Water quality classification approach based on bio-inspired Gray Wolf Optimization

Sweidan, Asmaa Hashem; El-Bendary, Nashwa; Hassanien, Aboul Ella; Hegazy, Osman; Mohamed, Abd El-karim

doi:10.1109/socpar.2015.7492777

Cited by 12 publications

(7 citation statements)

References 12 publications

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“…GWO has been suggested to be a meta-heuristic algorithm by [66]. GWO has been used to solve real world complex problems and is applied in many fields, such as [67] used GWO was used with SVM to evaluate the quality of the water and classify the pollution degree of the water using microscopic images of fish liver such that the performance is enhanced than using SVM alone in training images [67]. [68] presented GWO for the pointing applications of Laser…”

Section: Grey Wolf Optimization (Gwo)mentioning

confidence: 99%

Automatic grading for Arabic short answer questions using optimized deep learning model

Salam

El-Fatah

Hassan³

2022

PLoS ONE

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Auto-grading of short answer questions is considered a challenging problem in the processing of natural language. It requires a system to comprehend the free text answers to automatically assign a grade for a student answer compared to one or more model answers. This paper suggests an optimized deep learning model for grading short-answer questions automatically by using various sizes of datasets collected in the Science subject for students in seventh grade in Egypt. The proposed system is a hybrid approach that optimizes a deep learning technique called LSTM (Long Short Term Memory) with a recent optimization algorithm called a Grey Wolf Optimizer (GWO). The GWO is employed to optimize the LSTM by selecting the best dropout and recurrent dropout rates of LSTM hyperparameters rather than manual choice. Using GWO makes the LSTM model more generalized and can also avoid the problem of overfitting in forecasting the students’ scores to improve the learning process and save instructors’ time and effort. The model’s performance is measured in terms of the Root Mean Squared Error (RMSE), the Pearson correlation coefficient, and R-Square. According to the simulation results, the hybrid GWO with the LSTM model ensured the best performance and outperformed the classical LSTM model and other compared models such that it had the highest Pearson correlation coefficient value, the lowest RMSE value, and the best R square value in all experiments, but higher training time than the traditional deep learning model.

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Section: Grey Wolf Optimization (Gwo)mentioning

confidence: 99%

Automatic grading for Arabic short answer questions using optimized deep learning model

Salam

El-Fatah

Hassan³

2022

PLoS ONE

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“…[22] The grey wolves are encircling their prey during the hunt. Equations ( 1) and (2) [23] are numerical models of surrounding behavior:…”

Section: Gwomentioning

confidence: 99%

Optimization of Support Vector Machine Classifier Using Grey Wolf Optimization Algorithm for Chronic Kidney Disease Prediction

Sharma¹,

Kaur²

2022

APJHS

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The massive generation of medical data from smart health-care applications in recent years necessitates the development of big data classification strategies. Medical data classification can be used to visualize patterns in the data and detect the presence of the disease in medical data. We present an efficient support vector machine (SVM) hybridized with a grey wolf optimization (GWO) algorithm for chronic kidney disease (CKD) data classification in this work. Initially, infinite feature selection (IFS) algorithm is used to select the best features from a set of available features. The dataset’s selected features are processed and fed into the GWO optimized SVM algorithm. The proposed CKD classification strategy has been simulated in MATLAB. CKD dataset from UCI machine learning repository is utilized for testing the developed strategy. The performance of the proposed CKD classification strategy is examined by accuracy and root mean square error (RMSE) values. According to the investigational findings, the proposed CKD classification system achieved accuracy and RMSE value of 97.58% and 0.1581, respectively, for classifying subjects into the CKD and non-CKD categories. The performance of GWO optimized SVM algorithm is outstanding, according to the experimental observations.

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“…Amid the hunting process, the grey wolves surround their prey. Numerically, the model of surrounding behavior is modeled by the (1) and (2) [13]:…”

Section: Grey Wolf Optimization (Gwo)mentioning

confidence: 99%

“…When the problem cannot be separated linearly in the input space, the strategy is to apply kernel function in SVM. The kernel function defined in (13).…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Acute sinusitis data classification using grey wolf optimization-based support vector machine

Putri

Rustam

Pandelaki

et al. 2021

IJ-AI

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<span id="docs-internal-guid-ebf19048-7fff-9350-093e-7f1e8df23393"><span>Acute sinusitis is the most common form of sinusitis, and it causes swelling and inflammation within the nose. The main thing that can causes sinusitis is probably due to viruses, and also can be caused by other factors, namely bacteria, fungi, irritation, dust, and allergens. In this research, the CT scan data attributes will be used for classification and grey wolf optimization-support vector machine (GWO-SVM) will be the machine learning technique used, where the GWO technique will be used to tuned the parameters in SVM. The performance of methods was analyzed using the python programming language with different percentages of training data, which started from 10% to 90%. The GWO-SVM method proposed provides better accuracy than using SVM without GWO.</span></span>

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Water quality classification approach based on bio-inspired Gray Wolf Optimization

Cited by 12 publications

References 12 publications

Automatic grading for Arabic short answer questions using optimized deep learning model

Automatic grading for Arabic short answer questions using optimized deep learning model

Optimization of Support Vector Machine Classifier Using Grey Wolf Optimization Algorithm for Chronic Kidney Disease Prediction

Acute sinusitis data classification using grey wolf optimization-based support vector machine

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