scholarly journals Fast pornographic image recognition using compact holistic features and multi-layer neural network

2019 ◽  
Vol 5 (2) ◽  
pp. 89 ◽  
Author(s):  
I Gede Pasek Suta Wijaya ◽  
Ida Bagus Ketut Widiartha ◽  
Keiichi Uchimura ◽  
Muhamad Syamsu Iqbal ◽  
Ario Yudo Husodo
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
False Negative ◽  
False Negative Rate ◽  
Probability Scale ◽  
Scale Invariant ◽  
Large Size ◽  
Skin Region ◽  
Neuro Fuzzy ◽  
Scale Invariant Feature

The paper presents an alternative fast pornographic image recognition using compact holistic features and multi-layer neural network (MNN). The compact holistic features of pornographic images, which are invariant features against pose and scale, is extracted by shape and frequency analysis on pornographic images under skin region of interests (ROIs). The main objective of this work is to design pornographic recognition scheme which not only can improve performances of existing methods (i.e., methods based on skin probability, scale invariant feature transform, eigenporn, and Multilayer-Perceptron and Neuro-Fuzzy (MP-NF)) but also can works fast for recognition. The experimental outcome display that our proposed system can improve 0.3% of accuracy and reduce 6.60% the false negative rate (FNR) of the best existing method (skin probability and eigenporn on YCbCr, SEP), respectively. Additionally, our proposed method also provides almost similar robust performances to the MP-NF on large size dataset. However, our proposed method needs short recognition time by about 0.021 seconds per image for both tested datasets.

Clinically Meaningful Change

Methodology ◽  
2019 ◽  
Vol 15 (3) ◽  
pp. 97-105
Author(s):  
Rodrigo Ferrer ◽  
Antonio Pardo
Keyword(s):  
Effect Size ◽  
False Negative ◽  
False Negative Rate ◽  
Point Of View ◽  
Skewed Distribution ◽  
Effect Sizes ◽  
False Negatives ◽  
Large Size ◽  
Before And After ◽  
Post Test

Abstract. In a recent paper, Ferrer and Pardo (2014) tested several distribution-based methods designed to assess when test scores obtained before and after an intervention reflect a statistically reliable change. However, we still do not know how these methods perform from the point of view of false negatives. For this purpose, we have simulated change scenarios (different effect sizes in a pre-post-test design) with distributions of different shapes and with different sample sizes. For each simulated scenario, we generated 1,000 samples. In each sample, we recorded the false-negative rate of the five distribution-based methods with the best performance from the point of view of the false positives. Our results have revealed unacceptable rates of false negatives even with effects of very large size, starting from 31.8% in an optimistic scenario (effect size of 2.0 and a normal distribution) to 99.9% in the worst scenario (effect size of 0.2 and a highly skewed distribution). Therefore, our results suggest that the widely used distribution-based methods must be applied with caution in a clinical context, because they need huge effect sizes to detect a true change. However, we made some considerations regarding the effect size and the cut-off points commonly used which allow us to be more precise in our estimates.

Empirical mean curve decomposition with multiwavelet transformation for eye movements recognition using electrooculogram signals

2020 ◽  
Vol 234 (8) ◽  
pp. 794-811
Author(s):  
Harikrishna Mulam ◽  
Malini Mudigonda
Keyword(s):  
Neural Network ◽  
Eye Movements ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Principal Component ◽  
The Neural Network ◽  
Marquardt Algorithm ◽  
Positive Rate ◽  
Sensitivity Specificity

Many research works are in progress in classification of the eye movements using the electrooculography signals and employing them to control the human–computer interface systems. This article introduces a new model for recognizing various eye movements using electrooculography signals with the help of empirical mean curve decomposition and multiwavelet transformation. Furthermore, this article also adopts a principal component analysis algorithm to reduce the dimension of electrooculography signals. Accordingly, the dimensionally reduced decomposed signal is provided to the neural network classifier for classifying the electrooculography signals, along with this, the weight of the neural network is fine-tuned with the assistance of the Levenberg–Marquardt algorithm. Finally, the proposed method is compared with the existing methods and it is observed that the proposed methodology gives the better performance in correspondence with accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F1 score, and Mathews correlation coefficient.

scholarly journals Image recognition of individual cow based on SIFT in Lαβ color space

2018 ◽  
Vol 176 ◽  
pp. 01023
Author(s):  
Feng Lv ◽  
Chunmei ZHANG ◽  
Changwei Lv
Keyword(s):  
Image Recognition ◽  
Individual Recognition ◽  
Low Cost ◽  
Color Space ◽  
Strong Stability ◽  
Identification Accuracy ◽  
Scale Invariant ◽  
Biological Features ◽  
Electronic Tag ◽  
Scale Invariant Feature

Using image recognition technology to identify individual dairy cattle with her biological features shows strong stability. This kind of non-contact, high precision and low cost individual recognition methods based on image processing are more and more popular recently to replace the electronic tag and ear mark which can hurt the cattle’s psychology and physical health and can affect cattle’s behavior. By comparing the various color space transformations, he proposed a scale-invariant feature transform algorithm based on the Luminace of Lαβ color space. With this algorithm, a biological features recognition and management system of Holstein cow has been developed. The identification accuracy is higher than 98%, which is the best result than all the similar reports for cows’ identification.

scholarly journals Methods for Determining Nitrogen, Phosphorus, and Potassium (NPK) Nutrient Content Using Features from Accelerated Segment Test (FAST)

2020 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Raden Sumiharto ◽  
Ristya Ginanjar Putra ◽  
Samuel Demetouw
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nutrient Content ◽  
Scale Invariant ◽  
Rice Field Soil ◽  
Long Time ◽  
The Mean ◽  
Artificial Neural ◽  
Phosphorus And Potassium ◽  
Scale Invariant Feature

Nutrient Content NPK is macro nutrient content that important for the growth of a plant. The measurement of NPK conducted periodically, but the measurement using laboratories test need relatively long time. This Research is conducted to determine the nutrient content of the soil, consisted of nitrogen, phosphor, and calcium (NPK) using digital image processing based on Features from Accelerated Segment Test (FAST) and backpropagation artificial neural network. The data sample in this research taken from the rice field soil in Daerah Istimewa Yogyakarta province where the soil taken at the length of 30 cm to 110 cm with l20 cm interval, and -30° to 30° degree with interval 10°. The model from this measurement system based on texture’s characteristic that extracted using Scale Invariant Feature Transform from soil’s image that already passed pre-processing process. The characteristic result will be the input from the artificial neural network with a variation on parameter’s model. The model tested for the purpose of knowing the influence the distance and degree where the image taken and the influence of parameter’s artificial neural network. The result from the research, is a accurate value of the measurement for each nutrient in the soil, nitrogen (94.86%), phosphor (58.93%) and calcium (63.57%), with the mean 72,46%. The corresponding result obtained from image taken with optimal height of 70 cm and degree 0o

scholarly journals An Exploration into the Detection of COVID-19 from Chest X-ray Scans Using the xRGM-NET Convolutional Neural Network

Technologies ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 98
Author(s):  
Gabriel Ackall ◽  
Mohammed Elmzoudi ◽  
Richard Yuan ◽  
Cuixian Chen
Keyword(s):  
Neural Network ◽  
High School Students ◽  
Convolutional Neural Network ◽  
Rural Areas ◽  
False Negative ◽  
False Negative Rate ◽  
Mapping Technique ◽  
X Rays ◽  
School Students ◽  
X Ray

COVID-19 has spread rapidly across the world since late 2019. As of December, 2021, there are over 250 million documented COVID-19 cases and over 5 million deaths worldwide, which have caused businesses, schools, and government operations to shut down. The most common method of detecting COVID-19 is the RT-PCR swab test, which suffers from a high false-negative rate and a very slow turnaround for results, often up to two weeks. Because of this, specialists often manually review X-ray images of the lungs to detect the presence of COVID-19 with up to 97% accuracy. Neural network algorithms greatly accelerate this review process, analyzing hundreds of X-rays in seconds. Using the Cohen COVID-19 X-ray Database and the NIH ChestX-ray8 Database, we trained and constructed the xRGM-NET convolutional neural network (CNN) to detect COVID-19 in X-ray scans of the lungs. To further aid medical professionals in the manual review of X-rays, we implemented the CNN activation mapping technique Score-CAM, which generates a heat map over an X-ray to illustrate which areas in the scan are most influential over the ultimate diagnosis. xRGM-NET achieved an overall classification accuracy of 97% with a sensitivity of 94% and specificity of 97%. Lightweight models like xRGM-NET can serve to improve the efficiency and accuracy of COVID-19 detection in developing countries or rural areas. In this paper, we report on our model and methods that were developed as part of a STEM enrichment summer program for high school students. We hope that our model and methods will allow other researchers to create lightweight and accurate models as more COVID-19 X-ray scans become available.

scholarly journals Dog recognition in public places based on convolutional neural network

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771982967
Author(s):  
Jianquan Ouyang ◽  
Hao He ◽  
Yi He ◽  
Huanrong Tang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Public Places ◽  
Invariant Feature ◽  
Channel Information ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
The City

With the increase in the number of dogs in the city, the dogs can be seen everywhere in public places. At the same time, more and more stray dogs appear in public places where dogs are prohibited, which has a certain impact on the city environment and personal safety. In view of this, we propose a novel algorithm that combines dense–scale invariant feature transform and convolutional neural network to solve dog recognition problems in public places. First, the image is divided into several grids; then, the dense–scale invariant feature transform algorithm is used to split and combine the descriptors, and the channel information of the eight directions of the image is extracted as the input of the convolutional neural network; and finally, we design a convolutional neural network based on Adam optimization algorithm and cross-entropy to identify the dog species. The experimental results show that the algorithm can fully combine the advantages of dense–scale invariant feature transform and convolutional neural network to achieve dog recognition in public places, and the correct rate is 94.2%.

Human–Computer Interactive Gesture Feature Capture and Recognition in Virtual Reality

2020 ◽  
pp. 106480462092413 ◽  
Author(s):  
Fan Zhang
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Extraction Methods ◽  
Scale Invariant ◽  
Feature Vectors ◽  
Accuracy And Precision ◽  
Scale Invariant Feature

With the development of computer technology, the simulation authenticity of virtual reality technology is getting higher and higher, and the accurate recognition of human–computer interaction gestures is also the key technology to enhance the authenticity of virtual reality. This article briefly introduced three different gesture feature extraction methods: scale invariant feature transform, local binary pattern and histogram of oriented gradients (HOG), and back-propagation (BP) neural network for classifying and recognizing different gestures. The gesture feature vectors obtained by three feature extraction methods were used as input data of BP neural network respectively and were simulated in MATLAB software. The results showed that the information of feature gesture diagram extracted by HOG was the closest to the original one; the BP neural network that applied HOG extracted feature vectors converged to stability faster and had the smallest error when it was stable; in the aspect of gesture recognition, the BP neural network that applied HOG extracted feature vector had higher accuracy and precision and lower false alarm rate.

scholarly journals Using an artificial neural network to predict traumatic brain injury

2019 ◽  
Vol 23 (2) ◽  
pp. 219-226 ◽  
Author(s):  
Andrew T. Hale ◽  
David P. Stonko ◽  
Jaims Lim ◽  
Oscar D. Guillamondegui ◽  
Chevis N. Shannon ◽  
...  
Keyword(s):  
Neural Network ◽  
Traumatic Brain Injury ◽  
Artificial Neural Network ◽  
Brain Injury ◽  
False Negative ◽  
False Negative Rate ◽  
Research Network ◽  
Direct Result ◽  
Data Set ◽  
Artificial Neural

OBJECTIVEPediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling in patients who will have a clinically relevant TBI (CRTBI) would be valuable, providing an evidence-based way to safely discharge children who are at low risk for a CRTBI. The authors hypothesized that an artificial neural network (ANN) trained on clinical and radiologist-interpreted imaging metrics could provide a tool for identifying patients likely to suffer from a CRTBI.METHODSThe authors used the prospectively collected, publicly available, multicenter Pediatric Emergency Care Applied Research Network (PECARN) TBI data set. All patients under the age of 18 years with TBI and admission head CT imaging data were included. The authors constructed an ANN using clinical and radiologist-interpreted imaging metrics in order to predict a CRTBI, as previously defined by PECARN: 1) neurosurgical procedure, 2) intubation > 24 hours as direct result of the head trauma, 3) hospitalization ≥ 48 hours and evidence of TBI on a CT scan, or 4) death due to TBI.RESULTSAmong 12,902 patients included in this study, 480 were diagnosed with CRTBI. The authors’ ANN had a sensitivity of 99.73% with precision of 98.19%, accuracy of 97.98%, negative predictive value of 91.23%, false-negative rate of 0.0027%, and specificity for CRTBI of 60.47%. The area under the receiver operating characteristic curve was 0.9907.CONCLUSIONSThe authors are the first to utilize artificial intelligence to predict a CRTBI in a clinically meaningful manner, using radiologist-interpreted CT information, in order to identify pediatric patients likely to suffer from a CRTBI. This proof-of-concept study lays the groundwork for future studies incorporating iterations of this algorithm directly into the electronic medical record for real-time, data-driven predictive assistance to physicians.

Human Head Portrait Feature Extraction Based on SIFT

2014 ◽  
Vol 644-650 ◽  
pp. 4322-4324
Author(s):  
Xue Ding ◽  
Shun Han ◽  
Hong Hong Yang ◽  
Xiao Feng Wang ◽  
Kun Wu Yang
Keyword(s):  
Feature Extraction ◽  
Image Recognition ◽  
Recognition Performance ◽  
Human Head ◽  
Human Vision ◽  
Practical Application ◽  
Scale Invariant ◽  
The Arts ◽  
Scale Invariant Feature ◽  
Advanced Computer

As the number of students who attend the arts exams has been growing, each admission institutions need to score the human head portraits of art sketch with the number of ten thousand and even 100 thousand. Thus it is an innovative research on how to conduct image recognition with the help of advanced computer technology. Image Recognition Technology is to give the computer the intelligence of human vision, so that the computer can quickly and accurately recognize the object on the input images. However, in the recognition process such factors as light, rotation and shield increase the difficulty of identifying the human head portrait images. In order to get better recognition performance, this paper studies the feature extraction of human head portrait based on SIFT (Scale Invariant Feature Transform). From the practical application, it can be seen that the approach proposed in this paper is feasible and is of good recognition performance.

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