An Efficient Estimation Model for Induction Motor Using BMO-RBFNN Technique

Rajesh, P.; Shajin, Francis H.; Anand, N. Vijaya

doi:10.1007/s41660-021-00177-4

Cited by 34 publications

(25 citation statements)

References 35 publications

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“…Recently, Plug‐in Hybrid Electric Vehicles (PHEVs) have become famous around the world in the transportation industry 11 . In the world, 40 % of total energy consumption is utilized by residential sector; here most of energy consumption is based on electric uses and heat 12‐16 . MG is incorporated with RER, PHEV, and energy storage system and the ESSs provide extra power 17,18 .…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach for optimal energy management system of internet of things enabled residential buildings in smart grid

Vanitha

Vallimurugan²

2022

Intl J of Energy Research

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Nowadays, a large number of smart devices in residential buildings is integrated with the evolution of internet of things (IoT), where it is necessary to effectively manage energy to meet the increase in demand. Therefore, in this paper, an optimal energy management strategy using the hybrid Gradient Boosting Decision Tree ‐Artificial Transgender Longicorn Algorithm (GBDT‐ALTA) is proposed for IoT‐enabled residential buildings. The main objective of the proposed approach is to minimize the electricity bill of customer, thereby Peak‐to‐Average Ratio (PAR) is reduced. Due to the scheduling process of residential electric devices, the proposed approach utilizes the waiting time threshold. Here, three types of appliances, such as shiftable electrical devices, thermostatically controlled electrical devices, and generally operated electrical devices are considered. Related to real‐time price signals (RTPS) in utilities, scheduling the shiftable electrical devices is processed. The cost minimization with less acceptable time of waiting is achieved by GBDT‐ATLA method, which considers the trade‐off between the electricity cost and waiting time. Moreover, the proposed approach maintains the stability of the grid, because the stability of grid depends on the PAR. The proposed method is carried out in MATLAB/Simulink site and the simulated results prove that the better performance of GBDT‐ATLA approach compared to the existing approaches, like Slime Mould Optimization (SMO), Chaos Game Optimization Algorithm (CGO), and Side‐Blotched Lizard Optimization Algorithm (SBLO). The efficiency of the proposed approach under 100, 200, 500, and 1000 trails are 99.7300%, 99.6513%, 99.8363%, and 99.7916% respectively.

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Section: Introductionmentioning

confidence: 99%

A hybrid approach for optimal energy management system of internet of things enabled residential buildings in smart grid

Vanitha

Vallimurugan²

2022

Intl J of Energy Research

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“…Various collaborative filtering methods are set up in References 3–10 in which, collaborating mode included users and items. The combination of collaborative filtering and deep learning can be effective because deep learning contains intrinsic depth for embedding 11–14 . It is recognize from the literature that deep learning base models are superior to traditional machine learning methods for recommender systems.…”

Section: Introductionmentioning

confidence: 99%

“…The combination of collaborative filtering and deep learning can be effective because deep learning contains intrinsic depth for embedding. [11][12][13][14] It is recognize from the literature that deep learning base models are superior to traditional machine learning methods for recommender systems. It presents insights, namely, time and semantic irregularities could not deal with baseline long short term memory.…”

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confidence: 99%

A multi‐preference integrated algorithm for deep learning based recommender framework

Jaishankar

Naveen

Marimuthu

et al. 2022

Concurrency and Computation

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Summary Nowadays, the online recommender systems based collaborative filtering methods are widely employed to model long term user preferences (LTUP). The deep learning methods, like recurrent neural networks (RNN) have the potential to model short‐term user preferences (STUP). There is no dynamic integration of these two models in the existing recommender systems. Therefore, in this article, a multi‐preference integrated algorithm (MPIA) for deep learning based recommender framework (DLRF) is proposed to perform the dynamic integration of these two models. Moreover, the MPIA addresses improper data and to improve the performance for creating recommendations. This algorithm is depending on an enhanced long short term memory (LSTM) with additional controllers to consider relative information. Here, experiments are carried out by Amazon benchmark datasets, then obtained outcomes are compared with other existing recommender systems. From the comparison, the experimental outcomes show that the proposed MPIA outperforms existing systems under performance metrics, like area under curve, F1‐score. Consequently, the MPIA can be integrated with real time recommender systems.

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“…In developing software, predicting defective modules has examined by various trainings depending upon two decades 5,6 . Software defect identification technique depends on various factors, software metrics are extracted from the historical data 7–11 . By Increases the defective software modules within the development and maintained costs for customer dissatisfaction.…”

Section: Introductionmentioning

confidence: 99%

“…5,6 Software defect identification technique depends on various factors, software metrics are extracted from the historical data. [7][8][9][10][11] By Increases the defective software modules within the development and maintained costs for customer dissatisfaction. Controlling software quality and reducing development costs is a significant tool.…”

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confidence: 99%

Improved mayfly optimization deep stacked sparse auto encoder feature selection scorched gradient descent driven dropout XLM learning framework for software defect prediction

Anbu

2022

Concurrency and Computation

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Summary Software testing is the process of improving software quality by classifying and removing defects in the software development. Previously, several methods were used for software defect prediction, but any one method did not provide sufficient accuracy. To overcome this issue, an improved may fly optimization with deep stacked sparse auto encoder feature selection scorched gradient descent driven dropout extreme learning machine framework (SDP‐IMFOFS‐GDDDXLMC) is proposed in this article for software defect prediction. Here, an IMFO method is considered for feature selection techniques. In feature selection technique, features are selected, such as PC1, PC4, and MC1 for selecting the probable minimal attribute. Then, GDDDXLMC approach is used to classify buggy and clean software detection. The proposed approach is implemented in MATLAB platform. The performance metrics, such as accuracy, precision, F‐measure, sensitivity, specificity, Mathew correlation coefficient (MCC) is examined to examine the performance of the proposed method. The simulation performance of the proposed SDP‐IMFOFS‐GDDDXLMC method attains higher accuracy 99.75%, 97.85%, 95.13%, 14.89%, 16.34%, 17.89%, and 98.79, higher sensitivity 96.34%, 91.23%, 89.12%, 12.67%, 17.56%, 18.90%, and 87.25% higher specificity 14.89%, 16.89%, 20.67%, 93.67%, 92.37%, 98.47%, and 94.78% compared with the existing methods, like SDP‐MLP‐PSO, SDP‐BPNN‐RBFNN, SDP‐CNN‐RNN‐LSTM, SDP‐KBN‐LIME, SDP‐SLDeep‐LSTM, SDP‐K‐PCA‐ELM, and SDP‐CNN‐PHI forest, respectively.

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An Efficient Estimation Model for Induction Motor Using BMO-RBFNN Technique

Cited by 34 publications

References 35 publications

A hybrid approach for optimal energy management system of internet of things enabled residential buildings in smart grid

A hybrid approach for optimal energy management system of internet of things enabled residential buildings in smart grid

A multi‐preference integrated algorithm for deep learning based recommender framework

Improved mayfly optimization deep stacked sparse auto encoder feature selection scorched gradient descent driven dropout XLM learning framework for software defect prediction

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