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

   Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be

In this research Artificial Neural Network (ANN) technique was applied to study the filtration process in water treatment. Eight models have been developed and tested using data from a pilot filtration plant, working under different process design criteria; influent turbidity, bed depth, grain size, filtration rate and running time (length of the filtration run), recording effluent turbidity and head losses. The ANN models were constructed for the prediction of different performance criteria in the filtration process: effluent turbidity, head losses and running time. The results indicate that it is quite possible to use artificial neural networks in predicting effluent turbidity, head losses and running time in the filtration process, wi

The research addresses smart city concept as it is the latest urban design trends, by the investment of the capabilities of human, and artificial intelligence for the sake of the advancement of the city. The concept of a smart city is described as one of the most important manifestations of the information revolution, with the end of the twentieth, and the beginning of twenty – first century, The research attributes the emergence of the concept to: deficiencies of means, and traditional methods in building and development of cities, as well as The significant increase in the number of city and global metropolises dwellers. So, smart city approach has been adopted, along with innovative principles and methods which cons

The traffic congestion caused by the increase in the number of vehicles in the cities as a result of the increase in the population and the density of construction requires the provision of appropriate infrastructure and the provision of transport systems and logistics services that meet the needs of the population to meet the many challenges now and in the future by introducing various modes of transport , In accordance with integrated plans such as the use of (pedestrian friendly environments, bicycles and their own paths, light rail, metro, express bus, as well as public transport buses and others), through the development of Projects High-level roads, such as the annual and major roads, etc., and integrated with the urban planning of

Loud noise can be extremely harmful to the auditory system as well as to human health. Noise pollution is primarily caused by traffic noise. The study's goal was to determine how various vehicle types and speeds affected the amount of noise generated by traffic. The two factors were investigated at seven different arterial streets throughout Kirkuk city to measure the noise levels. The measurements were performed during peak hours to compare the result with WHO standards for noise specification. Traffic volume and vehicle speed are shown to be the key elements that determine an increase in noise level. 

Background. Material tribology has widely expanded in scope and depth and is extended from the mechanical field to the biomedical field. The present study aimed to characterize the nanocoating of highly pure (99.9%) niobium (Nb), tantalum (Ta), and vanadium (V) deposited on 316L stainless steel (SS) substrates which considered the most widely used alloys in the manufacturing of SS orthodontic components. To date, the coating of SS orthodontic archwires with Nb, Ta, and V using a plasma sputtering method has never been reported. Nanodeposition was performed using a DC plasma sputtering system with three different sputtering times (1, 2, and 3 hours). Results. Structural and elemental analyses were conducted on the deposited coating