The selection of optimal subset of features from highdimensional data sets still remains a major challenge during breast cancer detection and categorization.There exist several research works regarding optimal feature subset selection from high-dimensional data sets, but the obtained results are not satisfying when multidimensional data sets (MDDs) are employed in large amount during disease analysis. In this article, an effective feature subset selection and classification method suitable for MDD is proposed. At first, the important and distinct features are extracted from the mammogram images using a Deep Neural Network with wrapper-based extraction technique. Then, a novel two-phase mutation strategy integrated with grey wolf optimizer algorithm is employed for selecting the most relevant feature subsets. Finally, a learning-based semilazy Bayesian network classifier with parallel implementation is proposed for the precise categorization of the breast cancer stages. The proposed method is executed in MATLAB platform and analyzed using mammogram images taken from MAMMOSET database. The proposed method is likened with three state-of-the-art existing feature subset selection and classification approaches for
Viruses are the most abundant living things and a source of genetic variation. Despite recent research, we know little about their biodiversity and geographic distribution. We used different bioinformatics tools, MG-RAST, genome detective web tools, and GenomeVx, to describe the first metagenomic examination of haloviruses in Wadi Al-Natrun. The discovered viromes had remarkably different taxonomic compositions. Most sequences were derived from double-stranded DNA viruses, especially from Myoviridae, Podoviridae, Siphoviridae, Herpesviridae, Bicaudaviridae, and Phycodnaviridae families; single-stranded DNA viruses, especially from the family Microviridae; and positive-strand RNA viruses, especially from the family Potyviridae. Additionally, our results showed that Myohalovirus chaoS9 has eight Contigs and is annotated to 18 proteins as follows: tail sheath protein, tco, nep, five uncharacterized proteins, HCO, major capsid protein, putative pro head protease protein, putative head assembly protein, CxxC motive protein, terl, HTH domain protein, and terS Exon 2. Additionally, Halorubrum phage CGphi46 has 19 proteins in the brine sample as follows: portal protein, 17 hypothetical proteins, major capsid protein, etc. This study reveals viral lineages, suggesting the Virus's global dispersal more than other microorganisms. Our study clarifies how viral communities are connected and how the global environment changes.
Over the years, energy harvesting technologies have been used in various self-powered systems. These technologies have several methods of application depending on their usage. Renewable energy is one of the types of energy harvesting technologies where energy is generated from naturally replenished sources. One of the energy harvesting methods that is commonly used is piezoelectric transducers. Piezoelectric materials are groups of elements that can be used to generate electricity when mechanical energy is applied. When external mechanical stress is applied, the inner lattice is deformed, resulting in the separation of the positive and negative centers of the molecule and thus the generation of a small dipole. Therefore, this paper aims to discuss the output of the piezoelectric transducer by reviewing it depending on two different material types and in other energy harvesting structures. Furthermore, a comparison was made in order to compare the power output of the two materials. Similarly, the most used piezoelectric transducer structures for power harvesting applications were revised. In addition, the parameters that affect the value of the generated power output were discussed using the figures of merit (FOM) concept. Moreover, the according to the FOM concepts, when stress is applied, the electrical energy extracted from a piezoelectric energy harvesting material is determined by the change in stored electrical energy within a piezoelectric material. The figures of merit (FOM) depend on the piezoelectric strain and its permittivity. The piezoelectric strain directly relates to FOM, while the permittivity has an inverse relationship with FOM. Thus, the highest strain constant and low permittivity material will provide the highest energy output. Additionally, lead-based (PZT) material has a strain coefficient d33 equal to 390 Coul/Nx10-12, and permittivity value ranging from 1000 to 3500 and can generate power output that is equal to 52mW at 100Hz, which is higher than the output of the lead-free-based material Barium Titanate (BaTiO3). The output of piezoelectric also depends on the piezoelectric transducer’s structure. The circular diaphragm’s power output is greater than the bimorph cantilever’s power output due to the presence of a proof mass in the center of the diaphragm that provides prestress to the piezoelectric which improves the low-frequency performance of the energy harvester.
Real-time estimation of transmission line (TL) parameters is essential for proper management of transmission and distribution networks. These parameters can be used to detect incipient faults within the line and hence avoid any potential consequences. While some attempts can be found in the literature to estimate TL parameters, the presented techniques are either complex or impractical. Moreover, none of the presented techniques published in the literature so far can be implemented in real time. This paper presents a cost-effective technique to estimate TL parameters in real time. The proposed technique employs easily accessible voltage and current data measured at both ends of the line. For simplicity, only one quarter of the measured data is sampled and utilized in a developed objective function that is solved using the whale optimization algorithm (WOA) to estimate the TL parameters. The proposed objective function comprises the sum of square errors of the measured data and the corresponding estimated values. The robustness of the proposed technique is tested on a simple two-bus and the IEEE 14-bus systems. The impact of uncertainties in the measured data including magnitude, phase, and communication delay on the performance of the proposed estimation technique is also investigated. Results reveal the effectiveness of the proposed method that can be implemented in real time to detect any incipient variations in the TL parameters due to abnormal or fault events.
This work presents a novel methodology for variable speed high power Transfer Capability of a self-excited induction generator (SEIG). The proposed methodology is based on the selection of a suitable firing angle of Fixed Capacitor-Thyristor Controlled Reactor (FC-TCR) for achieving constant rated stator current. WDSEIG would produce a variable speed high power without overheating under variable wind speed and connected load. The analytical approach for the proposed methodology has been implemented to predict the optimal operating firing angle of FC-TCR for full load stator current achievement within the allowed operating range of load and prime mover speed. Also, Soft Computing (SC) techniques have been implemented based on Harmony Search Algorithm (HSA), Flower Pollination Algorithm (FPA), and Moth-Flame Optimization (MFO) algorithm to achieve the proposed methodology. A comparison between different SC techniques, analytical approach and experimental work are given and evaluated to verify SC techniques accuracy. This evaluation study can be useful in specifying the appropriateness of the SC techniques for High Power Transfer Capability for a SEIG.
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