Ant Colony Optimization Approach To Communications Network Design

Ant Colony Optimization (ACO) is a metaheuristic approach for solving hard combinatorial optimization problems. The pheromone trails in ACO serve as distributed, numerical information, which the ants use to probabilistically construct solutions to the problem being solved, and which the ants adapt during the algorithm's execution to reflect their search experience

Improving Machine Learning Algorithms for Breast Cancer Prediction

Early prediction of breast cancer can prevent death or receiving late treatment. The purpose of this research is to improve machine learning algorithms in predicting breast cancer that will assist patients and healthcare systems. The machine learning algorithms for the prediction of breast cancer are the methods applied in this research by using these following algorithms which are decision tree, random forest, naive Bayes, and gradient boosting due to their high performance. This research uses data from the breast cancer of Wisconsin (diagnostic) dataset of the general surgery department. The results from this research are that by using the stratified k-fold cross validation as a part of the random forest classifier achieved 100% for all four performance scores which are accuracy, recall, precision and F1. The stratified k-fold also improved two machine learning algorithms. In addition, data visualization was applied to the random forest algorithm for result understanding. The implication from the best method is that it could increase the number of accurate breast cancer detections. The values by selecting the best method from this research could assist doctors in early breast cancer detection and increase the number of breast cancer survival rates by receiving early treatment from accurate prediction

Ant Colony Optimization approaches to the degree-constrained minimum spanning tree problem

This paper presents the design of two Ant Colony Optimization (ACO) approaches and their improved variants on the degree-constrained minimum spanning tree (d-MST) problem. The first approach, which we call p-ACO, uses the vertices of the construction graph as solution components, and is motivated by the well-known Prim's algorithm for constructing MST. The second approach, known as k-ACO, uses the graph edges as solution components, and is motivated by Kruskal's algorithm for the MST problem. The proposed approaches are evaluated on two different data sets containing difficult d-MST instances. Empirical results show that k-ACO performs better than p-ACO. We then enhance the k-ACO approach by incorporating the tournament selection, global update and candidate lists strategies. Empirical evaluations of the enhanced k-ACO indicate that on average, it performs better than Prufer-coded evolutionary algorithm (F-EA), problem search space (PSS), simulated annealing (SA), branch and bound (B&B), Knowles and Come's evolutionary algorithm (K-EA) and ant-based algorithm (AB) on most problem instances from a well-known class of data set called structured hard graphs. Results also show that it is very competitive with two other evolutionary algorithm based methods, namely weight-coded evolutionary algorithm (W-EA), and edge-set representation evolutionary algorithm (S-EA) on the same class of data set

Effects of Coronavirus Disease on Trade for New Zealand

The coronavirus disease 2019 (COVID-19) is a humanitarian crisis that is spreading throughout the world. COVID-19 will be worse to countries that have weak healthcare and economic systems. Countries that are highly affected by coronavirus disease will have problems with international trade since the virus has a high infection rate. This will have effects on the trading economy which will cause export restrictions and trade barriers which make the country trade worse and can cause livelihood problems for the country. But there are countries that handle the pandemic excellently and manage to control the outbreak. Therefore, this research studies one country which is New Zealand on how the coronavirus disease affects their trading economy. This research consists of five phases of research methodology to be conducted before presenting the final findings. The five phases are dataset collection, data preprocessing, decision tree regressor, apriori algorithm under association rule mining and finally data visualizations. Using decision tree regressor, apriori algorithm and data visualizations for results, the outcomes of the findings show that the trade for New Zealand is not badly affected by the coronavirus pandemic and two association rules that support their economy have been discovered

Fast numerical threshold search algorithm for C4.5

This paper presents a new algorithm to improve the speed of threshold searching process in C4.5 by using the technique of genetic algorithms. In the threshold searching process in C4.5, the values in a numerical attribute are sorted first and then the mid-point between every two consecutive values is calculated and designated as a candidate threshold. This process can be time consuming and it is not practical for large data. Our algorithm generates a population of possible thresholds and converges to the best threshold value rapidly. Our experimental results have shown that significant time reduction has been achieved by using our algorithm in threshold searching process

Improved Canny Edges Using Ant Colony Optimization

Ant colony optimization (ACO) is a metaheuristic approach for solving hard optimization problem. It has been applied to solve various image processing problems such as image segmentation, classification, image analysis and edge detection. In this paper, we present an Improved Canny edges (ICE-ACO) algorithm which uses ACO to solve the problem of linking disjointed edges produced by Canny edge detector