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 the suggested
operator and its adjoin operator. These properties include the posinormality, inclusion property, isolating property and
achieving the Weyl’s and Browder’s theorems.

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 that while LSTM achieved the highest
accuracy on the ISOT dataset (99.95%), CNN-GRU stood out with an exceptional 99.97% accuracy on the FakeNewsNet
dataset. Both CNN-LSTM and LSTM exhibited almost perfect accuracies on Dataset1. On Dataset2, LSTM led with 98.64%.
However, LSTM AutoEncoder consistently demonstrated lower efficiency, with accuracies spanning between 49% and
62%. The study underscores the critical role of dataset-specific model selection in optimizing deep learning outcomes.

The rapid advancement of technology has resulted in many intellectual works, in the form of images requiring copy-
right 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 of IWT-DCT-SVD for copyright protection. The proposed technique is further assessed
and verified by using various attack types. The test findings indicate that the proposed watermarking technology exhibits
excellent invisibility and robustness performance, surpassing prior studies. The proposed scheme achieved a high SSIM
value of 0.99943, good robustness for NC value from various attacks. These methods are anticipated to provide an
alternative answer to watermarking techniques for enhancing the robustness of embedded digital watermarks.

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
PSNR value of 39.8489 dB and a SSIM value of 0.9993. The proposed scheme achieved a strong robustness performance,
with an average NC value of 0.92.

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 of Schur decomposition is also investigated. The experimental results
demonstrate that the proposed scheme achieved high invisibility with the SSIM value of about 0.9951. The proposed
scheme was also evaluated by several image processing attacks such as added noise, resized image, filtered image, and
compressed image. The proposed scheme obtained a high level of resilience with the NC value of 0.996.

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, this study employed a measurement model to assess indicator loadings, convergent validity,
and reliability. Additionally, a structural model was utilized for path analysis. This study selects the measurement or
dependent variable for e-wallet adoption as "usage behavior." The findings indicate a significant positive relationship
between e-wallet adoption behavior and online trans-action security. The inclusion of secure online transactions in
digital wallets reassures users, fostering confidence and promoting engagement in secure online transactions, thereby
bolstering the efficacy of e-wallet services, especially for critical financial activities. The significance of the research
project lies in its potential to overcome the barriers hindering e-wallet adoption by addressing the security concerns of
potential users. By enhancing security measures and increasing user trust, the adoption of e-wallets can be accelerated.
Through an in-depth analysis of these factors, the research may provide recommendations and contribute to the country’s
overall development an

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
layer and a delicate watermark in the blue layer of the host image. The watermark is incorporated into the singular
value of the Singular Value Decomposition (SVD). The red layers with watermarks are generated using a Hash function
and subsequently integrated into the blue layer using the Least Significant Bit (LSB) method. The experimental findings
demonstrate that the proposed strategy effectively enhances both imperceptibility and robustness in comparison to the
previous benchmark research. The suggested scheme attained an average PSNR value of 51,935 and an average SSIM
value of 0.976. The proposed scheme demonstrated an average improvement of 14.19% in PSNR compared to existing
schemes. This study offers efficient and suitable remedies for safeguarding digital images against unauthorised and
alteration without consent.

This paper introduces an innovative approach for solving fourth-order ordinary differential equations (ODEs) of the
form μ(iν ) = f (x, μ, μ′ ) and μ(iν) = f (x, μ, μ′, μ′′ ). 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 the lower-order counterpart providing optimal error
estimates. To facilitate practical implementation, we have devised a variable step-size code based on these methods,
which was applied to solve a set of fourth-order problems. Our method’s performance was rigorously assessed through
numerical experiments, with comparisons to existing embedded RK pairs that necessitate problem reduction into a
system of first-order ODEs. The results unequivocally demonstrate the efficiency of our ERKDGF method, both in terms
of accuracy and the number of function evaluations required. This research marks a significant advancement in the field,
offering a robust and efficient solution for directly solving fourth-order ordinary differential equations.

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 the analysis of molecular conformations,
structure-activity relationships, and drug design by identifying the smallest number of distinct clusters or resolving
partition sets necessary to uniquely dene the local environment of each atom in the molecule. The partition dimension
is a valuable metric in computational chemistry, o
ering insights into molecular complexity, aiding in the prediction
of molecular properties, and facilitating the discovery of new drug candidates with specic structural characteristics. In
this work, we have calculated the partition dimension of certain ANTI-HIV drug molecular structures.

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
planning, thereby maintaining stable and efficient vehicle operations. The SHTDR-EPP approach integrates advanced
techniques to analyze heterogeneous traffic flow. Firstly, effective localization is achieved among CAVs. Secondly,
a deep reinforcement learning model is developed using the Markov Decision Process to manage mixed traffic flow
efficiently. Thirdly, effective path planning is conducted using Extended Direction Analysis. These methods collectively
ensure efficient communication between CAVs and HDVs. Experiments were conducted using NS3 and SUMO, with
key parameters evaluated including vehicle delay, energy consumption, and safety metrics. The proposed SHTDR-EPP
approach significantly enhances vehicular network performance. Positional accuracy is improved through effective
localization techniques. Communication efficiency is increased by employing DRL to manage noise and stability. Path
planning is optimized through EDA, ensuring efficient and reliable data transmission routes. Comparative analysis with
prior methods such as OPP-CAVs and ROC-CAVs demonstrates that SHTDR-EPP achieves superior energy efficiency and
vehicle safety. The SHTDR-EPP model effectively addresses critical issues of localization, communication noise, and path
planning in mixed vehicular networks. By leveraging modern techniques, the proposed approach significantly enhances
the overall efficiency, stability, and reliability of CAV and HDV interactions.

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 Spectrum Disorder (C-ASD) cases. Two main feature selection methods namely Gain Ratio
(GR) and Chi-squared (χ 2) that used to rank then select best subset C-ASD attributes. In order to identify ASD cases, four
machine learning algorithms are used into proposed system for identification purpose namely, Neural Network (NN),
Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). A dataset contained 1045 cases
with 18 attributes employed for feature selection and C-ASD identification processes. C-ASDs are identified into binary
classification approach namely negative and positive C-ASD classes. The results indicate only 10 attributes instead of
18 are significant into identification process. We found that each of RF and NN performed better than other classifiers
where both classifiers score accuracy reach to 100 % based selected subset. This supports the idea that these models
might be used for screening for C-ASD at an early stage of test-bed applications.

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 and IDS, the aim is to develop a
comprehensive solution to address the complex problem of protecting IoT settings. The input emphasizes the significance
of exploring machine learning (ML) techniques to improve security protocols for IoT devices. It also highlights the
importance of validating the effectiveness of FL technology in safeguarding and transferring confidential information
within IoT systems. The integration of IDS is proposed as an extra measure to strengthen the security of IoT systems as a
whole. Ultimately, the objective of this research is to provide comprehensive and effective solutions to address security
challenges in the IoT, thereby increasing trust in the application of this technology across various domains.

Modelling some rare environmental events and obtaining accurate predictions is quite important to determine their
nature and to take action accordingly. In this article, a new three-parameter exponentiated Benini distribution based on
the exponential family is proposed for this purpose. It is called the three-parameter exponentiated Benini distribution. As
an additional motivation, although new distributions are derived by various methods in the literature, there is no study
on variants of the Benini distribution. We first investigate its mathematical properties and then evaluate its performance
in real applications by comparing it with other distributions. In particular, the corresponding probability density and
hazard rate functions, quantile function, stochastic ordering, moment and incomplete moment, and ordered statistics,
are presented in detail. The analysis of the shape of the distribution is also provided by calculating the skewness and
kurtosis coefficients for different parameter values. In addition, several classical estimates for the parameters of the three-
parameter exponential Benini distribution are computed, including maximum likelihood estimates, maximum product
of distance estimates, Anderson-Darling estimates, right and left tail Anderson-Darling estimates, and Cramér-von Mises
estimates. We examine the performance of these methods with simulation studies via different scenarios and sample sizes.
Applying the proposed distribution to two real environmental data sets, we find that the three-parameter exponential
Benini distribution gives the best results compared to the Benini, Ramous Louzada, inverse Pareto, Pareto, Pareto type
I, Pareto type II, Beta Pareto and Weibull Pareto distributions. Overall, it is particularly adapted to the analysis of
environmental data, mainly due to its long-tailed and flexible structure.

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 with a combination of the Synthetic Minority Over-sampling Technique (SMOTE) and
advanced over- and under-sampling methods. By implementing ordinal classification, the model effectively captures the
progressive nature of dementia, showing comparable or improved performance against current diagnostic benchmarks.
This approach highlights the benefits of ordinal classification in medical imaging, paving the way for enhanced severity
assessment and supporting better treatment planning.

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 specifi-
cally on Retinal Optical Coherence Tomography (OCT) images. The results highlight the framework’s ability to preserve
image quality and diagnostic utility, with minimal perceptual impact, as evidenced by strong evaluation metrics: a Mean
Squared Error (MSE) of 0.0001, a Peak Signal-to-Noise Ratio (PSNR) of 85.27, a Structural Similarity Index Measure
(SSIM) of 0.9999, a Normalized Correlation Coefficient (NC) of 1, and a Universal Average Change Index (UACI) of 0.
Overall, this framework represents a significant step forward in the secure sharing of medical images, ensuring that
patient data remains protected and accessible without compromising diagnostic fidelity.

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 confirm the analytical findings as well as to
test the model’s long-term behavior to understand the impact of changing the system’s parameters. It is noted that the
system exhibits a variety of local bifurcations, most notably the Hopf bifurcation, which is calculated numerically for
various parameters.

E-learning systems have transformed educational sectors and more interestingly, the use of these educational platforms
for laboratory-based practices has been trending in the last couple of years. Laboratory-usage e-learning system has
various tremendous benefits such as the skill of accessibility utilization and practical experimentation simulation. Several
intelligent tasks still need to be tackled to make the most of it. One of the biggest problems is how to recreate hands-
on experience in a virtual or remote environment. Some e-learning systems offer simulations and virtual experiments
but may also require additional tactile feedback through interaction with physical equipment for this challenge, it is
necessary to work toward creating more realistic virtual simulations and using haptic technologies that improve the
realism of experience senses. Training instructors to exploit the system effectively as well as modifying instructional
strategies and providing guidance for students during virtual or remote engagement and clear instructions or guidelines
for using the e-learning platform should be provided to students for effective understanding and learning throughout the
process. The continuous evaluation and improvement in the performance of these systems must be focused on making
e-learning systems more effective for laboratory use. Feedback from instructors and students regularly would help to
pinpoint what can be improved. Insights from research studies and evaluations can also inform the role of different
approaches, instructional strategies, and technologies in laboratory education. In this article, we introduce e-learning
systems for laboratory usage and then present their challenges and how they can be addressed.

The use of wireless sensor networks (WSNs) has become an inevitably necessary for a smart world, such as smart cities
and environmental fields. WSN consists of hundreds or even thousands of sensor nodes that have the ability to sense
physical conditions from the target field, and also consists of a device that acts as a link between the sensor nodes and
the base station (BS) called cluster head (CH). In the recent years, researchers have become interested in optimizing the
energy efficiency of the WSNs due to the limited and non-replenish energy sources of their sensor nodes. In this paper,
we present a review of main methodologies used in the literature to improve the energy efficiency of WSNs and extend
the lifetime of the network. In addition, we present the general energy model that is used in these networks. Moreover,
we discuss new research directions for efficient network-energy utilization that need to be considered for implementing
power-constrained WSNs.

Cyberbullying has arisen as a prevalent and worrying issue in the digital age, substantially influencing the well-being
and mental health of social media users. Previous studies have identified several factors and theories of cyberbullying.
Still more in-depth research is required to understand the key factors influencing cyberbullying intention in social media.
This study aims to identify the factors influencing cyberbullying intention in social media and examine the moderating
effect of fake accounts on cyberbullying intention. An extensive literature review has been conducted to examine the
gaps in existing studies on cyberbullying intention. As a result, this study uses the Theory of Planned Behavior (TPB)
as a basis of the proposed Cyber- bully Intention Model (CIM), with the addition of Anonymity and Fake Ac- count,
respectively, as external exogenous and moderating variables. This study is conducted based on a quantitative survey
of 273 social media users. It assessed respondents’ experiences with cyberbullying by considering various perceptions
of behavioral control, subjective norms, attitudes, anonymity, fake accounts, and their intentions toward committing
cyberbullying on social media platforms. Data was analyzed using the Partial Least Square Structural Equation Modeling
(PLS-SEM) in SmartPLS 4 software. The results reveal that Attitude (p = 0.008) and Subjective Norms (p = 0.014) are key
factors in Cyberbully Intention. On the other hand, Fake Account is seen as having a medium moderating effect on the
relationship between Perceived Behavioral Control and Cyberbully Intention (p = 0.046). Fake Account also moderate
the relationship between Anonymity and Cyberbully Intention (p = 0.041). The output from this study is multi-faceted
and holds implications for various stakeholders, including social media platforms, educational institutions, policymakers,
organizations, and individuals. The theoretical contribution of this study by introducing the concept of fake accounts as
medium effect moderating anonymity to become a cyberbully intention. The findings of this study give implications to
digital platform administrators and policy makers who aim to curb cyberbullying incidents. Educating users about the
possible con- sequences of leveraging fake accounts for malicious purposes can contribute to fostering more responsible
and empathetic digital interactions.

In the current landscape of technological advancement, data holds a pivotal role, shaping societal interactions and
daily routines. The rapid escalation in digital data volume, driven by technological strides, has underscored the critical
necessity for robust protective measures to safeguard its sensitive nature. This study aims to develop a secure software
specification for Android application ensuring effective data protection through a specialized Android application
tailored explicitly for data concealment, assuring utmost confidentiality and secure transmission. In this paper we revolve
around the integration of multifaceted security and privacy protocols, employing advanced information concealment
techniques, encryption mechanisms, secure key management, and meticulous access control by MAC address. Termed
‘Secure Data Applications,’ these interfaces are meticulously crafted using PHP interlinked with MySQL databases, and
developed using Flutter and Dart languages. The primary goal is to make a substantial contribution to the domain of
mobile data protection, enabling secure and streamlined data exchanges while placing paramount importance on an
intuitive user experience The ‘Secure Data App’ application, emerged successfully through a battery of rigorous testing
methodologies. Throughout the testing phases, the application seamlessly navigated without encountering any errors,
affirming its robustness and reliability. Moreover, the tested value (6.8%) of maximum CPU usage signifies that the
application utilizes the CPU at a relatively low rate. Also, the encryption ratio of 1.35 is relatively high which guarantees
a higher level of security.