scholarly journals Evaluation of Classification Accuracy with Original and Compressed Images

2019 ◽  
Vol 8 (4) ◽  
pp. 162-166
Keyword(s):  
Image Compression ◽  
Image Data ◽  
Classification Performance ◽  
Support Vector ◽  
Network Support ◽  
Data Standard ◽  
The Past ◽  
Compressed Images ◽  
The One ◽  
Compressed Data

The extent of communicated information through internet has augmented speedily over the past few years. Image compression is the preeminent way to lessen the size of the image. JPEG is the one the best technique related to lossy image compression. In this paper a novel JPEG compression algorithm with Fuzzy-Morphology techniques was proposed. The efficacy of the proposed algorithm compared to JPEG is presented with metrics like PSNR, MSE, No of bits transmitted. The proposed approaches lessen the number of encoded bits as a result tumbling the quantity of memory needed. The Planned approaches are best appropriate for the images corrupted with Gaussian, Speckle, Poisson, Salt & Pepper noises. In this paper the effect of compression on classification performance was envisaged, Artificial Neural Network, Support Vector Machine, and, KNN classifiers performance is evaluated with original image data, standard JPEG compressed data and the compressed image data with the proposed method.

scholarly journals Deep Convolutional Neural Networks for Hyperspectral Image Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Hu ◽  
Yangyu Huang ◽  
Li Wei ◽  
Fan Zhang ◽  
Hengchao Li
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hyperspectral Image ◽  
Image Data ◽  
Classification Performance ◽  
Spectral Signature ◽  
Support Vector ◽  
Data Sets ◽  
Deep Convolutional Neural Networks ◽  
Visual Tasks

Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.

scholarly journals Prolonged concussion effects: Constellations of cognitive deficits detected up to year after injury

2021 ◽  
Vol 5 ◽  
pp. 205970022110065
Author(s):  
Daniel D Leeds ◽  
Annie Nguyen ◽  
Christopher D’Lauro ◽  
Jonathan C Jackson ◽  
Brian R Johnson
Keyword(s):  
Cognitive Abilities ◽  
Cognitive Testing ◽  
Support Vector ◽  
Cognitive Measures ◽  
Post Injury ◽  
The Past ◽  
Neurocognitive Assessment ◽  
Non Linear ◽  
One Year ◽  
The One

Concussions are associated with an array of physical, emotional, cognitive, and sleep symptoms at multiple timescales. Cognitive recovery occurs relatively quickly – five-to-seven days on average. Yet, recent evidence suggests that some neurophysiological changes can be identified one year after a concussion. To that end, we examine more nuanced patterns in cognitive tests to determine whether cognitive abilities could identify a concussion within one-year post injury. A radial-basis (non-linear boundary) support vector machine classifier was trained to use cognitive performance measures to distinguish participants with no prior concussion from participants with prior concussion in the past year. After incorporating only 10 cognitive measures, or all 5 composite measures from the neurocognitive assessment (Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT)), over 90% accuracy was achieved in identifying both participants without prior concussions and participants with concussions in the past year, particularly when relying on non-linear patterns. Notably, classification accuracy stayed relatively constant between participants who had a concussion early or late in the one-year window. Thus, with substantial accuracy, a prior concussion can be identified using a non-linear combination of cognitive measures. Cognitive effects from concussion linger one-year post-injury, indicating the importance of continuing to follow concussion patients for many months after recovery and to take special note of constellations of cognitive abilities.

scholarly journals Explaining Machine Learning Models for Clinical Gait Analysis

2022 ◽  
Vol 3 (2) ◽  
pp. 1-27
Author(s):  
Djordje Slijepcevic ◽  
Fabian Horst ◽  
Sebastian Lapuschkin ◽  
Brian Horsak ◽  
Anna-Maria Raberger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reaction Force ◽  
Statistical Parametric Mapping ◽  
Qualitative Evaluation ◽  
Classification Performance ◽  
Healthcare Sector ◽  
Support Vector ◽  
Classification Methods ◽  
Network Support ◽  
Gait Classification

Machine Learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification performance, most share a central limitation, their black-box character. This article investigates the usefulness of Explainable Artificial Intelligence (XAI) methods to increase transparency in automated clinical gait classification based on time series. For this purpose, predictions of state-of-the-art classification methods are explained with a XAI method called Layer-wise Relevance Propagation (LRP). Our main contribution is an approach that explains class-specific characteristics learned by ML models that are trained for gait classification. We investigate several gait classification tasks and employ different classification methods, i.e., Convolutional Neural Network, Support Vector Machine, and Multi-layer Perceptron. We propose to evaluate the obtained explanations with two complementary approaches: a statistical analysis of the underlying data using Statistical Parametric Mapping and a qualitative evaluation by two clinical experts. A gait dataset comprising ground reaction force measurements from 132 patients with different lower-body gait disorders and 62 healthy controls is utilized. Our experiments show that explanations obtained by LRP exhibit promising statistical properties concerning inter-class discriminativity and are also in line with clinically relevant biomechanical gait characteristics.

scholarly journals Monte Carlo Optimization of a Combined Image Quality Assessment for Compressed Images Evaluation

2021 ◽  
Vol 38 (2) ◽  
pp. 281-289
Author(s):  
Ahmed Bouida ◽  
Mustapha Khelifi ◽  
Mohammed Beladgham ◽  
Fatima-Zohra Hamlili
Keyword(s):  
Monte Carlo ◽  
Image Quality ◽  
Image Compression ◽  
Image Quality Assessment ◽  
Image Data ◽  
Optimization Method ◽  
Data Systems ◽  
Monte Carlo Optimization ◽  
Compressed Images ◽  
Quality Aspects

In image processing, using compression is very important in various applications, especially those using data quantities in transmission and storing. This importance becomes most required with the evolution of image quantities and the big data systems explosion. The image compression allows reducing the required binary volume of image data by encoding the image for transmission goal or database saving. The principal problem with image compression when reducing its size is the degradation that enters the image. This degradation can affect the quality of use of the compressed image. To evaluate and qualify this quality, we investigate the use of textural combined image quality metrics (TCQ) based on the fusion of full reference structural, textural, and edge evaluation metrics. To optimize this metric, we use the Monte Carlo optimization method. This approach allows us to qualify our compressed images and propose the best metric that evaluates compressed images according to several textural quality aspects.

scholarly journals Method for Training Convolutional Neural Networks for In Situ Plankton Image Recognition and Classification Based on the Mechanisms of the Human Eye

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2592
Author(s):  
Xuemin Cheng ◽  
Yong Ren ◽  
Kaichang Cheng ◽  
Jie Cao ◽  
Qun Hao
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Image Data ◽  
Recall Rate ◽  
Classification Performance ◽  
Support Vector ◽  
Coordinate Systems ◽  
Polar Coordinate

In this study, we propose a method for training convolutional neural networks to make them identify and classify images with higher classification accuracy. By combining the Cartesian and polar coordinate systems when describing the images, the method of recognition and classification for plankton images is discussed. The optimized classification and recognition networks are constructed. They are available for in situ plankton images, exploiting the advantages of both coordinate systems in the network training process. Fusing the two types of vectors and using them as the input for conventional machine learning models for classification, support vector machines (SVMs) are selected as the classifiers to combine these two features of vectors, coming from different image coordinate descriptions. The accuracy of the proposed model was markedly higher than those of the initial classical convolutional neural networks when using the in situ plankton image data, with the increases in classification accuracy and recall rate being 5.3% and 5.1% respectively. In addition, the proposed training method can improve the classification performance considerably when used on the public CIFAR-10 dataset.

scholarly journals Lossless Image Compression Schemes: A Review

2021 ◽  
pp. 14-22
Author(s):  
I. Manga ◽  
E. J. Garba ◽  
A. S. Ahmadu
Keyword(s):  
Image Compression ◽  
Data Compression ◽  
Image Data ◽  
Original Data ◽  
Lossy Compression ◽  
Video Content ◽  
Textual Data ◽  
Audio Data ◽  
Lossless Data Compression ◽  
Compressed Data

Data compression refers to the process of representation of data using fewer number of bits. Data compression can be lossless or lossy. There are many schemes developed and used to perform either lossless or lossy compression. Lossless data compression allows the original data be conveniently reconstructed from the compressed data while lossy compression allow only an approximate of the original data to be constructed. The type of data to compressed can be classified as image data, textual data, audio data or even video content. Various researches are being carried out in the area of image compression. This paper presents various literatures in field of data compression and the techniques used to compress image using lossless type of compression. In conclusion, the paper reviewed some schemes used to compress an image using a single schemes or combination of two or more schemes methods.

scholarly journals Machine Learning-Based Hourly Frost-Prediction System Optimized for Orchards Using Automatic Weather Station and Digital Camera Image Data

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 846
Author(s):  
Ilseok Noh ◽  
Hae-Won Doh ◽  
Soo-Ock Kim ◽  
Su-Hyun Kim ◽  
Seoleun Shin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Digital Camera ◽  
Image Data ◽  
Classification Performance ◽  
Classification Model ◽  
Support Vector ◽  
Observation Data ◽  
Freezing Resistance

Spring frosts damage crops that have weakened freezing resistance after germination. We developed a machine learning (ML)-based frost-classification model and optimized it for orchard farming environments. First, logistic regression, decision tree, random forest, and support vector machine models were trained using balanced Korea Meteorological Administration (KMA) Automated Synoptic Observing System (ASOS) frost observation data for March from the last 10 years (2008–2017). Random forest and support vector machine models showed good classification performance and were selected as the main techniques, which were optimized for orchard fields based on initial frost occurrence times. The training period was then extended to March–April for 20 years (2000–2019). Finally, the model was applied to the KMA ASOS frost observation data from March to April 2020, which were not used in the previous steps, and RGB data were extracted by digital cameras installed in an orchard in Gyeonggi-do. The developed model successfully classified 117 of 139 frost observation cases from the domestic ASOS data and 35 of 37 orchard camera observations. The assumption of the initial frost occurrence time for training helped the most in improving the frost-classification model. These results clearly indicate that the frost-classification model using ML has applicable accuracy in orchard farming.

Stacked Convolutional Sparse Auto-Encoders for Representation Learning

2021 ◽  
Vol 15 (2) ◽  
pp. 1-21
Author(s):  
Yi Zhu ◽  
Lei Li ◽  
Xindong Wu
Keyword(s):  
Deep Learning ◽  
Image Data ◽  
Representation Learning ◽  
Classification Performance ◽  
Support Vector ◽  
Learning Models ◽  
Feature Representations ◽  
Learning Framework ◽  
Label Information ◽  
Unsupervised Deep Learning

Deep learning seeks to achieve excellent performance for representation learning in image datasets. However, supervised deep learning models such as convolutional neural networks require a large number of labeled image data, which is intractable in applications, while unsupervised deep learning models like stacked denoising auto-encoder cannot employ label information. Meanwhile, the redundancy of image data incurs performance degradation on representation learning for aforementioned models. To address these problems, we propose a semi-supervised deep learning framework called stacked convolutional sparse auto-encoder, which can learn robust and sparse representations from image data with fewer labeled data records. More specifically, the framework is constructed by stacking layers. In each layer, higher layer feature representations are generated by features of lower layers in a convolutional way with kernels learned by a sparse auto-encoder. Meanwhile, to solve the data redundance problem, the algorithm of Reconstruction Independent Component Analysis is designed to train on patches for sphering the input data. The label information is encoded using a Softmax Regression model for semi-supervised learning. With this framework, higher level representations are learned by layers mapping from image data. It can boost the performance of the base subsequent classifiers such as support vector machines. Extensive experiments demonstrate the superior classification performance of our framework compared to several state-of-the-art representation learning methods.

scholarly journals A Fast Classification Method of Faults in Power Electronic Circuits Based on Support Vector Machines

2017 ◽  
Vol 24 (4) ◽  
pp. 701-720 ◽  
Author(s):  
Jiang Cui ◽  
Ge Shi ◽  
Chunying Gong
Keyword(s):  
Classification Problem ◽  
Classification Performance ◽  
Classification Method ◽  
Fault Classification ◽  
Support Vector ◽  
Power Electronic ◽  
Electronic Circuits ◽  
Power Electronic Circuits ◽  
The One ◽  
Fast Classification

AbstractFault detection and location are important and front-end tasks in assuring the reliability of power electronic circuits. In essence, both tasks can be considered as the classification problem. This paper presents a fast fault classification method for power electronic circuits by using the support vector machine (SVM) as a classifier and the wavelet transform as a feature extraction technique. Using one-against-rest SVM and one-against-one SVM are two general approaches to fault classification in power electronic circuits. However, these methods have a high computational complexity, therefore in this design we employ a directed acyclic graph (DAG) SVM to implement the fault classification. The DAG SVM is close to the one-against-one SVM regarding its classification performance, but it is much faster. Moreover, in the presented approach, the DAG SVM is improved by introducing the method of Knearest neighbours to reduce some computations, so that the classification time can be further reduced. A rectifier and an inverter are demonstrated to prove effectiveness of the presented design.

scholarly journals The Construction of Support Vector Machine Classifier Using the Firefly Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Chih-Feng Chao ◽  
Ming-Huwi Horng
Keyword(s):  
Feature Selection ◽  
Firefly Algorithm ◽  
Binary Classification ◽  
Classification Performance ◽  
Support Vector ◽  
Lagrangian Multiplier ◽  
Data Sets ◽  
Smoothness Parameter ◽  
Multiclass Svm ◽  
The One

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

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