DNA microarrays allow simultaneous measurements of expression levels for a large number of genes across a number of different experimental conditions (samples). The algorithms for mining association rules are used to reveal biologically relevant associations between different genes under different experimental samples. This paper presents a new column-enumeration based method algorithm (abbreviated by MCR-Miner) for mining maximal high confidence association rules for up/down-expressed genes. MCR-Miner algorithm uses an efficient maximal association rules tree data structure (abbreviated by MAR-Tree). MAR-tree enumerates (lists) all genes with their binary representations, the binary representation of a gene saves the status (normal, up, and downexpressed) of a gene in all experiments. The binary representation has many advantages, scan the dataset only once, the measurements of confidences for association rules are made in one step, and it makes MCR-Miner algorithm easily finds all maximal high confidence association rules. In the experimental results on a real microarray datasets, MCR-Miner algorithm attained very promising results and outperformed other counterparts.
A software design is often modeled as a collection of unified Modeling Language (UML) diagrams. There are different aspects of the software system that are covered by many different UML diagrams. This leads for big risk that the overall specification of the system becomes inconsistent and incompleteness. This inherits the necessary to check the consistency between these related UML diagrams. In addition, as the software system gets evolution, those diagrams get modified that leads again to possible inconsistency and incompleteness between the different versions of these diagrams. In this paper, we plan to employ our previous novel XML semantics approach, which proposed for checking the semantic consistency of XML documents using attribute grammar techniques, to check the consistency of UML diagrams. The key idea here is translating the UML diagrams to its equivalent XMI documents. Then checking the consistency of these XMI documents, they are special forms of XML, by employing them to our previous XML semantics approach.
Factoring a composite odd integer into its prime factors is one of the security problems for some public-key cryptosystems such as the Rivest-Shamir-Adleman cryptosystem. Many strategies have been proposed to solve factorization problem in a fast running time. However, the main drawback of the algorithms used in such strategies is the high computational time needed to find prime factors. Therefore, in this study, we focus on one of the factorization algorithms that is used when the two prime factors are of the same size, namely, the Fermat factorization (FF) algorithm. We investigate the performance of the FF method using three parameters: (1) the number of bits for the composite odd integer, (2) size of the difference between the two prime factors, and (3) number of threads used. The results of our experiments in which we used different parameters values indicate that the running time of the parallel FF algorithm is faster than that of the sequential FF algorithm. The maximum speed up achieved by the parallel FF algorithm is 6.7 times that of the sequential FF algorithm using 12 cores. Moreover, the parallel FF algorithm has near-linear scalability.
A minimal length addition chain for a positive integer m is a finite sequence of positive integers such that (1) the first and last elements in the sequence are 1 and m, respectively, (2) any element greater than 1 in the sequence is the addition of two earlier elements (not necessarily distinct), and (3) the length of the sequence is minimal. Generating the minimal length addition chain for m is challenging due to the running time, which increases with the size of m and particularly with the number of 1s in the binary representation of m. In this paper, we introduce a new parallel algorithm to find the minimal length addition chain for m. The experimental studies on multicore systems show that the running time of the proposed algorithm is faster than the sequential algorithm. Moreover, the maximum speedup obtained by the proposed algorithm is 2.5 times the best known sequential algorithm.
The MCR-Miner algorithm is aimed to mine all maximal high confident association rules form the microarray up/down-expressed genes data set. This paper introduces two new algorithms: IMCR-Miner and PMCR-Miner. The IMCR-Miner algorithm is an extension of the MCR-Miner algorithm with some improvements. These improvements implement a novel way to store the samples of each gene into a list of unsigned integers in order to benefit using the bitwise operations. In addition, the IMCR-Miner algorithm overcomes the drawbacks faced by the MCR-Miner algorithm by setting some restrictions to ignore repeated comparisons. The PMCR-Miner algorithm is a parallel version of the new proposed IMCR-Miner algorithm. The PMCR-Miner algorithm is based on shared-memory systems and task parallelism, where no time is needed in the process of sharing and combining data between processors. The experimental results on real microarray data sets show that the PMCR-Miner algorithm is more efficient and scalable than the counterparts.
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With the rapid development in the area of Machine Learning (ML) and Deep learning, it is important to exploit these tools to contribute to mitigating the effects of the coronavirus pandemic. Early diagnosis of the presence of this virus in the human body can be crucially helpful to healthcare professionals. In this paper, three well-known Convolutional Neural Network deep learning algorithms (VGGNet 16, GoogleNet and ResNet50) are applied to measure their ability to distinguish COVID-19 patients from other patients and to evaluate the best performance among these algorithms with a large dataset. Two stages are conducted, the first stage with 14994 x-ray images and the second one with 33178. Each model has been applied with different batch sizes 16, 32 and 64 in each stage to measure the impact of data size and batch size factors on the accuracy results. The second stage achieved accuracy better than the first one and the 64 batch size gain best results than the 16 and 32. ResNet50 achieves a high rate of 99.31, GoogleNet model achieves 95.55, while VGG16 achieves 96.5. Ultimately, the results affect the process of expediting the diagnosis and referral of these treatable conditions, thereby facilitating earlier treatment, and resulting in improved clinical outcomes.
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