Search Results - subject:"Convolutional neural networks"

A supervised multimodal search re-ranking technique using visual semantics

Bhuvan, Nikhila T.; Elayidom, M. Sudheep - In: International journal of intelligent enterprise 7 (2020) 1/2/3, pp. 279-290

Persistent link: https://www.econbiz.de/10012177450

Convolutional and generative adversarial neural networks in manufacturing

Kusiak, Andrew - In: International journal of production research 58 (2020) 5, pp. 1594-1604

Persistent link: https://www.econbiz.de/10012194100

Deep learning for time series forecasting

Yalçın Kayacan, Eda - In: Selected topics in applied econometrics, (pp. 243-254). 2019

Persistent link: https://www.econbiz.de/10012286997

A Novel Approach to Classify Nailfold Capillary Images in Indian Population Using USB Digital Microscope

Suma K. V.; Sasi, Vishwajit; Rao, Bheemsain - In: International Journal of Biomedical and Clinical … 7 (2018) 1, pp. 25-39

Nailfold capillaroscopy is a simple, non-invasive clinical aid used for the diagnosis of vascular dysfunction. The method proposed emphasizes on the USB Digital Microscope which is a cheaper and useful alternative to commercially available expensive videocapillaroscopes which produces high...

Persistent link: https://www.econbiz.de/10012043236

Copy-Move Forgery Localization Using Convolutional Neural Networks and CFA Features

Liu, Lu; Zhao, Yao; Ni, Rongrong; Tian, Qi - In: International Journal of Digital Crime and Forensics (IJDCF) 10 (2018) 4, pp. 140-155

This article describes how images could be forged using different techniques, and the most common forgery is copy-move forgery, in which a part of an image is duplicated and placed elsewhere in the same image. This article describes a convolutional neural network (CNN)-based method to accurately...

Persistent link: https://www.econbiz.de/10012043989

Learning and Interpreting Features to Rank: A Case Study on Age Estimation

Dong, Ming; Chen, Shixing; Zhu, Dongxiao - In: International Journal of Multimedia Data Engineering … 9 (2018) 3, pp. 17-36

Previously, it was taken for granted that features learned for classification can also be used for ranking. However, ranking problems possess some distinctive properties, e.g., the ordinal class labels, which indicates the necessity of developing new feature learning procedures dedicated for...

Persistent link: https://www.econbiz.de/10012046542

Recognition on Images from Internet Street View Based on Hierarchical Features Learning with CNNs

Liu, Jian-min; Yang, Min-hua - In: Journal of Information Technology Research (JITR) 11 (2018) 3, pp. 62-74

focuses on the activation of each layer of with convolutional neural networks (CNNs) on Internet street view images detection …

Persistent link: https://www.econbiz.de/10012049170

Design of an Artificial Neural Network battery for an optimal recognition of patterns in financial time series

Fioribello, Simone; Giribone, Pier Giuseppe - In: International journal of financial engineering 5 (2018) 4, pp. 1-17

Persistent link: https://www.econbiz.de/10012028806

Classifying Behaviours in Videos with Recurrent Neural Networks

Abellan-Abenza, Javier; Garcia-Garcia, Alberto; Oprea, … - In: International Journal of Computer Vision and Image … 7 (2017) 4, pp. 1-15

Learning. In particular, convolutional neural networks are used to detect features in the video images, meanwhile Recurrent …

Persistent link: https://www.econbiz.de/10012043838

Use of Images of Leaves and Fruits of Apple Trees for Automatic Identification of Symptoms of Diseases and Nutritional Disorders

Nachtigall, Lucas Garcia; Araujo, Ricardo Matsumura; … - In: International Journal of Monitoring and Surveillance … 5 (2017) 2, pp. 1-14

orchards is essential to avoid greater losses. This paper aimed to evaluate the efficiency of Convolutional Neural Networks … images, with an accuracy of 97.3% and obtain 91.1% accuracy with fruit images. In this way, the use of Convolutional Neural … Networks may enable the diagnosis of symptoms in apple trees in a fast, precise and usual way. …

Persistent link: https://www.econbiz.de/10012046691