Fuzzy modeling has many advantages over the non-fuzzy methods, such as robustness against uncertainties and less sensitivity to the varying dynamics of nonlinear systems. Data-driven fuzzy modeling needs to extract fuzzy rules from the input/output data, and train the fuzzy parameters. This paper takes advantages from deep learning, probability theory, fuzzy modeling, and extreme learning machi...

The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids. However, existing methods for can struggle handle large consumption datasets because missing values, data variance and nonlinear relationship problems, there a lack integrated infrastructure coordinating load analysis procedures. To help address these simple yet effective ETD model developed. T...

Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implemen...

—In this work we propose a new deep learning tool – deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion – one layer at a time. This requires solving a simple (shallow) dictionary learning problem; the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like s...

It is very important for financial institutions which are capable of accurately predicting business failure. In literature, numbers of bankruptcy prediction models have been developed based on statistical and machine learning techniques. In particular, many machine learning techniques, such as neural networks, decision trees, etc. have shown better prediction performances than statistical ones....

The wind speed forecasting in Hong Kong is more difficult than in other places in the same latitude for two reasons: the great affect from the urbanization of Hong Kong in the long term, and the very high wind speeds brought by the tropical cyclones. Therefore, prediction model with higher learning ability is in need for the wind speed forecast in Hong Kong. In this paper, we try to employ the ...

Accurate and reliable prediction of exhaust emissions is crucial for combustion optimization control environmental protection. This study proposes a novel ensemble deep learning model (NOx CO2) prediction. In this model, the stacked denoising autoencoder established to extract features flame images. Four forecasting engines include artificial neural network, extreme machine, support vector mach...

Numerous machine learning algorithms applied on Intrusion Detection System (IDS) to detect enormous attacks. However, it is difficult for machine to learn attack properties globally since there are huge and complex input features. Feature selection can overcome this problem by selecting the most important features only to reduce the dimensionality of input features. We leverage Artificial Neura...