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An Efficient honey badger optimization Based Solar MPPT Under Partial Shading Conditions

Intelligent Automation & Soft Computing 2023年第2期35卷 1311-1322页

作者： N.Rajeswari S.Venkatanarayanan Department of Electrical and Electronics Engineering Sri Ràaja Raajan College of Engineering and TechnologySivagangaTamilnadu630301India Department of Electrical and Electronics Engineering K.L.N.College of EngineeringSivagangaiTamilnadu630612India

Due to the enormous utilization of solar energy,the photovoltaic(PV)system is *** PV system is functioned based on a maximum power point(MPP).Due to the climatic change,the Partial shading conditions have occurred under non-uniform irradiance *** the PV system,the global maximum power point(GMPP)is complex to track in the P-V curve due to the Partial ***,several tracking processes are performed using various methods like perturb and observe(P&O),hill climbing(HC),incremental conductance(INC),Fuzzy Logic,Whale optimization Algorithm(WOA),Grey Wolf Optimi-zation(GWO)and Flying Squirrel Search optimization(FSSO)***,the MPPT is not so efﬁcient when the partial shading is *** increase the efﬁciency and convergences in MMPT,the honey badger optimization(HBO)algorithm is *** HBO model is motivated by the excellent foraging behaviour of honey *** HBO model is used to achieve the best solution in GMPP tracking and speed *** HBO methodology is also com-pared with prior P&O,WOA and FSSO methods using ***,the experiment shows that the HBO method is performed a higher tracking than all prior methods.

关键词： PV system gmpp tracking convergence honey badger optimization digging and honey phase

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EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network

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Intelligent Automation & Soft Computing 2023年第5期36卷 1745-1766页

作者： S.Kavi Priya K.Pon Karthika Mepco Schlenk Engineering College(Autonomous) Sivakasi626005TamilnaduIndia

Globally,depression is perceived as the most recurrent and risky disor-der among young people and adults under the age of *** has a strong inﬂuence on the usage of words which can be observed in the form of written texts or stories posted on social *** the help of Natural Language Proces-sing(NLP)and Machine Learning(ML)techniques,the depressive signs expressed by people can be identiﬁed at the earliest stage from their Social Media *** proposed work aims to introduce an efﬁcacious depression detection model unifying an exemplary feature extraction scheme and a hybrid Long Short-Term Memory network(LSTM)*** feature extraction process combines a novel feature selection method called Elite Term Score(ETS)and Word2Vec to extract the syntactic and semantic information ***,the ETS method leverages the document level,class level,and corpus level prob-abilities for computing the weightage/score of the ***,the ideal and per-tinent set of features with a high ETS score is selected,and the Word2vec model is trained to generate the intense feature vector representation for the set of selected ***,the resultant word vector obtained is called EliteVec,which is fed to the hybrid LSTM model based on honey badger optimizer with population reduction technique(PHB)which predicts whether the input textual content is depressive or *** PHB algorithm is integrated to explore and exploit the opti-mal hyperparameters for strengthening the performance of the LSTM *** comprehensive experiments are carried out with two different Twitter depres-sion corpus based on accuracy and Root Mean Square Error(RMSE)*** results demonstrated that the proposed EliteVec+LSTM+PHB model outperforms the state-of-art models with 98.1%accuracy and 0.0559 RMSE.

关键词： Depression detection dimensionality reduction feature extraction feature selection hybrid LSTM network population reduction honey badger optimization social media twitter

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