An intelligent ensemble classification method based on multi-layer perceptron neural network and evolutionary algorithms for breast cancer diagnosis

Azad, Saeed Talatian; Ahmadi, Gholamreza; Rezaeipanah, Amin

doi:10.1080/0952813x.2021.1938698

Cited by 32 publications

(13 citation statements)

References 47 publications

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“…The MLP classifier, as implied by its name, is connected to a Neural Network. When it comes to performing the job of classification, MLP Classifier depends on an underlying Neural Network [41]. Suppose there is N number of hidden layers present, and each hidden layer is associated with η number of neurons.…”

Section: F Multi Layer Perceptron (Mlp)mentioning

confidence: 99%

En-MinWhale: An Ensemble Approach Based on MRMR and Whale Optimization for Cancer Diagnosis

Panigrahi,

Pati,

Sahu

et al. 2023

IEEE Access

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According to the WHO, Cancer is a prominent cause of mortality worldwide, accounting for ∼ 10 million fatalities at the end of 2020. The most common types of cancers include Lung, Breast, CNS, Leukemia, Colon, and Cervical Cancer. Early detection of cancer can decrease the death toll. According to the study, if the cancer is identified at its early stage, the death rate can be reduced to ∼85%. In order to reduce the death toll, machine learning (ML) emerges as a significant solution. When it comes to cancer research with ML, biopsy and microarray data come into the front. The biopsy data is less useful as it excludes a patient's genetic information. However, due to the genetic information, the microarray data emerges as a solution to detecting cancer disease. Dealing with microarray data also has some consequences, and high dimensionality is one of them. This article reports an ML-based ensemble model to tackle microarray data issues and provide an effective model for cancer detection. The reported model uses Minimum Redundancy Maximum Relevance (MRMR) as the feature selection algorithm. The Whale Optimization Algorithm (WOA) is implemented for the featured dataset to select the optimistic number of features without affecting microarray data relevance. Then, four ML-based classification models, including Support Vector Machine, Decision Tree, Multi-Layer Perceptron, and Random Forest, are applied as the base learners to make initial predictions. Finally, the voting ensemble technique is applied to the initial prediction to develop an ensemble model for cancer prediction. The proposed En-MinWhale model is evaluated over six different types of cancer microarray datasets, including Lung, Leukemia, Ovarian, CNS, Breast, and Colon Cancer. Finally, the performance of the proposed model is evaluated using 11 various evaluative parameters, including accuracy, precision, specificity, sensitivity, F-β score, etc. The En-MinWhale model shows 94.09%, 95.83%, 94.86%, 95.00%, 94.85%, and 96.77% accuracy for Lung, Leukemia, Ovarian, CNS, Breast, and Colon Cancer datasets, respectively, that outperforms other considered hybrid models and can help out the physicians in Cancer diagnosis.

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Section: F Multi Layer Perceptron (Mlp)mentioning

confidence: 99%

En-MinWhale: An Ensemble Approach Based on MRMR and Whale Optimization for Cancer Diagnosis

Panigrahi,

Pati,

Sahu

et al. 2023

IEEE Access

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“…Modeling the policy of attracting the imperialists : The policy of attracting the imperialists means that the colonial countries must move toward their imperialists. This section is based on the differential evolution (DE) technique according to 38 …”

Section: Proposed Approachmentioning

confidence: 99%

Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach

2022

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Internet of Things (IoT) represents a new generation of information and communication technology for anyone, anytime and anywhere. Cloud service‐based IoT applications significantly increase latency and network utilization. The fog environment is closer to the user to perform computing, communication, and storage tasks on network edge devices. Therefore, it can greatly reduce the latency of real‐time applications. It is an essential feature of fog computing and its most important advantage compared to cloud computing. This study proposed a new approach to service placement generated by running applications on IoT devices in the fog computing. IoT devices send applications to the fog environment that each application contains a set of services. The purpose of solving the IoT services placement problem is to efficiently deploy these services on fog cells. For this purpose, it is assumed that the received services from the IoT applications are received as a directed acyclic graph that depicts the communication between the cells within the graph that shows the communication between the services. Then, the imperialist competitive algorithm is used to place and select the destination for IoT services. The simulation results of the iFogSim simulator in different experiments showed that the imperialist competitive algorithm with the proposed graph partitioning approach has improved service placement on the fog infrastructure compared to the genetic algorithm and best‐fit algorithm.

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“…On this basis, the metaheuristic algorithm is utilized to traverse the entire space of the model to obtain the optimal solution of the model. It has been successfully applied to many tasks, such as neuroevolutionary algorithms for optimizing deep reinforcement learning models [20], genetic algorithms (GA) for optimizing BP neural networks [21], and evolutionary algorithms (EA) for optimizing the hyperparameters of neural networks [22]. In a recent prediction study, Xie Hailun et al proposed a prediction model based on an improved gray wolf optimization (GWO) algorithm optimizing CNN-LSTM, which enhances the optimization finding ability of the GWO algorithm by introducing a different searching mechanism, and the optimized CNN-LSTM network provides a better characterization ability, which is not only capable of capturing the interactions of the important features but also capable of encapsulating the complex temporal complex dependencies in the background for time series tasks [23].…”

Section: Introduction 1literature Reviewmentioning

confidence: 99%

A boiler oxygen content and furnace temperature prediction model based on honey badger algorithm optimized neural network

Ji,

Tao,

Zhang

2024

Eng. Res. Express

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Aiming at the problem that it is difficult to accurately predict the oxygen content and furnace temperature during the combustion process inside the circulating fluidized bed boiler, this paper proposes a prediction model with a mixture of the convolutional neural network(CNN), bi-directional long-short term memory network(biLSTM), and honey badger algorithm(HBA). First, process variables related to furnace temperature and oxygen content were selected as input variables using actual test data from circulating fluidized bed boilers. Secondly, the parsed input variables are analyzed by CNN through an analytical mechanism to extract the data into smaller details and features, which are used as inputs to the biLSTM layer. The BiLSTM layer selectively memorizes the information of the input temporal data and learns the information dependence of the temporal data sequences, which then solves the temporal problem of the training of the irregular trend of the oxygen content and the furnace temperature. Finally, the HBA is utilized to continuously traverse and search the CNN-biLSTM model to find the optimal parameters of the model. The experimental results show: the CNN-biLSTM neural network model mixed with the HBA is able to accurately predict the oxygen content and furnace temperature. The experimental results show that the CNN-biLSTM neural network model with hybrid HBA is able to accurately predict the oxygen content and furnace temperature, and the average MAPE errors for the oxygen content are HBA-CNN-biLSTM (2.92E-03), CNN (7.96E-02), LSTM (5.13E-02), GRU (4.83E-02), RF (4.96E-02), RBF (8.41E-02), SVM (5.71E-02), RNN ( 5.53E-02), CNN-LSTM (4.79E-02).

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An intelligent ensemble classification method based on multi-layer perceptron neural network and evolutionary algorithms for breast cancer diagnosis

Cited by 32 publications

References 47 publications

En-MinWhale: An Ensemble Approach Based on MRMR and Whale Optimization for Cancer Diagnosis

En-MinWhale: An Ensemble Approach Based on MRMR and Whale Optimization for Cancer Diagnosis

Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach

A boiler oxygen content and furnace temperature prediction model based on honey badger algorithm optimized neural network

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