The combination of gray level co-occurrence matrix and back propagation neural network for classifying stairs descent and floor

Utaminingrum, Fitri; Sarosa, Syam Julio A.; Karim, Corina; Gapsari, Femiana; Wihandika, Randy Cahya

doi:10.1016/j.icte.2021.05.010

Cited by 11 publications

(3 citation statements)

References 12 publications

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“…The State Council issued the most among them, reaching 26, indicating that the state attaches importance to the education of MC. The analysis of policy tools shows that there are relatively few capacity-building tools and system change tools, which reserves space for adjustment and repair for the introduction of followup policies and is conducive to the long-term benefits of policy development (Nicolas et al, 2019;Utaminingrum et al, 2021). Huang (2021) studied how AI courses cultivate students' key abilities, and analyzed the influence of AI education on students' knowledge ability, team ability, and learning ability in the stage of basic education.…”

Section: Discussionmentioning

confidence: 99%

Influence Analysis of Education Policy on Migrant Children’s Education Integration Using Artificial Intelligence and Deep Learning

Chen

Song²,

Yuan³

et al. 2022

Front. Psychol.

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This work intends to solve the problem that the traditional education system cannot reasonably adjust the educational integration of children with the arrival of labor force in a short time, and support the education of migrant children (MC) in the education policy (EP) to integrate them into the local educational environment as soon as possible. Firstly, this work defines the surplus labor force and MC. Secondly, the principles of Artificial Intelligence (AI) and Deep Learning (DL) are introduced. Thirdly, it analyzes the education of MC and relevant policies, and the data of the education effect of MC are collected and the evaluation effect model is built. Finally, the evaluation model of MC’s education effect is applied to test the effect of EP. The results show that using AI technology combined with DL technology to model the education effect of MC can establish an effective and accurate evaluation model of the education effect of MC, effectively evaluate the impact of local education policies on the education of MC, and give an effective effect analysis of relevant education policies in each period. The result of Adaptive Resonance Theory (ART)–Back Propagation algorithm is 65 ∼ 96%, which is much higher than the efficiency of traditional algorithms. This shows that the education integration evaluation model of MC based on AI technology and DL technology can effectively and accurately evaluate the integration effect of MC on the local education system, and then provide reference for local and even national adjustment of education policies. The results provide a new idea for the application of new technology in EP.

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Section: Discussionmentioning

confidence: 99%

Influence Analysis of Education Policy on Migrant Children’s Education Integration Using Artificial Intelligence and Deep Learning

Chen

Song²,

Yuan³

et al. 2022

Front. Psychol.

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“…After getting the hybrid dataset composed of real electricity data and specific electricity stealing data, various detection models can be adopted [9][10][11][12][13][14][15][16][17][18][19]. However, due to two features of S , i.e., small scale of electricity stealing and random coherence with time, the performance of these models is not optimal and deep features are needed.…”

Section: Method: Bi-resnet Modelmentioning

confidence: 99%

“…However, because of the massive data, this method brings heavy workload to energy systems, which makes detection efficiency become low. To handle this issue, some new techniques, e.g., grey model (GM) [10,11] and neural network (NN) [12,13], are adopted to predict LLR automatically, but the detection models are still limited by the downside of LLR. It means that only long-term electricity stealing can be identified, while the short-term electricity stealing cannot be detected.…”

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confidence: 99%

Timing shift-based bi-residual network model for the detection of electricity stealing

Feng

et al. 2022

EURASIP J. Adv. Signal Process.

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With the increasing number of electricity stealing users, the interests of countries are jeopardized and it brings economic burden to the government. However, due to the small-scale stealing and its random time coherence, it is difficult to find electricity stealing users. To solve this issue, we first generate the hybrid dataset composed of real electricity data and specific electricity stealing data. Then, we put forward the timing shift-based bi-residual network (TS-BiResNet) model. It learns the features of electricity consumption data on two aspects, i.e., shallow features and deep features, and meanwhile takes time factor into consideration. The simulation results show that TS-BiResNet model can detect electricity stealing behaviors that are small scaled and randomly coherent with time. Besides, its detection accuracy is superior to the benchmark schemes, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), combined convolutional neural network and LSTM (CNN-LSTM) and Bi-ResNet.

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Cancer Mammography Detection Using Four Features Extractions on Gray Level Co-occurrence Matrix with SVM Kernel Analysis

Hendri,

Utaminingrum

2022

Proceedings of the 2nd International Conference on Electronics, Biomedical Engineering, and Health Informatics

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The combination of gray level co-occurrence matrix and back propagation neural network for classifying stairs descent and floor

Cited by 11 publications

References 12 publications

Influence Analysis of Education Policy on Migrant Children’s Education Integration Using Artificial Intelligence and Deep Learning

Influence Analysis of Education Policy on Migrant Children’s Education Integration Using Artificial Intelligence and Deep Learning

Timing shift-based bi-residual network model for the detection of electricity stealing

Cancer Mammography Detection Using Four Features Extractions on Gray Level Co-occurrence Matrix with SVM Kernel Analysis

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