Abstract-Breast cancer is one of the causes of female death in the world. Mammography is commonly used for distinguishing malignant tumors from benign ones. In this research, a mammographic diagnostic method is presented for breast cancer biopsy outcome predictions using five machine learning which includes: Logistic Regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Random Forest (RF) and Support Vector Machine (SVM) classification. The testing results showed that SVM learning classification performs better than other with accuracy of 95.8% in diagnosing both malignant and benign breast cancer, a sensitivity of 98.4% in diagnosing disease, a specificity of 94.4%. Furthermore, an estimated area of the receiver operating characteristic (ROC) curve analysis for Support vector machine (SVM) was 99.9% for breast cancer outcome predictions, outperformed the diagnostic accuracies of Logistic Regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Random Forest (RF) methods. Therefore, Support Vector Machine (SVM) learning classification with mammography can provide highly accurate and consistent diagnoses in distinguishing malignant and benign cases for breast cancer predictions.
ABSTRACT:The Genetic Algorithm (GA) is an evolutionary algorithms and technique based on natural selections of individuals called chromosomes. In this paper, a method for solving Knapsack problem via GA (Genetic Algorithm) is presented. We compared six different crossovers: Crossover single point, Crossover Two point, Crossover Scattered, Crossover Heuristic, Crossover Arithmetic and Crossover Intermediate. Three different dimensions of knapsack problems are used to test the convergence of knapsack problem. Based on our experimental results, two point crossovers (TP) emerged the best result to solve knapsack problem. ©JASEM http://dx.doi.org/10.4314/jasem.v20i3.13Keywords: Genetic Algorithm, Crossover, Heuristic, Arithmetic, Intermediate, Evolutionary AlgorithmThe knapsack problem (KP) has been used in many real life problem such as investment decision making (Peeta, 2010), project selection (Mavrotas, 2008) and (Hartvigsen, 2006) applied it in vote-trading problem. The Knapsack problem can be defined as a set of items, each with a weight(w) and a profit(p), determine the number(n) of each item to include in a collection(j) so that the total weight is less than or equal to a given limit and the total profit(p) is as large as possible. Mathematically it can be represented as follows:The difficulty of the problem is caused by the integrality requirement of equation (3 MATERIALS AND METHODSIn GA Crossover operators is used to divide a pair of selected chromosomes into two or more parts. It consists of combining the chromosomes of two parents to produce a new offspring (child). The reason behind using crossover is that the new chromosomes being formed (child) may be better than both of the parents, if it takes the best chromosomes from both parents. For the purpose of this work, the following Crossover will be use: Single point Crossover (SP)A single point crossover involves the two mating chromosomes (parent) are cut once at corresponding points and the selection after the cuts exchanged. Fig.
Abstract:Nigerian educational system has gone through various developments and change. The main aim of this paper, which is knowledge and awareness of teachers to universal basic education in Nigeria is to investigate the perceived knowledge and awareness of teachers to universal basic education to aid proper implementation of the education policy through the proper involvement of the stake holders. The training and production of the manpower required for the attainment of Education policy objectives should be framed not only on the quality and quantity of teachers but also their involvement in policy formation. Analysis of the Nigerian education sector reveals the challenges of incoherence in policy formulation and implementation. One positive note is that both the government and the people are seeking better ways of doing things and achieving results that would benefit the majority of the people. Based on the findings, government is strictly advised to be proactive in the implementation of Universal Basic Education (UBE) for better monitoring strategy. It is also recommended that government needs to provide more in terms of library facilities, laboratory facilities and play materials for schools.
In this paper, we developed new model on replacement policy for some machines in some economic setup using discounted factors and Markov chain processes. Computational processes were applied to solve some proposed unbounded optimization problem which included iterative method for replacement and maintenance policies using inventory model. The conventional inventory model only balanced off manufacturing with inventory holding costs while the economic cost of varying production levels from one period to the next was ignored and equally ignored discount factor knowing fully that money depreciates with time.All these deficiencies are taken cared by our newly developed inventory model. The new model is also very efficient even in large systems unlike the existing exhaustive enumeration algorithm which can be used only if the number of stationary policies is reasonably small. From the numerically simulated results, it was observed that the optimal values of unbounded horizon problems were 126 D. Hakimi et al. obtained at the last peak of the model before shooting into non-convergence state. It was also observed that after the optimum report was acquired, a local minimum was achieved and thereafter, a non-convergence positive result that went into infinity followed. Manufacturing industries can apply the inventory model developed in the acquisition of raw materials as the stocks are replenished at the right time.
This paper uses iterative method for replacement and maintenance policies leading to a very efficient model in large systems unlike the existing exhaustive enumeration algorithm which can be used only if the number of stationary policies is reasonably small. From the numerically simulated results, it was observed that the optimal values of unbounded horizon problems are obtained at the last peak of the model before shooting into non convergence state. Immediately after the optimum report is acquired, a local minimum is achieved. Thereafter, a non convergence positive result that goes into infinity is achieved. The last trough clearly gives a false positive respond. Therefore, the optimum value observed before non-convergence state should be the decisive state to quit the stage. All equipment must be constantly kept under good maintenance since the cost of maintenance increases with time. At a stage of deterioration, equipment could be refurbished and sold out at less expensive cost as their life-span cannot stand the test of time. 114Danladi Hakimi and Victor O. Waziri
In this paper, an investigation of a performance of population size on the genetic algorithm (GA) for a knapsack problem is considered. Population sizes between 10 and 200 chromosomes in the population are tested. In order to obtain meaningful information about the performance of the population size, a considerable number of independent runs of the GA are performed. Accurate model parameters values are obtained in reasonable computational time. Further increase of the population size, does not improve the solution accuracy. Moreover, the computational time is increased significantly.
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