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
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).
Sequence alignment is widely used in Bioinformatics for Genome Sequence difference identification. It is the main problem of computational biology. Any sequence of Deoxyribonucleic acid (DNA), Ribonucleic acid (RNA), and protein can be alignment by many algorithms called bioinformatics algorithms. This paper presents a new implemented algorithm for sequence alignment based on concepts from bioinformatics algorithms .The implemented algorithm is called fast dynamic algorithm for sequence alignment (FDASA). This implemented algorithm based on making a matrix of M×N (M is the length of the first sequence, N is the length of the second sequence), After that filling the three main diagonal without filling the unused data and at the same time get the optimal solution; so that the execution time is decreased, the performance is high and the memory location decreased. The implementation introduced in this paper made a comparison between the dynamic algorithms Needleman-Wunsch algorithm, Smith-Waterman and our algorithm FDASA to test the execution time. The results show that our algorithm FDASA decreased the execution time when compared with Needleman-Wunsch and Smith-Waterman algorithms.
Cloud computing services are becoming ubiquitous, and are becoming the primary source of computing power for both enterprises and personal computing applications. One of the fundamental issues in this environment is related to task scheduling. The scheduler should do the scheduling process efficiently in order to utilize the available resources. In this paper a cloud task scheduling policy based on artificial bee colony algorithm compared with different scheduling algorithms has been proposed. The main goal of the proposed algorithm is minimizing the makespan of a given tasks set. Artificial bee colony algorithm models the behavior of honey bees and can be used to find solutions for difficult or impossible combinatorial problems. Algorithms have been simulated using Cloudsim toolkit package. Experimental results showed that the artificial bee colony algorithm outperformed ACO, FPLTF and FCFS algorithms.
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