Maximum likelihood based estimation with quasi oppositional chemical reaction optimization algorithm for speech signal enhancement

Kaur, Inderjeet; Nassa, Vinay Kumar; Kavitha, T.; Mohan, Prakash; Velmurugan, S.

doi:10.1007/s41870-022-01032-6

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…After the fuzzification of attributes, the next step is calculating automated weights for each fuzzified value. Here weights are computed using the Maximum Likelihood Estimation (MLE) method [ 33 ]. MLE is a statistical method in which parameter estimation is done using probability distribution on the observed data.…”

Section: Methodsmentioning

confidence: 99%

Fuzzy weighted Bayesian belief network: a medical knowledge-driven Bayesian model using fuzzy weighted rules

Kharya

Soni

Swarnkar

2023

Int. j. inf. tecnol.

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In this current work, Weighted Bayesian Association rules using the Fuzzy set theory are proposed with the new concept of Fuzzy Weighted Bayesian Association Rules to design and develop a Clinical Decision Support System on the Bayesian Belief Network, which is an appropriate area to work in Clinical Domain as it has a higher degree of unpredictability and causality. Weighted Bayesian Association rules to construct a Bayesian network are already proposed. A "Sharp boundary" issue related to quantitative attribute domains may cause erroneous predictions in medicine and treatment in the medical environment. So to eradicate sharp boundary problems in the medical field, the fuzzy theory is applied in attributes to deal with real-life situations. A new algorithm is designed and implemented in this paper to set up a new Bayesian belief network using the concept of Fuzzy Weighted Association rule mining under the Predictive Modeling paradigm named Fuzzy weighted Bayesian belief network using numerous clinical datasets with outshone results.

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Section: Methodsmentioning

confidence: 99%

Fuzzy weighted Bayesian belief network: a medical knowledge-driven Bayesian model using fuzzy weighted rules

Kharya

Soni

Swarnkar

2023

Int. j. inf. tecnol.

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“…Fig. 7 investigates the average classification outcomes analysis of the MOFSS-ODL model on the test dataset [30][31][32][33][34][35]. The figure depicted that the MOFSS-ODL methodology has resulted in maximal average prec n , reca l , F measure , accu y and kappa of 0.9764, 0.9822, 0.9793, 0.96499 and 0.8263.…”

Section: Experimental Validationmentioning

confidence: 99%

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Babu¹,

MathuSoothana²,

Kumar³

2023

Intelligent Automation &Amp; Soft Computing

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Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools to improve students' grades and retention. Prediction of students' performance is a difficult process owing to the massive quantity of educational data. Therefore, Artificial Intelligence (AI) techniques can be used for educational data mining in a big data environment. At the same time, in EDM, the feature selection process becomes necessary in creation of feature subsets. Since the feature selection performance affects the predictive performance of any model, it is important to elaborately investigate the outcome of students' performance model related to the feature selection techniques. With this motivation, this paper presents a new Metaheuristic Optimization-based Feature Subset Selection with an Optimal Deep Learning model (MOFSS-ODL) for predicting students' performance. In addition, the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers. Besides, the Chaotic Monarch Butterfly Optimization Algorithm (CBOA) is used for the selection of highly related features with low complexity and high performance. Then, a sailfish optimizer with stacked sparse autoencoder (SFO-SSAE) approach is utilized for the classification of educational data. The MOFSS-ODL model is tested against a benchmark student's performance data set from the UCI repository. A wide-ranging simulation analysis portrayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures. Compared to other methods, experimental results prove that the proposed (MOFSS-ODL) classification model does a great job of predicting students' academic progress, with an accuracy of 96.49%.

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Maximum likelihood based estimation with quasi oppositional chemical reaction optimization algorithm for speech signal enhancement

Cited by 2 publications

References 31 publications

Fuzzy weighted Bayesian belief network: a medical knowledge-driven Bayesian model using fuzzy weighted rules

Fuzzy weighted Bayesian belief network: a medical knowledge-driven Bayesian model using fuzzy weighted rules

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

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