Optimization of Neural Network with Genetic Algorithm for Breast Cancer Classification

General Election is one of the characteristics of a democratic country. One of the countries that embrace the democratic system is the state of Indonesia. Elections are a party of democracy in Indonesia to elect representatives of the people who will sit in parliament and provide great opportunities for the people of Indonesia to compete to appoint themselves to become members of the legislature. Research related to the election has been done by researchers is by using decision tree method or by using neural network. each method has its own weaknesses and advantages, but neural network methods can cover the weaknesses of the decision tree. The result of research using neural network method in predicting election result has accurate result value is still less accurate. In this research, we create neural network algorithm model and optimization with particle swarm optimization algorithm to increase attribute weight to all attributes or variables used, select attributes, and feature selection. whereas the Genetic Algorithm for predicting the performance of generalizations based on static properties of networks such as activation function and hidden neurons will be strong enough to find solutions. After testing with neural network algorithm to produce accurate value of 98.50% and AUC value of 0.982, further optimization done with particle swarm optimization obtained an accuracy of 98.85% and AUC value of 0.996. and then done the optimization testing with genetic algorithm obtained an accuracy value of 96.56% and AUC value of 0.925 So that both methods have a difference of accuracy that is equal to 0,35% and difference of AUC value equal to 0,14.

In this article we developed a method for optimizing the structure of a fuzzy artificial neural networks through genetic algorithms. This genetic algorithm is used by optimizing the number of weight connections in a neural network structure, by the evolution of those structures as individuals in a population. It is found that the optimization of the neural network provides higher confidence accuracy of the suggested solution in a case based diagnostic system. The computational cost of the optimized network also improved considerably high.

Abaqus finite element simulation technology and BP neural network and genetic algorithm are applied to the study on the optimization of the cutting parameters of the thin-walled ring gear. A simulation model of ring gear shaping was established by Abaqus finite element analysis software, a cutting force prediction model was built using BP neural network. With the help of the BP neural network and genetic algorithm function extreme value optimization feature, the gear ring gear shaping parameter optimization model was established, and the Abaqus simulation model was used to test it. The relevant slotting parameters, namely the slotting speed, the amount of back-cutting and the circumferential feed speed have been successfully optimized, and the slotting force has been reduced. The experimental results show that the parameter optimization model of ring gear shaping based on BP neural network and genetic algorithm can effectively realize the optimization of slotting parameters.

Forecasting the adoption of innovative products is an important managerial task. In this paper we examine the usefulness of a probabilistic neural network (PNN) algorithm for forecasting new product adoption. We compare this approach with one widely accepted forecasting procedure, the binomial logit model, and two other neural network algorithms: a feed-forward neural network model estimated with backward propagation (NNBP), and a feed-forward neural network model estimated with a genetic algorithm (NNGA). To test the relative forecasting accuracy of these algorithms, we examine the first-time adoption of DVD players. Our analysis is based on longitudinal consumer data collected between March 2000 and March 2001. We find that the PNN algorithm significantly outperforms the logit model and the two remaining neural network algorithms.

Path planning is a well-known problem in mobile robot. The robot needs to move from the starting point to the destination point and avoid obstacles. To create an agent that able to reach some destinations in the given environment, such agent need some abilities to processing a collection of information obtained by its sensors and roam freely in the environment. In this paper, we design mobile agents to solve local path planning problems in 3D environment by using Evolutionary Neural Network (ENN) algorithm. ENN combines Evolutionary Algorithm (EA) and Neural Network algorithm. We chose Genetic Algorithm (GA) for the EA part and designing a simple feed forward neural network for the neural networks part. We evaluate what kind of ENN configuration values that works best in a local path planning problem. Experiment results show that the lowest iterations rate is 1.8 with one hidden layer and 50 hidden nodes when the population size is 50.

The combination of neural network and genetic algorithm not only can improve the operating efficiency and economy of the boiler, but also give the recommended values of the operating parameters for the boiler operation. Based on the field data from power plant, the errors of the 3 and 4-layer neural network were compared according to the data experiments. The 4-layer neural network was used to model the steam temperature system. Taking the standard heat consumption as the target value, the adjustable and non-adjustable quantities of the input parameters of the steam temperature system are determined, and the genetic algorithm is used for optimization. The results show that the standard heat consumptions of all the 100 groups of working conditions are reduced. The maximum reduction of heat consumption is 186.88 kJ/(kW·h), and the average reduction is 153.93 kJ/(kW·h). This indicates that the combination of neural network and genetic algorithm can optimize the boiler steam temperature system by optimizing parameters such as superheater de-superheating water flow, superheater flue gas baffle opening and reheater flue gas baffle opening, which provides guidance for actual production.

Antenna modeling based on meta-heuristic intelligent algorithms and neural networks

A way in which a neural network can implement a genetic algorithm as its learning algorithm is shown. This model is called GLANN (genetic learning algorithm for neural networks). The components of GLANN can be shown to be biologically plausible. The algorithm itself can be classified as a reinforcement learning algorithm. The neural network has a fixed architecture and processes binary strings using genetic operators. Learning is stored in the form of newly created patterns, which can then be stored in some kind of associative memory. The benefits of GLANN reside in the proven optimizing capabilities of genetic algorithms, and in its parallel implementation. The shallow two-level architecture translates into system scalability, an issue that has not been successfully resolved in the case of other neural network algorithms. >

COVID-19, one of the most dangerous pandemics, is currently affecting humanity. COVID-19 is spreading rapidly due to its high reliability transmissibility. Patients who test positive more often have mild to severe symptoms such as a cough, fever, raw throat, and muscle aches. Diseased people experience severe symptoms in more severe cases. such as shortness of breath, which can lead to respiratory failure and death. Machine learning techniques for detection and classification are commonly used in current medical diagnoses. However, for treatment using neural networks based on improved Particle Swarm Optimization (PSO), known as PSONN, the accuracy and performance of current models must be improved. This hybridization implements Particle Swarm Optimization and a neural network to improve results while slowing convergence and improving efficiency. The purpose of this study is to contribute to resolving this issue by presenting the implementation and assessment of Machine Learning models. Using Neural Networks and Particle Swarm Optimization to help in the detection of COVID-19 in its early stages. To begin, we preprocessed data from a Brazilian dataset consisted primarily of early-stage symptoms. Following that, we implemented Neural Network and Particle Swarm Optimization algorithms. We used precision, accuracy score, recall, and F-Measure tests to evaluate the Neural Network with Particle Swarm Optimization algorithms. Based on the comparison, this paper grouped the top seven ML models such as Neural Networks, Logistic Regression, Nave Bayes Classifier, Multilayer Perceptron, Support Vector Machine, BF Tree, Bayesian Networks algorithms and measured feature importance, and other, to justify the differences between classification models. Particle Swarm Optimization with Neural Network is being deployed to improve the efficiency of the detection method by more accurately predicting COVID-19 detection. Preprocessed datasets with important features are then fed into the testing and training phases as inputs. Particle Swarm Optimization was used for the training phase of a neural net to identify the best weights and biases. On training data, the highest rate of accuracy gained is 0.98.738 and on testing data, it is 98.689.

In today's world, in which computerized recognition is expanding its horizons in the field of medicine, breast cancer classification is receiving wide attention. In this application, artificial neural networks have achieved reasonable recognition rates. However, to improve performance, a technique is needed to screen the features of the input data, to extract the important ones and suppress those that are irrelevant. Although neural networks do have this capability to some extent, here it is shown that by using a hybrid genetic algorithm and neural network (GANN), the feature extraction can be performed more effectively. Another advantage of augmenting NN training with a GA is that the extracted features using GA are explicit and perceivable. Although the authors evaluated the technique using breast cancer data, the methodology is designed to handle any other kind of classification task.

Product design is a complicated activity that is highly reliant on individual impressions, feelings and emotions. Back-propagated neural networks have already been applied in Kansei engineering to solve difficult design problems. However, artificial neural networks (ANNs) have a slow rate of convergence, and find it difficult to devise a suitable network structure and find the global optimal solution. This study developed an ANN-based predictive model enhanced with a genetic algorithm (GA) optimization technique to search for close-to-optimal sports shoe color schemes for a given product image. The design factors of the sports shoe were set as the network inputs, and the Kansei objective value was the output of the GA-based ANN model. The results show that a model built with three hidden layers (28 × 38 × 19) could predict the object value reliably. The R2 of the preference objective was equal to 0.834, suggesting that the developed model is a feasible and efficient tool for predicting the objective value of product images. This study also found that the prediction accuracy for shoes with two colors was higher than that for shoes with only one color. In addition, the prediction accuracy for shoes with a relatively familiar shape was also higher. However, the prediction of color preferences is relatively difficult, because the respondents had different individual color preferences. Exploring the sensitivity and importance of the visual factors (form, color, texture) for various image words is a worthy topic for future research in this field.

Parameters exploration of SOFC for dynamic simulation using adaptive chaotic grey wolf optimization algorithm

This paper aimed to establish a nonlinear relationship between laser cladding process parameters and the crack density of a high-hardness, nickel-based laser cladding layer, and to control the cracking of the cladding layer via an intelligent algorithm. By using three main process parameters (overlap rate, powder feed rate, and scanning speed), an orthogonal experiment was designed, and the experimental results were used as training and testing datasets for a neural network. A neural network prediction model between the laser cladding process parameters and coating crack density was established, and a genetic algorithm was used to optimize the prediction results. To improve their prediction accuracy, genetic algorithms were used to optimize the weights and thresholds of the neural networks. In addition, the performance of the neural network was tested. The results show that the order of influence on the coating crack sensitivity was as follows: overlap rate > powder feed rate > scanning speed. The relative error between the predicted value and the experimental value of the three-group test genetic algorithm-optimized neural network model was less than 9.8%. The genetic algorithm optimized the predicted results, and the technological parameters that resulted in the smallest crack density were as follows: powder feed rate of 15.0726 g/min, overlap rate of 49.797%, scanning speed of 5.9275 mm/s, crack density of 0.001272 mm/mm2. Therefore, the amount of crack generation was controlled by the optimization of the neural network and genetic algorithm process.

Cross-cultural learning competence, a critical skill in our globally interconnected world, is advanced through the application of the Backpropagation (BP) neural network algorithm. This innovative approach involves leveraging neural network techniques to model and enhance individuals' abilities to navigate and understand diverse cultural contexts. The BP neural network algorithm facilitates personalized learning experiences by adapting to individuals' cultural backgrounds and preferences. This research explores a comprehensive approach for assessing and enhancing cross-cultural learning competence among Chinese college students, integrating the Word Embedding Multilingual Model with the Back Propagation Neural Network (WEMM-BPNN) algorithm. Recognizing the importance of global competencies in higher education, our study focuses on leveraging advanced neural network techniques to evaluate and elevate students' cross-cultural learning abilities. The WEMM-BPNN model combines the power of word embedding and multilingual considerations, tailoring the learning experience to individual cultural backgrounds. Through a meticulous analysis of cross-cultural data and linguistic patterns, the algorithm refines its recommendations for personalized learning strategies. The research aims not only to assess the current state of cross-cultural learning competence but also to provide targeted interventions to enhance students' intercultural understanding and adaptability. By merging linguistic models with neural network algorithms, this study offers a pioneering approach to cultivating cross-cultural competencies, contributing valuable insights to the ongoing discourse on globalized education.

The Sixth International Symposium on Neural Networks (ISNN 2009)