Journal of Education for Pure Science-University of Thi-Qar

Epilepsy seizure (ES) monitoring and detection are only two examples of the many problems that may
be addressed by combining the Internet of Medical Things (IoMT) with machine learning (ML)
techniques and cloud computing services. Epilepsy, a potentially fatal neurological disorder, is a
worldwide problem that poses a significant threat to human health. There is an urgent need for a
reliable way of identifying epileptic seizures in their early stages to save thousands of epileptic patients
every year. With the use of IoMT, several medical treatments, such as epileptic monitoring, diagnosis,
and other procedures, may be performed remotely, hence lowering healthcare costs and enhancing
service quality. EEG datasets have made use of feature importance-based data reduction to address the
problem of a high number of data points and improve the delivery of service to the end user. In this
article, we use the feature importance method by applying two popular machine learning techniques
extra tree classifier (ETC) and the extreme gradient boosting classifier (XGBoost). Finally, the
performance of a number of tests is evaluated using experimental data from Bonn University. Also
achieved is a comparison of the two approaches used. The collected findings demonstrate the efficacy
of the XGBoost technique and its greater accuracy in comparison to the ETC strategy.

The general concept of algebraic statistics is to employ algebra tools to provide a better view of the
structure of statistical problems and to contribute to finding solutions. The algebraic and statistical
mixture represented by the method of Markov basis for the independent model (MBIM) on the
contingency table is considered highly efficient to study and analyze the target problem which is
Rheumatoid arthritis. The verification of the proposed method is carried out by taking advantage of the
effect of the entropy property by using the law of Renyi entropy to show which of the alternate matrices
that exist within the fiber elements are more independent than others

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that first appeared in Wuhan, China, gave rise to the coronavirus disease 2019 (COVID-19), a highly contagious and pathogenic viral infection. COVID-19 is not currently being treated with a specific therapy. Remdesivir is the first medication to be approved by the Food and Drug Administration (FDA) to treat COVID-19. However, numerous alternative therapy modalities are being looked for as COVID-19 therapies. As part of this review, we discussed the development of various drugs, their mechanism of action, and how they might be applied to different cases of COVID-19 patients. This review also offers an update on the development of novel COVID-19 preventive or therapeutic vaccines. We aimed to include the most recent published data from phase III trials about various COVID-19 vaccines and include clinical data made available on networks or peer-reviewed journals in addition to vaccines approved by the Food and Drug Administration (FDA) or The World Health Organization (WHO).

Background: Spontaneous abortion (SA) is one of the major pregnancy disorders and poses a serious risk
to both the mother and the fetus. A large number of evidence confirms that there may be an immune
background to SA. The major possibility of apparently unexplained SA is thought to be caused by an
imbalance of immunological tolerance, a condition in which Natural killer NK cells may play an
important role. Increased peripheral Natural Killer Cell (pNK) activity may cause SA. The aim of the
current study is to find out whether natural killer cells have a role in abortion in women by measuring
their concentration in the blood using the ELISA technique.
Materials and methods: The current study includes 50 samples from spontaneous abortion SA women
and 30 samples from healthy pregnancy outcomes (control). Where it was collected from: Babylon
Teaching Hospital for Maternity and Children and AL-Imam AL-Sadiq Teaching Hospital in Hilla City
during the period from October 2022 to December 2022. When an ultrasound scan confirmed the death of
the fetus and the signs of SA appeared on the patient, 3 ml of venous blood from patients and healthy
controls were collected by the physician, and the samples were used for immunological detection of pNK
cell levels by the ELISA method.
Results: Depending on the data that appeared in the work, there is a discrepancy in the concentrations
between patients and controls, but it doesn't reach the degree of significance. The statistical analysis
showed that the concentration of NK cells in patients was not significant compared with the controls'
means (107.33 and 80.229) respectively and the standard deviation (46.64 and 23.73).
Conclusion: Results from this study suggest that the NK cells themselves may not cause SA, but rather
that this rise may be due to another cause. This is because the concentration of natural killer cells is slightly elevated in the peripheral blood of aborted women compared to healthy women, and it is only
associated with cases of recurrent spontaneous abortion.

Asthma is a chronic disease of the airways of the lungs caused by inflammation and narrowing of the
airways. Asthma patients are sometimes misdiagnosed because of the similarity of asthma symptoms to
other respiratory diseases. This leads to administering inappropriate medications, and thus the patient's
condition worsens. In this search, we propose a new approach based on ontology, the aiming this method
is that to develop a system that can diagnose asthma and suggest the appropriate treatment for each
patient. The proposed approach consists of three steps: the knowledge base, four modules that act as
mediators between the user interface and ontology, and the web application that act as an interface to the
approach. Semantic Web Rule Language (SWRL) was used for asthma diagnosis. The proposed system
was tested using several patients previously diagnosed with asthma. The obtained results showed that the
approach could diagnose the disease and recommend treatments. It diagnosed 56 out of 59 (accuracy rate
of 94.91%) and correctly recommended treatment for 20 out of 22 patients (accuracy rate of 90.9%). The
approach also showed good speed in the previous two procedures, as it took 3.6 seconds to diagnose and
15.4 seconds to recommend the appropriate treatment.

Estimation for reliability of a parallel redundant system with independent stress and strength Weibull-
Ryleigh probability density functions is considered. Estimation of the reliability parameters was
conducted according to three methods, namely maximum likelihood, moments and percentiles methods.
Finally, the reliability estimate was calculated and the best method for estimation for each case was given
using the mean squared error criteria. It was found that the best estimation method is the percentiles
method.

Breast cancer (BC) is the most common types of cancer among women. Based on international
organizations, hundreds of women die from this disease each year around the world. Most common risk
factors of BC in women are hormone-related pathway. Some cases of BC show an increase in the level of
steroid-receptor protein in the breast tissue, these cases of cancers are called hormone dependent. In the
other hand, a clear relationship between high level of thyroid hormone and BC has been reported in
some studies. In this work, the relationship between steroid and thyroid hormones with BC was studied in
Iraqi women who are diagnosed with BC and before starting any kind of therapy. Serum concentrations
of steroid and thyroid hormones was measured using a fully automatic device. Blood samples were
collected from 60 women affected with BC and 60 healthy women, those groups are dividing into two
sub-groups; premenopausal and postmenopausal.

This study is a review of both the continuous Galerkin finite element method (CGFEM)and the
discontinuous Galerkin finite element method (DGFEM).A group of the most important research over the
last 13 years has been compared to both methods and their historical development, as well as comparing
the advantages and disadvantages of both methods.This review is aimed at determining the best and most
efficient method to solve complex problems in the future.

In this paper,we present the conditions that must be met in order to obtain a general solution to the
bounded operator equation ???????????????? + ???????????????????? = ???? , where ???? is a closed range operator and ???? is any
operator on the Hilbert space H. We only propose what is necessary and sufficient. Several theorems are
proved by the existence conditions. that provide explicit forms for solutions and conditions for their
existence. The general explicit solution of the operator equation is one of the main results, as well as
bounded solutions for cases whereP has a pseudo-inverse operator, The authors also provide proofs for
each of the theorems.

In this study, we construct the fuzzy soft rectangular b-metric space first, and then define the fuzzy soft
convergence sequence, also known as the fuzzy soft Cauchy sequence, in this space. In addition, we
defined fuzzy soft contraction mapping and proved its fixed point in fuzzy soft rectangular b-metric
space.

In this paper, we discuss the action for topological groupoids. In our work, some definitions and
propositions are introduced and in addition to the basic construction of topological groupoid which are
needed and we studied the different properties of categories and the properties of topological groupoid,
and we define concepts such as topos, , functor and sheave.

In this paper, a DNA molecule is modeled as a basis for studying the properties of a single electron
transistor (SET). Solid binding model (SET) characterization and stationary-state equation are adopted to
elucidate the electron transport properties through this model. In this work, a good pattern is presented for
the calculation of curves (IV) for a single electron transistor and theoretical calculations for the drain
current as a function of the source bank voltage and study the effect of heat and gate voltages on it. All
calculations of this model are based on the transmittance spectrum. The results of the transmittance
spectrum were calculated at equilibrium. The current was evaluated using the Landauers formula.
Through the results obtained, it is clear that there is a mutual effect between the gate voltage and the
source-drain voltage on the current of the source-drain. Also, there is a clear effect of the temperature of
the electrodes on the values of the source-drain in all the studied cases. Through all these calculations of
the DNA molecule the results were encouraging and gave the common features of SET. These results are
very useful for getting closer and closer to the fabrication of nanoscale electronic parts.

Undoubtedly, electronic goods made life easier for people and supplied It made life easier for everyone
and gave comfort. However ongoing updates, the short lifespan of electronic products, affordable prices,
and illegal disposal of electronic waste, as a result, electronic waste has made electronic waste faster
growing. This review highlights current bioremediation techniques, bacterial and fungal bioleaching
utilize normally occurring microorganisms and their by-products to extract valuable metals, which,
because of its affordability, environmental friendliness, and sustainability, is a promising technique.
Bacterial species are resistant to high concentrations of heavy metals and organic pollutants were
highlighted which have possibly potential remediation properties.

The Internet of Things, or IoT, is now an important technology that is the basis for various innovations
in intelligent environments, including smart homes and innovative healthcare. Due to their architecture,
many IoT devices suffer from security issues, leading to increased electronic threats targeting IoT
devices, facilitating abuse and lack of security control. The most common attack in IoT environments is
the botnet attack, which supports various criminal activities. In this study, This article aims to study
different methods for selecting features and comparing them to find the best possible strategies for
selecting and reducing features to detect botnet attacks in IoT devices. By using the UNSW-NB15
dataset, we will analyze the system's performance suggested to solve the classification issue. On the
UNSW-NB15 dataset, the results obtained using the LSTM, BRNN, and GRU classifiers were analyzed
to solve the binary classification issue and multiclass classification issues and compare the performance
of three different selection features (Correlation method, GNDO method, and Lasso method) of network
intrusion detection. The proposed system was evaluated by using different performance metrics and
comparing the various techniques to show better performance. The results showed with that a Filter
method (Correlation) for selecting features is better than other methods, and the model GRU in deep
learning got the highest accuracy, amounting to 92.71% and 78.62% for both binary and multiple
classifications, respectively. This study can potentially be applied in practical settings to detect real-time
network intrusions with a dynamic nature

Objective in the newborn critical care unit, seizures are one of the most frequent medical emergencies.
They are identified by visually electroencephalography (EEG) results assessment, and neuroscientific
professionals are responsible for their care. This method takes a long time, and the results are
inconsistent. In order to solve this issue, a system for identifying epileptic seizures from EEG data
collected from healthy individuals and epileptic patients is presented in this research. This system is
based on a study of EEG data utilizing the discrete wavelet transform (DWT) and both linear and
nonlinear classifiers.
Methods: Neonatal human EEG recordings are included in the dataset used in our method. A 19-channel
EEG equipment was used to record the brainwave activity of 79 term newborns referred to the NICU at
the Helsinki University Hospital. Five patients with structural seizure and non-seizure annotations were
chosen for the pilot study from these databases. Three experts independently identified the presence of
seizures and non-seizures in the EEGs using visual interpretation, while statistical features were retrieved
using the k-mean Algorithm and RS was utilized to identify seizures.

Major Depressive Disorder (MDD) is the primary cause of impairment and one of the primary reasons
for suicide. It is a serious ailment that millions of people are currently suffering from it. Any decline in
brain function during MDD or depression is evident in the Electroencephalogram (EEG) signals. In this
study, a Fast Fourier transform (FFT) method-based automated framework for depression recognition is
proposed. Firstly, EEG signals from each channel are divideGXVLQJ))7LQWRILYHIUHTXHQF\EDQGV Įȕ
Ȗ į DQG ș 1H[W nonlinear features are extracted from each band, including approximation entropy,
dispersion entropy, Shannon entropy, and Second Order Different Plot (SODP). The Mann-Whitney U
test is used to get rid of undesirable features that could harm learners' performance. The selected features
are passed to the least square support vector machine (LS-SVM) for comparison with other classifiers,
including the support vector machine (SVM), k-nearest Neighbour (KNN), and decision tree (DT). The
performance of the suggested model is assessed using K-fold cross-validation. A publicly accessible
dataset was used to model our investigations. The results showed that the reduction of features can lead to
high accuracy, where the highest accuracy obtained was 0.96%, it was using the LS-SVM classifier, with
Shannon Entropy and SODP features only from the delta and gamma bands

Parkinson's disease (PD) is a brain disorder that causes speech and communication problems. The speech
issues are often described as slow speech and difficulty with articulation because Jaw muscles don’t
move with enough strength. Mainly, clinical experts make a voice assessment to analyze voice signals, to
detect PD. In this paper, we designed an intelligent model based on discrete wavelet transform (DWT) for
the detection of Parkinson's. Firstly, the voice signals are passed through DWT. The suggested DWT-
based model is then used to extract a collection of statistical features from voice data. The extracted
features are evaluated, and the significant ones are chosen. The features that have been chosen are fed
into a least squares support vector machine (LS-SVM), as well as another four classifiers K-nearest
neighbor (KNN), bagged tree, SVM (support vector machine), and k means for the comparison. The
proposed model is evaluated using a publicly available dataset named UCI machine learning repository.
Several tests were carried out, and the findings revealed that the proposed framework can classify voice
signals with a100% accuracy in the LS-SVM technique