scholarly journals Multiweight Cross-Multimedia Logistics Optimal Path Exploration by Integrating High-Dimensional Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Huiying Zhang ◽  
Jinjin Guo ◽  
Guie Sun
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Optimal Path ◽  
High Dimensional ◽  
Learning Technology ◽  
Logistics Industry ◽  
Management Level ◽  
Intelligent Decision Making ◽  
Deep Learning Algorithm ◽  
Logistics Enterprises

High-dimensional deep learning has been applied in all walks of life at present, among which the most representative one is the logistics path optimization combining multimedia with high-dimensional deep learning. Using multimedia logistics to explore and operate the best path can make the whole logistics industry get innovation and leap forward. How to use high-dimensional deep learning to conduct visual logistics operation management is an opportunity and a problem facing the whole logistics industry at present. The application of high-dimensional deep learning technology can help logistics enterprises improve their management level, realize intelligent decision-making, and enable accurate prediction. Starting from the total amount of logistics, regional layout, enterprise scale, and high-dimensional deep learning algorithm, this paper analyzes the current situation of China’s logistic development through multiweight analysis and explores the best path for multimedia logistics.

scholarly journals Research on Network Security Application Based on Deep Learning

CONVERTER ◽  
2021 ◽  
pp. 598-605
Author(s):  
Zhao Jianchao
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Internet Security ◽  
Detection Accuracy ◽  
Data Sets ◽  
Learning Technology ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Internet Industry

Behind the rapid development of the Internet industry, Internet security has become a hidden danger. In recent years, the outstanding performance of deep learning in classification and behavior prediction based on massive data makes people begin to study how to use deep learning technology. Therefore, this paper attempts to apply deep learning to intrusion detection to learn and classify network attacks. Aiming at the nsl-kdd data set, this paper first uses the traditional classification methods and several different deep learning algorithms for learning classification. This paper deeply analyzes the correlation among data sets, algorithm characteristics and experimental classification results, and finds out the deep learning algorithm which is relatively good at. Then, a normalized coding algorithm is proposed. The experimental results show that the algorithm can improve the detection accuracy and reduce the false alarm rate.

scholarly journals Transfer Learning Strategies for Deep Learning-based PHM Algorithms

2020 ◽  
Vol 10 (7) ◽  
pp. 2361
Author(s):  
Fan Yang ◽  
Wenjin Zhang ◽  
Laifa Tao ◽  
Jian Ma
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Learning Strategies ◽  
Transfer Learning ◽  
High Speed ◽  
Health Management ◽  
Learning Algorithm ◽  
Learning Technology ◽  
Deep Learning Algorithm ◽  
Transfer Strategies

As we enter the era of big data, we have to face big data generated by industrial systems that are massive, diverse, high-speed, and variability. In order to effectively deal with big data possessing these characteristics, deep learning technology has been widely used. However, the existing methods require great human involvement that is heavily depend on domain expertise and may thus be non-representative and biased from task to similar task, so for a wide variety of prognostic and health management (PHM) tasks, how to apply the developed deep learning algorithms to similar tasks to reduce the amount of development and data collection costs has become an urgent problem. Based on the idea of transfer learning and the structures of deep learning PHM algorithms, this paper proposes two transfer strategies via transferring different elements of deep learning PHM algorithms, analyzes the possible transfer scenarios in practical application, and proposes transfer strategies applicable in each scenario. At the end of this paper, the deep learning algorithm of bearing fault diagnosis based on convolutional neural networks (CNN) is transferred based on the proposed method, which was carried out under different working conditions and for different objects, respectively. The experiments verify the value and effectiveness of the proposed method and give the best choice of transfer strategy.

scholarly journals Comparison and Prediction of Hyperthyroidism Accuracy Rate Using Novel Deep Learning Technology and Vivo Monitoring

2021 ◽  
Vol 36 (1) ◽  
pp. 698-703
Author(s):  
Krushitha Reddy ◽  
D. Jenila Rani
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Research Work ◽  
Learning Technology ◽  
Accuracy Rate ◽  
Data Set ◽  
In Vivo Monitoring ◽  
Deep Learning Algorithm

Aim: The aim of this research work is to determine the presence of hyperthyroidism using modern algorithms, and comparing the accuracy rate between deep learning algorithms and vivo monitoring. Materials and methods: Data collection containing ultrasound images from kaggle's website was used in this research. Samples were considered as (N=23) for Deep learning algorithm and (N=23) for vivo monitoring in accordance to total sample size calculated using clinical.com. The accuracy was calculated by using DPLA with a standard data set. Results: Comparison of accuracy rate is done by independent sample test using SPSS software. There is a statistically indifference between Deep learning algorithm and in vivo monitoring. Deep learning algorithm (87.89%) showed better results in comparison to vivo monitoring (83.32%). Conclusion: Deep learning algorithms appear to give better accuracy than in vivo monitoring to predict hyperthyroidism.

scholarly journals Deep Learning Based Coronary Angiography in Diagnosis of Myocardial Ischemia

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yiwen Shu ◽  
Xiwen Wu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Coronary Angiography ◽  
Myocardial Ischemia ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Control Group ◽  
Learning Technology ◽  
Observation Group ◽  
Deep Learning Algorithm

Objective. This study was to explore the diagnostic effect of the coronary angiography (CAG) based on the fully convolutional neural network (FCNN) algorithm for patients with coronary heart disease (CHD) and suspected (not diagnosed) myocardial ischemia. Methods. In this study, 150 patients with undiagnosed CHD with myocardial ischemia in hospital were selected as the research objects. They were divided into an observation group and a control group by random number method. The patients in observation group were examined with CAG with the assistance of convolutional neural network (CNN) algorithm, while patients in the control group received conventional CAG. Results. The Dice coefficient of the segmentation effect evaluation index was 0.89, which showed that the image processing effect of the algorithm was good. There was no statistical difference in positive rates of single/double-vessel lesions between the two groups ( P > 0.05 ), and the positive rates of multivessel lesions and total lesions in the observation group were higher than those in the control group, showing statistically obvious difference ( P < 0.05 ). The examination sensitivity, specificity, accuracy, and Kappa value of the observation group were −90.9%, −60%, −82.7%, and −0.72, which were all higher in contrast to those of the control group. The proportion of positive myocardial ischemia and coronary artery stenosis (CAS) (82%) was higher than other cases (18%), and the comparison was statistically significant ( P < 0.05 ). Conclusion. CAG based on the deep learning algorithm showed a good detection effect and can better display the coronary lesions and reflect the good development prospects of deep learning technology in medical imaging.

scholarly journals utomated Skin Disease Identification using Deep Learning Algorithm

2018 ◽  
Vol 11 (3) ◽  
pp. 1429-1436 ◽  
Author(s):  
Sourav Kumar Patnaik ◽  
Mansher Singh Sidhu ◽  
Yaagyanika Gehlot ◽  
Bhairvi Sharma ◽  
P. Muthu
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Skin Disease ◽  
Skin Diseases ◽  
Learning Algorithm ◽  
Disease Prediction ◽  
Learning Technology ◽  
Deep Learning Algorithm ◽  
Tone Color ◽  
Dermatological Disorders

Dermatological disorders are one of the most widespread diseases in the world. Despite being common its diagnosis is extremely difficult because of its complexities of skin tone, color, presence of hair. This paper provides an approach to use various computer vision based techniques (deep learning) to automatically predict the various kinds of skin diseases. The system uses three publicly available image recognition architectures namely Inception V3, Inception Resnet V2, Mobile Net with modifications for skin disease application and successfully predicts the skin disease based on maximum voting from the three networks. These models are pretrained to recognize images upto 1000 classes like panda, parrot etc. The architectures are published by image recognition giants for public usage for various applications. The system consists of three phases- The feature extraction phase, the training phase and the testing /validation phase. The system makes use of deep learning technology to train itself with the various skin images. The main objective of this system is to achieve maximum accuracy of skin disease prediction.

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