Iraqi Journal for Computer Science and Mathematics

The aim of this study is to investigate the efficacy of Artificial Intelligence (AI) techniques and programs in developing
Critical Thinking Skills (CTSs) in mathematics among secondary school students, as well as their attitudes towards it. This
study employed an experimental methodology, which was applied to a sample of 91 students. A critical thinking test and
a scale to measure students’ Attitudes Towards Mathematics (ATM) were also utilized. This study revealed significant
improvements in the mean scores of critical thinking skills among secondary students who were exposed to Artificial
Intelligence Techniques (AITs), particularly in deduction, interpretation, inference, and evaluation. Additionally, the
experimental group showed enhanced attitudes toward mathematics, as indicated by the post-application of the attitudes
towards mathematics scale across all its domains. The findings underscore the efficacy of AI-powered methods in
improving both critical thinking skills and attitudes toward mathematics. This study suggests integrating AI programs
into curriculum content and providing teacher training to effectively utilize these technologies into mathematics
education.

Whenmulticollinearity arises in the inverse Gaussian regression (IGR), there is a substantially unstable variance in the maximum likelihood estimator. Based on the (r-(k-d)) class estimation method, we present a novel Liu-type estimator in the IGR model in this study. The study examines the effectiveness of the suggested estimator and draws comparisons with alternative estimators. Based on simulation and real data results, the suggested estimate performs better than the other estimators in terms of mean squared error.

The dynamic nature of e-commerce necessitates the adoption of cutting-edge technologies to improve the online
shopping experience. Our research introduces a groundbreaking methodology called Fuzzy Association Rule Mining
(FARM), combining fuzzy set theory with traditional Association Rule Mining (ARM). Unlike conventional ARM, which
focuses solely on the frequency of jointly purchased items, FARM also considers the sold quantities, leveraging the
Apriori algorithm to discern customer preferences from historical sales data across the UCI Online Retail II, Market
Basket, and Movielens datasets. This hybrid of fuzzy set theory with ARM enables a better understanding of complicated
consumer behaviors and associations between products. A variety of comprehensive statistical techniques, including
support, confidence, lift metrics, Chi-square test, and P-value hypothesis test, are applied in our approach to validating
the statistical robustness of the mined rules. The result shows that FARM offers a broader range of insight into
product associations and markedly enhances customer recommendations’ precision and relevance. The above-described
innovation is a great addition to any e-commerce portal that aims to refine its recommendation system, and it also
captures an aspect of more granularity in consumer preference. Through this work, we aspire to contribute to the
academic discourse on data mining by presenting a robust analytical framework that gives a finer definition to the subtle
nuances underlying their buying behavior. We debate and highlight how important it is to merge these statistical methods
with data mining in e-commerce recommendation systems to improve them by carefully analyzing transactional data.

This paper introduces new concepts such as permutation BCK–algebra, permutation involutory ideal, commutative per-
mutation BCK–algebra, and prime permutation ideal. Additionally, their attributes are examined. This paper elucidates a
method for determining a relationship between the chemical structure of atoms for the chemical element Cadmium, and
some of our suggestions are given here. In this work, the structure of the sets A‡ and λβ∗∗
n A are defined. Next, we show
that if A is a permutation ideal, then λβ∗∗
n A is a permutation ideal that contains A. Also, in any commutative permutation
BCK–algebra the inequality (λβ
i # λβ
j ) # λβ
m = (λβ
i # λβ
m ) # λβ
j , for any λβ
i , λβ
j , λβ
m ∈ X is provided. After that, we show that
(λβ
n ~ λβ
m ) # (λβ
n ~ λβ
k ) ≤ (λβ
n ~ λβ
m ) ≤ λβ
m, for any λβ
i , λβ
j , λβ
m ∈ X in any commutative permutation BCK–algebra, where
λβ
n ~ λβ
j = λβ
n # (λβ
n # λβ
j ). The notion of triple merge permutation (TMP) in Sn+k+m for any permutations β, δ and γ in
symmetric groups Sm, Sk and Sn respectively is shown and then the concepts of triple merge permutation BCK–algebra
and triple merge permutation bounded are studied. Also, the atomic shells for three chemical elements they are Carbon,
Beryllium and Hydrogen are studied and the structure of the triple merge permutation is given from their atomic shells.
Some interesting inequalities in commutative permutation BCK–algebra and in triple merge permutation BCK–algebra
are given to support this work.

This research investigates how deep learning might be used to optimize beamforming in wireless communication
systems that are helped by Reconfigurable Intelligent Surfaces (RIS). Our goal is to increase the possible data rates by
dynamically forecasting the best phase shifts for RIS elements by utilizing Convolutional Neural Networks (CNN) and
hybrid CNN-Long Short-Term Memory (CNN-LSTM) models. We assess the performance of these deep learning models
against conventional genie-aided techniques by simulating real-world wireless settings using the DeepMIMO dataset. The
findings demonstrate that beamforming based on deep learning can reach near-optimal performance, greatly lowering
the overhead associated with channel estimation while improving communication efficiency. In order to support the
development of next-generation wireless networks like 5G and 6G, this study shows how deep learning approaches can
be used to increase the effectiveness of RIS-assisted systems.

These days, assistive robotics and their applications for people with disabilities have become a revolutionary field in
medical care. It combines edge-cutting technologies such as the Internet of Things (IoT), edge computing networks, and
federated learning, offering the best services to disabled people for mobility and navigation in the environment. However,
in the state of the art, many conceptual models are presented, and less effort is put into the practical implementation
of assistive robots for disabled people in the environment. With this motivation, we propose an intelligent assistive
robotics wheelchair system that enhances disabled care in federated learning-enabled IoT and edge cloud networks.
In the proposed system, we connect IoT-enabled robots with various sensors and devices, facilitating real-time data
gathering and processing, which is vital for tracking the health conditions and immediate requirements of disabled
patients. The proposed system enables the robots to continuously learn and adapt to the specific needs trained on
different clinicals based on federated learning securely. For example, a user who uses a wheelchair might benefit from
a robot that learns to navigate various settings based on previous interactions, optimize paths, and adjust assistance
based on real-time conditions based on multi-modal trained data. We exploited the deep convolutional neural network
to train, validate, and test data. These navigation operations are performed with higher accuracy and secure data. The
system’s goal is to offer wheelchairs to disabled people for performing pilgrimages and monitoring their health, as well
as autonomous help to search locations and objects. Simulation outcomes show that we achieved optimal results for
patients with disabilities, a time and deadline missing ratio of 50%, data training accuracy of 98%, and minimized
power consumption of wheelchairs during pilgrimages and Umrah in Saudia Arabia. The overall paper focused on the
disability services during their pilgrimage in Saudia Arabia.

Artificial intelligence, especially in the field of “deep learning”, is still promising when it comes to skin cancer detection
and diagnosis. Among deep learning algorithms, convolutional neural networks (CNNs) give a high level of accuracy
in identifying and classifying different types of skin cancer. CNNs have a strong coordination due to understanding the
important features from medical images that are extracted from convolutional layers. However, there is still a problem
which is the high imbalance in the dataset with high noise in the images. This paper presents a new solution that
combines different architectural structures of convolutional neural networks (CNNs) to extract features from skin cancer
images with two forms of linear algebra methodologies, Principal Component Analysis (PCA) and Factor Analysis (FA)
to eliminate the curse of combining the extracted features and removing the redundant features and passing them to
a fully connected classifier to perform multiple classification, in this paper the HAM10000 dataset is used. The idea
here is to retain the vital features of the image and extract the important features from it and eliminate the irrelevant
features which leads to achieving higher classification accuracy for all classes. The proposed method was accurate and
achieved a macro average test accuracy of 97.90% with a test loss of 0.14% and precision, recall and f1 score of 0.97%,
0.93% and 0.95% respectively and achieved a weighted average of 0.98% in precision, recall and f1 score in multi-class
classification.

Most real-world technological systems are highly complex, making it challenging to examine their reliability. Many systems can be represented as Complex Parallel-Series Networks (CPSN). The large number of components and subnetworks, along with their intricate connection, complicates the identification, evaluation, and potential failure of the CPSN. The concept of minimal path sets for a CPSN refers to a specific set of components whose failure leads to the failure of the whole CPSN. The primary research problem is to identify these minimal path sets, both for the overall CPSN and for its complex subnetworks. This paper presents a mathematical technique for analyzing subnetworks instead of the whole CPSN to reduce computational effort and simplifies intricate calculations. Specialized algorithms and techniques are presented and used to identify minimal path sets, with numerical results funded by general formulas. Numerical cases show the effectiveness and applicability of this technique for CPSN.

The face morphing process blends two or more facial images to produce a singular morphed facial image that shows
the vulnerabilities of Face Recognition Systems (FRS). The widespread use of facial recognition algorithms, especially
in Automatic Border Control (ABC) systems, has elicited concerns about potential attacks, as modified passports pose a
significant risk to national security. This research presents a hybrid approach for feature extraction from facial images.
The suggested approach involves three stages: The initial phase involves preprocessing the image through resizing and
face identification, using the Viola-Jones algorithm to detect and locate the human face in the image, regardless of its
size, context, or environment. In the second step, we extract features using three different techniques: Transfer learning
using ResNet50, Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP); we produce a one-dimensional
feature vector that merges the outputs of each technique. The third phase includes the classification process utilizing
the Deep Neural Network (DNN) classifier and the Support Vector Machine (SVM) as a secondary classifier. The AMSL
dataset that contains real face and morphed face images has been used for training and testing the proposed approach.
The DNN classifier achieved an average accuracy of 98.62%, surpassing the SVM, which achieved an accuracy of 97.39%.
The results demonstrate a higher accuracy in identifying morphing attacks relative to previous studies.

Speech recognition-based applications increased and developed as a result of artificial intelligence’s rapid growth,
particularly Machine Learning, which play a crucial role in many aspects of daily life, such as applications related to
human-computer interaction, and natural language processing. The complexity and diversity of speech signals provides
challenges in maximizing the rate of accuracy and efficiency of speech recognition systems. Hyperparameter tuning
is a crucial step in machine learning that has a significant role in optimizing the performance and generalization by
determining the optimal values for the model’s hyperparameters. This paper employed the recently developed WAR
Strategy optimization algorithm for optimizing the features related to the speech signal and tuning the hyperparameters
of machine learning typical models for accurate and rapid speech recognition. Two types of features are extracted from
the speech signal including the spectral feature using the Mel-Frequency Cepstral Coefficients (MFCCs) technique and
the statistical features. Afterward these features are optimized using the WAR Strategy optimization algorithm to obtain
the optimum features set that describe the speech signal important information. Finally, the hyperparameters of six
classical machine learning models are tuned to serve as newly designed classifiers in the final classification phase of the
proposed system. Three different language speech datasets are used to evaluate the proposed system (i.e. English, Arabic,
Malaysian) to prove the high generalization property of the proposed system. The obtained recognition accuracy that
was ranging from 98.38% to 100% in a training time between 0.001 to 19.8 second demonstrate the high effectiveness
of the proposed speech recognition system in dealing with the many obstacles facing the recognition of speech signal
within high accuracy, low resources requirements, and minimum training time.

Image denoising is an important area of computer vision. Rudin-Osher-Fatemi model based on a gradient is one of
the simplest models used in image denoising to solve the problem of restoring the clear image. The challenge in solving
this model is the non-differentiability of Total Variation function (TV-function) minimization. Image transmission is
widespread over wireless systems, including the fifth generation (5G) cellular network. Transmission impairment can
affect transmitted images, including noise, attenuation, and distortion. This study proposed a new smoothing technique
to make the TV-function differentiable and smooth. The new smoothed function was used for de-noising images with
the help of the gradient descent method in minimization. Two transmission systems were proposed, additive white
Gaussian noise (AWGN) and 5G enhanced mobile broadband (eMBB), to evaluate the performance of the proposed
approach. The denoising technique proved convincing over AWGN and 5G eMBB channel models, reducing the noise
effect on the transmitted images. Compared with the noisy image, the gains achieved by the denoised image reached
7.4 and 4174.1 for peak signal-to-noise ratio and mean square error, respectively. These gains are achieved at the
low and moderate signal-to-noise ratio regions, while at the high signal-to-noise region, the quality of the noisy and
denoised images is almost the same.

This paper comprehensively reviews the classification of breast cancer histological images. The paper discusses the
research objectives, methodologies used, and conclusions drawn, as well as suggestions for the future. The study is
based on the ICIAR 2018 database, which is considered one of the largest databases available to support this research.
The paper also addresses major challenges such as lack of data, variation in tissue preparation, class imbalance, and
computational requirements. Advanced techniques such as deep learning (DL), transfer learning and data augmentation
are explored, along with innovative models such as convolutional neural networks (CNNs) and generative adversarial
networks (GANs). The study focuses on integrating multimodal (histological, genetic, and clinical) data to enhance
diagnostic accuracy and enable personalized treatments. Breast cancer is responsible for 25.4% of cancer cases, with
1 in every 12 women developing this fatal disease. In 2020, a large number of women died as a result of the disease
spreading through lymphatic and blood vessels, with the number of deaths reaching about 685,000 cases. Improving
outcomes depends greatly on early detection of the disease and providing appropriate and effective treatment. Future
directions include addressing challenges related to texture image classification and promoting standardized data sets
with the goal of developing diagnostic tools and treatment strategies.

Email spam is a significant issue confronting both email consumers and providers. The evolution of spam filtering has progressed considerably, transitioning from basic rule-based filters to more sophisticated machine learning algorithms. Deep learning has become a potent collection of techniques for addressing intricate issues such as spam classification in recent times. A thorough literature evaluation is required to have a comprehensive overview of the current research on utilizing deep learning methods for email spam classification. This review aims to identify the various deep learning techniques used for email spam, their effectiveness, and areas for future research. By synthesizing the outcomes of pertinent studies, this review delineates the strengths and drawbacks of various approaches, offering valuable insights into the challenges that must be tackled to enhance the precision and efficacy of email spam classification.

In recent years, Advancements in Artificial Intelligence (AI), particularly deep learning (DL), have made great strides
in the creation of highly realistic deepfakes, which manipulate facial forensics to generate convincing fake faces or
expressions. These manipulations pose significant threats to individual privacy and the integrity of legal, political,
and social institutions. In fact, several existing studies have recently pursued the development of machine learning
techniques for detecting deepfake content, with the overarching aim of protecting the victim’s privacy or curbing the rise
of picture fabrication. Despite extensive research on DL-based deepfake detection systems, challenges such as detecting
facial swaps under occlusion or subtle alteration remain insufficiently addressed. This study provides a comprehensive
detailed evaluation of state-of the art DL approaches for detecting such manipulations, focusing on their strengths and
limitations. Additionally, it reviews recent deepfake datasets (2019 to date) to identify their adequacy for adequacy
for training and testing these models. By addressing the gaps and limitations in current existing methods and datasets,
this study aims to pave the way for redefining DL-based detection techniques tailored to facial-swap-based deepfakes.
It aspires to enhance the integrity and reliability of image media and contributes to the ongoing effort to mitigate the
risk posed by advanced image manipulation.