scholarly journals Local information pattern descriptor for corneal diseases diagnosis

2021 ◽  
Vol 11 (6) ◽  
pp. 4972
Author(s):  
Samer Kais Jameel ◽  
Sezgin Aydin ◽  
Nebras H. Ghaeb
Keyword(s):  
Feature Vector ◽  
Outer Part ◽  
Local Binary Patterns ◽  
Local Information ◽  
Machine Learning Algorithms ◽  
Local Feature ◽  
Human Vision ◽  
Human Cornea ◽  
Image Center ◽  
Information Pattern

<span lang="EN-US">Light penetrates the human eye through the cornea, which is the outer part of the eye, and then the cornea directs it to the pupil to determine the amount of light that reaches the lens of the eye. Accordingly, the human cornea must not be exposed to any damage or disease that may lead to human vision disturbances. Such damages can be revealed by topographic images used by ophthalmologists. Consequently, an important priority is the early and accurate diagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms, particularly, use of local feature extractions for the image. Accordingly, we suggest a new algorithm called local information pattern (LIP) descriptor to overcome the lack of local binary patterns that loss of information from the image and solve the problem of image rotation. The LIP based on utilizing the sub-image center intensity for estimating neighbors' weights that can use to calculate what so-called contrast based centre (CBC). On the other hand, calculating local pattern (LP) for each block image, to distinguish between two sub-images having the same CBC. LP is the sum of transitions of neighbors' weights, from sub-image center value to one and vice versa. Finally, creating histograms for both CBC and LP, then blending them to represent a robust local feature vector. Which can use for diagnosing, detecting.</span>

Emotion recognition using time–frequency ridges of EEG signals based on multivariate synchrosqueezing transform

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ahmet Mert ◽  
Hasan Huseyin Celik
Keyword(s):  
Emotion Recognition ◽  
Feature Vector ◽  
Dimensional Space ◽  
Computational Cost ◽  
Feature Space ◽  
Maximum Energy ◽  
Machine Learning Algorithms ◽  
Eeg Signals ◽  
Time Frequency ◽  
Synchrosqueezing Transform

Abstract The feasibility of using time–frequency (TF) ridges estimation is investigated on multi-channel electroencephalogram (EEG) signals for emotional recognition. Without decreasing accuracy rate of the valence/arousal recognition, the informative component extraction with low computational cost will be examined using multivariate ridge estimation. The advanced TF representation technique called multivariate synchrosqueezing transform (MSST) is used to obtain well-localized components of multi-channel EEG signals. Maximum-energy components in the 2D TF distribution are determined using TF-ridges estimation to extract instantaneous frequency and instantaneous amplitude, respectively. The statistical values of the estimated ridges are used as a feature vector to the inputs of machine learning algorithms. Thus, component information in multi-channel EEG signals can be captured and compressed into low dimensional space for emotion recognition. Mean and variance values of the five maximum-energy ridges in the MSST based TF distribution are adopted as feature vector. Properties of five TF-ridges in frequency and energy plane (e.g., mean frequency, frequency deviation, mean energy, and energy deviation over time) are computed to obtain 20-dimensional feature space. The proposed method is performed on the DEAP emotional EEG recordings for benchmarking, and the recognition rates are yielded up to 71.55, and 70.02% for high/low arousal, and high/low valence, respectively.

scholarly journals Modified Decision Tree Technique for Ransomware Detection at Runtime through API Calls

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Faizan Ullah ◽  
Qaisar Javaid ◽  
Abdu Salam ◽  
Masood Ahmad ◽  
Nadeem Sarwar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Feature Vector ◽  
Machine Learning Algorithms ◽  
The Novel ◽  
Proposed Model ◽  
Testing Accuracy ◽  
Financial Losses

Ransomware (RW) is a distinctive variety of malware that encrypts the files or locks the user’s system by keeping and taking their files hostage, which leads to huge financial losses to users. In this article, we propose a new model that extracts the novel features from the RW dataset and performs classification of the RW and benign files. The proposed model can detect a large number of RW from various families at runtime and scan the network, registry activities, and file system throughout the execution. API-call series was reutilized to represent the behavior-based features of RW. The technique extracts fourteen-feature vector at runtime and analyzes it by applying online machine learning algorithms to predict the RW. To validate the effectiveness and scalability, we test 78550 recent malign and benign RW and compare with the random forest and AdaBoost, and the testing accuracy is extended at 99.56%.

scholarly journals Threshold of permanent cornea thermal damage due to incidental continuous wave CO2 laser irradiation

2010 ◽  
Vol 14 (2) ◽  
pp. 459-467 ◽  
Author(s):  
Mayada Tahir
Keyword(s):  
Laser Irradiation ◽  
Co2 Laser ◽  
Thermal Damage ◽  
Continuous Wave ◽  
Anterior Part ◽  
Outer Part ◽  
Human Cornea ◽  
Thermal Dose ◽  
Element Analysis ◽  
Reflex Blink

Cornea thermal damage due to incidental continuous wave CO2 laser irradiation is studied numerically based on bio-heat equation. The interaction of laser with tissue leads to a rapid temperature increased in target and the nearby tissue. As the temperature of the eye surface reaches 44?C, a sensation of pain will cause aversion response of the reflex blink and/or shifting away from the source of pain. The aim of the work is to predict numerically the threshold limit of incidental laser power that causes damage to the anterior part of the cornea, which can be healed within 2-5 days as long as damage is not exceeding the outer part of the eye (epithelium). A finite element analysis is used to predict temperature distribution through the cornea where the necroses region can be obtained using thermal dose equation. The thermal dose that required for damaging the cornea is predicted from previously published experimental data on rhesus monkeys and used later as a limit for shrinkage to human cornea. The result of this work is compared by international standard of safety and a good nearby result is obtained which verified the result of this work.

Automatically Evolving Texture Image Descriptors using the Multi-tree Representation in Genetic Programming using Few Instances

2020 ◽  
pp. 1-34
Author(s):  
Harith Al-Sahaf ◽  
Ausama Al-Sahaf ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Feature Vector ◽  
Local Binary Patterns ◽  
Texture Image ◽  
Tree Representation ◽  
Line Segments ◽  
Image Descriptor ◽  
Image Descriptors ◽  
The Cost

The performance of image classification is highly dependent on the quality of the extracted features that are used to build a model. Designing such features usually requires prior knowledge of the domain and is often undertaken by a domain expert who, if available, is very costly to employ. Automating the process of designing such features can largely reduce the cost and efforts associated with this task. Image descriptors, such as local binary patterns, have emerged in computer vision, and aim at detecting keypoints, e.g., corners, line-segments and shapes, in an image and extracting features from those keypoints. In this paper, genetic programming (GP) is used to automatically evolve an image descriptor using only two instances per class by utilising a multi-tree program representation. The automatically evolved descriptor operates directly on the raw pixel values of an image and generates the corresponding feature vector. Seven well-known datasets were adapted to the few-shot setting and used to assess the performance of the proposed method and compared against six hand-crafted and one evolutionary computation-based image descriptor as well as three convolutional neural network (CNN) based methods. The experimental results show that the new method has significantly outperformed the competitor image descriptors and CNN-based methods. Furthermore, different patterns have been identified from analysing the evolved programs.

scholarly journals Statistical Comparison of Classifiers Applied to the Interferential Tear Film Lipid Layer Automatic Classification

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
B. Remeseiro ◽  
M. Penas ◽  
A. Mosquera ◽  
J. Novo ◽  
M. G. Penedo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Healthy Subjects ◽  
Feature Vector ◽  
Tear Film ◽  
Region Of Interest ◽  
Machine Learning Algorithms ◽  
Lipid Layer ◽  
Statistical Comparison ◽  
Texture Pattern ◽  
Exhaustive Study

The tear film lipid layer is heterogeneous among the population. Its classification depends on its thickness and can be done using the interference pattern categories proposed by Guillon. The interference phenomena can be characterised as a colour texture pattern, which can be automatically classified into one of these categories. From a photography of the eye, a region of interest is detected and its low-level features are extracted, generating a feature vector that describes it, to be finally classified in one of the target categories. This paper presents an exhaustive study about the problem at hand using different texture analysis methods in three colour spaces and different machine learning algorithms. All these methods and classifiers have been tested on a dataset composed of 105 images from healthy subjects and the results have been statistically analysed. As a result, the manual process done by experts can be automated with the benefits of being faster and unaffected by subjective factors, with maximum accuracy over 95%.

scholarly journals Multiple Sclerosis Detection in Multispectral Magnetic Resonance Images with Principal Components Analysis.

2008 ◽  
Author(s):  
Dirk-jan Kroon ◽  
Erik van Oort ◽  
Kees Slump
Keyword(s):  
Multiple Sclerosis ◽  
Feature Vector ◽  
Principal Component ◽  
Bias Field ◽  
Magnetic Resonance Images ◽  
Local Feature ◽  
Training Set ◽  
Edge Preserving ◽  
Vector Method ◽  
Components Analysis

This paper presents a local feature vector based method for automated Multiple Sclerosis (MS) lesion segmentation of multi spectral MRI data. Twenty datasets from MS patients with FLAIR, T1,T2, MD and FA data with expert annotations are available as training set from the MICCAI 2008 challenge on MS, and 24 test datasets. Our local feature vector method contains neighbourhood voxel intensities, histogram and MS probability atlas information. Principal Component Analysis(PCA) with log-likelihood ratio is used to classify each voxel. MRI suffers from intensity inhomogenities. We try to correct this ‘’bias field’’ with 3 methods: a genetic algorithm, edge preserving filtering and atlas based correction. A large observer variability exist between expert classifications, but the similarity scores between model and expert classifications are often lower. Our model gives the best classification results with raw data, because bias correction gives artifacts at the edges and flatten large MS lesions.

scholarly journals Building Detection using Two-Layered Novel Convolutional Neural Networks

2021 ◽  
Vol 3 (1) ◽  
pp. 29-37
Author(s):  
Karuppusamy P
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Video Processing ◽  
Feature Vector ◽  
State Of The Art ◽  
Local Binary Patterns ◽  
Building Detection ◽  
Histogram Of Oriented Gradients ◽  
Sensing Applications ◽  
Remote Sensing Applications

In the recent years, there has been a high surge in the use of convolutional neural networks (CNNs) because of the state-of-the art performance in a number of areas like text, audio and video processing. The field of remote sensing applications is however a field that has not fully incorporated the use of CNN. To address this issue, we introduced a novel CNN that can be used to increase the performance of detectors built that use Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). Moreover, in this paper, we have also increased the accuracy of the CNN using two improvements. The first improvement involves feature vector transformation with Euler methodology and combining normalized and raw features. Based on the results observed, we have also performed a comparative study using similar methods and it has been identified that the proposed CNN proves to be an improvement over the others.

DELAUNAY REFINEMENT ALGORITHMS FOR ESTIMATING LOCAL FEATURE SIZE IN 2D AND 3D

2011 ◽  
Vol 21 (05) ◽  
pp. 507-543
Author(s):  
ALEXANDER RAND ◽  
NOEL WALKINGTON
Keyword(s):  
Delaunay Triangulation ◽  
Mesh Generation ◽  
Piecewise Linear ◽  
Local Information ◽  
Local Feature ◽  
Delaunay Refinement ◽  
Linear Complex ◽  
Feature Size ◽  
2D And 3D ◽  
Local Feature Size

We present Delaunay refinement algorithms for estimating local feature size on the input vertices of a 2D piecewise linear complex and on the input vertices and segments of a 3D piecewise linear complex. These algorithms are designed to eliminate the need for a local feature size oracle during quality mesh generation of domains containing acute input angles. In keeping with Ruppert's algorithm, encroachment in these algorithms can be determined through only local information in the current Delaunay triangulation. The algorithms are practical to implement and several examples are given.

Neural Network-Based Approach for Face Recognition Across Varying Pose

2015 ◽  
Vol 29 (08) ◽  
pp. 1556015 ◽  
Author(s):  
Seyed Omid Shahdi ◽  
S. A. R. Abu-Bakar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Local Information ◽  
Discrete Wavelet ◽  
Face Images ◽  
The Face ◽  
The Relationship

At present, frontal or even near frontal face recognition problem is no longer considered as a challenge. Recently, the shift has been to improve the recognition rate for the nonfrontal face. In this work, a neural network paradigm based on the radial basis function approach is proposed to tackle the challenge of recognizing faces in different poses. Exploiting the symmetrical properties of human face, our work takes the advantage of the existence of even half of the face. The strategy is to maximize the linearity relationship based on the local information of the face rather than on the global information. To establish the relationship, our proposed method employs discrete wavelet transform and multi-color uniform local binary pattern (ULBP) in order to obtain features for the local information. The local information will then be represented by a single vector known as the face feature vector. This face feature vector will be used to estimate the frontal face feature vector which will be used for matching with the actual vector. With such an approach, our proposed method relies on a database that contains only single frontal face images. The results shown in this paper demonstrate the robustness of our proposed method even at low-resolution conditions.

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