The research topic was chosen as a result of the importance of human resource in business organizations in general and the industrial process in particular. Without the human resource, business organizations cannot continue and achieve success and excellence, and the research problem has been diagnosed in the lack of sales of General Cement Company’s northern products, despite their distinctiveness, standing, and reputation in The market and its products with standard specifications, and through this problem, the following questions were raised:                                                    &nbs

The speech recognition system has been widely used by many researchers using different
methods to fulfill a fast and accurate system. Speech signal recognition is a typical
classification problem, which generally includes two main parts: feature extraction and
classification. In this paper, a new approach to achieve speech recognition task is proposed by
using transformation techniques for feature extraction methods; namely, slantlet transform
(SLT), discrete wavelet transforms (DWT) type Daubechies Db1 and Db4. Furthermore, a
modified artificial neural network (ANN) with dynamic time warping (DTW) algorithm is
developed to train a speech recognition system to be used for classification and recognition
purposes. T

In this paper, we derive and prove the stability bounds of the momentum coefficient µ and the learning rate ? of the back propagation updating rule in Artificial Neural Networks .The theoretical upper bound of learning rate ? is derived and its practical approximation is obtained

Artificial Neural Networks (ANN) is one of the important statistical methods that are widely used in a range of applications in various fields, which simulates the work of the human brain in terms of receiving a signal, processing data in a human cell and sending to the next cell. It is a system consisting of a number of modules (layers) linked together (input, hidden, output). A comparison was made between three types of neural networks (Feed Forward Neural Network (FFNN), Back propagation network (BPL), Recurrent Neural Network (RNN). he study found that the lowest false prediction rate was for the recurrentt network architecture and using the Data on graduate students at the College of Administration and Economics, Univer

In this paper, we investigate and characterize the effects of multi-channel and rendezvous protocols on the connectivity of dynamic spectrum access networks using percolation theory. In particular, we focus on the scenario where the secondary nodes have plenty of vacant channels to choose from a phenomenon which we define as channel abundance. To cope with the existence of multi-channel, we use two types of rendezvous protocols: naive ones which do not guarantee a common channel and advanced ones which do. We show that, with more channel abundance, even with the use of either type of rendezvous protocols, it becomes difficult for two nodes to agree on a common channel, thereby, potentially remaining invisible to each other. We model this in

This paper presents a minimum delay congestion control in differentiated Service communication networks. The premium and ordinary passage services based fluid flow theory is used to build the suggested structure in high efficient manage. The established system is capable to adeptly manage both the physical network resource limitations and indefinite time delay related to networking system structure.

A comprehensive review focuses on 3D network-on-chip (NoC) simulators and plugins while paying attention to the 2D simulators as the baseline is presented. Discussions include the programming languages, installation configuration, platforms and operating systems for the respective simulators. In addition, the simulator’s properties and plugins for design metrics evaluations are addressed. This review is intended for the early career researchers starting in 3D NoC, offering selection guidelines on the right tools for the targeted NoC architecture, design, and requirements.

The road networks is considered to be one of the determinants that controls to specify the areas of human activities, which it depend on to specify the arrival cost , in addition it is useful to achieve the connectivity for interaction and human activities , and shorten the distance and time between the population and places of service. The density of the road network in any space directly affected by the density of population and the type of economic activities and administrative functions performed by the space. On this basis, the subject of this study is reflected in the quantitative analysis of the roads network in the Governorate of Karbala. The study consists the quantitative analysis for the roads network and the Urban Nodes in th

Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the