Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection

Abisoye, Opeyemi Aderiike; Jimoh, Rasheed Gbenga; Uthman, Muhammed

doi:10.32890/jict2022.21.1.6

Cited by 7 publications

(6 citation statements)

References 48 publications

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“…In conclusion, the three primary objectives stated were accomplished Other than that, online stores can create special promotions, such as happy hour sales for customers. In terms of the model architecture, this work can be further extended by using different variants of ANN, ranging from the radial basis function neural network, ensemble feedforward neural network, and support vector machine (Jimoh et al, 2022) and convolutional neural network (Ong et al, 2022). Other structures of SAT can also be implemented in the selected ANN as a symbolic logical rule, such as Boolean satisfiability with majority logic (Amarú et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm

Mohd Kasihmuddin,

Abdul Halim,

Mohd Jamaludin

et al. 2023

Journal of Information and Communication Technology

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Online shopping is a multi-billion-dollar industry worldwide. However, several challenges related to purchase intention can impact the sales of e-commerce. For example, e-commerce platforms are unable to identify which factors contribute to the high sales of a product. Besides, online sellers have difficulty finding products that align with customers’ preferences. Therefore, this work will utilize an artificial neural network to provide knowledge extraction for the online shopping industry or e-commerce platforms that might improve their sales and services. There are limited attempts to propose knowledge extraction with neural network models in the online shopping field, especially research revolving around online shoppers’ purchasing intentions. In this study, 2-satisfiability logic was used to represent the shopping attribute and a special recurrent artificial neural network named Hopfield neural network was employed. In reducing the learning complexity, a genetic algorithm was implemented to optimize the logical rule throughout the learning phase in performing a 2-satisfiability-based reverse analysis method, implemented during the learning phase as this method was compared. The performance of the genetic algorithm with 2-satisfiability-based reverse analysis was measured according to the selected performance evaluation metrics. The simulation suggested that the proposed model outperformed the existing model in doing logic mining for the online shoppers dataset.

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Section: Discussionmentioning

confidence: 99%

Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm

Mohd Kasihmuddin,

Abdul Halim,

Mohd Jamaludin

et al. 2023

Journal of Information and Communication Technology

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“…If 𝜀𝜀 has a negative value, it implies that overfitting has occurred. Equations 28 and 29 are applied to evaluate the performance of the classification methods by focusing on the proportion of correct group membership prediction (Jimoh et al, 2022;Huberty & Holmes, 1983;Alf & Abrahams, 1968;Levy, 1967). Another method similar to Equation 29to analyse the performance of classifiers was discussed in Mohd Noor (continued) Figure 1 displays the breakdown of the different methods based on the PEC and the number of random outliers introduced.…”

Section: 𝜀𝜀𝜀𝜀 = 1 − 𝑂𝑂𝑂𝑂𝑂𝑂 (30)mentioning

confidence: 99%

“…Classification can be applied to determine ICT knowledge awareness (Dávideková et al, 2019). Jimoh et al (2022) applied classification methods to classify malaria infection. The classification method can also be used to classify students as first-class or second-class upper based on their final CGPA.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Robust Univariate Classification Methods for Solving Outliers and Overfitting Problems

Okwonu

Ahad

Hamid

et al. 2023

Journal of Information and Communication Technology

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The robustness of some classical univariate classifiers is hampered if the data are contaminated. Overfitting is another hiccup when the data sets are uncontaminated with a considerable sample size. The performance of the classification models can be easily biased by the outliers’ problems, of which the constructed model tends to be overfitted. Previous studies often used the Bayes Classifier (BC) and the Predictive Classifier (PC) to address two groups of univariate classification problems. Unfortunately for substantial large sample sizes and uncontaminated data, the BC method overfits when the Optimal Probability of Exact Classification (OPEC) is used as an evaluation benchmark. Meanwhile, for small sample sizes, the BC and PC methods are extremely susceptible to outliers. To overcome these two problems, we proposed two methods: the Smart Univariate Classifier (SUC) and the hybrid classifier. The latter is a combination of the SUC and the BC methods, known as the Smart Univariate Bayes Classifier (SUBC). The performance of the new classification methods was evaluated and compared with the conventional BC and PC methods using the OPEC as a benchmark value. To validate the performance of these classification methods, the Probability of Exact Classification (PEC) was compared with the OPEC value. The results showed that the proposed methods outperformed the conventional BC and PC methods based on the real data sets applied. Numerical results also revealed that the SUC method could solve the overfitting problem. The results further indicated that the two proposed methods were robust against outliers. Therefore, these new methods are helpful when practitioners are confronted with overfitting and data contamination problems.

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“…In the literature, several optimizations, clustering, and classification techniques are widely used for the analysis of malaria cells, some of which are discussed in this section ( 8 , 10 – 14 ). The classification techniques used for the diagnosis of the malaria cells, in which AdaBoost ( 15 ), Naïve Bayes Tree ( 16 ), SVM ( 17 ), DT ( 18 ), and Linear Discriminant ( 19 ), classifiers are involved. Custom convolutional neural models are widely used for the analysis of malaria cells and provide an accuracy of 97.37%.…”

Section: Related Workmentioning

confidence: 99%

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

Amin

Sharif

Mallah

et al. 2022

Front. Public Health

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Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far.

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Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection

Cited by 7 publications

References 48 publications

Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm

Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm

Enhanced Robust Univariate Classification Methods for Solving Outliers and Overfitting Problems

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

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