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Breast cancer (BC) is one of the primary causes of cancer death among women. Early detection of BC allows patients to receive appropriate treatment, thus increasing the possibility of survival. In this work, a new deep-learning (DL) model based on the transfer-learning (TL) technique is developed to efficiently assist in the automatic detection and diagnosis of the BC suspected area based on two techniques namely 80-20 and cross-validation. DL architectures are modeled to be problem-specific. TL uses the knowledge gained during solving one problem in another relevant problem. In the proposed model, the features are extracted from the mammographic image analysis-society (MIAS) dataset using a pretrained convolutional neural network (CNN) architecture such as Inception V3, ResNet50, Visual Geometry Group networks (VGG)-19, VGG-16, and Inception-V2 ResNet. Six evaluation metrics for evaluating the performance of the proposed model in terms of accuracy, sensitivity, specificity, precision, F-score, and area under the ROC curve (AUC) has been chosen. Experimental results show that the TL of the VGG16 model is powerful for BC diagnosis by classifying the mammogram breast images with overall

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Abstract-Cloud computing is a type of parallel and distributed system consisting of a co llect ion of interconnected and virtual computers. With the increasing demand and benefits of cloud computing infrastructure, different co mputing can be performed on cloud environment. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorith ms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper a cloud task scheduling policy based on ant colony optimizat ion algorith m for load balancing compared with different scheduling algorith ms has been proposed. Ant Co lony Optimization (ACO) is random optimization search approach that will be used for allocating the incoming jobs to the virtual mach ines. The main contribution of our work is to balance the system load while t rying to min imizing the make span of a given tasks set. The load balancing factor, related to the job fin ishing rate, is proposed to make the job fin ishing rate at different resource being similar and the ability of the load balancing will be improved. The proposed scheduling strategy was simu lated using Cloudsim toolkit package. Experimental results showed that, the proposed algorith m outperformed scheduling algorith ms that are based on the basic ACO or Modified Ant Colony Optimization (MACO).

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