Audio-visual detection and recognition system is thought to become the most promising methods for many applications includes surveillance, speech recognition, eavesdropping devices, intelligence operations, etc. In the recent field of human recognition, the majority of the research be- coming performed presently is focused on the reidentification of various body images taken by several cameras or its focuses on recognized audio-only. However, in some cases these traditional methods can- not be useful when used alone such as in indoor surveillance systems, that are installed close to the ceiling and capture images right from above in a downwards direction and in some cases people don't look straight the cameras or it cannot be added in some

Information from 54 Magnetic Resonance Imaging (MRI) brain tumor images (27 benign and 27 malignant) were collected and subjected to multilayer perceptron artificial neural network available on the well know software of IBM SPSS 17 (Statistical Package for the Social Sciences). After many attempts, automatic architecture was decided to be adopted in this research work. Thirteen shape and statistical characteristics of images were considered. The neural network revealed an 89.1 % of correct classification for the training sample and 100 % of correct classification for the test sample. The normalized importance of the considered characteristics showed that kurtosis accounted for 100 % which means that this variable has a substantial effect

    Predicting the network traffic of web pages is one of the areas that has increased focus in recent years. Modeling traffic helps find strategies for distributing network loads, identifying user behaviors and malicious traffic, and predicting future trends. Many statistical and intelligent methods have been studied to predict web traffic using time series of network traffic. In this paper, the use of machine learning algorithms to model Wikipedia traffic using Google's time series dataset is studied. Two data sets were used for time series, data generalization, building a set of machine learning models (XGboost, Logistic Regression, Linear Regression, and Random Forest), and comparing the performance of the models using (SMAPE) and

Speech recognition is a very important field that can be used in many applications such as controlling to protect area, banking, transaction over telephone network database access service, voice email, investigations, House controlling and management ... etc. Speech recognition systems can be used in two modes: to identify a particular person or to verify a person’s claimed identity. The family speaker recognition is a modern field in the speaker recognition. Many family speakers have similarity in the characteristics and hard to identify between them. Today, the scope of speech recognition is limited to speech collected from cooperative users in real world office environments and without adverse microphone or channel impairments.

The work in this paper involves the planning, design and implementation of a mobile learning system called Nahrain Mobile Learning System (NMLS). This system provides complete teaching resources, which can be accessed by the students, instructors and administrators through the mobile phones. It presents a viable alternative to Electronic learning. It focuses on the mobility and flexibility of the learning practice, and emphasizes the interaction between the learner and learning content. System users are categorized into three categories: administrators, instructors and students. Different learning activities can be carried out throughout the system, offering necessary communication tools to allow the users to communicate with each other

Nuclear medicine is important for both diagnosis and treatment. The most common treatment for diseases is radiation therapy used against cancer. The radiation intensity of the treatment is often less than its ability to cause damage, so radiation must be carefully controlled. The interactions of alpha particle with matter were studied and the stopping powers of alpha particle with ovary tissue were calculated using Beth-Bloch equation, Zeigler’s formula and SRIM Software also the range and Liner Energy Transfer (LET) and ovary thickness as well as dose and dose equivalent for this particle were calculated by using Matlab language for (0.01-200) MeV alpha energy.

This paper presents the application of a framework of fast and efficient compressive sampling based on the concept of random sampling of sparse Audio signal. It provides four important features. (i) It is universal with a variety of sparse signals. (ii) The number of measurements required for exact reconstruction is nearly optimal and much less then the sampling frequency and below the Nyquist frequency. (iii) It has very low complexity and fast computation. (iv) It is developed on the provable mathematical model from which we are able to quantify trade-offs among streaming capability, computation/memory requirement and quality of reconstruction of the audio signal. Compressed sensing CS is an attractive compression scheme due to its uni

Gas sensors based on titanium dioxide (TiO₂) and zinc oxide (ZnO) nanocomposites are considered energy-saving devices that are utilized to find dangerous or harmful gases in an environment. The performance of nitrogen dioxide (NO₂) gas sensors have been improved by spin-coating a TiO₂ and TiO₂:ZnO nanocomposite with varying concentrations (₉₀TiO₂:₁₀ZnO, ₇₀TiO₂:₃₀ZnO, and ₅₀TiO₂:₅₀ZnO). To correlate structural properties with gas-sensing behavior, structural and morphological characterization has been done using FESEM, XRD, and EDX.  Without any ZnO-specific crystalline phase, TiO₂

In this research, we studied the structural properties of SnO2 films nanostructure which prepared by chemical spray pyrolysis method at room temperature on the rules of glass heated (400oC) with rate of spraying (2.5 ml/ min). The effect of annealing temperaturs (450,500,550,600 and 650oC) for two hours on those properties has been indicated. The results of x-ray diffraction showed that all of the prepared films were polycrystalline with tetragonal type and orientation was (110) for all models before and after annealing, and the annealing led to an increase in the grain size. The full width at half maximum (FWHM) values of the (110) peaks of the films decreased from 1.492o to 1.064o with increasing annealing temperature .The surface morp

In this work, porous silicon gas sensor hs been fabricated on n-type crystalline silicon (c-Si) wafers of (100) orientation denoted by n-PS using electrochemical etching (ECE) process at etching time 10 min and etching current density 40 mA/cm2. Deposition of the catalyst (Cu) is done by immersing porous silicon (PS) layer in solution consists of 3ml from (Cu) chloride with 4ml (HF) and 12ml (ethanol) and 1 ml (H2O2). The structural, morphological and gas sensing behavior of porous silicon has been studied. The formation of nanostructured silicon is confirmed by using X-ray diffraction (XRD) measurement as well as it shows the formation of an oxide silicon layer due to chemical reaction. Atomic force microscope for PS illustrates that the p