Cancer Categorization Using Genetic Algorithm to Identify Biomarker Genes

Bio inspired heuristic computing scheme for the human liver nonlinear model

In this research, a bio-inspired heuristic computing approach has been developed to solve the nonlinear behavior of the human liver, which is categorized into the liver and blood. The solutions of the human liver model are presented by using the stochastic computation procedure based on the artificial neural network (ANN) along with the optimization of genetic algorithm (GA) and interior-point (IP). A fitness function is designed through the differential form of the nonlinear human liver model and then optimized by using the hybrid competency of GAIP scheme. The correctness and exactness of the proposed approach are observed through the overlapping of the obtained (GAIP) and reference (Adams scheme) solutions, while the calculated absolute error values in good order enhance the worth of the proposed solver. The log-sigmoid transfer function together with ten numbers of neurons is executed to perform the solutions of the human liver nonlinear model. Furthermore, the statistical approaches have been applied in order to observe the reliability of the designed approach for solving the nonlinear human liver model.

Heuristic computing with active set method for the nonlinear Rabinovich-Fabrikant model

The current study shows a reliable stochastic computing heuristic approach for solving the nonlinear Rabinovich-Fabrikant model. This nonlinear model contains three ordinary differential equations. The process of stochastic computing artificial neural networks (ANNs) has been applied along with the competences of global heuristic genetic algorithm (GA) and local search active set (AS) methodologies, i.e., ANNs-GAAS. The construction of merit function is performed through the differential Rabinovich-Fabrikant model. The results obtained through this scheme are simple, reliable, and accurate, which have been calculated to optimize the merit function by using the GAAS method. The comparison of the obtained results through this scheme and the conventional reference solutions strengthens the correctness of the proposed method. Ten numbers of neurons along with the log-sigmoid transfer function in the neural network structure have been used to solve the model. The values of the absolute error are performed around 10-07 and 10-08 for each class of the Rabinovich-Fabrikant model. Moreover, the reliability of the ANNs-GAAS approach is observed by using different statistical approaches for solving the Rabinovich-Fabrikant model.

Retracted: Cancer Categorization Using Genetic Algorithm to Identify Biomarker Genes

[This retracts the article DOI: 10.1155/2022/5821938.].

A Breast Cancer Image Classification Algorithm with 2c Multiclass Support Vector Machine

Breast cancer is the most frequent type of cancer in women; however, early identification has reduced the mortality rate associated with the condition. Studies have demonstrated that the earlier this sickness is detected by mammography, the lower the death rate. Breast mammography is a critical technique in the early identification of breast cancer since it can detect abnormalities in the breast months or years before a patient is aware of the presence of such abnormalities. Mammography is a type of breast scanning used in medical imaging that involves using x-rays to image the breasts. It is a method that produces high-resolution digital pictures of the breasts known as mammography. Immediately following the capture of digital images and transmission of those images to a piece of high-tech digital mammography equipment, our radiologists evaluate the photos to establish the specific position and degree of the sickness in the breast. When compared to the many classifiers typically used in the literature, the suggested Multiclass Support Vector Machine (MSVM) approach produces promising results, according to the authors. This method may pave the way for developing more advanced statistical characteristics based on most cancer prognostic models shortly. It is demonstrated in this paper that the suggested 2C algorithm with MSVM outperforms a decision tree model in terms of accuracy, which follows prior findings. According to our findings, new screening mammography technologies can increase the accuracy and accessibility of screening mammography around the world.