scholarly journals Research on Chest Disease Recognition Based on Deep Hierarchical Learning Algorithm

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Lingling Li ◽  
Yangyang Long ◽  
Bangtong Huang ◽  
Zihong Chen ◽  
Zheng Liu ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Vital Role ◽  
Image Features ◽  
Diagnostic Radiology ◽  
Efficient Computation ◽  
Computation Cost ◽  
Short Period ◽  
Chest Disease ◽  
Fast Prediction ◽  
Hierarchical Features

Chest X-ray has become one of the most common ways in diagnostic radiology exams, and this technology assists expert radiologists with finding the patients at potential risk of cardiopathy and lung diseases. However, it is still a challenge for expert radiologists to assess thousands of cases in a short period so that deep learning methods are introduced to tackle this problem. Since the diseases have correlations with each other and have hierarchical features, the traditional classification scheme could not achieve a good performance. In order to extract the correlation features among the diseases, some GCN-based models are introduced to combine the features extracted from the images to make prediction. This scheme can work well with the high quality of image features, so backbone with high computation cost plays a vital role in this scheme. However, a fast prediction in diagnostic radiology is also needed especially in case of emergency or region with low computation facilities, so we proposed an efficient convolutional neural network with GCN, which is named SGGCN, to meet the need of efficient computation and considerable accuracy. SGGCN used SGNet-101 as backbone, which is built by ShuffleGhost Block (Huang et al., 2021) to extract features with a low computation cost. In order to make sufficient usage of the information in GCN, a new GCN architecture is designed to combine information from different layers together in GCNM module so that we can utilize various hierarchical features and meanwhile make the GCN scheme faster. The experiment on CheXPert datasets illustrated that SGGCN achieves a considerable performance. Compared with GCN and ResNet-101 (He et al., 2015) backbone (test AUC 0.8080, parameters 4.7M and FLOPs 16.0B), the SGGCN achieves 0.7831 (−3.08%) test AUC with parameters 1.2M (−73.73%) and FLOPs 3.1B (−80.82%), where GCN with MobileNet (Sandler and Howard, 2018) backbone achieves 0.7531 (−6.79%) test AUC with parameters 0.5M (−88.46%) and FLOPs 0.66B (−95.88%).

scholarly journals An Assessment of the Effectiveness of CALL in Teaching English Language Writing Skills in Saudi Arabia

2019 ◽  
Author(s):  
Hailah Alhujaylan
Keyword(s):  
Saudi Arabia ◽  
Language Learning ◽  
Quantitative Research ◽  
English Language ◽  
Writing Skills ◽  
Vital Role ◽  
Computer Assisted ◽  
Teaching English ◽  
Main Research ◽  
Short Period

Computer-Assisted Language Learning (CALL) is playing a vital role in teaching English language to English as a Foreign Language (EFL) students. However, to best of my knowledge a little has been done in this regard to keep the students in line with the most recent advancements in this paradigm in Saudi Arabia. This paper evaluates the efficacy of CALL in improving students’ writing skills and provides innovative techniques and robust strategies for long-lasting learning. The research seeks to fill in the knowledge gap regarding prospects of using CALL in the Kingdom with these main research questions; 1) how is the technology presently used for teaching the writing skills?; 2) what is the true impact of using CALL on students’ writing skills?; 3) which area of the language (organization, structure, content, grammar) sees the most improvements by CALL to make them better writers? A quantitative research design was used for this study. The sample was sixty female students of a Saudi University divided equally into control and experimental groups. The elicited data analysis indicates that the performance scores of two groups differ significantly when taught through CALL. The research contends that using CALL can enhance students’ writing skills over a short period of time when compared to the traditional ways of improving the writing skills. The current study also recommends that language classrooms should be equipped with all the latest technological facilities to encourage the use of CALL.

scholarly journals Does Tail Label Help for Large-Scale Multi-Label Learning

2018 ◽  
Author(s):  
Tong Wei ◽  
Yu-Feng Li
Keyword(s):  
Large Scale ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Low Complexity ◽  
Tail Distribution ◽  
Prediction Time ◽  
Unseen Data ◽  
Model Size ◽  
Fast Prediction

Large-scale multi-label learning annotates relevant labels for unseen data from a huge number of candidate labels. It is well known that in large-scale multi-label learning, labels exhibit a long tail distribution in which a significant fraction of labels are tail labels. Nonetheless, how tail labels make impact on the performance metrics in large-scale multi-label learning was not explicitly quantified. In this paper, we disclose that whatever labels are randomly missing or misclassified, tail labels impact much less than common labels in terms of commonly used performance metrics (Top-$k$ precision and nDCG@$k$). With the observation above, we develop a low-complexity large-scale multi-label learning algorithm with the goal of facilitating fast prediction and compact models by trimming tail labels adaptively. Experiments clearly verify that both the prediction time and the model size are significantly reduced without sacrificing much predictive performance for state-of-the-art approaches.

scholarly journals Achieving High-Speed Retraction in a Stretchable Hydrogel

2020 ◽  
Author(s):  
Rosa Maria Badani Prado ◽  
Satish Mishra ◽  
Buckston Morgan ◽  
Rangana Wijayapala ◽  
Seyed Meysam Hashemnejad ◽  
...  
Keyword(s):  
High Speed ◽  
Biological Tissues ◽  
Biological Species ◽  
Vital Role ◽  
Polypropylene Glycol ◽  
Glycol Diacrylate ◽  
Mantis Shrimp ◽  
Power Amplification ◽  
Short Period ◽  
Very High

Many biological species apply the power amplification mechanism for locomotion, feeding, and protection. In power amplification, a biological system rapidly releases stored-energy by achieving a very high velocity over a short period of time, resulting in high power output. Such power amplification allows insects such as locust to jump and Mantis shrimp to kill prey by its appendage strike. Biological elastomeric polymers such as resilin play a vital role in the power amplification process because of their high stretchability and resilience. In synthetic materials, although<br>crosslinked rubbers display high stretchability and resilience, such is difficult to achieve in the water-containing systems such as in hydrogels, commonly considered materials for mimicking biological tissues. Here, we have used a simple free-radical polymerization of acrylic acid (AAc), methacrylamide (MAAm), and polypropylene glycol diacrylate (PPGDA) to obtain hydrogels. In these gels, the polymerized AAc and MAAm act as hydrophilic blocks and PPG as hydrophobic, and the gel structure resemble that of resilin consisting of hydrophilic and hydrophobic components. The bioinspired gels display very high stretchability, as high as eight times the original length, and greater than 90% resilience. In addition, the gel samples can reach a retraction velocity of 16 m/s with an acceleration of 4X10^3 m/s2. These values are similar or better than those observed in water containing biological systems, such as appendage strikes in Mantis shrimp, etc. To the best of our knowledge, such performance has not been reported in the<br>literature for any water containing networks.

scholarly journals Smart Face Detection and Recognition in Low Resolution Images Using Alexnet CNN Compare Accuracy with SVM

2021 ◽  
Vol 36 (1) ◽  
pp. 721-726
Author(s):  
S. Mahesh ◽  
Dr.G. Ramkumar
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Low Resolution ◽  
Accuracy Ratio ◽  
Low Resolution Images

Aim: Machine learning algorithm plays a vital role in various biometric applications due to its admirable result in detection, recognition and classification. The main objective of this work is to perform comparative analysis on two different machine learning algorithms to recognize the person from low resolution images with high accuracy. Materials & Methods: AlexNet Convolutional Neural Network (ACNN) and Support Vector Machine (SVM) classifiers are implemented to recognize the face in a low resolution image dataset with 20 samples each. Results: Simulation result shows that ACNN achieves a significant recognition rate with 98% accuracy over SVM (89%). Attained significant accuracy ratio (p=0.002) in SPSS statistical analysis as well. Conclusion: For the considered low resolution images ACNN classifier provides better accuracy than SVM Classifier.

scholarly journals Problems and Prospects of SME Financing in Bangladesh

2021 ◽  
Vol 10 (2) ◽  
pp. 10
Author(s):  
Md. Shahin Alam Khan ◽  
Aminul Hasan ◽  
Jitesh Paul ◽  
Salmana Chowdhury
Keyword(s):  
National Income ◽  
Value Added ◽  
Vital Role ◽  
Market Condition ◽  
Main Concern ◽  
Industrial Workers ◽  
Economic Activities ◽  
Manufacturing Enterprises ◽  
Short Period ◽  
Sme Financing

To conduct a comprehensive study on the SME sector of Bangladesh of its present state, prospects, issues and emerging challenges this “problems & Prospects of SME Financing in Bangladesh” research is carried out. The main objective of the study is to find out the SME financing’s different types of strategies & policies in Bangladesh. The SMEs are playing gradually more significant role as a mainstream of economic escalation in Bangladesh as well as all over the world. The SME usually creates the opportunities of employment at lower costs and render flexibility to the economy. The SMEs are playing an indispensable role for overall economic development. Since this sector is a manual labor intensivewith the short period of time, usually it is capable of increasing the national income as well as hasty employmentcreation by achieving the Millennium Development Goals (MDGs) as well as the abolition of extremepoverty and starvation, gender equality and women empowerment. This SME sector has played a vital role intheeconomic progress of some prosperous countries of Asia. In national economy of Bangladesh SMEs play mainly vital role by making manufacturing enterprises by providing the employment of industrial workers and contributing to the over one-third of industrial value-added to gross domestic product (GDP) and become accustomed quickly to change the market condition, create employment, help diversify economic activities, and also make a significantcontribution to the exports and trade. The economic competence and the overall performance of the SMEs are considerably depended upon the policy of environmental and specific promotional policies pursued for their benefits. Consequently, the policies and initiatives to develop the condition of SMEs and toincrease their competitiveness are a main concern of Bangladesh.

scholarly journals https://jusst.org/developing-the-rural-economy-structure-in-agriculture-towards-the-betterment-of-indias-economy/

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Amit Doegar ◽  
◽  
Maitreyee Dutta ◽  
Gaurav Kumar ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Rural Economy ◽  
Learning Algorithm ◽  
Learning Model ◽  
Image Features ◽  
Forgery Detection ◽  
Image Forgery ◽  
Image Forgery Detection ◽  
Testing Dataset ◽  
Deep Learning Model

In the present scenario, one of the threats of trust on images for digital and online applications as well as on social media. Individual’s reputation can be turnish using misinformation or manipulation in the digital images. Image forgery detection is an approach for detection and localization of forged components in the image which is manipulated. For effective image forgery detection, an adequate number of features are required which can be accomplished by a deep learning model, which does not require manual feature engineering or handcraft feature approaches. In this paper we have implemented GoogleNet deep learning model to extract the image features and employ Random Forest machine learning algorithm to detect whether the image is forged or not. The proposed approach is implemented on the publicly available benchmark dataset MICC-F220 with k-fold cross validation approach to split the dataset into training and testing dataset and also compared with the state-of-the-art approaches.

scholarly journals Image Features of Magnetic Resonance Imaging under the Deep Learning Algorithm in the Diagnosis and Nursing of Malignant Tumors

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lifang Sun ◽  
Xi Hu ◽  
Yutao Liu ◽  
Hengyu Cai
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Malignant Tumors ◽  
Image Features ◽  
Control Group ◽  
Deep Learning Algorithm ◽  
Image Characteristics ◽  
Traditional Algorithm ◽  
Mri Image

In order to explore the effect of convolutional neural network (CNN) algorithm based on deep learning on magnetic resonance imaging (MRI) images of brain tumor patients and evaluate the practical value of MRI image features based on deep learning algorithm in the clinical diagnosis and nursing of malignant tumors, in this study, a brain tumor MRI image model based on the CNN algorithm was constructed, and 80 patients with brain tumors were selected as the research objects. They were divided into an experimental group (CNN algorithm) and a control group (traditional algorithm). The patients were nursed in the whole process. The macroscopic characteristics and imaging index of the MRI image and anxiety of patients in two groups were compared and analyzed. In addition, the image quality after nursing was checked. The results of the study revealed that the MRI characteristics of brain tumors based on CNN algorithm were clearer and more accurate in the fluid-attenuated inversion recovery (FLAIR), MRI T1, T1c, and T2; in terms of accuracy, sensitivity, and specificity, the mean value was 0.83, 0.84, and 0.83, which had obvious advantages compared with the traditional algorithm ( P < 0.05 ). The patients in the nursing group showed lower depression scores and better MRI images in contrast to the control group ( P < 0.05 ). Therefore, the deep learning algorithm can further accurately analyze the MRI image characteristics of brain tumor patients on the basis of conventional algorithms, showing high sensitivity and specificity, which improved the application value of MRI image characteristics in the diagnosis of malignant tumors. In addition, effective nursing for patients undergoing analysis and diagnosis on brain tumor MRI image characteristics can alleviate the patient’s anxiety and ensure that high-quality MRI images were obtained after the examination.

scholarly journals Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Junping Hu ◽  
Shitu Abubakar ◽  
Shengjun Liu ◽  
Xiaobiao Dai ◽  
Gen Yang ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Near Infrared ◽  
Feature Fusion ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Vital Role ◽  
Image Features ◽  
Fusion Technique ◽  
Detection Model ◽  
Road Marking

Pedestrians, motorist, and cyclist remain the victims of poor vision and negligence of human drivers, especially in the night. Millions of people die or sustain physical injury yearly as a result of traffic accidents. Detection and recognition of road markings play a vital role in many applications such as traffic surveillance and autonomous driving. In this study, we have trained a nighttime road-marking detection model using NIR camera images. We have modified the VGG-16 base network of the state-of-the-art faster R-CNN algorithm by using a multilayer feature fusion technique. We have demonstrated another promising feature fusion technique of concatenating all the convolutional layers within a stage to extract image features. The modification boosts the overall detection performance of the model by utilizing the advantages of the shallow layers and the deep layers of the VGG-16 network. The training samples were augmented using random rotation and translation to enhance the heterogeneity of the detection algorithm. We have achieved a mean average precision (mAP) of 89.48% and 92.83% for the baseline faster R-CNN and our modified method, respectively.

Feature Dimension Reduction and Graph Based Ranking Based Image Classification

2013 ◽  
Vol 380-384 ◽  
pp. 4035-4038 ◽  
Author(s):  
Nan Yao ◽  
Feng Qian ◽  
Zuo Lei Sun
Keyword(s):  
Dimensionality Reduction ◽  
Dimension Reduction ◽  
Image Classification ◽  
Learning Algorithm ◽  
Contextual Information ◽  
Original Data ◽  
Classification Problem ◽  
Image Features ◽  
Similarity Learning ◽  
Low Dimensional

Dimensionality reduction (DR) of image features plays an important role in image retrieval and classification tasks. Recently, two types of methods have been proposed to improve both the accuracy and efficiency for the dimensionality reduction problem. One uses Non-negative matrix factorization (NMF) to describe the image distribution on the space of base matrix. Another one for dimension reduction trains a subspace projection matrix to project original data space into some low-dimensional subspaces which have deep architecture, so that the low-dimensional codes would be learned. At the same time, the graph based similarity learning algorithm which tries to exploit contextual information for improving the effectiveness of image rankings is also proposed for image class and retrieval problem. In this paper, after above two methods mentioned are utilized to reduce the high-dimensional features of images respectively, we learn the graph based similarity for the image classification problem. This paper compares the proposed approach with other approaches on an image database.

scholarly journals Learned Features are Better for Ethnicity Classification

2017 ◽  
Vol 17 (3) ◽  
pp. 152-164 ◽  
Author(s):  
Inzamam Anwar ◽  
Naeem Ul Islam
Keyword(s):  
African American ◽  
Vital Role ◽  
Support Vector ◽  
Human Beings ◽  
Novel Approach ◽  
Hierarchical Features ◽  
Facial Images ◽  
Learned Features ◽  
Ethnicity Classification ◽  
Demographic Attribute

Abstract Ethnicity is a key demographic attribute of human beings and it plays a vital role in automatic facial recognition and have extensive real world applications such as Human Computer Interaction (HCI); demographic based classification; biometric based recognition; security and defense to name a few. In this paper, we present a novel approach for extracting ethnicity from the facial images. The proposed method makes use of a pre trained Convolutional Neural Network (CNN) to extract the features, then Support Vector Machine (SVM) with linear kernel is used as a classifier. This technique uses translational invariant hierarchical features learned by the network, in contrast to previous works, which use hand crafted features such as Local Binary Pattern (LBP); Gabor, etc. Thorough experiments are presented on ten different facial databases, which strongly suggest that our approach is robust to different expressions and illuminations conditions. Here we consider ethnicity classification as a three class problem including Asian, African-American and Caucasian. Average classification accuracy over all databases is 98.28%, 99.66% and 99.05% for Asian, African-American and Caucasian respectively. All the codes are available for reproducing the results on request.

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