Iraqi Journal for Computer Science and Mathematics

This paper proposes an implementation of the secant optimization method on the watermark
embedding in DTCWT domain. An objective function combining PSNR and NC (The Normalized Correlation)
values calculated for a set of common attacks is used in the optimization process. The aim is to enhance the tradeoff between robustness and imperceptibility against the various image processing attacks via an automatic and
careful selection of the scaling factor using optimization. The watermark is inserted in the high energy bands of
DCT-transformed sub-vectors, which were generated from the DTCWT coefficient decomposition of the host
image. Experimental results performed on a set of 512×512 greyscale images, show better results than the
existing schemes for most common image attacks such as compression, low-pass filtering and noise addition.

T Through the article, we will provide an entirely novel type of quasi operators base on the concept of
D razin inverse in Operator theory which is named (n,D) -quasi operators on the Hilbert spaces, also we explain the
sum, product, and other features of these classes are discussed. and restriction via some necessary conditions have
been holds in order to obtain these operations, in addition that we investigate the scalar and power of this concept,
moreover we submit prove of the Tensor product and direct product of (n,D)-quasi operators.

Text mining, a subfield of natural language processing (NLP), has received considerable attention in recent years
due to its ability to extract valuable insights from large volumes of unstructured textual data. This review aims to
provide a comprehensive evaluation of the applicability of text mining techniques across various domains and
industries.
The review starts off with a dialogue of the basic ideas and methodologies that are concerned with textual content
mining together with preprocessing, feature extraction, and machine learning algorithms.
Furthermore, this survey highlights the challenges faced at some stage in implementing textual content mining
strategies. Additionally, the review explores emerging tendencies and possibilities in text-mining research. It
discusses advancements in deep learning models for text evaluation, integration with different AI technologies like
image or speech recognition for multimodal analysis, utilization of domain-unique ontologies or information graphs
for more desirable information of textual facts, and incorporation of explainable AI strategies to improve
interpretability. The findings from this overview are analyzed to identify common developments and patterns in text
mining packages across extraordinary domain names.
The consequences of this paper will advantage researchers by means of imparting updated expertise of modern
practices in textual content mining. Additionally, it will manual practitioners in selecting suitable strategies for their
unique application domain names while addressing capacity-demanding situations.

Heart disease is the leading cause of death in developed countries, as it causes many deaths annually. Despite the availability of effective treatments, heart disease remains a significant challenge to public health, so early detection is essential in enhancing patient outcomes and reducing mortality. Artificial intelligence seeks to help physicians make the right decisions about a patient's health condition. In this regard, the authors decided to utilize machine learning techniques (k-nearest neighbor, decision tree, linear regression, support vector machine, naïve bayes, multilayer perceptron, random forest) to contribute to the classification of the heart disease dataset, where it is determined whether a person is suffering or not. After that, the execution of all techniques will be measured, and the accuracy of each technique will be compared to determine the most suitable performer. The public dataset is organized from the UC Irvine machine learning repository and have significantly different characteristics. The dataset will be divided such that 80% of the data is designated for training and 20% is designated for testing. This article concluded that the adequate performance is for the multilayer perceptron technique, as it gained an accuracy of more than 88%, while the poor performance is for the decision tree technique, as it gained an accuracy of more than 79%.

The prostate cancer is a deadly form of cancer that assassinates a significant number of men due of its mediocre identification process. Images from people with cancer include important and intricate details that are difficult for conventional diagnostic methods to extract. This work establishes a novel Automated Prostate-cancer Prediction System (APPS) model for the goal of detecting and classifying prostate cancer utilizing MRI imaging sequences. The supplied medical image is normalized using a Coherence Diffusion Filtering (CDFilter) approach for improved quality and contrast. The appropriate properties are also extracted from the normalized image using the morphological and texture feature extraction approach, which helps to increase the classifier's accuracy. In order to train the classifier, the most important properties are also selected utilizing the cutting-edge Dragon Fly Optimized Feature Selection (DFO-FS) algorithm. Using this method greatly improves the classifier's overall disease diagnosis performance in less time and with faster processing. More specifically, the provided MRI input data are used to categorize the prostate cancer-affected and healthy tissues using the new Convoluted Gated Axial Attention Learning Model (ConGA2L) based on the selected features. This study compares and validates the performance of the APPS model by looking at several aspects using publicly available prostate cancer data.

In this study, a network created by converting the Petri net into a network for shutdown system, by calculating
a network’s nonlinear reliability polynomial by splitting the network into two, each having a source node and an
terminal node. This the procedure converts the system into two parallel-series block diagrams that are connected in
a series. The metaheuristic algorithm using the honey badger algorithm (HBA) and Dwarf Mongoose Optimization
Algorithm (DMO) have been used to improve the problem for the shutdown network hybridizing these algorithms
employing the penalty method, we will obtained the (PFMHBA, PFMDMO) algorithms. Thereafter, we compared
the results of the use of the hybridization technique with the results of the process that does not use this technique. The
objective of this comparison was to ascertain whether the use of hybridization makes the results increasing reliable,
reducing cost, and shortening the duration of implementation.

Handwritten signature identification is the process of identifying the true identity of an individual by analyzing their signature. This is an important task in applications such as financial transactions, legal documents, and biometric systems. Various techniques have been developed for signature identification, including feature-based methods and machine learning-based methods. This paper proposes an authentic signature identification method based on integrating static (off-line) signature data and proposed deep-based model, this is done by fused three types of signature features, Linear Discriminant Analysis (LDA) as appearance-based features, Fast Fourier Transform (FFT) as frequency-features, and Gray-Level Co-occurrence Matrix (GLCM) as texture-features. Then, the fused features are inputted to the proposed deep-based model of 25 layers for identifying each person. For experiments, we have used three datasets: Our own private collected dataset, called SigArab, and two public datasets called SigComp11 and CEDAR respectively. The proposed deep model achieves 99.23%, 100%, 100% accuracy on SigArab, CEDAR and SigComp2011 datasets.

This study investigates how differential geometry ideas can be used to effectively carry out structural optimization and reliability analysis. Strong mathematical representations and methods for examining intricate surfaces and forms are provided by differential geometry. The basic ideas of differential geometry, such as tensors, manifolds, and curvature, are initially introduced in the work. Then, to account for ambiguities in the geometry, the probability theory in tangent spaces is developed. As a result, structural reliability can be determined using propagating uncertainty. To enable reliability-based design optimization, differential geometry representations are linked with optimization techniques. The proposed differential geometry-based approach is applied in a number of case studies to trusses, airplane wings, car bodies, and ship hulls. The outcomes show a significant increase in productivity and scalability compared to conventional finite element methods. The article offers new tools for dealing with uncertainty.

Localization in wireless sensor networks (WSNs) plays a crucial role in various applications that
rely on spatial information. This paper introduces the Multi-Strategy Fusion for Localization model, which
integrates optimization techniques (ABO, DSA, EHO, and KNN) and neurocomputing techniques (BP, MTLSTM,
BILSTM, and Autoencoder) to enhance localization accuracy in WSNs. The work is divided into three phases: data
collection, model building, and implementation. The first and the last are carried out in the field, while the second
is made in the laboratory. The three phases involve a few general steps. The (1) Data Collection Phase includes
four steps: (a) Deploy three anchors at known locations, forming an equilateral triangle. (b) Each anchor starts
broadcasting its location. (c) Using an ordinary sensor, the RSSI of each anchor is measured at every possible
location where the signal of the three anchors can reach it. (d) Data is logged to a CSV file containing the
measuring location and the RSSI of the three anchors and their locations. The Model Building Phase includes (a)
preprocessing of the collected data, and (b) building a model based on optimization and optimization techniques.
The Implementation phase includes five steps: (a) Convoy sensors to the target field. (b) Manually deploy anchors
according to the distribution plan. (c) Randomly deploy ordinary sensors. (d) Each ordinary sensor starts in
initialization mode. When receiving a signal from three anchors, a sensor computes its location and stores it for
future use. When a sensor has its location, it turns into operational mode. (e) A sensor in the operational mode
attaches the location with sensed data each time it sends it to the sink or neighbors according to routing protocols
(routing is not considered in this study). ABO and DSA optimization techniques show similar performance, with
lower Mean Squared Error (MSE) values compared to EHO and KNN. ABO and DSA also have similar Mean
Absolute Error (MAE) values, indicating lesser average absolute errors. BP emerges as the top performer among
the neurocomputing techniques, demonstrating better accuracy with lower MSE and MAE values compared to
MTLSTM, BILSTM, and Autoencoder. Finally; The Multi-Strategy Fusion for Localization model offers an
effective approach to enhance localization accuracy in wireless sensor networks. The paper focuses on addressing
the correlation between wireless device positions and signal intensities to improve the localization process. The
obtained results and provided justification emphasize the significance and value of the model in the field of
localization in WSNs. The model represents a valuable contribution to the development of localization techniques
and improving their accuracy to meet the needs of various applications. The model opens up opportunities for its
utilization in diverse domains such as environmental monitoring, healthcare, smart cities, and disaster
management, enhancing its practical applications and practical significance.

Cryptographic tools like Hill's encryption algorithm protect digital data. In this work, we present a novel Hill cipher security method that utilizes Gaussian integers from number theory. Using these intriguing mathematical entities to disguise plaintext values dramatically boosts assault resistance and duration. This research includes a three-pass protocol for encryption and decryption without key exchange, ensuring a safe, efficient, and dependable solution. Pandas is used for efficient data processing, and Numpy for computational tasks, notably matrices. Hill cipher-based encryption and decryption can be utilized in real life. It also demonstrates how to save Pandas DataFrame data to Excel. This strategy assures progress in cryptography. Uniquely designed for banking, it emphasizes its applicability and possible influence on present financial systems.

It is well known that there are many mathematical financial failure models that have been proposed
in the financial literature for specific stock markets. Some researchers are not aware these mathematical models
were constructed to be fitted for that market data, not for other markets. Iraq stock market exchange is one of these
markets in which the researchers used imported models such as Kida, Sherrod, Altman, and others to predict
financial failure. Therefore, the development of a financial failure warning model for banks has become very
crucial for the Iraqi bank sector in the stock market exchange. Unfortunately, there is no clear information about
the financial failure of Iraqi banks as a response variable, and the financial indicators contain outliers. The
objective of this paper is to propose an algorithm to know the performance of efficient and inefficient banks based
on their indicators during specific time periods. The output of this algorithm will be considered as response
variables. Then, a weighted adaptive lasso logistic regression algorithm that has a high breakdown point is used to
tackle outliers’ problem. Thirteen banks have been chosen as the most traded during the period (2010-2017), and
for each bank (27) financial indicators were collected. Our proposed model is compared with adaptive lasso
logistic regression by using Deviance, Misclassification, Area Under Curve, Mean Square Errors, and Mean
Absolute Errors. Consequently, the results showed that the weighted Adaptive Lasso Logistic Regression model is
more robust and relevant than others to be a financial model to warn of the failure of the banks in Iraq's stock
market.

The proliferation of fake news or misinformation, commonly referred to as fake news, has a significant effect on a global scale, as it is aimed at influencing public opinion as well as crowd decision-making. It is therefore crucial to verify the truthfulness of news before it is released to the public. Today, most studies on early detection of Arabic misinformation rely on machine learning methods and transformer-based models. Therefore, in the current study, we used deep learning techniques to propose a model for detecting Arabic misinformation by leveraging the contextual features of news article content. The proposed model was built based on BiLSTM and the attention mechanism. To extract features from Arabic text, we utilized a pre-trained AraBERT model, which generates contextual embeddings from text, then are fed to the BiLSTM layer as input features. Moreover, we investigated the effectiveness of the attention mechanism in improving the overall performance of the model by configuring model architecture to exclude the attention mechanism and comparing the results. Two datasets were utilized to train and evaluate the proposed model, namely, the AraNews and ArCovid19-Rumors datasets. Experimental results showed that the proposed model outperformed other existing models, achieving an accuracy of 0.96 on the ArCovid19-Rumors dataset and 0.90 on the AraNews dataset. This remarkable performance was due to the capability of the attention mechanism to enhance the overall performance, allowing the model to capture the relationship between textual features.

Routing techniques (RTs) play a critical role in modern computing systems that use network-on-chip (NoC) communication infrastructure within multiprocessor system-on-chip (MPSoC) platforms. RTs contribute greatly to the successful performance of NoC-based MPSoCs due to traffic congestion avoidance, quality-of-service assurance, fault handling and optimisation of power usage. This paper outlines our efforts to catalogue RTs, limitations, recommendations and key challenges associated with these RTs used in NoC-based MPSoC systems for the IoT domain. We utilized the PRISMA method to collect data from credible resources, including IEEE Xplore ®, ScienceDirect, Association for Computing Machinery and Web of Science. Out of the 906 research papers reviewed, only 51 were considered relevant to the investigation on NoC RTs. The study addresses issues related to NoC routing and suggests new approaches for in-package data negotiating. In addition, it gives an overview of the recent research on routing strategies and numerous algorithms that can be used for NoC-based MPSoCs. The literature analysis addresses current obstacles and delineates potential future avenues, recommendations, and challenges analyzing techniques to assess performance utilizing metrics within the TCCM framework.

Science and technology researchers are currently focused on the creation of self-driving cars. This can have a profound effect on social and economic progress; self- driving vehicles can help reduce auto accidents dramatically and enhance the quality of life of people the world over. Self-driving cars have had a tremendous increase in popularity in the recent past because of artificial intelligence development. However, there is a lot of research work to be done to manufacture fully-automated cars because a self-driving carshas tto be able to sense its environment and operate without human involvement. A human passenger is not required to take control of the vehicle at any time, nor are they required to be present in the vehicle at all.

Currently, self-driving cars are still at level 3 and are not allowed ply the roads due to many challenges which usually cause blurred images, including irregular roads, weather factors (rain and fog).

This paper is a review study on self-driving cars, and will be examining the obstacles that self-driving cars face, as well as how they might overcome them. The paper will provide the researchers with pieces of information about self-driving cars, the challenges they face, the recent methods used to overcome these challenges, and the advantage, disadvantage, and accuracy of these methods. The paper aims to encourage researchers to work on solving the problems that inhibit the evolution of self-driving vehicles.

Diabetes disease is prevalent worldwide, and predicting its progression is crucial. Several model have been
proposed to predict such disease. Those models only determine the disease label, leaving the likelihood of developing the disease
unclear. Proposing a model for predicting the progression of disease becomes essential. Therefore, this article proposes a logistic
regression model to anticipate the likelihood of Diabetes syndrome incidence. The model exploit capabilities of logistic regression
by using sigmoid function. The model's performance was evaluated using the Pima Indians Diabetes dataset and demonstrated
high accuracy, sensitivity, and specificity. The prediction accuracy rate was 77.6%, with a sensitivity of 72.4%, specificity of
79.6%, Type I Error of 27.6%, and Type II Error of 20.4%. Furthermore, the model indicates the feasibility of using laboratory
tests, such as Pregnancies, Glucose, Blood Pressure, BMI, and DiabetesPedigreeFunction, to predict disease progress. The
proposed model can aid patients and physicians in understanding the disease's progression and implementing timely interventions.

In this study, the Chaotic Sinusoidal Map (CSM)-enhanced Zebra Optimization Algorithm (CZOA)
is introduced. CZOA combines CSM's integration strengths with ZOA's optimization skills. ZOA already exhibits
great optimization capabilities, but the addition of CSM increases its potential even more. This addition greatly
strengthens ZOA's exploration and exploitation skills and increases its flexibility for various optimization tasks.
CZOA outperforms both the original ZOA and contemporary optimisation methods on 23 benchmark functions,
including high-dimensional (FD), multimodal (MM), and unimodal (UM) challenges. Using the chaos of CSM to
investigate regional optimal and determine better convergence and exploration-exploitation equilibrium are shown
by CZOA, which also shows more profitable solution locations. CZOA demonstrates its resilience and versatility
through multiple benchmark activities, underscoring its potential as an adaptable optimisation tool. CZOA
becomes a potent metaheuristic by combining biological inspiration and chaotic dynamics to solve difficult
optimisation problems. Inspired by the natural behaviour of zebras, the Zebra Optimisation Algorithm (ZOA) is a
relatively new optimisation technique. It makes use of a herd behaviour mechanism and the ideas of leadership and
following, in which members of the population—zebras in this case—cooperate to solve optimisation issues in the
best possible ways.

Biometric systems are a continuously evolving and promising technological domain that can be used in automatic systems for the unique and efficient identification and authentication of individuals without necessitating users to carry or remember any physical tokens or passwords, in contrast to traditional methods such as password IDs. Biometrics are biological measurements or physical characteristics that can be used to ascertain and validate the identity of individuals. Recently, considerable interest has emerged in exploiting brain activity as a biometric identifier in automatic recognition systems, particularly focusing on data acquired through electroencephalography (EEG). Multiple research endeavors have indeed confirmed the presence of discriminative characteristics within brain signals recorded while performing specific cognitive tasks. However, EEG signals are inherently complex due to their nonstationary and high-dimensional properties, thus demanding careful consideration during both the feature extraction and classification processes. This study applied a hybridization technique integrating a pre-trained convolutional neural network (CNN) with a classical classifier and the short-time Fourier transform (STFT) spectrum. We used a hybrid model to decode two-class motor imagery (MI) signals for mobile biometric authentication tasks, which include subject identification and lock and unlock classification. To this purpose, nine potential classifiers (mostly classification algorithms) were utilized to build nine distinct hybrid models, with the ultimate goal of selecting the most effective one. Practically, six experiments were conducted in the experimental part of this study. The first experiment aims to develop a hybrid model for biometric authentication tasks. To do this, nine possible classifiers (mostly classification algorithms) were used to build nine hybrid models. It can be seen that the RF-VGG model achieved better performance compared with other models. Therefore, it was chosen to be utilized for mobile biometric authentication. The fourth experiment is to apply the RF-VGG model for doing the lock and unlock classification process, and their mean accuracy is 97.50%. Consequently, the fifth experiment was conducted to validate the RF-VGG model for the lock and unlock task, and their mean accuracy was 97.40%. Practically, the sixth experiment was to verify the RF-VGG model for the lock and unlock task over another dataset (unseen data), and their accuracy is 94.4%. It can be deduced that the hybrid model appraises the capability of decoding the MI signal for the left and right hand. Therefore, the RF-VGG model can contribute to the BCI-MI community by facilitating the deployment of the mobile biometric authentication task for (the subject identification and the lock and unlock classification).

Integrating artificial intelligence (AI) techniques in public relations and media is an emerging interdisciplinary research domain warranting greater attention. This study presents the first bibliometric analysis of recent literature at the nexus of AI, public relations, and media. Publications from 2018-2023 were retrieved from Scopus and analyzed to uncover productivity, impact, collaborations, and topics. Results showed rising annual outputs with over 2000 articles published in 2021, confirming intensifying research activity. Recent publications also demonstrated higher citation impact, indicating their contemporary influence. Prolific authors were predominantly China-based, while the US led overall production. China, Western nations, and India dominated but opportunities exist to improve geographic diversity. Initial activity focused on justifying AI's value, evolving to technical applications for social media analytics, predictive modeling, and content creation. International collaborations centered around Western regions, though China's partnerships increased. This quantitative intelligence provides a benchmark to inform future work in this high-potential domain. Bibliometric monitoring should continue as the discourse progresses. Broader participation from underrepresented stakeholders is needed to responsibly shape AI integration in public relations and media.

Blockchain technology era is triggered due to current advancement in the decentralized paradigms via facilitating secure collaboration among untrusted entities consequently superseding the necessity for the trusted third parties’ existence. Notwithstanding its potential, blockchain scalability faces severe limitations such as low throughput, high fees, and confirmation time latency. Several scaling solutions have been proposed, including layer one solutions that substantially require fundamentally amending blockchain underlying infrastructure resulted in further scalability complications. Layer two solutions, specifically Lightning Network, is a successful cryptographic layer built atop of the Bitcoin blockchain that aims to alleviate these implications by deporting transaction processing outside blockchain while ensuring security and consensus inherited from blockchain without compromising its infrastructure. Payment channels form the core of the lightning network, facilitating fast and secure transactions between participants. However, the network encounters substantial obstacles associated with transaction amounts that exceed the current channel capacity, leading to payment failures and limiting its effectiveness. Payment Channel Networks (PCN) have been risen as an evolved replacement that solves payment channel’s issues, seeking to enhance transaction throughput and reduce confirmation delays by solving limited capacities shortages. This paper thoroughly examines PCN efficiency impacting factors that hinder its current adoption growth and avoiding its widespread employment. Additionally, existing solutions are reviewed and categorized based on the PCN building architecture. The research concludes by outlining potential research avenues and future directions to improve the efficiency and practicality of the Lightning Network PCN. This research contribution aids the advent of blockchain layer two technology and participates in its integration into real-world applications.

Efforts have been made to include artificial intelligence (AI) in teaching and learning; nevertheless,
the successful deployment of new instructional technology depends on the attitudes of the teachers who conduct
the lesson. Few scholars have researched teachers' perspectives on AI use due to a general lack of expertise on how
it can be used in the classroom, as well as a lack of specific knowledge about what AI-adopted tools would be like.
This study investigated mathematics teachers’ perceptions of implemented AI systems and applications in Abu
Dhabi Emirate schools. The sample study consists of 580 male and female math teachers from public and private
schools across three educational regions in Abu Dhabi selected based on several qualifications and experiences.
The research followed the descriptive analytical approach due to its suitability to the study’s context. The results
revealed that AI could be used as an educational tool to facilitate teaching and develop students’ performance by
including AI systems and applications in the curricula. They increased motivation for learning, encouraging
challenge, competition, and suspense among students and considering their differences. The results also showed
the most critical challenges that math teachers face in applying AI systems and applications, the most prominent of
which are the need to exert more effort than the traditional method when using different AI systems and
applications and the pressures placed on them, which prevent them from using AI in teaching. Additionally, the
findings revealed no statistically significant differences in mathematics teachers’ perspectives regarding the
importance of using systems and applications of AI in teaching; however, statistically significant differences were
found in the math teachers’ challenges when applying AI systems and applications in teaching according to the
educational qualifications, especially among math teachers who have masters’ degrees. These results can be used
as a foundation for creating guidelines for the future integration of AI education in schools since they report
teachers’ experiences utilizing the system and various considerations regarding its implementation.

The problem of finding the cyclic decomposition (c.d.) for the groups ), where prime upper than 9 is determined in this work. Also, we compute the Artin characters (A.ch.) and Artin indicator (A.ind.) for the same groups, we obtain that after computing the conjugacy classes, cyclic subgroups, the ordinary character table (o.ch.ta.) and the rational valued character table for each group.

When there is collinearity among the regressors in gamma regression models, we present a new
two-parameter ridge estimator in this study. We look into the new estimator's mean squared error characteristics.
Additionally, we offer several theorems to contrast the new estimators with the current ones. To compare the
estimators under various collinearity designs in terms of mean squared error, we run a Monte Carlo simulation
analysis. We also offer a real data application to demonstrate the usefulness of the new estimator. The results from
simulations and actual data reveal that the proposed estimator is superior to competing estimators.