In many oil fields only the BHC logs (borehole compensated sonic tool) are available to provide interval transit time (Δtp), the reciprocal of compressional wave velocity VP.

   To calculate the rock elastic or inelastic properties, to detect gas-bearing formations, the shear wave velocity VS is needed. Also VS is useful in fluid identification and matrix mineral identification.

   Because of the lack of wells with shear wave velocity data, so many empirical models have been developed to predict the shear wave velocity from compressional wave velocity. Some are mathematical models others used the multiple regression method and neural network technique.

   In this study a number of em

In this work , a hybrid scheme tor Arabic speech for the recognition

of  the speaker  verification  is presented  . The scheme is hybrid as utilizes the traditional digi tal signal processi ng and neural network . Kohonen neural  network has been used as a recognizer  tor speaker verification after extract spectral  features from an acoustic signal  by Fast Fourier Transformation Algorithm(FFT) .

The system was im plemented using a PENTIUM  processor , I000

MHZ compatible and MS-dos 6.2 .

A total of 243 serum samples  were tested  for the presence of

Chlamydia antibodies by ind irect immunofluorescent antibody test.Ninety

nine females were suffering from abortions, 64 were infertile and other 80 were  none  aborted  women.  The  incidence of  Ch lamydia  were  (15%,

9.4%)   and   (3.8%)   in  abortion,   infertile   and   non   aborted   group,

respecti vely.  The  results  also  showed  a difference  in  prevalence rate between the age groups. The  highest  incidence was found  in the age group  20-39 &

The present calculation covers the building shield during irradiation process and under water storage of three milion curries Cobalt-60 radiation source the calculation results in design requirement of 8m depth of water in the source stoeage pool

Nowadays, due to our everyday stress and current stressful lifestyle, the loss of items appears a frequent issue and may be very inconvenient. In this regard, until the IoT becomes part of everyday life, we can use the software as an efficient tool to assist a person's searching, verifying, and finding lost belongings. This paper presents an Android-based application that we proposed and implemented to help users find lost items. Utilizing this software will enable the subscriber to record his request to the relevant authority. In addition, a special section offers to insert a contact telephone number or email to communicate between the person who found the item and the person who lost it. During testing, among other services, the p

There are many methods of forecasting, and these methods take data only, analyze it, make a prediction by analyzing, neglect the prior information side and do not considering the fluctuations that occur overtime. The best way to forecast oil prices that takes the fluctuations that occur overtime and is updated by entering prior information is the Bayesian structural time series (BSTS) method. Oil prices fluctuations have an important role in economic so predictions of future oil prices that are crucial for many countries whose economies depend mainly on oil, such as Iraq. Oil prices directly affect the health of the economy. Thus, it is necessary to forecast future oil price with models adapted for emerging events. In this article, we st

Activity recognition (AR) is a new interesting and challenging research area with many applications (e.g. healthcare, security, and event detection). Basically, activity recognition (e.g. identifying user’s physical activity) is more likely to be considered as a classification problem. In this paper, a combination of 7 classification methods is employed and experimented on accelerometer data collected via smartphones, and compared for best performance. The dataset is collected from 59 individuals who performed 6 different activities (i.e. walk, jog, sit, stand, upstairs, and downstairs). The total number of dataset instances is 5418 with 46 labeled features. The results show that the proposed method of ensemble boost-based classif

Researchers are increasingly using multimodal biometrics to strengthen the security of biometric applications. In this study, a strong multimodal human identification model was developed to address the growing problem of spoofing attacks in biometric security systems. Through the use of metaheuristic optimization methods, such as the Genetic Algorithm(GA), Ant Colony Optimization(ACO), and Particle Swarm Optimization (PSO) for feature selection, this unique model incorporates three biometric modalities: face, iris, and fingerprint. Image pre-processing, feature extraction, critical image feature selection, and multibiometric recognition are the four main steps in the workflow of the system. To determine its performance, the model wa