With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se

The most important topic for psychologist generally is factor of education and it's active tools because learning needs active perception for stimulus that recived by the educator and give it avalue and meaning , Need for cognition is
very important in the various daily fields of life , especially in learning and teaching and the academy work , it help with shifting the learning level for people , and icreas the intense and challenge between them
The research endeavored to achieve the following aim :
1- Measuring the level of peripheral perception for the university student .
2- Measuring the level of need for cognition for the university student .
3- Measuring the level of peripheral perception for the university student

The aim of the research is to study the comparison between (ARIMA) Auto Regressive Integrated Moving Average and(ANNs) Artificial Neural Networks models and to select the best one for prediction the monthly relative humidity values depending upon the standard errors between estimated and observe values . It has been noted that both can be used for estimation and the best on among is (ANNs) as the values (MAE,RMSE, R2) is )0.036816,0.0466,0.91) respectively for the best formula for model (ARIMA) (6,0,2)(6,0,1) whereas the values of estimates relative to model (ANNs) for the best formula (5,5,1) is (0.0109, 0.0139 ,0.991) respectively. so that model (ANNs) is superior than (ARIMA) in a such evaluation.

We propose a novel strategy to optimize the test suite required for testing both hardware and software in a production line. Here, the strategy is based on two processes: Quality Signing Process and Quality Verification Process, respectively. Unlike earlier work, the proposed strategy is based on integration of black box and white box techniques in order to derive an optimum test suite during the Quality Signing Process. In this case, the generated optimal test suite significantly improves the Quality Verification Process. Considering both processes, the novelty of the proposed strategy is the fact that the optimization and reduction of test suite is performed by selecting only mutant killing test cases from cumulating t-way test ca

In a connected graph , the distance function between each pair of two vertices from a set vertex  is the shortest distance between them and the vertex degree  denoted by  is the number of edges which are incident to the vertex  The Schultz and modified Schultz polynomials of  are have defined as:

 respectively, where the summations are taken over all unordered pairs of distinct vertices in  and  is the distance between  and  in  The general forms of Schultz and modified Schultz polynomials shall be found and indices of the edge – identification chain and ring – square graphs in the present work.

ABSTRUCT

In This Paper, some semi- parametric spatial models were estimated, these models are, the semi – parametric spatial error model (SPSEM), which suffer from the problem of spatial errors dependence, and the semi – parametric spatial auto regressive model (SPSAR). Where the method of maximum likelihood was used in estimating the parameter of spatial error          ( λ ) in the model (SPSEM), estimated  the parameter of spatial dependence ( ρ ) in the model ( SPSAR ), and using the non-parametric method in estimating the smoothing function m(x) for these two models, these non-parametric methods are; the local linear estimator (LLE) which require finding the smoo

There are many tools and S/W systems to generate finite state automata, FSA, due to its importance in modeling and simulation and its wide variety of applications. However, no appropriate tool that can generate finite state automata, FSA, for DNA motif template due to the huge size of the motif template. In addition to the optional paths in the motif structure which are represented by the gap. These reasons lead to the unavailability of the specifications of the automata to be generated. This absence of specifications makes the generating process very difficult. This paper presents a novel algorithm to construct FSAs for DNA motif templates. This research is the first research presents the problem of generating FSAs for DNA motif temp

Abstract 

     This research aims to analyze the reality of the production process in an assembly line Cars (RUNNA) in the public company for the automotive industry / Alexandria through the use of some Lean production tools, and data were collected through permanence in the company to identify the problems of the line in order to find appropriate to adopt some Lean production tools solutions, and results showed the presence of Lead time in some stations, which is reflected on the customer's waiting time to get the car, as well as some of the problems existing in the car produced such as high temperature of the car, as the company does not take into account customer preferences,

Oil recovery could be impacted by the relation between vertical permeability (K_v) and horizontal permeability (K_h) (K_v/K_h). 4816 plugs that have been getting hold of 18 wells of Mishrif formation in the West Qurna oilfield were used. K_v/K_h data provided some scatter, but the mean is ~1. K_v/K_h =1 was used for the Petrel model before upscaling according to the heterogeneity of each layer.

K_v/K_h values for Mishrif Formation in West Qurna Oilfield are 0.8 for relatively homogeneous, 0.4 for heterogeneous rock, and 0.1 for cap rocks (CRII).

   Eclipse ^TM was used for reservoir simulation. PVT and SCAL data e