An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to

Many letters and theses written on the subject of consensus, as well as in measurement,
But we tried to address a topic of consensus

Building a blind measuring guide.
 We have tried to explain the meaning of convening, then the statement of consensus in language and terminology and then the statement of measurement
Also, we have shown the types of consensus mentioned by the jurists, and this is how much was in the first topic, either
The second section included the statement of the doctrines of the blind in the matter, and then the evidence of each doctrine and discussed.
We followed it with the most correct opinion statement and concluded the research with some of the conclusions we reached through
search.

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

The issue of measurement of operational efficiency and productivity plays a major role in determining the weakness of the company, especially in relation to the productive processes, and thus starting to address these points and improve their performance. Hence the problem of research on how to determine the constraints in the production process, to identify weaknesses in the company. The research aims to clarify the role of throughput accounting in measuring the operational efficiency of the company in addition to clarify some of the constraints and causes behind them. The study concluded a set of conclusions, the most important of which is the availability of throughput accounting indicators that help in measuring operational efficienc

The university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a higher-level method that selects low-level heuristics (LLH) sequence which further generates an optimal solution. The proposed a

Measuring the efficiency of postgraduate and undergraduate programs is one of the essential elements in educational process. In this study, colleges of Baghdad University and data for the academic year (2011-2012) have been chosen to measure the relative efficiencies of postgraduate and undergraduate programs in terms of their inputs and outputs. A relevant method to conduct the analysis of this data is Data Envelopment Analysis (DEA). The effect of academic staff to the number of enrolled and alumni students to the postgraduate and undergraduate programs are the main focus of the study.

The field of identification of similarity was considered a subject of research strongly recommended in the areas of semantic web, artificial intelligence and linguistic literature. In this paper a new similarity measure is proposed based on the Wu and Palmer measure. This measure is applied on the domain of the pedagogical ontology. Results obtained showed that the new measure gives better relevance of the similarity values compared to the approach of Wu and Palmer and it appears that the proposed measure is advantageous because obtained results showed that it is faster and capable of obtaining results to concepts not located in the same path.

The method of Similarity of important topics in Arabic Rhetoric has ample evidence in the Qur'an and the Hadith and the words of the Arabs and this study is limited to only the Koran.
There have been Similarity in the Holy Quran on both types : investigative and discretion , but the verses are more Similarity investigative and in verse 49 Similarity investigative and in verse 25.
The rush of Albulageyen and many other examples of Quranic Vmtheloa on Similarity , with a linguistic analysis and return to the origins of the meanings are not properly counted Similarity , Kolvaz cunning , deception , and maliciousness , the bad and ridicule.
This study seeks as much as possible to the statement of this phenomenon and stand on these t

In this paper, the memorization capability of a multilayer interpolative neural network is exploited to estimate a mobile position based on three angles of arrival. The neural network is trained with ideal angles-position patterns distributed uniformly throughout the region. This approach is compared with two other analytical methods, the average-position method which relies on finding the average position of the vertices of the uncertainty triangular region and the optimal position method which relies on finding the nearest ideal angles-position pattern to the measured angles. Simulation results based on estimations of the mobile position of particles moving along a nonlinear path show that the interpolative neural network approach outperf