Iraqi Journal of Information and communication technology

Finger Texture (FT) is one of the most recent attractive biometric characteristic. It refers 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. Individual verification systems are established in this paper by using multiple spectrum FT specifications. The key idea here is that by combining two various spectrum lightings of FTs, high personal recognitions can be attained. 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, examined for different rules and analysed. Then, the best performance procedure is benchmarked to be considered. 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 was recorded for the FLF based concatenation rule by improving the Equal Error Rate (EER) percentages from 5% for the BLU and7% for the WHT to 2%

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 cost analogue 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 dynamical programmable Analog Signal Processor (dpASP) board is used as a target device for the implementation. The simulation results of system closely matched the programmable hardware testing results

In this paper, feature extraction methods such as time domain, frequency domain and spatial domain were investigated. Where Mean Absolute Value (MAV), Integrated Absolute Value (IAV), Zero Crossing (ZC), Root Mean Square (RMS), Waveform Length (WL) and Slope Sign Change (SSC) are the used time domain features. Autoregressive Feature (AR) is the frequency domain feature and the spatial domain feature is the Common Spatial Patterns (CSP). Channel selection algorithm was proposed for dimensionality reduction using Matlab code. Results of the above algorithm were compared with Matlab library of Principle Component Analysis (PCA). The extracted feature vectors were fed into Support Vector Machine with Radial Basis Function kernel (SVM-RBF) to train the classifier. The pair of algorithms (feature extraction plus dimensionality reduction) that owned the lowest classification error rate were used to control a Humanoid Robotic Hand (HRH) in offline mode. EEG dataset of two classes and three bipolar channels was used. Results showed that CSP features achieved the lowest classification error rate for both dimensionality reduction techniques with 2.14%. Results recommends to use (CSP plus channel selection algorithm) over (PCA plus PCA) since the former owned lowest classification processing time of 8.2s over8.5s for the later.

The speed of learning in neural network environment is considered as an effective parameter 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 epochs 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

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