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Impact of Low Resolution on Image Recognition with Deep Neural Networks: An Experimental Study

Michał Koziarski, Bogusław Cyganek
2018 International Journal of Applied Mathematics and Computer Science  
In this paper we experimentally evaluate the impact of low resolution on the classification accuracy of several notable neural architectures of recent years.  ...  Furthermore, we examine the possibility of improving neural networks' performance in the task of low resolution image recognition by applying super-resolution prior to classification.  ...  The support of the PLGrid infrastructure is also greatly appreciated.  ... 
doi:10.2478/amcs-2018-0056 fatcat:56yfhvmthvfwrpy7pqv4bt6gh4

A way to improve an architecture of neural network classifier for remote sensing applications

Jerzy Korczak, Fatiha Hammadi-Mesmoudi
1994 Neural Processing Letters  
It is shown how to create in an iterative way a neural classifier architecture, and how to refine a network organization using performance evaluation criteria. I.  ...  Most of neural network applications have been developed using an ad hoc approach resulting in poor efficiency and performance.  ...  Hence, there is an inherent necessity to develop neural network architectures in a systematic way to avoid a high cost of experimentations.  ... 
doi:10.1007/bf02312395 fatcat:xkzb77gvxvacplxo5uhhtvm53e

Graphite Epoxy Defect Classification of Ultrasonic Signatures Using Statistical and Neural Network Techniques [chapter]

L. M. Brownt, R. W. Newman, R. DeNale, C. A. Lebowitz, F. G. Arcella
1992 Review of Progress in Quantitative Nondestructive Evaluation  
Another type of defect which has a very deleterious effect on the physical properties of a composite component is an inclusion.  ...  The use of graphite epoxy composite materials in thick sections for structural applications in naval vessels is achieving worldwide interest [1).  ...  Several artificial neural network paradigms are available for classification of data [16] .  ... 
doi:10.1007/978-1-4615-3344-3_87 fatcat:dwsqez3dencvfchot7tdey6gby

Abstract: Adversarial Examples as Benchmark for Medical Imaging Neural Networks [chapter]

Magdalini Paschali, Sailesh Conjeti, Fernando Navarro, Nassir Navab
2019 Handbook of Experimental Pharmacology  
Extensive evaluation was performed on state-of-the-art classification and segmentation deep neural networks, for the challenging tasks of fine-grained skin lesion classification and whole brain segmentation  ...  Specifically, networks that performed equally well regarding their generalizability had an astounding 20% difference in robustness, highlighting the need for the proposed, more thorough evaluation technique  ...  Extensive evaluation was performed on state-of-the-art classification and segmentation deep neural networks, for the challenging tasks of fine-grained skin lesion classification and whole brain segmentation  ... 
doi:10.1007/978-3-658-25326-4_4 fatcat:bch6xie7tfebdbereerp4dwwmu

Boosting Mapping Functionality of Neural Networks via Latent Feature Generation based on Reversible Learning [article]

Jongmin Yu
2019 arXiv   pre-print
This paper addresses a boosting method for mapping functionality of neural networks in visual recognition such as image classification and face recognition.  ...  We present reversible learning for generating and learning latent features using the network itself.  ...  In classification problem setting, the feed-forward process generates an output of networks for classification: p • f (X) = p(f (x)|θ i ).  ... 
arXiv:1910.09108v1 fatcat:7b6rxoxdx5hu5pif66co6n4bku

Hybrid ANN optimized artificial fish swarm algorithm based classifier for classification of suspicious lesions in breast DCE-MRI

D Janaki Sathya, K Geetha
2017 Polish Journal of Medical Physics And Engineering  
The basic idea behind the proposed classifier is to use AFSO algorithm for searching the best combination of synaptic weights for the neural network.  ...  A new classifier (a set of features together with a classification method) based on artificial neural networks trained using artificial fish swarm optimization (AFSO) algorithm is proposed in this paper  ...  for testing the classifier algorithm proposed.  ... 
doi:10.1515/pjmpe-2017-0014 fatcat:eqcwy7njg5brli3dxkp3grk35a

Deep Convolutional Neural Network and Weighted Bayesian Model for Evaluation of College Foreign Language Multimedia Teaching

Tingting Liu, Le Ning, Shan Zhong
2021 Wireless Communications and Mobile Computing  
In colleges and universities, teaching quality evaluation is an integral part of the teaching management process.  ...  This paper develops a teaching assessment model using a deep convolutional neural network and the weighted Naive Bayes algorithm.  ...  Acknowledgments This study was supported by the Shandong Social Science and Research Foundation of China (Grant No.17CWZJ07\ 18CWZJ34).  ... 
doi:10.1155/2021/1859065 fatcat:4bjdio43kbahxghudryetmn3yu

Deep Learning Techniques for Classification of P300 Component

Jiří Vaněk, Roman Mouček
2018 Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies  
of various types of neural network layers.  ...  The main aim of this article is to improve the success rate of deep learning algorithms, especially stacked autoencoders, when they are used for detection and classification of P300 event-related potential  ...  Successful results of such classification approaches could be subsequently used for developing an evaluation tool that would be suitable for the P300 component detection and classification in many applications  ... 
doi:10.5220/0006594104460453 dblp:conf/biostec/VanekM18 fatcat:cmfhwzejnvhztlp6oe6ej5cmpm

Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Xiaoling Liu, Syed Hassan Ahmed
2021 Computational Intelligence and Neuroscience  
Then the errors of deep belief network classification algorithm and Big Data analysis are evaluated.  ...  Second, this paper describes the quantitative evaluation of deep belief network classification algorithm and Big Data analysis in this system.  ...  Acknowledgments is work was supported by Anyang Institute of Technology.  ... 
doi:10.1155/2021/5609885 fatcat:mc4kvdrsifcujkhtw4qjayo5xu

An Evolving Hybrid Deep Learning Framework for Legal Document Classification

Neha Bansal, Arun Sharma, R.K. Singh
2019 Ingénierie des Systèmes d'Information  
In this paper, an automatic classification method is developed for online judgement documents from Washington University School of Law Supreme Court Database (SCDB).  ...  Our approach was designed under a hybrid framework of deep learning, which couples convolution neural network (CNN) and with a recurrent neural network called bidirectional long short-term memory (BiLSTM  ...  Bi-directional LSTM layer for classification Bidirectional LSTM is a variant of LSTM recurrent neural networks.  ... 
doi:10.18280/isi.240410 fatcat:gi3zzxzp5jb3pbvlijdqdb5jbm

Audio Classification of Bit-Representation Waveform

Masaki Okawa, Takuya Saito, Naoki Sawada, Hiromitsu Nishizaki
2019 Interspeech 2019  
Most studies on audio waveform classification have proposed the use of a deep learning (neural network) framework.  ...  The experimental results showed that the bit representation waveform achieved the best classification performance for both the tasks.  ...  Neural network for the bit pattern image Experiments Our bit transformation method and the neural network architecture were evaluated using two tasks: one was an acoustic event detection task and the  ... 
doi:10.21437/interspeech.2019-1855 dblp:conf/interspeech/OkawaSSN19 fatcat:mqvj6txx3zdzfa4cpfokvjszfy

A Neural Adaptive Algorithm for Feature Selection and Classification of High Dimensionality Data [chapter]

Elisabetta Binaghi, Ignazio Gallo, Mirco Boschetti, P. Alessandro Brivio
2005 Lecture Notes in Computer Science  
Performances were evaluated experimentally within a Remote Sensing study, aimed to classify hyperspectral data.  ...  In this paper, we propose a novel method which involves neural adaptive techniques for identifying salient features and for classifying high dimensionality data.  ...  Conclusion In this paper, we have proposed the use of an adaptive neural network model for the twofold task of feature selection and classification.  ... 
doi:10.1007/11553595_92 fatcat:erbf6uhsfzd5vnetgzpbkif7aq

Recognition of Dry Fruits using Deep Convolutional Neural Network

Roohi Jan
2021 International Journal for Research in Applied Science and Engineering Technology  
Deep Convolutional Neural Network (CNN) with a unique structure for combining the feature extraction and classification stages has been considered to be a state-of-the-art computer vision technique for  ...  The CNN model performs Dry fruit recognition and was able to achieve an overall classification accuracy of 94%.  ...  Finally, model performance was evaluated based on experimental results. IV. RESULT AND DISCUSSION In this study, convolutional neural network has been implemented for dry fruits classification.  ... 
doi:10.22214/ijraset.2021.34415 fatcat:uvnueig4qzh5zn7mibpruocesi

A neural network approach to the inspection of ball grid array solder joints on printed circuit boards

Kuk Won Ko, Young Jun Roh, Hyung Suck Cho, Hyung Cheol Kimn
2000 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium  
This problem has been major obstacle to extract suitable features for classification. To solve this problem, a neural network based classification method is proposed.  ...  In this paper; we described an approach to automation of visual inspection of BGA solder joint defects of surface mounted components on printed circuit board by using neural network.  ...  Experimental results and discussions After training the two neural networks differently, classification performance of the proposed method was evaluated for the training images.  ... 
doi:10.1109/ijcnn.2000.861463 dblp:conf/ijcnn/KoRCK00 fatcat:xnjz63dbgfdu3jlvqcjbe6tqoa

TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM

Dr. C. Arunabala, P. Jwalitha, Soniya Nuthalapati
2019 International journal of research - granthaalayah  
The experimental results show that the proposed method improves the accuracy of text sentiment classification effectively compared with traditional CNN, and confirms the effectiveness of sentiment analysis  ...  In this paper, a Convolution Neural Networks (CNNs) model combined with SVM text sentiment analysis is proposed.  ...  Experimental Results and Analysis The evaluation criteria of emotional analysis task based on deep learning technology in NLPCC2014 is regarded as the evaluation index of experimental results, according  ... 
doi:10.29121/granthaalayah.v7.i6.2019.761 fatcat:hefcomkfmzblln57vu2cktwzu4
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