scholarly journals Abnormal Respiratory Sounds Classification Using Deep CNN Through Artificial Noise Addition

2021 ◽  
Vol 8 ◽  
Author(s):  
Rizwana Zulfiqar ◽  
Fiaz Majeed ◽  
Rizwana Irfan ◽  
Hafiz Tayyab Rauf ◽  
Elhadj Benkhelifa ◽  
...  
Keyword(s):  
Visual Inspection ◽  
Similar Type ◽  
Respiratory Sounds ◽  
Artificial Noise ◽  
Adaptive Mechanism ◽  
Respiratory Sound ◽  
Deep Convolutional Neural Networks ◽  
The Third ◽  
Deep Cnn ◽  
Sound Features

Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

scholarly journals Mating Disruption of Pseudococcus calceolariae (Maskell) (Hemiptera, Pseudococcidae) in Fruit Crops

Insects ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 343
Author(s):  
Carolina Ballesteros ◽  
Alda Romero ◽  
María Colomba Castro ◽  
Sofía Miranda ◽  
Jan Bergmann ◽  
...  
Keyword(s):  
Visual Inspection ◽  
Mating Disruption ◽  
Economic Importance ◽  
Fruit Crops ◽  
Potential Control ◽  
The Third ◽  
Pseudococcus Calceolariae ◽  
And Control ◽  
Potential Use ◽  
Control Tool

Pseudococcus calceolariae, the citrophilous mealybug, is a species of economic importance. Mating disruption (MD) is a potential control tool. During 2017–2020, trials were conducted to evaluate the potential of P. calceolariae MD in an apple and a tangerine orchard. Two pheromone doses, 6.32 g/ha (2017–2018) and 9.45 g/ha (2019–2020), were tested. The intermediate season (2018–2019) was evaluated without pheromone renewal to study the persistence of the pheromone effect. Male captures in pheromone traps, mealybug population/plant, percentage of infested fruit at harvest and mating disruption index (MDI) were recorded regularly. In both orchards, in the first season, male captures were significantly lower in MD plots compared to control plots, with an MDI > 94% in the first month after pheromone deployment. During the second season, significantly lower male captures in MD plots were still observed, with an average MDI of 80%. At the third season, male captures were again significant lower in MD than control plots shortly after pheromone applications. In both orchards, population by visual inspection and infested fruits were very low, without differences between MD and control plots. These results show the potential use of mating disruption for the control of P. calceolariae.

scholarly journals The occurrence rate and clinical application of Osteo-line on the femur neck

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Mingchen Zou ◽  
Haotian Wu ◽  
Shuangquan Yao ◽  
Dong Ren ◽  
Song Liu ◽  
...  
Keyword(s):  
Clinical Application ◽  
Visual Inspection ◽  
Occurrence Rate ◽  
Control Group ◽  
Leg Length ◽  
X Ray ◽  
The Third ◽  
Femur Neck ◽  
Medical University ◽  
Experimental Group

Abstract Background This study was done to observe the incidence of Osteo-line on the femur neck and to explore the clinical application of Osteo-line in osteotomy. Methods Eighty-nine adult femur specimens were selected to observe the incidence of Osteo-line on the femur neck. From August 2015 to January 2019, a total of 278 patients who completed unilateral hip arthroplasty at the Third Hospital of Hebei Medical University were retrospectively included. Patients who accepted osteotomy via Osteo-line on the femur neck were defined as the experimental group (n = 139), and patients who accepted osteotomy via traditional method (The femoral distance 1.5 cm above the trochanter was retained for osteotomy by visual inspection.) were defined as the control group (n = 139). According to the postoperative pelvic X-ray, Photoshop was used to evaluate the leg length discrepancy (LLD) by the CFR-T-LT method. Results Among the 89 specimens, the incidence of anterior Osteo-line was 75.28%, and the incidence of posterior Osteo-line was 100%. According to the clinical application results, the incidence of anterior Osteo-line on the femur neck was 80%, and the incidence of posterior Osteo-line was 100%. The Osteo-line was clearer than those on the femoral specimens. Twenty-six cases had LLD greater than 1 cm (9.29%), including 2 cases in the experimental group and 24 cases in the control group. The average postoperative LLD in the experimental group (0.19 ± 0.38 mm) was significantly shorter than in the control group (0.54 ± 0.51 mm)(P = 0.005). Conclusion The incidence of Osteo-line on the femur neck was high, and patients who accepted osteotomy via Osteo-line on the femur neck can achieve shorter postoperative LLD than the control group.

scholarly journals Classification of Lung Sounds and Disease Prediction using Dense CNN Network

2021 ◽  
Vol 11 (1) ◽  
pp. 195-198
Author(s):  
Suyash Lakhani ◽  
◽  
Ridhi Jhamb ◽  
Keyword(s):  
Respiratory Sounds ◽  
Respiratory Sound ◽  
Lung Sounds ◽  
Dense Network ◽  
Lung Sound ◽  
Respiratory Dysfunction ◽  
Respiratory Disorders ◽  
Respiratory Illnesses ◽  
In The Beginning

Respiratory illnesses are a main source of death in the world and exact lung sound identification is very significant for the conclusion and assessment of sickness. Be that as it may, this method is vulnerable to doctors and instrument limitations. As a result, the automated investigation and analysis of respiratory sounds has been a field of great research and exploration during the last decades. The classification of respiratory sounds has the potential to distinguish anomalies and diseases in the beginning phases of a respiratory dysfunction and hence improve the accuracy of decision making. In this paper, we explore the publically available respiratory sound database and deploy three different convolutional neural networks (CNN) and combine them to form a dense network to diagnose the respiratory disorders. The results demonstrate that this dense network classifies the sounds accurately and diagnoses the corresponding respiratory disorders associated with them.

scholarly journals Respiratory sound recorder – continued Innovation

2021 ◽  
pp. 146-151
Author(s):  
Lada S. Starostina ◽  
Natalia A. Geppe ◽  
Vladimir S. Malyshev ◽  
Saniia I. Valieva ◽  
Irina L. Ginesina ◽  
...  
Keyword(s):  
Respiratory Tract ◽  
Computer Technology ◽  
Respiratory Function ◽  
Age Groups ◽  
Patient Treatment ◽  
Respiratory Sounds ◽  
Early Age ◽  
Respiratory Sound ◽  
Respiratory Noises ◽  
Inner Diameter

The study of external respiratory function (ERF) is important in the diagnosis of respiratory tract abnormalities in various diseases. In children, especially at an early age, there are many difficulties in conducting studies. In recent decades, due to the development of computer technology, there is great interest in the study of respiratory sounds, methods of their registration, processing and use in the assessment of the respiratory system in children and adults. Russian scientists have developed the method of respiratory airway sound investigation, which has proved its effectiveness, reliability and necessity of use in practice. Computer bronchophonography is based on the analysis of time and frequency characteristics of the spectrum of respiratory noises, arising from changes in the bronchial diameter due to increase in the stiffness of their walls or decrease in the inner diameter. Computed bronchophonography may be used for diagnostics of EFD disorders in patients of all age groups both in the in-patient and out-patient treatment.

Effective Filter Pruning Method Using Additional Downsampled Image for Global Pooling Applied CNN

2021 ◽  
pp. 2154029
Author(s):  
Hyunseok Kim ◽  
Bunyodbek Ibrokhimov ◽  
Sanggil Kang
Keyword(s):  
Evaluation Method ◽  
Computational Cost ◽  
Information Loss ◽  
Deep Convolutional Neural Networks ◽  
Embedded Devices ◽  
Computational Costs ◽  
Pruning Method ◽  
Original Dataset ◽  
High Resolution Images ◽  
Deep Cnn

Deep Convolutional Neural Networks (CNNs) show remarkable performance in many areas. However, most of the applications require huge computational costs and massive memory, which are hard to obtain in devices with a relatively weak performance like embedded devices. To reduce the computational cost, and meantime, to preserve the performance of the trained deep CNN, we propose a new filter pruning method using an additional dataset derived by downsampling the original dataset. Our method takes advantage of the fact that information in high-resolution images is lost in the downsampling process. Each trained convolutional filter reacts differently to this information loss. Based on this, the importance of the filter is evaluated by comparing the gradient obtained from two different resolution images. We validate the superiority of our filter evaluation method using a VGG-16 model trained on CIFAR-10 and CUB-200-2011 datasets. The pruned network with our method shows an average of 2.66% higher accuracy in the latter dataset, compared to existing pruning methods when about 75% of the parameters are removed.

Biometric Authentication Using Finger-Vein Patterns with Deep-Learning and Discriminant Correlation Analysis

2021 ◽  
pp. 2250013
Author(s):  
Aldjia Boucetta ◽  
Leila Boussaad
Keyword(s):  
Correlation Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Identification System ◽  
Deep Convolutional Neural Networks ◽  
Finger Vein ◽  
Deep Cnn ◽  
Identification Stage ◽  
Human Finger ◽  
The Right

Finger-vein identification, a biometric technology that uses vein patterns in the human finger to identify people. In recent years, it has received increasing attention due to its tremendous advantages compared to fingerprint characteristics. Moreover, Deep-Convolutional Neural Networks (Deep-CNN) appeared to be highly successful for feature extraction in the finger-vein area, and most of the proposed works focus on new Convolutional Neural Network (CNN) models, which require huge databases for training, a solution that may be more practicable in real world applications, is to reuse pretrained Deep-CNN models. In this paper, a finger-vein identification system is proposed, which uses Squeezenet pretrained Deep-CNN model as feature extractor from the left and the right finger vein patterns. Then, combines this Deep-based features by using a feature-level Discriminant Correlation Analysis (DCA) to reduce feature dimensions and to give the most relevant features. Finally, these composite feature vectors are used as input data for a Support Vector Machine (SVM) classifier, in an identification stage. This method is tested on two widely available finger vein databases, namely SDUMLA-HMT and FV-USM. Experimental results show that the proposed finger vein identification system achieves significant high mean accuracy rates.

scholarly journals About the QSOs contained in the Cerro el Roble Survey and the density of UVX-QSOs in the SGP field

1986 ◽  
Vol 119 ◽  
pp. 49-50
Author(s):  
Luis E. Campusano
Keyword(s):  
Visual Inspection ◽  
The Other ◽  
Small Areas ◽  
The Third ◽  
Machine Selection ◽  
Objective Prism ◽  
Selection Of

A region containing the SGP, centered at α 00h 53m (1950) δ −28°03', is becoming a selected region for QSO research. Three lists of QSO candidates have been published for this field. One consists of candidates discovered visually on an objective prism plate, selected in a 25-deg2 area and with B(lim) ⋍ 20 mag (Clowes and Savage, 1983; the CS sample). The other list came from visual inspection of U and B plates (UVX stars), covering a region of 44-deg2 and with approximately the same limiting magnitude of the CS sample (Campusano and Torres, 1983; the CT sample). The third survey of QSO-candidates involved a machine selection of UVX stars (Shanks et al., 1983), whose published components correspond to two small areas of 1.6 and 8.2 deg2 with B(lim) = 19 mag (Boyle et al., 1985).

scholarly journals Criteria and Applications of the MK Classification System

1979 ◽  
Vol 47 ◽  
pp. 23-30
Author(s):  
R. F. Garrison
Keyword(s):  
Present State ◽  
Classification System ◽  
Visual Inspection ◽  
Higher Dimensions ◽  
Variable Stars ◽  
Specific Reference ◽  
The Sun ◽  
The Third ◽  
Future Developments ◽  
Inspection Techniques

AbstractA review is given of the present state of MK classification, with a view to future developments in techniques and instrumentation. The principle of the complementarity of quantitative and visual inspection techniques is stressed.Included in the discussion are examples of problems which are currently outstanding. Among these are variable stars, marginal peculiarities, fundamental standards (with specific reference to the Sun), and representation of the third and higher dimensions.

scholarly journals Face Verification with Multi-Task and Multi-Scale Features Fusion

2017 ◽  
Author(s):  
Xiaojun Lu ◽  
Yue Yang ◽  
Weilin Zhang ◽  
Qi Wang ◽  
Yang Wang
Keyword(s):  
Training Dataset ◽  
Face Verification ◽  
Deep Convolutional Neural Networks ◽  
Computation Cost ◽  
Face Representation ◽  
Features Fusion ◽  
Multi Scale ◽  
The Face ◽  
In The Wild ◽  
Deep Cnn

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

Sign in / Sign up

Export Citation Format

Share Document