs) were inspired by the organization of the primate visual system, and in turn have become effective models of the visual cortex, allowing for accurate predictions of neural stimulus responses. While training CNNs on brain-relevant object-recognition tasks may be an important…
Sanket Kadulkar, Michael Howard, Thomas Truskett, Venkat Ganesan
We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes, and use it to identify the characteristics of morphologies which exhibit optimal transport properties. The ground truth data is obtained from kinetic Monte Carlo (kMC) simulations of cation…
Chen-Ping Yu, Huidong Liu, Dimitris Samaras, Gregory Zelinsky
Recently we proposed that people represent object categories using category-consistent features (CCFs), those features that occur both frequently and consistently across a categorys exemplars [70]. Here we designed a Convolutional Neural Network (CNN) after the primate ventral stream (VsNet) and used it to extract CCFs…
Authors not listed
X-ray diffraction (XRD) is an immediate and powerful characterization technique that provides detailed information on the lattice structure and long-range order in crystalline materials. In recent decades, the quality and quantity of available crystal structure data has exploded, in large part due to the advent of…
Kelsey Hatzell, Yanjie Zheng
X-ray Computed Tomography (CT) is a non-invasive, non-destructive approach to imaging materials, material systems and engineered components in two- and three- dimensions. Acquisition of 3D images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such…
Hongyang Dong, Simon D.M. Jacques, Winfried Kockelmann, Stephen W. T. Price + 10 more
Hongyang Dong 3 , Simon D.M. Jacques 1 , Winfried Kockelmann 4 , Stephen W. T. Price 1 , Robert Emberson 5 , Dorota Matras 6,7 , Yaroslav Odarchenko 1 , Vesna Middelkoop 10 , Athanasios Giokaris 1 , Olof Gutowski 8 , Ann-Christin Dippel 8 , Martin v. Zimmermann 8 , Andrew M. Beale 3 , Keith T. Butler 9 , Antonis…