Computer Engineering Commons^™

The Significance Of Cannibalism, Panic, And Sanctuary In The Interactions Between Prey And Predator With A Stage Structure, Ahmed Sami Abdulghafour, Raid Kamel Naji

Iraqi Journal for Computer Science and Mathematics

This paper analyzes a novel prey-predator model that takes into account the predator's stage structure, cannibalism within the predator population, panicky behavior, and the existence of a sanctuary where the prey might hide from the predator. The Holling type II functional response is used in the predation process. The behavior of the identified fixed points of the proposed system has been closely analyzed. The analysis focuses on the local stability and potential bifurcations that could happen close to the system's fixed points. The Lyapunov function approach is used to investigate the fixed-point stability zone globally. Numerical simulations were run to …

Juegos De Rol En El Desarrollo De Projectpipe: Una Inmersión En El Uso De Metodologías De Diseño, Julieth A. Gómez Hernández, Cristian C. Orozco Ospina, Nicolas Restrepo Henao

Journal of Roleplaying Studies and STEAM

En el entorno digital actual, la evolución del concepto de juegos de rol ha llevado al desarrollo de aplicaciones web interactivas y educativas. En sintonía con este planteamiento, se presenta la siguiente propuesta, que adopta una metodología integrada para aprovechar la versatilidad del método de Bruno Munari y el Diseño Centrado en el Usuario (DCU). La metodología de Munari descompone la gestión macro del proyecto en pasos manejables; mientras que el DCU se enfoca en la creación de una interfaz intuitiva y funcional mediante prototipos iterativos y pruebas continuas de usabilidad. Como complemento a estas estrategias se integra la metodología …

Benchmarking Pretrained Models For Speech Emotion Recognition: A Focus On Xception, Ahmed Hassan, Tehroom Masood, Hassan A. Ahmed, H. M. Shahzad, Hafiz Muhammad T. Khushi

Business Faculty Publications

Speech emotion recognition (SER) is an emerging technology that utilizes speech sounds to identify a speaker’s emotional state. Computational intelligence is receiving increasing attention from academics, health, and social media applications. This research was conducted to identify emotional states in verbal communication. We applied a publicly available dataset called RAVDEES. The data augmentation process involved adding noise, applying time stretching, shifting, and pitch, and extracting the features zero cross rate (ZCR), chroma shift, Mel-Frequency Cepstral Coefficients (MFCC), and a spectrogram. In addition, we used many pretrained deep learning models, such as VGG16, ResNet50, Xception, InceptionV3, and DenseNet121. Out of all …

Secure Blind Medical Image Watermarking Using Hybrid Feature Extraction Techniques, Sawsan D. Mahmood, Yassine Aribi, Fadoua Drira, Adel M. Alimi

Iraqi Journal for Computer Science and Mathematics

Watermarking offers great potential for medical images by embedding identifiable information that ensures secure and authenticated sharing of patient data while maintaining both integrity and diagnostic quality. In this paper, we present an innovative framework for blind medical image watermarking that harnesses advanced feature extraction techniques, including K-Means clustering, BRISK (Binary Robust Invariant Scalable Key-points), GFTT (Good Features to Track), and chaotic systems algorithms.

We conducted extensive experiments on the Ocular Disease Intelligent Recognition (ODIR) dataset, focusing specifically on Retinal Optical Coherence Tomography (OCT) images. The results highlight the framework's ability to preserve image quality and diagnostic utility, with minimal …

Identifying Redundant Audio Content Over Cloud Environment Using Deduplication Techniques, Venkatesh K

Theses and Dissertations

Cloud computing has become an integral part of modern internet-based services, with users relying heavily on cloud environments as primary storage solutions. However, the exponential growth in data volume presents a challenge (i.e) the proliferation of duplicated content within cloud repositories. Deduplication techniques provide a promising approach to mitigate this issue. This research focuses on detecting redundant audio content within a cloud environment, specifically targeting the sharing of extensive audio files, such as those in Waveform Audio File Format (WAV). The study proposes the Refined Super Subset Identification Algorithm (RSSIA) to efficiently identify redundant content and segments within existing audio …

Data Security In The Apple Ecosystem: An Evaluation, Kayrene Woods

Cybersecurity Undergraduate Research Showcase

This study provides a comprehensive evaluation of data security within the Apple ecosystem, focusing on the company’s privacy policies, user perceptions, and the effectiveness of its App Store review processes. Employing an interdisciplinary methodology, the research examines Apple’s commitment to data protection, emphasizing transparency and user trust. A survey of user experiences revealed varying levels of engagement and understanding of Apple’s privacy practices, with only 32.8% of respondents having read the Privacy Policy and mixed opinions on its clarity. Additionally, concerns persist about third-party app security, with 39.7% of users expressing apprehension and skepticism about Apple’s App Store review process. …

Convolutional Neural Networks For Dementia Severity Classification: Ordinal Versus Regular Methods, Ambresh Bhadrashetty, P. Sandhya

Iraqi Journal for Computer Science and Mathematics

Dementia, a chronic neurodegenerative disorder, progressively impairs cognitive functions such as memory, reasoning, learning, and recall, placing a significant burden on patients and healthcare systems. Early and accurate classification of dementia severity is crucial for personalized care and intervention. This study introduces a novel Convolutional Neural Network (CNN) designed to classify dementia into four ordinal severity levels (None, Very Mild, Mild, and Moderate) based on MRI brain scans. Utilizing the extensive Open Access Series of Imaging Studies (OASIS) dataset, which includes 86,437 MRI scans (67,222 ‘none,’ 13,725 ‘very mild,’ 5,002 ‘mild,’ and 488 ‘moderate’), our model addresses severe class imbalance …

New Three-Parameter Exponentiated Benini Distribution: Properties And Applications, Elif Yıldırım, Gamze Özel, Christophe Chesneaul, Farrukh Jamal, Ahmed M. Gemeay

Iraqi Journal for Computer Science and Mathematics

Modeling of some rarely occurring environmental events, obtaining and analyzing accurate predictions is quite important in determining the nature of such events and taking measures accordingly. Modeling these rarely occurring environmental events with the statistical distributions in the literature may cause some problems. For this reason, different statistical distributions are needed for modeling this type of rare data. In this paper, a new three-parameter exponentiated Benini distribution based on the exponential family is proposed as a new lifetime distribution. It is called the three-parameter exponentiated Benini distribution. Although new distributions are derived by different methods in the literature, there is …

Investigating Intrusion Detection System Using Federated Learning For Iot Security Challenges, Mohammed Q. Mohammed, Zena Abd Alrahman, Aouf R. Shehab

Iraqi Journal for Computer Science and Mathematics

The Internet of Things (IoT) is a decentralized and ever-changing network, which poses challenges in terms of security. The input highlights the need for robust security measures to protect IoT devices and their data from potential threats. The study focuses on Federated Learning (FL) technology as a potential solution to enhance IoT security. FL models are designed to protect sensitive data while allowing its exchange with other systems, making it a promising approach for securing IoT environments. Additionally, the input suggests the implementation of intrusion detection systems (IDS) as an additional strategy to enhance overall IoT security. By combining FL …

Explainable Machine Learning Approach Enables Computer-Aided Identification System For Children Autism Spectrum Disorder (C-Asd), Karrar Hameed Abdulkareem, Zainab Hussein Arif, Mazin Abed Mohammed

Iraqi Journal for Computer Science and Mathematics

Neurodevelopmental disorders like autism spectrum disorder (ASD) cause significant cognitive, linguistic, object identification, communication, and social skills deficits. Although there is currently no cure for autism spectrum disorder (ASD), early detection can aid in diagnosis and implementing effective preventative measures. Artificial intelligence (AI) tools allow for an earlier diagnosis of ASD than was previously possible. Furthermore, many clinical and not clinical attributes can be used for identification of ASD but select the most proper ones still challenge. Therefore, in this study we propose a Computer-Aided Identification System based on machine learning concept and feature selection methods to diagnosis Children Autism …

Stable Heterogeneous Traffic Flow With Effective Localization And Path Planning In Wireless Network Connected And Automated Vehicles In~Internet Of Vehicular Things (Iovt), Ahmed N. Rashid, Ahmed Mahdi Jubair

Iraqi Journal for Computer Science and Mathematics

The increasing complexity of networks comprising both Connected Autonomous Vehicles (CAVs) and Human-Driven Vehicles (HDVs) presents substantial challenges in achieving accurate positioning, efficient communication, and optimal route planning. Current methodologies fall short in enhancing vehicular network efficiency and reliability due to noise interference, inefficient data transmission, and unstable data transfer. This study aims to improve localization accuracy, reduce communication noise, and enhance path planning efficiency in mixed CAV and HDV environments through the Stable Heterogeneous Traffic Flow using Deep Reinforcement Learning and Effective Path Planning (SHTDR-EPP) approach. The primary goals are to ensure dependable localization, efficient communication, and reliable route …

A Comprehensive Analysis Of Partition Dimensions In Efavirenz Abacavir Lamivudine Doravirine Of Anti-Hiv Drug Structures, R. Nithya Raj, R. Sundara Rajan, Hijaz Ahmad

Iraqi Journal for Computer Science and Mathematics

The partition dimension of a graph in chemical graph theory refers to a graph invariant used to analyze the structural properties of molecules. It represents the minimum number of clusters or resolving partition set required to uniquely identify each vertex in the graph based on the neighborhoods within their respective clusters. In the context of chemical graph theory, the vertices of the graph correspond to atoms, and edges represent bonds between these atoms in a molecular structure. Determining the partition dimension of a chemical graph helps in understanding the relationships between molecular components and their spatial arrangements. It assists in …

Embedded Schemes Of The Runge-Kutta Type For The Direct Solution Of Fourth-Order Ordinary Differential Equations, F. A. Fawzi, Nizam G. Ghawadri

Iraqi Journal for Computer Science and Mathematics

This paper introduces an innovative approach for solving fourth-order ordinary differential equations (ODEs) of the form. We present the embedded Runge-Kutta (RK) Direct Explicit (ERKDGF) method, a family of embedded direct explicit RK type methods tailored specifically for this purpose. Through meticulous application of Taylor expansion, we have derived algebraic equations with order conditions up to the sixth order, ensuring the accuracy and reliability of our proposed integrator. We have developed two key variants within this method, namely RKDF5(4) and ERKDGF5(4), with orders five and four, respectively. Our approach is strategically designed, with the higher-order method ensuring exceptional accuracy, and …

Robust-Fragile Watermarking Using Integer Wavelet Transform For Tampered Detection And Copyright Protection, Hendra Budi, Ferda Ernawan, Agit Amrullah

Iraqi Journal for Computer Science and Mathematics

The use of the internet and advanced technologies enables the distribution of information and data through diverse digital images. Nevertheless, this ease of use comes with the potential risk of data misappropriation, encompassing unauthorized alterations, duplications, and reproductions of digital images. Ongoing research is being conducted in the field of watermarking to enhance the capabilities of protecting digital images. The objective of this work is to enhance the strength of copyright protection and the vulnerability of watermarking for authentication in digital images by utilizing the Integer Wavelet Transform (IWT). The watermarking approach involves embedding a durable watermark in the red …

Modelling Security Factors Influencing E-Wallet Adoption In Malaysia, Adi Badiozaman Ruhani, Nur Suhaili Mansor, Hapini Awang, Mohamad Fadli Zolkipli, Khuzairi Mohd Zaini, Abderrahmane Benlahcene

Iraqi Journal for Computer Science and Mathematics

This study aims to develop an effective framework that addresses the security concerns and user behavior related to e-wallet adoption. The research methodology entails quantitative data collection through a literature review and surveys of e-wallet users using convenience sampling, and the proposed model is tested using the Partial Least Squares Structural Equation Modelling (PLS-SEM). The proposed security factors in this study include phone stolen protection, app security performance, secure authentication, data privacy protection, secure online transaction and banking info security. The online survey form was disseminated to Malaysian citizens, and 186 respondents participated in the survey. Using a two-step approach, …

Robust Image Watermarking Based On Schur Decomposition, Ahmad Toha, Ferda Ernawan, Agit Amrullah

Iraqi Journal for Computer Science and Mathematics

The advanced of internet technology allows unauthorized people to distribute multimedia data. Copyright protection for digital images is needed to protect the intellectual properties of the digital image. This study presents a colour image watermarking using schur decomposition for protecting the digital copyright. This study investigates the significant contribution of the orthogonal U of schur decomposition with the size of 8´8 pixels. The proposed scheme embeds a watermark on the (U₂, U₆) of the U matrix to achieve high invisibly and robustness of the embedded watermark. The relationship of each coefficient on the U matrix …

Image Watermarking Using Firefly Algorithm–Iwt-Svd For Copyright Protection, Anjahul Khuluq, Ferda Ernawan, Agit Amrullah, Mohd Arfian Ismail

Iraqi Journal for Computer Science and Mathematics

Image watermarking is a technique used to ensure the legitimacy of ownership by safeguarding images. This research presented the Firefly algorithm–IWT-SVD to enhance resistance and robustness performance against different type of attacks. The cover image is split into blocks of 4×4 pixels, and each block is then computed by IWT-SVD. The Firefly algorithm is used to determine the appropriate scaling factor to incorporate the watermark using a predefined set of principles. The watermarked images have been evaluated under various attacks such as noise addition, filtered image, compressed image and scaled image. The experimental results demonstrate exceptional imperceptibility, with an average …

Robust Image Watermarking Based On Iwt-Dct-Svd For Copyright Protection, Syafiqul Shubuh, Ferda Ernawan, Agit Amrullah, Prajanto Wahyu

Iraqi Journal for Computer Science and Mathematics

The rapid advancement of technology has resulted in many intellectual works, in the form of images requiring copyright protection to prevent unauthorized use by irresponsible parties. This research examines the hybrid watermarking method that utilises IWT-DCT-SVD domain to enhance the durability of the embedded watermark. The host image is used as the input for the R-Level process, if the watermark logo is small, then the cover image is computed by 3-level of IWT. Whereas, the watermark logo has a large size, the cover image is then computed by 1-level of IWT. The watermark logo is embedded into the singular value …

Automated Fake News Detection System, Saja A. Al-Obaidi, Tuba Çağlıkantar

Iraqi Journal for Computer Science and Mathematics

Online news has been the majority of people’s information source in recent decades. However, a lot of the information that is accessible online is fake and sometimes even designed to mislead. It might be difficult for individuals to distinguish between certain false newspaper items and the real ones since they are so similar. Deep learning (DL) and machine learning (ML) models, among other automated false news detection (FND) techniques, are quickly becoming essential. A comparative study was conducted to analyze the performance of five prominent deep learning models across four distinct datasets, namely ISOT, FakeNewsNet, Dataset1, and Dataset2. Results indicated …

Posinormality Of Operators Treated By Weyl's Theorem On Unbounded Hilbert Space, Abbas G. Rajij, Dalia S. Ali, Huseyin Cakalli, Nadia M. G. Al-Saidi

Iraqi Journal for Computer Science and Mathematics

Hamiltonians, momentum operators, and other quantum-mechanical perceptible take the form of self-adjoint operators when understood in quantized physical schemes. Unbounded and self-adjoint recognition are required in the situation of positive measurements. The selection of the proper Hilbert space(s) and the selection of the self-adjoint extension must be made in order for this to operate. In this effort, we define a new extension positive measure depending on the measurable field of nonzero positive self-adjoint operator in unbounded Hilbert space of analytic functions of complex variables. Consequently, we define an extension norm in the same space. We show several new properties of …