The design of future will still be the most confusing and puzzling issue and misgivings that arouse worry and leading to the spirit of adventures to make progress and arrive at the ways of reviving, creativity and modernism. The idea of prevailing of a certain culture or certain product in design depends on the given and available techniques, due to the fact that the computer and their artistic techniques become very important and vital to reinforce the image in the design. Thus, it is very necessary to link between these techniques and suitable way to reform the mentality by which the design will be reformed, from what has been said, (there has no utilization for the whole modern and available graphic techniques in the design proce

Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions. The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication. For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier. In

Cognitive radios have the potential to greatly improve spectral efficiency in wireless networks. Cognitive radios are considered lower priority or secondary users of spectrum allocated to a primary user. Their fundamental requirement is to avoid interference to potential primary users in their vicinity. Spectrum sensing has been identified as a key enabling functionality to ensure that cognitive radios would not interfere with primary users, by reliably detecting primary user signals. In addition, reliable sensing creates spectrum opportunities for capacity increase of cognitive networks. One of the key challenges in spectrum sensing is the robust detection of primary signals in highly negative signal-to-noise regimes (SNR).In this paper ,

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

The fingerprinting DNA method which depends on the unique pattern in this study was employed to detect the hydatid cyst of Echinococcus granulosus and to determine the genetic variation among their strains in different intermediate hosts (cows and sheep).  The unique pattern represents the number of amplified bands and their molecular weights with specialized sequences to one sample which different from the other samples.   Five hydatitd cysts samples  from cows and sheep were  collected, genetic analysis for  isolated DNA was done using PCR technique and  Random Amplified Polymorphic DNA reaction(RAPD) depending on (4) random primers, and the results showed:      

The present study is concerned with the role of income tax in implementing economic goals in Iraq and treating the problems and pitfalls in the Iraq economy.

The study also aims at investigating the role of income tax in attracting promising favorite effects into economy.

The study was performed on data covering the period (2003 - 2012) with respect to the variables of (income tax, oil profits) as independent variables and (private consuming expenditure, private investmental expenditure, and standard figure of prices) as dependent variables. To analyze these data, a number of statistical descriptive and analytical techniques were used such as (percentage, standard variance, mediums, F test, T test and SPSS). It has been c

This paper presents an enhancement technique for tracking and regulating the blood glucose level for diabetic patients using an intelligent auto-tuning Proportional-Integral-Derivative PID controller. The proposed controller aims to generate the best insulin control action responsible for regulating the blood glucose level precisely, accurately, and quickly. The tuning control algorithm used the Dolphin Echolocation Optimization (DEO) algorithm for obtaining the near-optimal PID controller parameters with a proposed time domain specification performance index. The MATLAB simulation results for three different patients showed that the effectiveness and the robustness of the proposed control algorithm in terms of fast gene

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor