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Enhanced EEG Signal Classification Using Machine Learning and Optimization Algorithm
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This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance while minimizing redundancy. This optimization process improves the performance of the classification model in general. In case of classification, the Support Vector Machine (SVM) and Neural Network (NN) hybrid model is presented. This combines an SVM classifier's capacity to manage functions in high dimensional space, as well as a neural network capacity to learn non-linearly with its feature (pattern learning). The model was trained and tested on an EEG dataset and performed a classification accuracy of 97%, indicating the robustness and efficacy of our method. The results indicate that this improved classifier is able to be used in brain–computer interface systems and neurologic evaluations. The combination of machine learning and optimization techniques has established this paradigm as a highly effective way to pursue further research in EEG signal processing for brain language recognition.

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Publication Date
Mon Oct 22 2018
Journal Name
Journal Of Economics And Administrative Sciences
Using simulation to compare between parametric and nonparametric transfer function model
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In this paper, The transfer function model in the time series was estimated using different methods, including parametric Represented by the method of the Conditional Likelihood Function, as well as the use of abilities nonparametric are in two methods  local linear regression and cubic smoothing spline method, This research aims to compare those capabilities with the nonlinear transfer function model by using the style of simulation and the study of two models as output variable and one model as input variable in addition t

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Publication Date
Wed Feb 29 2012
Journal Name
Al-khwarizmi Engineering Journal
Color Image Denoising Using Stationary Wavelet Transform and Adaptive Wiener Filter
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The denoising of a natural image corrupted by Gaussian noise is a problem in signal or image processing.  Much work has been done in the field of wavelet thresholding but most of it was focused on statistical modeling of wavelet coefficients and the optimal choice of thresholds.  This paper describes a new method for the suppression of noise in image by fusing the stationary wavelet denoising technique with adaptive wiener filter. The wiener filter is applied to the reconstructed image for the approximation coefficients only, while the thresholding technique is applied to the details coefficients of the transform, then get the final denoised image is obtained by combining the two results. The proposed method was applied by usin

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Publication Date
Wed Dec 01 2021
Journal Name
Baghdad Science Journal
Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks
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Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. This work aims to design a stego method that can effectively hide a message inside the images of the video file.  In this work, a video steganography model has been proposed through training a model to hiding video (or images) within another video using convolutional neural networks (CNN). By using a CNN in this approach, two main goals can be achieved for any steganographic methods which are, increasing security (hardness to observed and broken by used steganalysis program), this was achieved in this work as the weights and architecture are randomized. Thus,

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Publication Date
Fri Mar 12 2021
Journal Name
Sensors
A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments
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Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential a

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Publication Date
Fri Sep 16 2022
Journal Name
Sustainability
Intellectual Capital History and Trends: A Bibliometric Analysis Using Scopus Database
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This article aims to provide a bibliometric analysis of intellectual capital research published in the Scopus database from 1956 to 2020 to trace the development of scientific activities that can pave the way for future studies by shedding light on the gaps in the field. The analysis focuses on 638 intellectual capital-related papers published in the Scopus database over 60 years, drawing upon a bibliometric analysis using VOSviewer. This paper highlights the mainstream of the current research in the intellectual capital field, based on the Scopus database, by presenting a detailed bibliometric analysis of the trend and development of intellectual capital research in the past six decades, including journals, authors, countries, inst

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Publication Date
Wed Feb 01 2023
Journal Name
Baghdad Science Journal
Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption
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A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a

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Publication Date
Sun Mar 03 2024
Journal Name
Mesopotamian Journal Of Cybersecurity
Using Information Technology for Comprehensive Analysis and Prediction in Forensic Evidence
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With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Lev

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Publication Date
Wed Dec 12 2018
Journal Name
Iop Conference Series: Materials Science And Engineering
Preparation and characterization DLC thin films using atmospheric pressure plasma Jet
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Diamond-like carbon, amorphous hydrogenated films forms of carbon, were pretreated from cyclohexane (C6H12) liquid using plasma jet which operates with alternating voltage 7.5kv and frequency 28kHz. The plasma Separates molecules of cyclohexane and Transform it into carbon nanoparticles. The effect of argon flow rate (0.5, 1 and 1.5 L/min) on the optical and chemical bonding properties of the films were investigated. These films were characterized by UV-Visible spectrophotometer, X-ray diffractometer (XRD) Raman spectroscopy and scanning electron microscopy (SEM). The main absorption appears around 296, 299 and 309nm at the three flow rate of argon gas. The value of the optical energy gap is 3.37, 3.55 and 3.68 eV at a different flow rate o

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Publication Date
Mon Apr 01 2024
Journal Name
Wasit Journal Of Engineering Sciences
Analysis attackers’ methods with hashing secure password using CSPRNG and PBKDF2
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Using the Internet, nothing is secure and as we are in need of means of protecting our data, the use of passwords has become important in the electronic world. To ensure that there is no hacking and to protect the database that contains important information such as the ID card and banking information, the proposed system stores the username after hashing it using the 256 hash algorithm and strong passwords are saved to repel attackers using one of two methods: -The first method is to add a random salt to the password using the CSPRNG algorithm, then hash it using hash 256 and store it on the website. -The second method is to use the PBKDF2 algorithm, which salts the passwords and extends them (deriving the password) before being ha

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Publication Date
Tue Jun 20 2023
Journal Name
Baghdad Science Journal
Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network
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Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

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