Parameter Estimation of Solar PV Using Ali Baba and Forty Thieves Optimization Technique

Sharma, Pankaj; Thangavel, Saravanakumar; Raju, S. Sivanaga; Prusty, B Rajanarayan

doi:10.1155/2022/5013146

Cited by 19 publications

(6 citation statements)

References 43 publications

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“…State one: Based on data gathered from a source, the thieves' chase after Ali Baba can be simulated using Eq. ( 3), which shows their relative positions [27], [28].…”

Section: Alibaba and The Forty Thieves (Aft)mentioning

confidence: 99%

Meta-Model Classification Based on the Naïve Bias Technique Auto-Regulated via Novel Metaheuristic Methods to Define Optimal Attributes of Student Performance

Ren,

2024

IJACSA

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Accurately assessing and predicting student performance is critical in today's educational environment. Schools are dependent on evaluating students' skills, forecasting their grades, and providing customized instruction to improve their academic performance. Early intervention is essential for pinpointing areas in need of development. By predicting students' futures in particular subjects, data mining, a potent technique for revealing hidden patterns within large datasets, helps lower failure rates. These methods are combined in the field of educational data mining, which focuses on the analysis of data from educators and students with the aim of raising academic achievement. In this study, the Naive Bayes classification (NBC) model is given the main responsibility for predicting student performance. However, two cutting-edge optimization strategies, Alibaba and the Forty Thieves (AFT) and Leader Harris Hawk's optimization (LHHO), have been used to improve the model's accuracy. The study's findings show that the NBC+AFT model performs more accurately than the other models. Accuracy, Precision, Recall, and F1-Score all display impressive performance metrics for a superior model, with values of 0.891, 0.9, 0.89, and 0.89, respectively. These metrics outperform those of competing models, highlighting how successful this strategy is. Because of the NBC+AFT model's strong performance, educational institutions are getting closer to a time when they will be able to predict students' success more precisely and help them along the way, making everyone's academic journey more promising and brighter.

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“…State one: Based on data gathered from a source, the thieves' chase after Ali Baba can be simulated using Eq. ( 3), which shows their relative positions [27], [28].…”

Section: Alibaba and The Forty Thieves (Aft)mentioning

confidence: 99%

Meta-Model Classification Based on the Naïve Bias Technique Auto-Regulated via Novel Metaheuristic Methods to Define Optimal Attributes of Student Performance

Ren,

2024

IJACSA

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“…The framework of AFT encompasses three discernible states, each of which can be analyzed and delineated as follows [38,39]:…”

Section: Alibaba and The Forty Thieves (Aft)mentioning

confidence: 99%

Unconfined compressive strength prediction of rock materials based on machine learning

Niu,

Cui,

Luo

et al. 2024

J. Eng. Appl. Sci.

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It is costly, time-consuming, and difficult to measure unconfined compressive strength (UCS) using typical laboratory procedures, particularly when dealing with weak, extremely porous, and fractured rock. By efficiently choosing the variables from a subset of the dataset that includes the Schmidt hammer rebound number (SRn), bulk density (BD), bulk tensile strength (BTS), dry density (DD) test, p-wave velocity test (Vp), and point load index test (Is(50)), this study seeks to establish predictive models for the UCS of rocks. A prediction model for UCS was prepared using K-nearest neighbor (KNN). KNN was preferred over machine learning algorithms because it is simple, versatile, and interpretable. It is particularly useful when it has limited training time, faces non-parametric data with changing distributions, or requires straightforward explanations for predictions. In order to improve KNN’s prediction performance in this research, two optimization procedures (namely, Alibaba and the Forty Thieves (AFT) and Improved Manta-Ray Foraging Optimizer (IMRFO)) were used. Through comparison of KNN single modal performance with that of optimized versions, it is concluded that the KNIM (KNN model optimized with IMRFO) is an excellent possible applicant for the forecast of the UCS of rocks. This study’s results showed that the KNIM model is more suitable than the KNN single model and its counterpart KNAF in terms of accuracy as its correlation of determination (R2) values were 1.1% and 2% higher than KNN and KNAF and its root mean squared error (RMSE) values were 37.9% and 43.7% lower than KNN and KNAF. The improvement in R2 and RMSE values for the KNIM model compared to KNN and KNAF is highly significant for the reliability and accuracy of the predictive model. R2, measuring the proportion of variance predictable in the dependent variable (UCS of rocks) from the independent variables (model predictions), signifies a better fit to observed data. The elevated R2 values for KNIM indicate a stronger correlation with actual UCS values, enhancing the model’s accuracy in representing underlying patterns. Additionally, the reduction in RMSE values for KNIM implies that its predictions are, on average, closer to actual UCS values, contributing to a more accurate and reliable estimation of rock strength.

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“…A BSS is often used in the charging station when the grid is at peak demand, which raises the load on EVs. The government must construct more Photovoltaic public charging stations, and EV owners should improve the residential charging points that already exist [101].…”

Section: B Solar Energy For Ev Charging Stationmentioning

confidence: 99%

A Comprehensive Review on Electric Vehicle: Battery Management System, Charging Station, Traction Motors

et al. 2023

Self Cite

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Electric vehicles (EVs) are widespread, and their usage is increasing as a result of air pollution and rising fuel costs. EVs are quickly gaining popularity as a green means of transportation. By 2030, most cars will probably be battery-powered EVs. However, the development of EV power transmission is packed with important challenges and is an active topic of research. In EVs, the battery serves to store electrical energy. The DC-DC converter provides a direct current (DC) link between the battery and the inverter. A motor provides the transmission for the vehicle's motion. Hence, this state-of-the-art provides exhaustive information about battery management systems (BMS), power electronics converters, and motors. Lithiumion batteries are more efficient for EV applications, and boost converters and full bridge converters are commonly used in EVs. EVs use permanent magnet synchronous motors (PMSM) and induction motors (IM). The renewable energy-based charging station and the fast charging specifications are also clearly addressed for EV applications. INDEX TERMSElectric vehicle, BMS, power converters, motors, charging station, cyber security. NOMENCLATURE ABBREVIATION BLDC Brushless DC Motor. BMS Battery Management Systems. CO Carbon monoxide. CO 2 Carbon dioxide. DC Direct Current. EVs Electric Vehicles. EIS Electro-chemical impedance spectroscopy. ESS Energy storage systems. FC Fuel cell. HEVs Hybrid Electric Vehicles. IM Induction Motor.The associate editor coordinating the review of this manuscript and approving it for publication was Ramazan Bayindir .

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Parameter Estimation of Solar PV Using Ali Baba and Forty Thieves Optimization Technique

Cited by 19 publications

References 43 publications

Meta-Model Classification Based on the Naïve Bias Technique Auto-Regulated via Novel Metaheuristic Methods to Define Optimal Attributes of Student Performance

Meta-Model Classification Based on the Naïve Bias Technique Auto-Regulated via Novel Metaheuristic Methods to Define Optimal Attributes of Student Performance

Unconfined compressive strength prediction of rock materials based on machine learning

A Comprehensive Review on Electric Vehicle: Battery Management System, Charging Station, Traction Motors

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