Multinomial Bayesian extreme learning machine for sparse and accurate classification model

Luo, Jiahua; Wong, Chi-Man; Vong, Chi‐Man

doi:10.1016/j.neucom.2020.09.061

Cited by 18 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ML techniques simulate people's learning process and enable the identification and gaining of new information and, accordingly, enhance the effectiveness of particular functions based on the resulting information [85]. ML has been used to address the sparsity problem in several studies and by adopting various techniques such as sparse Bayesian extreme learning machine [86], spectral coclustering [87], and KNN [83].…”

Section: Discussion and Research Implicationsmentioning

confidence: 99%

A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach

Nilashi

Abumalloh

Alrizq

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Travel recommendation agents have been a helpful tool for travelers in their decision-making for destination choices. It has been shown that sparsity can significantly impact on the accuracy of recommendation agents. The COVID-19 outbreak has affected the tourism and hospitality industry of almost all countries in the world. Tourists who have planned to travel are canceling or postponing trips due to this pandemic. Accordingly, this will impact the rate of travelers’ online reviews on tourism products. Hence, the lack of data, in terms of ratings and textual reviews on hotels, will be a major issue for travel recommendation agents during the COVID-19 outbreak in the context of tourism and hospitality. This will be a new challenge for the researchers in the development of travel recommendation agents. Machine learning has been found to be effective in dealing with the data sparsity in recommendation agents. Therefore, developing new algorithms would be helpful to overcome the sparsity issue in travel recommendation agents. This research provides a new method through neurofuzzy, dimensionality reduction, and clustering techniques and evaluates it on the TripAdvisor dataset to see its effectiveness in solving the sparsity issue. The results showed that the method which used the fuzzy logic technique with the aid of clustering, dimensionality reduction, and fuzzy logic is more efficient in addressing the sparsity problem and presenting more accurate results. The results of the method evaluation are presented and discussed, and several suggestions are provided for future studies.

show abstract

Section: Discussion and Research Implicationsmentioning

confidence: 99%

A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach

Nilashi

Abumalloh

Alrizq

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Support Vector Machine (SVM) performs well in classification models with a balanced data set (Da Silva et al ., 2020; Zhao et al ., 2019). But in classification, there is a chance of ambiguity situation (Luo et al ., 2021). LSTM can be used as a regression model to forecast the RUL of engines, parts or rotary equipment efficiently (Namuduri et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

LSTM based artificial intelligence predictive maintenance technique for availability rate and OEE improvement in a TPM implementing plant through Industry 4.0 transformation

Mohan

Roselyn

Uthra

2023

JQME

View full text Add to dashboard Cite

PurposeThe artificial intelligence (AI) based total productive maintenance (TPM) condition based maintenance (CBM) approach through Industry 4.0 transformation can well predict the breakdown in advance to eliminate breakdown.Design/methodology/approachMeeting the customer requirement as per the delivery schedule with the existing resources are always a big challenge in industries. Any catastrophic breakdown in the equipment leads to increase in production loss, damage to machines, repair cost, time and affects delivery. If these breakdowns are predicted in advance, the breakdown can be addressed before its occurrence and the demand supply chain can be met. TPM is one of the essential operational excellence tool used in industries to utilize the existing resources of a plant in a optimal way. The conventional time based maintenance (TBM) and CBM approach of TPM in Industry 3.0 is time consuming and not accurate enough to achieve zero down time.FindingsThe proposed AI and IIoT based TPM is achieved in a digitalized data oriented platform to monitor and control the health status of the machine which may reduce the catastrophic breakdown by 95% and also improves the quality rate and machine performance rate. Based on the identified key signature parameters related to major breakdown are measured using the sensors, digitalised by programmable logic controller (PLC) and monitored by supervisory control and data acquisition (SCADA) and predicted in server or cloud.Originality/valueLong short term memory based deep learning network was developed as a regression forecasting model to predict the remaining useful life RUL of the part or assembly and based on the predictions, corrective action has been implemented before the occurrence of breakdown. The reliability and consistency of the proposed approach are validated and horizontally deployed in similar machines to achieve zero downtime.

show abstract

“…It has been widely used in recent years [23,24]. In [25], ELM with error correction was used for short-term wind speed prediction; in [26], the multinomial Bayesian extreme learning machine (MBELM) was proposed for multi-class classification; on-line sequential outlier robust extreme learning machine was applied in [27] for probabilistic wind speed forecasting; in [28], a self-adaptive kernel extreme learning machine was proposed for short-term wind speed forecasting; according to the training time shown in [29], ELM can obtain more accurate forecasting results with a faster calculation speed than comparison models, its structural advantages are fully demonstrated. In general, ELM is a powerful algorithm that is well suited for further study.…”

Section: Introductionmentioning

confidence: 99%

ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power

Niu

Zeng

et al. 2021

Energies

View full text Add to dashboard Cite

Wind power has significant randomness. Probabilistic prediction of wind power is necessary to solve the problem of safe and stable power grid dispatching with the integration of large-scale wind power. Therefore, this paper proposes a novel nonparametric probabilistic prediction model for wind power based on extreme learning machine-quantile regression (ELM-QR). Firstly, the ELM-QR models of multiple quantiles are established, and then the new comprehensive index (NCI) is optimized by particle swarm optimization (PSO) to obtain the weighting coefficients corresponding to the lower and upper bounds of the prediction intervals. The final prediction interval is obtained by integrating the outputs of ELM-QR models and the weighting coefficients. Finally, case studies are carried out with the real wind farm operation data, simulation results show that the proposed algorithm can obtain narrower prediction intervals while ensuring high reliability. Through sensitivity analysis and comparison with other algorithms, the effectiveness of the proposed algorithm is further verified.

show abstract

Multinomial Bayesian extreme learning machine for sparse and accurate classification model

Cited by 18 publications

References 27 publications

A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach

A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach

LSTM based artificial intelligence predictive maintenance technique for availability rate and OEE improvement in a TPM implementing plant through Industry 4.0 transformation

ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power

Contact Info

Product

Resources

About