Deep Convolutional Neural Network for Image Classification on CUDA Platform

Gavali, Pralhad; Banu, J. Saira

doi:10.1016/b978-0-12-816718-2.00013-0

Cited by 38 publications

(19 citation statements)

References 8 publications

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“…On the contrary DL specializes in solving problems such as image classification, voice recognition etc. For example, image classification of 1000 kinds of images provided a classification error rate of 3.5% which is higher than the accuracy of ordinary people [27].…”

Section: Literature Reviewmentioning

confidence: 87%

Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks

Ιωάννου¹,

Myronidis

2021

Sustainability

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The number of solar photovoltaic (PV) arrays in Greece has increased rapidly during the recent years. As a result, there is an increasing need for high quality updated information regarding the status of PV farms. This information includes the number of PV farms, power capacity and the energy generated. However, access to this data is obsolete, mainly due to the fact that there is a difficulty tracking PV investment status (from licensing to investment completion and energy production). This article presents a novel approach, which uses free access high resolution satellite imagery and a deep learning algorithm (a convolutional neural network—CNN) for the automatic detection of PV farms. Furthermore, in an effort to create an algorithm capable of generalizing better, all the current locations with installed PV farms (data provided from the Greek Energy Regulator Authority) in the Greek Territory (131,957 km2) were used. According to our knowledge this is the first time such an algorithm is used in order to determine the existence of PV farms and the results showed satisfying accuracy.

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Section: Literature Reviewmentioning

confidence: 87%

Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks

Ιωάννου¹,

Myronidis

2021

Sustainability

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“…The main idea underpinning this experiment is to use the YOLOv5 Detector (Redmon et al 2016), which is an object detection model based on DenseNet/CNN backbone and train the weights for the model using inference (Gavali and Banu, 2019). Our model uses Keras on Tensorflow.…”

Section: Training Data On Tensorflowmentioning

confidence: 99%

Measuring Resilient Communities: an Analytical and Predictive Tool

Carta¹,

Turchi²,

Pintacuda³

2022

CAADRIA Proceedings

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This work presents the initial results of an analytical tool designed to quantitatively assess the level of resilience of urban areas. We use Deep Neural Networks to extract features of resilience from a trained model that classifies urban areas using a pre-assigned value range of resilience. The model returns the resilience value for any urban area, indicating the distance between the centre of the selected area and relevant typologies, including green areas, buildings, natural elements and infrastructures. Our tool also indicates the urban morphological characteristics that have a larger impact on the resilience score. In this way we can learn why a neighbourhood is successful (or not) and how to improve its level of resilience. The model employs Convolutional Neural Networks (CNNs) with Keras on Tensorflow for the computation. The outputs are loaded onto a Node.JS environment and bootstrapped with React.js to generate the online demo.

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“…Apart from multi-modal techniques, deep learning based AD classification approaches is also using widely. Some of the major advantages of using deep learning based approach are; 1) it can extract the hidden features from the data, 2) it can produce convincing results, even if the data are unstructured, 3) it allows parallel processing, etc [208]. Some of the recently published articles on the deep learning based AD classification are discussed below.…”

Section: A Current Trends In the Field Of Ad Classification Using Brain Imagesmentioning

confidence: 99%

A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques

et al. 2021

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Alzheimer's disease (AD) is one of the most serious neurological disorders for elderly people. AD affected patient experiences severe memory loss. One of the main reasons for memory loss in AD patients is atrophy in the hippocampus, amygdala, etc. Due to the enormous growth of AD patients and the paucity of proper diagnostic tools, detection and classification of AD are considered as a challenging research area. Before a Cognitively normal (CN) person develops symptoms of AD, he may pass through an intermediate stage, commonly known as Mild Cognitive Impairment (MCI). MCI is having two stages, namely StableMCI (SMCI) and Progressive MCI (PMCI). In SMCI, a patient remains stable, whereas, in the case of PMCI, a person gradually develops few symptoms of AD. Several research works are in progress on the detection and classification of AD based on changes in the brain. In this paper, we have analyzed few existing state-of-art works for AD detection and classification, based on different feature extraction approaches. We have summarized the existing research articles with detailed observations. We have also compared the performance and research issues in each of the feature extraction mechanisms and observed that the AD classification using the wavelet transform-based feature extraction approaches might achieve convincing results.

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Deep Convolutional Neural Network for Image Classification on CUDA Platform

Cited by 38 publications

References 8 publications

Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks

Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks

Measuring Resilient Communities: an Analytical and Predictive Tool

A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques

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