Point Based Forecasting Model of Vehicle Queue with Extreme Learning Machine Method and Correlation Analysis

Kasliono,; Suprapto, Suprapto; Makhrus, Faizal

doi:10.5815/ijisa.2021.03.02

Cited by 6 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Correlation analysis [25,26] was conducted with the help of SPSS 25.0, and the results showed that psychological contract breakdown was positively correlated with turnover intention (r=0.420**) and perceived opportunity (r=0.373**), negatively correlated with job satisfaction (r=-0.260**), and job embeddedness (r=-0.473**), which preliminarily verified that H1, H2a, H3a, and H4a. Note：** indicates significant correlation at the 0.01 level (two-tailed).…”

Section: Correlation Analysismentioning

confidence: 99%

A Study on Leaving of Tibetan Cadres Under the Perspective of Psychological Contracts-Take Teachers and Civil Servants as an Example

Wang

2024

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

The high turnover rate of Tibetan cadres has seriously affected the development of Tibet. Existing studies have mainly explored the turnover of rural teachers, and primary and secondary school teachers, and have paid less attention to the extra-occupational factors. In this paper, we take teachers and civil servants working on the Tibetan Plateau as examples to explore the turnover problems, and analyze their mediating and moderating mechanisms. The study finds that psychological contract breakdown significantly and positively affects the turnover intention of Tibetan cadres, while job satisfaction, job embeddedness, and perceived opportunities play a mediating role, and kinship responsibility moderates the relationship between job satisfaction, job embeddedness, and turnover intention. The results of this study expand the antecedents of job embeddedness theory and turnover modeling studies, enrich the mediation and boundary mechanisms of turnover research, explain the reason and internal mechanism of Tibetan cadres leaving and retention, and provide ideas for Tibetan cadres’ retention.

show abstract

Section: Correlation Analysismentioning

confidence: 99%

A Study on Leaving of Tibetan Cadres Under the Perspective of Psychological Contracts-Take Teachers and Civil Servants as an Example

Wang

2024

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

show abstract

“…𝐻 † = (𝐻 𝑇 𝐻) −1 𝐻 𝑇 (8) Secara lebih ringkas, algoritma ELM dengan SLFN dapat dimodelkan dengan langkah sebagai berikut [20], [12], [21], [22]. Input : Data training (𝑥 𝑖 , 𝑦 𝑖 ) dengan 𝑥 𝑖 = [𝑥 𝑖1 , 𝑥 𝑖2 , … , 𝑥 𝑖𝑑 ] 𝑇 ∈ 𝑅 𝑑 dan 𝑦 𝑖 = [𝑦 𝑖1 , 𝑦 𝑖2 , … , 𝑦 𝑖𝑚 ] 𝑇 ∈ 𝑅 𝑚 .…”

Section: 𝐻𝛽 = 𝑌unclassified

Pemodelan Prediksi Harga Ethereum (Atribut: Open, High dan Low) dengan Algoritma Extreme Learning Machine

Kasliono,

Candraningrum,

Sari

2023

bits

View full text Add to dashboard Cite

The price of cryptocurrencies such as Ethereum often experiences high fluctuations and is difficult to predict. This study aims to predict Ethereum prices using the Extreme Learning Machine (ELM) algorithm which is a fast and efficient machine learning method. Ethereum price data is collected from CoinMarketCap by scraping the data using CoinmarketCap Scraper from the cryptocmd library using Python. An ELM model is built by changing the number of hidden nodes to determine the optimal prediction model of Ethereum prices based on the smallest average MAPE. Model performance was evaluated using the mean absolute percentage error (MAPE) on the test data set. The results show that the ELM model built can predict Ethereum prices with an accuracy of 96.96%. The MAPE obtained is 3.035334%, with 9 hidden nodes in the ELM network architecture model that was built. This shows that the model can explain about 96.96% of the variation in Ethereum price data. Therefore, the ELM model can be used as an aid in making investment decisions

show abstract

“…After the basic simulation of the type and number of faults and the extraction of the characteristic quantities for the building electrical systems and fault states, the appropriate algorithm must be used to model them, and then the model must be used for fault identification and diagnosis. BP neural networks are widely used in current fault diagnosis due to their multihidden layer structure, which increases the computational power of diagnosis [26]. However, BP neural networks also have some obvious drawbacks, resulting in slow convergence of the computational results, or not easy convergence.…”

Section: Building Electrical System Fault Diagnosis and Monitoring Co...mentioning

confidence: 99%

The application of fault diagnosis techniques and monitoring methods in building electrical systems – based on ELM algorithm

Liu

2023

J. meas. eng.

View full text Add to dashboard Cite

The reliability of modern building electrical systems are receiving increasing attention as they become more intelligent and complex. As the majority of building electrical systems use neutral point grounding, earth faults or short circuits can get worse over time and damage both the distribution system and the electrical equipment. To this end, the corresponding three phases and four categories, namely three-phase voltage, three-phase current after fault, three-phase voltage distortion rate, three-phase current distortion rate, a total of 12 dimensional fault feature vectors and 10 fault simulation types, were summarised and extracted in conjunction with the actual operating conditions of the system. Using traditional fault identification ideas and neural network algorithm as reference, a 12-dimensional fault feature vector is used as the model input to construct a building electrical fault diagnosis and detection model based on ELM algorithm. Results showed that the ELM-based model’s classification accuracy for this experimental sample was 97.56 %, its AUC was 0.92, and its RMSE was 0.3521. These figures were higher than the classification accuracy and performance of the BP algorithm and GA-BP algorithm fault diagnosis models, and they also demonstrate better robustness and generalizability. The model also has a 97.27 % correct rate in fault discrimination, while the computation time is only 0.201 s, and its fault identification and diagnosis speed is faster than other algorithmic models. At the same time, this research model has a good fault monitoring accuracy of up to 98.6 % for building electrical systems. The research can provide a more sensitive, accurate and rapid fault monitoring method for the current building electrical system. It also improves the reliability of the building electrical system in a complex environment and achieves better protection of the system. This has a certain significance for the development of the building electrical industry.

show abstract

Point Based Forecasting Model of Vehicle Queue with Extreme Learning Machine Method and Correlation Analysis

Cited by 6 publications

References 17 publications

A Study on Leaving of Tibetan Cadres Under the Perspective of Psychological Contracts-Take Teachers and Civil Servants as an Example

A Study on Leaving of Tibetan Cadres Under the Perspective of Psychological Contracts-Take Teachers and Civil Servants as an Example

Pemodelan Prediksi Harga Ethereum (Atribut: Open, High dan Low) dengan Algoritma Extreme Learning Machine

The application of fault diagnosis techniques and monitoring methods in building electrical systems – based on ELM algorithm

Contact Info

Product

Resources

About