Machine Learning-Based Sine-Cosine Algorithm for Wastewater Quality Assessment Using Activated Carbon

Alsolai, Hadeel; Asiri, Mashael M.; Alabdan, Rana; Al-Hagery, Mohammed Abdullah; Hilal, Anwer Mustafa; Rizwanullah, Mohammed; Motwakel, Abdelwahed; Yaseen, Ishfaq

doi:https://doi.org/10.1155/2022/3410872

Adsorption Science & Technology

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Futuristic Carbon Based Adsorbents and their Versatile Applications

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Research Article | Open Access

Volume 2022 | Article ID 3410872 | https://doi.org/10.1155/2022/3410872

Machine Learning-Based Sine-Cosine Algorithm for Wastewater Quality Assessment Using Activated Carbon

Hadeel Alsolai,¹Mashael M. Asiri,²Rana Alabdan,³Mohammed Abdullah Al-Hagery,⁴Anwer Mustafa Hilal,⁵Mohammed Rizwanullah,⁵Abdelwahed Motwakel,⁵and Ishfaq Yaseen⁵

Academic Editor: S.K. Khadheer Pasha

Received13 Jan 2022

Revised17 Feb 2022

Accepted06 Apr 2022

Published09 May 2022

Abstract

Activated carbon is one of the most highly proven adsorbents for organic chemicals from wastewater. It acts as a filter and adsorbs various chemicals from the wastewater. It has large pore size and strong adsorptive capacity. The quality of wastewater is generally determined by chemical oxygen demand (COD), biochemical oxygen demand (BOD₅₎, total suspended solids (TSSs), total phosphorus (TP), and total nitrogen (TN). Wastewater contaminant measurement is significant for saving aquatic life and reusing treated water. Adsorption of contaminants that contribute for wastewater quality indicators uses machine learning algorithm for prediction. Many research works have been done, and the issues are inefficiency and time consuming in the adsorption of contaminants by activated carbon in wastewater management. To overcome these issues, this paper introduces hybrid technique of Voting-Based Extreme Learning Machine with sine cosine algorithm (VELM-SCA). The accuracy of VELM-SCA algorithm in classification of water quality status produced improved accuracy is 0.97.

1. Introduction

Removal of dissolved chemical components and pollutants from the wastewater plant by surface phenomenon is called as adsorption. The wastewater treatment is a complicated process that ensures effective purification of wastewater. The adsorption of the organic pollutant to maintain the reduction of pollutant level in the wastewater treatment plant is an essential one. In the wastewater, treatment of plants depends to the indicator values of parameters which describe the quality of wastewater. For maintaining adequate level of indicators of wastewater quality in the aspect of reducing the pollutant in wastewater, many techniques are available for predicting both quality and quantity of influent wastewater. The wastewater quality indicator parameters are total nitrogen (TN), total phosphorus (TP), biochemical oxygen demand (BOD₅), total suspended solids (TSS), and chemical oxygen demand (COD).

United nation sustainable goal 6 that states “ensure access to water and sanitation for all” motivates us to research on wastewater treatment. Survey points that one person out of every three does not have safe drinking infrastructure. Water is basic necessities for all living creature. It is essential to monitor wastewater and its contaminants present in it.

Adsorption utilizes carbon-based material like activated carbon, and biochar was one of the prominent methods in the wastewater treatment [1]. To implement this, so many existing algorithms like random forest (RF), decision tree (DT), Gradient Boosting algorithm (GBA) are available. The issues in the existing algorithms are high efficiency but inaccurate in the reduction of pollutant in the wastewater plants. To overcome this issue, this paper promotes hybrid technique of Voting-Based Extreme Learning Machine with sine cosine algorithm (VELM-SCA). It produces high variation in the prediction of carbon in the wastewater plants and gives high variation in quality and quantity of reduction of the pollutant in the wastewater. The concept of voting ELM is a majority voting system which classified the adsorption of carbon in the wastewater. To improve the accuracy rate, this voting approach is used [2]. For improving the exploitation sine-cosine, the algorithm (SCA) allows to select the prediction of carbon by using search operations in a mathematical function of the sine and cosine [3].

Machine learning algorithm plays a powerful tool in elaborating the complicated multivariate and interpretation for optimizing the adsorption of the dissolved component in the wastewater system. The algorithms like response to surface methodology (RSM), and boosted regression tree (BRT) and general regression neural network (GRNN) were applied in the prediction of carbon in the wastewater model [4, 5]. This paper [6] proposed the polluted natural environment for a long period because of the usage of persistent contaminants which can easily enter into the surface and groundwater. The most powerful and reusable adsorbent is the activated carbon for wastewater treatment [7]. The main contributions of this paper are as follows: (1)Implementing the hybrid technique of the proposed method by using VELM based on the concepts of prediction of carbon absorption in the wastewater by taking decision making of the majority of the voting system(2)To improve the accuracy of sine-cosine, a mathematical function-based optimization algorithm is implemented

The paper has been organized as follows: Section 2 describes the review of the literature, Section 3 introduces the prediction of wastewater treatment using VELM-SCA, Section 4 discusses the experimented results, and Section 5 concludes the paper with future directions.

2. Review of Literature

Adsorption is the removal of the organic and inorganic pollutants on the surface of the adsorbents as well as in wastewater treatment. In recent years, searching for low-cost adsorbents in the pollutant component which are available in the water and wastewater treatment and activated carbon plays a vital role in the adsorption of pollutant material [8]. Adsorbents are categorized into two types, namely, natural and synthetic adsorbents. Natural adsorbents are clays, charcoal, zeolites, clay minerals, and ores. Synthetic adsorbents are formed from household wastage, agricultural product wastage, industrial wastage, polymeric adsorbents, and sewage sludge.

Activated carbon acts as an adsorbent for both natural and synthetic adsorbents because of it is an easily available product and cheap, enhancing the properties of adsorption capabilities. Similarly, granular activated carbon (GAC) and powdered activated carbons (PAC) are commonly available adsorbents in the removal of organic and inorganic and removal of industrial wastage water in the land surface area [9]. In the carbon-based adsorbent, predicting the capacity of adsorption is by (Q, mg/g), and it is compared with various influencers of properties of carbon-based properties which are total carbon content (C, wt.%), the molar ratio of oxygen to carbon (O/C), the molar ratio of hydrogen to carbon (H/C),molar ratio of oxygen and nitrogen to carbon [(O+N)/C], ash content (ash, wt.%), point of zero charges (pHpzc), and Brunauer-Emmett-Teller surface area (SBET, m2/g) [1]. This paper [10] proposed the sine-cosine algorithm (SCA) for improving the metaheuristic search. In improving the concept of ELM, this voting ELM can be implemented. This VELM has difference between samples of class forming various vector set values. The multiple independent ELMs are impended for taking majority voting method for making decisions [11]. Table 1 shows the survey on activated carbon in various techniques.

3. Proposed Methodology

Nowadays, domestic wastage and industrial wastage pollute the eco-friendly environment, which has been adsorbed by activated carbon. This wastage contains soluble and insoluble content and chemicals. In this proposed work, we implement the hybrid of Voting-Based Extreme Learning Machine with sine-cosine algorithm (VELM-SCA) to improve the efficiency in the prediction of wastewater treatment plants [20–29]. The architecture of the proposed work (VELM-SCA) is given in Figure 1.

Figure 1 describes three phases, namely, data collection, preprocessing, and analysis of VELM-SCA.

3.1. Data Collection

In this work, the data were collected from the wastewater treatment plant (WWTP) in Busan, Korea, for three years from January 2008 to December 2010 [19], which are total nitrogen (TN) and total phosphorous (TP) elements, chemical oxygen demand (COD), and biochemical demand (BCD).

3.2. Preprocessing

To enhance the quality in the prediction of carbon in the wastewater, plant needed normalization as a preprocessing technique. Data normalization is based on linear normal function by using:

where is the normalized value for each data point; is the sample data and .

3.3. Analysis of VELMSCA

3.3.1. Evolutionary-Based Voting ELM

Extreme learning machine (ELM) is based on the structure of input layer-single hidden layer with feed forward neural network. This voting-based ELM employs the concept of multiple independent ELM training models, and the final output decision is based on majority voting. The main advantage of using this VELM is the improvement in high quality of detecting available of carbon in the wastewater plant. In addition to this, it lowered the variance. The Algorithm 1 steps of VELM are given below:

Input: sample of training data set , with label of
is the water quality indicator of wastewater plant ; is the total number of organic materials in the wastewater at ; is the crossover probability and its mutation.
Output: prediction of carbon in the wastewater plant using max voting decision.
Step 1: randomly select the water quality indicator parameter
Step 2: evaluate fitness value of using
(2)
Step 3: choose the next parameter of water quality indicator . Crossover and mutation on are used. Repeat the following steps until the criteria of genetic algorithm (GA) is met.
(1) Probability of crossover is determined by members of , and its fitness value is selected probabilistically to .
(2) Probability of chromosomes is in for the mutation, and store the weights of which are the outputs, and predict its label of chemical component in the wastewater plant.
Step 4: voting-based ELM is the produced output taken as decision making of multiple ELMs by using
(3)

Algorithm 1 describes that as per Equation 3, contains samples of and , where is class label. Here, assume that is an input and is predicted by . As per voting based ELM, it predicts output of multiple ELM training models. Ifpredicts theclassifier, thenand, andand its ensembled decision (max voting) is defined by.

3.3.2. Sine-Cosine Algorithm (SCA)

For getting the best prediction of carbon adsorption in the wastewater plant, this SCA function is implemented. It is based on the mathematical function of sine and cosine. SCA algorithm employs searching process by using sine or cosine equation as

Combining Equations (2) and (3) as

whereis the destination solution,is current solution || that indicates random variables ofwhich contains absolute values. The parameter is a random variable to determine the path of next solution, and it may be towards or outside between and . That is switch between exploration and exploitation. The update of is given by

where is a constant value; is maximum number of iterations; and is the current iteration. The random variable is used to identify the next movement towards destination solution or best solution Also, random variable adds the random weight to , and it is stochastically emphasized or deemphasize . The random variable is used to switch between the function sine and cosine using Equation (4).The algorithm 2 describes the SCA algorithm.

Step 1: initialize the random population solutions, .
Step 2: by using Equation (4), the value of is used to switch from explore to exploit. It gradually decreases from 2 to 0.
Step 3: while do.
Step 4: compute the fitness value for each solution.
Step 5: express that has the best fitness value .
Step 6: update values.
Step 7: use Equation (4) to update
Step 8: end while
Step 9: output

Algorithm 2 describes that the concept of SCA is randomly choose the solution from the set of solution with various dimensions. For every solution it computes fitness values. The output of best solution of SCA is best fitness.

3.3.3. Proposed Hybrid of Voting ELM with Sine-Cosine Algorithm (VELM-SCA)

VELM-SCA enhances the accuracy and efficiency in terms of classification of the wastewater quality and reduces the computation time for both training and testing dataset. Procedure for VELM-SCA is given below.

Input: sample of training dataset , with label of
is the water quality indicator of wastewater plant ; is the total number of organic materials in the wastewater at ; is the crossover probability and its mutation.
Output: prediction of carbon in the wastewater plant.
Step 1: randomly select the water quality indicator parameter
Step 2: evaluate fitness value of using Equation (2).
Step 3: choose the next parameter of water quality indicator . Crossover and mutation on are calculated.
Step 4: using Algorithm 2, determine the best fitness .
Step 5: update the best solution out of all water quality indicator parameter value.
Step 6: update the position of adsorption of carbon in the wastewater plant by using
If , then It merges all related carbon particle.
Step 7: Else discard that particle.
Step 8: End.

Algorithm 3 describes that the training of the dataset , with input parameter values. Calculate the fitness for each parameter and choose the best fitness that denotes the carbon content in the wastewater plant. Update the value and choose the next set of data. Update the position of carbon particle using new random variable , and it merges all related carbon particle.

4. Result and Discussion

4.1. Performance Metric Measures

The metric measures for implementing VELM-SCA of Spearman’s rank correlation coefficient (SPCC), the coefficient of determination (), root mean square error (RMSE), and mean absolute error (MAE) are discuss in the following sections.

4.1.1. Spearman’s Rank Correlation Coefficient (SPCC)

It is the relationship the between actual and predicted variables that is in relationship between positive or negative. The formula is

where is the difference in ranks between two variables, and is the number of samples. The range of values is between -1 and +1.

4.1.2. Coefficient of Determination ()

It explains the fraction of variations in its output, and it is defined by

The maximum and minimum values of are 1 and 0. The higher the value it indicates, the best the model.

4.1.3. Mean Absolute Error (MAE)

The MAE is defined as an average of absolute differences between the model outputs and the

actual outputs. The MAE can be calculated using the following formula:

where is the predicted value, and is the actual value.

4.1.4. Root Mean Square Error (RMSE)

It is an error prediction measure. It calculates the overall error of this system, and it can be calculated as

In this work, VELM-SCA, the metric measures its performance based on accuracy, specificity, and sensitivity, and precision and F-score evaluation matrices were employed.

Table 2 shows that performance metric measures of VELM-SCA.

From Table 2, it seems that the performance of evaluation is based on SRCC, RMSE, MAE, andimplemented in VELM, SCA, and our proposed work (VELM-SCA) algorithms. For the SRCC shows strongly correlation of 0.96. In RMSE, VELM got 4.56, SCA got 3.87, and VELM-SCA got 2.45. For the value, proposed work VELM-SCA got 0.97. For the mean absolute error, VELM got 2.43, SCA got 1.67, and VELM-SCA got 1.22.

From the above Table 3, the accuracy of VELM-SCA (proposed work) is higher as compared to other classifier algorithms of VELM and SCA. In Table 3, the next higher accuracy is SCA which is also closer to VELM-SCA. Figure 2 shows the computation time for CO₂ adsorption in various algorithms like VELM, SCA, and VELM-SCA.

Figure 2 shows that the proposed work needs less computation time for the adsorption of carbon. Table 4 shows that parameter selection. In the wastewater plants, these parameters are used to predict the parametric values in the dataset.

Table 4 shows that in the influent parameter values of wastewater quantity and quality, the ranges of variation were established [20].The parametric values in Table 5 indicate substantial variation in TN, TP,BOD, COD, TSS, and Q values. These indicators of wastewater quality constitute input data for the models of VELM-SCA describing the changes in carbon, nitrogen, phosphorus, and biochemical changes in the compounds of bioreactors.

By using the Table 5 parametric values, our proposed work VELM-SCA predicted activated carbon-based TN, TP, BOD, COD, and TSS values using mean absolute error (MAE), mean relative error (MAPE), and coefficient of determination(). The analysis of these parameters are shown in Table 6.

The analysis of Table 4 shows that TN prediction activated carbon based on VELM inand SCA in, and fusion of VELM-SCA gives, similar for MAPE and VELM in, and SCA inand hybrid of VELM-SCA gives. In addition, TP prediction activated carbon based on VELM inand SCA in, and hybrid of VELM-SCA gives, similar for MAPE and VELM inand SCA in, and hybrid of VELM-SCA gives. Prediction of wastewater quality requires parametric indicators of TN, and TP shown that our proposed work VELM-SCA gives better performance. Similarly, for other indicators of BOD and COD, TSS also shows that the lowest prediction errors were observed for the VELM-SCA approach. Figure 3 shows the probability of prediction based on carbon in the wastewater plant for our proposed work.

From Figure 3, it seems that our proposed work VELM-SCA gives the better probability prediction of wastewater treatment for plants. Table 6 shows the effectiveness of various machine learning algorithm in terms of various influent indicators by using

where is the total number of testing data, and is the total number of losing test data.

From Table 7, it seems that the effectiveness of our proposed work produces better result compared with other existing algorithms. Figure 4 shows the VELM-SCA model for predicting the influent water based on activated carbon with various indicators.

In Figure 4, it seems that our proposed work gives effective prediction of WWTP that measured various indicators for the treatment of plants, which is the convergence curves of VELM-SCA for all datasets. Our proposed work has a fast convergence compared with other two algorithms.

5. Conclusion

The prediction of wastewater treatment plants used the adsorption of unwanted influent indicators using activated carbon. In this work, VELM-SCA uses the predicting influent indicators in the wastewater and identify the maximum and minimum point values with boundary set values. It also implemented various parameters like total phosphorous (TP), total nitrogen (TN), BOD, COD, and TSS. VELM-SCA accurately predicts the unwanted influent indictors and filtered it using activated carbon with minimum error prediction and also proves the efficient and robust performance. The accuracy of VELM-SCA algorithm in classification of water quality status that produced highest accuracy is 0.97. Our proposed work VELM-SCA outperforms the best result compared with existing classifier. In future to work this will be extended by using various ML algorithms and also upgrade our work in various influent and Effluent indicators for predicting the wastewater treatment for plants.

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant numbers (RGP 2/45/43). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R303), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under Project numbers (R-2022-50 and R-2022-119).

References

X. Zhu, Z. Wan, D. C. W. Tsang et al., “Machine learning for the selection of carbon-based materials for tetracycline and sulfamethoxazole adsorption,” Chemical Engineering Journal, vol. 406, article 126782, 2021.
View at: Publisher Site | Google Scholar
M. Mengcan, C. Xiaofang, and X. Yongfang, “Constrained voting extreme learning machine and its application,” Journal of Systems Engineering and Electronics, vol. 32, no. 1, pp. 209–219, 2021.
View at: Publisher Site | Google Scholar
M. Z. Rehman, A. Khan, R. Ghazali, M. Aamir, and N. M. Nawi, “A new multi sine-cosine algorithm for unconstrained optimization problems,” PLoS One, vol. 16, no. 8, article e0255269, 2021.
View at: Publisher Site | Google Scholar
X. Zhu, D. C. W. Tsang, L. Wang et al., “Machine learning exploration of the critical factors for CO₂ adsorption capacity on porous carbon materials at different pressures,” Journal of Cleaner Production, vol. 273, article 122915, 2020.
View at: Publisher Site | Google Scholar
M. Foroughi, M. H. A. Azqhandi, and S. Kakhki, “Bio-inspired, high, and fast adsorption of tetracycline from aqueous media using Fe3O4-g-CN@PEI-β-CD nanocomposite: modeling by response surface methodology (RSM), boosted regression tree (BRT), and general regression neural network (GRNN),” Journal of Hazardous Materials, vol. 388, article 121769, 2020.
View at: Publisher Site | Google Scholar
M. Bahrami, M. J. Amiri, and B. Beigzadeh, “Adsorption of 2,4-dichlorophenoxyacetic acid using rice husk biochar, granular activated carbon, and multi-walled carbon nanotubes in a fixed bed column system,” Water Science and Technology, vol. 78, no. 8, pp. 1812–1821, 2018.
View at: Publisher Site | Google Scholar
A. Spaltro, M. Pila, S. Simonetti et al., “Adsorption and removal of phenoxy acetic herbicides from water by using commercial activated carbons: experimental and computational studies,” Journal of Contaminant Hydrology, vol. 218, pp. 84–93, 2018.
View at: Publisher Site | Google Scholar
G. Crini, “Recent developments in polysaccharide-based materials used as adsorbents in wastewater treatment,” Progress in Polymer Science, vol. 30, no. 1, pp. 38–70, 2005.
View at: Publisher Site | Google Scholar
C. Moreno-Castilla, “Adsorption of organic molecules from aqueous solutions on carbon materials,” Carbon, vol. 42, no. 1, pp. 83–94, 2004.
View at: Publisher Site | Google Scholar
S. Mirjalili, “SCA: a sine cosine algorithm for solving optimization problems,” Knowledge-Based Systems, vol. 96, pp. 120–133, 2016.
View at: Publisher Site | Google Scholar
R. Polikar, “Ensemble based systems in decision making,” IEEE Circuits and Systems Magazine, vol. 6, no. 3, pp. 21–45, 2006.
View at: Publisher Site | Google Scholar
M. Aravind and M. Amalanathan, “Structural, morphological, and optical properties of country egg shell derived activated carbon for dye removal,” Materials Today: Proceedings, vol. 43, pp. 1491–1495, 2021.
View at: Publisher Site | Google Scholar
M. Alshabib, M. A. Oluwadamilare, A. Tanimu, I. Abdulazeez, K. Alhooshani, and S. A. Ganiyu, “Experimental and DFT investigation of ceria-nanocomposite decorated AC derived from groundnut shell for efficient removal of methylene-blue from wastewater effluent,” Applied Surface Science, vol. 536, article 147749, 2021.
View at: Publisher Site | Google Scholar
H. Wang, Z. Li, S. Yahyaoui et al., “Effective adsorption of dyes on an activated carbon prepared from carboxymethyl cellulose: experiments, characterization and advanced modelling,” Chemical Engineering Journal, vol. 417, article 128116, 2021.
View at: Publisher Site | Google Scholar
F. Yang, Q. Zhang, H. Jian et al., “Effect of biochar-derived dissolved organic matter on adsorption of sulfamethoxazole and chloramphenicol,” Journal of Hazardous Materials, vol. 396, article 122598, 2020.
View at: Publisher Site | Google Scholar
X. Li, C. Wang, J. Zhang, J. Liu, B. Liu, and G. Chen, “Preparation and application of magnetic biochar in water treatment: a critical review,” Science of the Total Environment, vol. 711, article 134847, 2020.
View at: Publisher Site | Google Scholar
Y. Xiang, Z. Xu, Y. Wei et al., “Carbon-based materials as adsorbent for antibiotics removal: mechanisms and influencing factors,” Journal of Environmental Management, vol. 237, pp. 128–138, 2019.
View at: Publisher Site | Google Scholar
M. Hasan, M. M. Rashid, M. M. Hossain et al., “Fabrication of polyaniline/activated carbon composite and its testing for methyl orange removal: Optimization, equilibrium, isotherm and kinetic study,” Polymer Testing, vol. 77, article 105909, 2019.
View at: Publisher Site | Google Scholar
K. Kaetzl, M. Lübken, G. Uzun et al., “On-farm wastewater treatment using biochar from local agroresidues reduces pathogens from irrigation water for safer food production in developing countries,” Science of the Total Environment, vol. 682, pp. 601–610, 2019.
View at: Publisher Site | Google Scholar
S. Moosavi, O. Manta, Y. A. El-Badry, E. E. Hussein, Z. M. El-Bahy, and M. Fawzi, “A Study on Machine Learning Methods’ Application for Dye Adsorption Prediction onto Agricultural Waste Activated Carbon,” Nanomaterials, vol. 11, p. 2734, 2021.
View at: Publisher Site | Google Scholar
B. Szeląg, K. Barbusiński, J. Studziński, and L. Bartkiewicz, “Prediction of wastewater quality indicators at the inflow to the wastewater treatment plant using data mining methods,” E3S Web of Conferences, vol. 22, article 00174, 2017.
View at: Publisher Site | Google Scholar
A. S. Mohammed, K. Venkatachalam, S. Hubálovský, P. Trojovský, and P. Prabu, “Smart Edge Computing for 5 g/6 g Satellite IOT for Reducing Inter Transmission Delay,” Mobile Networks and Applications, vol. 178, pp. 1–10, 2022.
View at: Publisher Site | Google Scholar
N. Bacanin, M. Zivkovic, T. Bezdan, K. Venkatachalam, and M. Abouhawwash, “Modified firefly algorithm for workflow scheduling in cloud-edge environment,” Neural Computing and Applications, pp. 1–26, 2022.
View at: Publisher Site | Google Scholar
K. Venkatachalam, P. Prabu, A. S. Alluhaidan, S. Hubálovský, and P. Trojovský, “Deep belief neural network for 5G diabetes monitoring in big data on edge IoT,” Mobile Networks and Applications, pp. 1–10, 2022.
View at: Publisher Site | Google Scholar
N. Bacanin, B. Timea, K. Venkatachalam, and F. Al-Turjman, “Optimized convolutional neural network by firefly algorithm for magnetic resonance image classification of glioma brain tumor grade,” Journal of Real-Time Image Processing, vol. 18, no. 4, pp. 1085–1098, 2021.
View at: Publisher Site | Google Scholar
K. Venkatachalam, P. Prabu, A. Almutairi, and M. Abouhawwash, “Secure biometric authentication with de-duplication on distributed cloud storage,” PeerJ Computer Science, vol. 7, article e569, 2021.
View at: Publisher Site | Google Scholar
K. Venkatachalam, S. Siuly, N. Bacanin, S. Hubalovsky, and P. Trojovsky, “An efficient Gabor Walsh-Hadamard transform based approach for retrieving brain tumor images from MRI,” IEEE Access, vol. 9, pp. 119078–119089, 2021.
View at: Publisher Site | Google Scholar
S. Ramamoorthy, G. Ravikumar, B. Saravana Balaji, S. Balakrishnan, and K. Venkatachalam, “MCAMO: multi constraint aware multi-objective resource scheduling optimization technique for cloud infrastructure services,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 6, pp. 5909–5916, 2021.
View at: Publisher Site | Google Scholar
R. S. Ponmagal, S. Karthick, B. Dhiyanesh, S. Balakrishnan, and K. Venkatachalam, “Optimized virtual network function provisioning technique for mobile edge cloud computing,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 6, pp. 5807–5815, 2021.
View at: Publisher Site | Google Scholar

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Copyright © 2022 Hadeel Alsolai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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