AesTech - A Deep Learning Based Image Evaluation Model

2021 ◽  
Author(s):  
Anshul Rankawat ◽  
Manisa Mondal ◽  
Arun Kumar
Keyword(s):  
Deep Learning ◽  
Evaluation Model ◽  
Image Evaluation

scholarly journals Evaluation Model of Operation State Based on Deep Learning for Smart Meter

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4674
Author(s):  
Qingsheng Zhao ◽  
Juwen Mu ◽  
Xiaoqing Han ◽  
Dingkang Liang ◽  
Xuping Wang
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Detection Efficiency ◽  
Evaluation Model ◽  
Smart Meter ◽  
Smart Meters ◽  
Abnormal Detection ◽  
Prediction Residual ◽  
Threshold Setting ◽  
Training Efficiency

The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.

A deep learning-based sports player evaluation model based on game statistics and news articles

2017 ◽  
Vol 138 ◽  
pp. 15-26 ◽  
Author(s):  
Young Joon Park ◽  
Hyung Seok Kim ◽  
Donghwa Kim ◽  
Hankyu Lee ◽  
Seoung Bum Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Evaluation Model ◽  
Model Based

scholarly journals An Intelligent Evaluation Model for Decision Scheme Based on Deep Learning

2019 ◽  
Vol 1176 ◽  
pp. 032014
Author(s):  
Wei Ou ◽  
Zhaohui Yi ◽  
Lan Cheng ◽  
Ying Liao
Keyword(s):  
Deep Learning ◽  
Evaluation Model ◽  
Decision Scheme

scholarly journals A Teaching Quality Evaluation Model for Preschool Teachers Based on Deep Learning

2021 ◽  
Vol 16 (03) ◽  
pp. 127
Author(s):  
Dongjun Ge ◽  
Xiaoyue Wang ◽  
Jingting Liu
Keyword(s):  
Deep Learning ◽  
Quality Evaluation ◽  
Preschool Teachers ◽  
Evaluation Model ◽  
Preschool Education ◽  
Teaching Quality ◽  
Gradient Boosting ◽  
Starting Point ◽  
Extreme Gradient Boosting ◽  
Quality Evaluation Model

Developed countries regard preschool education as an important starting point and foundation for elite training. In recent years, preschool education has also attracted a growing attention in developing countries like China. Considering the significance of the teaching quality of preschool teachers to lifelong academic achievement, this paper designs a teaching quality evaluation model for preschool teachers based on deep learning. Firstly, a progressive system with a hierarchical structure was developed for the relevant evaluation indices. Then, the fuzzy comprehensive evaluation of each index layer and evaluation criterion was determined by the principle of fuzzy relationship synthesis. Finally, an evaluation prediction model was established based on extreme gradient boosting (XGBoost) algorithm and technology services’ ResNet (TS-ResNet), and proved effective and accurate through experiments. The research results provide a reference for the application of the proposed model in other evaluation prediction scenarios.

Toy-IoT-Oriented data-driven CDN performance evaluation model with deep learning

2018 ◽  
Vol 88 ◽  
pp. 13-22 ◽  
Author(s):  
Wei Zhang ◽  
Zhihui Lu ◽  
Ziyan Wu ◽  
Jie Wu ◽  
Huanying Zou ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Deep Learning ◽  
Evaluation Model ◽  
Data Driven ◽  
Performance Evaluation Model

scholarly journals Application of Deep Learning in Financial Management Evaluation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wenlei Shi ◽  
Lei Xu ◽  
Dongli Peng
Keyword(s):  
Deep Learning ◽  
Financial Management ◽  
Evaluation Model ◽  
Principal Component ◽  
Model Learning ◽  
Analytic Hierarchy ◽  
Management Evaluation ◽  
Learning Evaluation ◽  
Hierarchy Process ◽  
Satisfactory Prediction

The competition among enterprises is becoming increasingly fierce. The research on the financial management evaluation model is helpful for enterprises to find possible risks as soon as possible. This paper constructs the financial management evaluation model based on the deep belief network and applies the analytic hierarchy process to determine the weight of financial management evaluation indicators, which is compared with other classical deep learning evaluation methods, such as KNN, SVM-RBF, and SVM linear. It has achieved an accuracy of more than 81%, showing a satisfactory prediction effect, which is of great significance to formulate corresponding countermeasures, strengthen financial management, improve the capital market system, and promote high-quality economic development. In addition, aiming at the problem of abnormal financial data, this paper uses the new sample dataset obtained by principal component analysis for convolution neural network model learning, which enhances the prediction accuracy of the model and fully shows that deep learning is feasible in the research of financial management prediction, and there is still a lot of space to explore.

An Automated Method for Detecting the Scar Tissue in the Left Ventricular Endocardial Wall Using Deep Learning Approach

2020 ◽  
Vol 16 (3) ◽  
pp. 206-213
Author(s):  
Yashbir Singh ◽  
Deepa Shakyawar ◽  
Weichih Hu
Keyword(s):  
Deep Learning ◽  
Cardiac Ct ◽  
Scar Tissue ◽  
Ct Images ◽  
Left Ventricular ◽  
Learning Approach ◽  
Morphological Operations ◽  
Image Evaluation ◽  
Automated Method ◽  
Pixel Value

Background: Image evaluation of scar tissue plays a significant role in the diagnosis of cardiovascular diseases. Segmentation of the scar tissue is the first step towards evaluating the morphology of the scar tissue. Then, with the use of CT images, the deep learning approach can be applied to identify possible scar tissue in the left ventricular endocardial wall. Objective: To develop an automated method for detecting the endocardial scar tissue in the left ventricular using Deep learning approach. Method: Pixel values of the endocardial wall for each image in the sequence were extracted. Morphological operations, including defining regions of the endocardial wall of the LV where scar tissue could predominate, were performed. Convolutional Neural Networks (CNN) is a deep learning application, which allowed choosing appropriate features from delayed enhancement cardiac CT images to distinguish between endocardial scar and healthy tissues of the LV by applying pixel value-based concepts. Result: We achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity in the detection of endocardial scars using the CNN-based method. Conclusion: Our findings reveal that the CNN-based method yielded robust accuracies in LV endocardial scar detection, which is currently the most extensively used pixel-based method of deep learning. This study provides a new direction for the assessment of scar tissue in imaging modalities and provides a potential avenue for clinical adaptations of these algorithms. Additionally this methodology, in comparison with those in the literature, provides specific advantages in its translational ability to clinical use.

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