The development of wireless sensor networks (WSNs) in the underwater environment leads to underwater WSN (UWSN). It has severe impact over the research field due to its extensive and real-time applications. However effective execution of underwater WSNs undergoes several problems. The main concern in the UWSN is sensor nodes’ energy depletion issue. Energy saving and maintaining quality of service (QoS) becomes highly essential for UWASN because of necessity of QoS application and confined sensor nodes (SNs). To overcome this problem, numerous prevailing methods like adaptive data forwarding techniques, QoS-based congestion control approaches, and various methods have been devised with maximum throughput and minimum network lifesp

The development of wireless sensor networks (WSNs) in the underwater environment leads to underwater WSN (UWSN). It has severe impact over the research field due to its extensive and real-time applications. However effective execution of underwater WSNs undergoes several problems. The main concern in the UWSN is sensor nodes’ energy depletion issue. Energy saving and maintaining quality of service (QoS) becomes highly essential for UWASN because of necessity of QoS application and confined sensor nodes (SNs). To overcome this problem, numerous prevailing methods like adaptive data forwarding techniques, QoS-based congestion control approaches, and various methods have been devised with maximum throughput and minimum network lifesp

 The aim of this paper, is to discuss several high performance training algorithms fall into two main categories. The first category uses heuristic techniques, which were developed from an analysis of the performance of the standard gradient descent algorithm. The second category of fast algorithms uses standard numerical optimization techniques such as: quasi-Newton . Other aim is to solve the drawbacks related with these training algorithms and propose an efficient training algorithm for FFNN

Pseudomonas aeruginosa, a ubiquitous environmental organism, is a difficult-to-treat opportunistic pathogen due to its broad-spectrum antibiotic resistance and its ability to form biofilms. In this study, we investigate the link between resistance to a clinically important antibiotic, imipenem, and biofilm formation. First, we observed that the laboratory strain P. aeruginosa PAO1 carrying a mutation in the oprD gene, which confers resistance to imipenem, showed a modest reduction in biofilm formation.We also observed an inverse relationship between imipenem resistance and biofilm formation for imipenem-resistant strains selected in vitro, as well as for clinical isolates.We identified two clinical isolates of P. aeruginosa from the sputum

The process of selection assure the objective of receiving for chosen ones to high levels more than other ways , and the problem of this research came by these inquires (what is the variables of limits we must considered when first preliminaries selections for mini basket ? and what is the proper test that suits this category ? and is there any standards references it can be depend on it ?) also the aims of this research that knowing the limits variables to basketball mini and their tests as a indicators for preliminaries for mini basketball category in ages (9-12) years and specifies standards (modified standards degrees in following method) to tests results to some limits variables for research sample. Also the researchers depends on (16)

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri

   SMNs like Facebook, YouTube, Twitter, WhatsApp,..etc. are among the most popular sites on the Internet. These sites can provide a powerful means of sharing, organizing, finding information and knowledge. The popularity of these sites provides an opportunity to measure the use them in knowledge sharing, which needs a special scale, but unfortunately, there is no special scale for that. Thus, this study supposes to use SCT as a scale to measure the use of SMNs in electronic knowledge sharing due to it has been used to measure knowledge sharing with its traditional form. This study can help the decision-makers to use these SMNs to share the academics’ knowledge in educational institutes to the communi

<p>Energy and memory limitations are considerable constraints of sensor nodes in wireless sensor networks (WSNs). The limited energy supplied to network nodes causes WSNs to face crucial functional limitations. Therefore, the problem of limited energy resource on sensor nodes can only be addressed by using them efficiently. In this research work, an energy-balancing routing scheme for in-network data aggregation is presented. This scheme is referred to as Energy-aware and load-Balancing Routing scheme for Data Aggregation (hereinafter referred to as EBR-DA). The EBRDA aims to provide an energy efficient multiple-hop routing to the destination on the basis of the quality of the links between the source and destination. In

Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall