Iraqi Journal of Information & Communications Technology 2019-07-21T17:52:40+00:00 Prof. Dr. Mohammed Z. Al-Faiz Open Journal Systems <p>The Iraqi Journal of Information &amp; Communication Technology&nbsp;(IJICT) is a specialized, referred, and indexed journal published by the college of Information Engineering at Al–Nahrain University, Baghdad, Iraq. IJICT invites contributions from researchers, scientists and practitioners from all over the world.</p> The effect of Z-Score standardization (normalization) on binary input due the speed of learning in back-propagation neural network 2019-07-21T17:52:36+00:00 Mohammed Z. Al-Faiz Ali A. Ibrahim Sarmad M. Hadi <p style="text-align: justify;">The speed of learning in neural network environment is considered as the most effective parameter spatially in large data sets. This paper tries to minimize the time required for the neural network to fully understand and learn about the data by standardize input data. The paper showed that the Z-Score standardization of input data significantly decreased the number of epoochs required for the network to learn. This paper also proved that the binary dataset is a serious limitation for the convergence of neural network, so the standardization is a must in such case where the 0’s inputs simply neglect the connections in the neural network. The data set used in this paper are features extracted from gel electrophoresis images and that open the door for using artificial intelligence in such areas.</p> 2019-02-01T00:00:00+00:00 ##submission.copyrightStatement## FPAA Implementation of Chaotic Modulation Based on Nahrain Map 2019-07-21T17:52:40+00:00 Hamsa A. Abdullah Hikmat N. Abdullah <p style="text-align: justify;">Due to characteristic of chaotic systems in terms of nonlinearity, sensitivity to initial values, and non-periodicity, they are used in many applications like security and multiuser transmission. Nahrain chaotic map is an example of such systems that are recently proposed with excellent features for the use in multimedia security applications. Although the implementation of chaotic systems is easy using low costanalogue ICs, this approach does not provide the flexibility that the reconfigurable analogue devices have in design possibilities such as reducing the complexity of design, real-time modification, software control and adjustment within the system. This paper presents a description of data modulation and demodulation based on Nahrain chaotic system and there hardware implementation using field programmable analogue array (FPAA) device. AN231E04 dpASP board is used as a target device for the implementation. The simulation results of system closely matched the programmable hardware testing results.</p> 2019-02-01T00:00:00+00:00 ##submission.copyrightStatement## Analysis and Implementation of Brain Waves Feature Extraction and Classification to Control Robotic Hand 2019-07-21T17:52:38+00:00 Mohammed Z. Al-Faiz Ammar A. Al-hamadani <p style="text-align: justify;">In this paper, (i) time domain, frequency domain and spatial domain feature extraction methods were investigated. (ii) Two dimensionality reduction methods were proposed, implemented and compared. (iii) The method pair (feature extraction + dimensionality reduction) that owns the lowest classification error rate will be used to learn a machine learning algorithm to control robotic hand in offline mode. Two classes EEG dataset of three bipolar channels was used. The extracted feature vectors were fed into Support Vector Machine with Radial Basis Function kernel (SVM-RBF) to train the classifier. The experimented time domain feature extraction methods were: Mean Absolute Value (MAV), integrated Absolute Value (IAV), Zero Crossing (ZC), Root Mean Square (RMS), Waveform Length (WL) and Slope Sign Change (SSC). Frequency domain feature was the Autoregressive Feature (AR). Finally, the spatial domain feature was the Common Spatial Patterns (CSP). Matlab codes for Principal Component Analysis (PCA) and channel selection algorithm were designed and used to reduce the dimensionality of the features vector. Results showed that CSP features got the lowest error rate for both dimensionality reduction technique with 2.14%. Results recommends to use channel selection algorithm over PCA since it owns the lowest processing time of 8.2s over 8.5s for PCA.</p> 2019-02-01T00:00:00+00:00 ##submission.copyrightStatement## Finger Texture Verification Systems Based on multiple spectrum Lighting Sensors with Four Fusion Levels 2019-07-21T17:52:39+00:00 Musab T. Al-Kaltakchi Raid R. Omar Hikmat N. Abdullah Tingting Han Jonathon A. Chambers <p style="text-align: justify;">Finger Texture (FT) is one of the most recent attractive biometric characteristic. Itrefers to a finger skin area which is restricted between the fingerprint and the palm print (just after including the lower knuckle). Different specifications for the FT can be obtained by employing multiple images spectrum of lights. This inspired the insight of applying a combination between the FT features that acquired by utilizing two various spectrum lightings in order to attain high personal recognitions. Four types of fusion will be listed and explained here: Sensor Level Fusion (SLF), Feature Level Fusion (FLF), Score Level Fusion (ScLF) and Decision Level Fusion (DLF). Each fusion method is employed and examined for an FT verification system. From the database of Multiple Spectrum CASIA (MSCASIA), FT images have been collected. Two types of spectrum lights have been exploited (the wavelength of 460 nm, which represents a Blue (BLU) light, and the White (WHT) light). Supporting comparisons were performed, including the state-of-the-art. Best recognition performance were recorded for the FLF based concatenation rule by improving the Equal Error Rate (EER) percentages from 5% for the BLU and 7% for the WHT to 2%.</p> 2019-02-01T00:00:00+00:00 ##submission.copyrightStatement## Evaluation of DDoS attacks Detection in a New Intrusion Dataset Based on Classification Algorithms 2019-07-21T17:52:37+00:00 Amer A. Abdulrahman Mahmood K. Ibrahem <p style="text-align: justify;">Intrusion detection system is an imperative role in increasing security and decreasing the harm of the computer security system and information system when using of network. It observes different events in a network or system to decide occurring an intrusion or not and it is used to make strategic decision, security purposes and analyzing directions. This paper describes host based intrusion detection system architecture for DDoS attack, which intelligently detects the intrusion periodically and dynamically by evaluating the intruder group respective to the present node with its neighbors. We analyze a dependable dataset named CICIDS 2017 that contains benign and DDoS attack network flows, which meets certifiable criteria and is openly accessible. It evaluates the performance of a complete arrangement of machine learning algorithms and network traffic features to indicate the best features for detecting the assured attack classes. Our goal is storing the address of destination IP that is utilized to detect an intruder by method of misuse detection.</p> 2019-02-01T00:00:00+00:00 ##submission.copyrightStatement##