Wasit Journal for Pure Sciences

In this research, I wanted to create a new beautiful class defined by subordination and the integral
operator and the combination between them and I named it ????????????(????, ????, ????,????,????) so that I inferred results from the
geometric properties of this nice class in terms of studying the radius of convexity and estimating the coefficient and
other new results. I also presented the previous information related to the distinguished researchers who before me
studied the subordination and the integral operator that I used in my beautiful research.

In the realm of cybersecurity, the increasing sophistication of cyber-attacks demands the creation of
sophisticated intrusion detection systems (IDS) designed to accurately detect and counteract threats in real-time. This
study presents an innovative framework that integrates the Improved Dynamic Arithmetic Optimization Algorithm
(IDAOA) with a Bagging technique to enhance the performance of intelligent cyber intrusion detection systems. The
IDAOA serves as a wrapper-based feature selection method, optimizing the identification of the most impactful
features while balancing local exploration and global exploitation. The Bagging technique further strengthens the
classification phase by combining predictions from multiple classifiers, effectively addressing issues of class
imbalance and improving overall system robustness. Evaluation of the proposed system using the NSL-KDD dataset
demonstrates its superior performance, achieving an accuracy of 99.45%, significantly outperforming state-of-the-art
approaches. Moreover, the proposed method achieved Precision, Recall, and F-score values of 99.63, 99.52, and
99.57, respectively, indicating its high reliability. These findings underscore the potential of intelligent optimization
and ensemble learning techniques in advancing cybersecurity for IoT networks.

Evolutionary algorithms suffer significantly from stagnation at local optima. This paper proposes a
new strategy that detects when the search gets stuck in a local optimum and then adopts a dynamic search strategy to
escape stagnation. The proposed model is based on simulating eight pattern movements and embedded with a Grey
Wolf Optimizer algorithm (GWO). It is called the Eight-Figure Grey Wolf Optimizer (Eight-GWO). The proposed
model combines two phases: regular search when searching progresses over time while the second phase, searching
by eight patterns when the algorithm reaches stuck. The Eight-pattern updates the gray position based on the sin and
cos function. The Eight-pattern updates the gray position using sine and cosine functions to introduce controlled
oscillations in the search process. These trigonometric functions help balance exploration and exploitation by
dynamically periodically adjusting positions. The proposed Eight-GWO on the 24 functions of the CEC2005
benchmark suite and compared its results with both the standard GWO and Particle Swarm Optimization (PSO). The
experiments result show the proposed Eight-GWO gets better results than GWO and PSO where it achieved the best
results on 80% of the test functions. The proposed Eight-GWO runs 23% faster than the original GWO and 44% faster
than PSO.

Protection from floods is one of the most significant activities aimed at risk reduction. Flood prediction
and disaster preparedness have relevance for reducing the association between floods and people and infrastructure.
The goal of this study was to find out how well the Support Vector Machine (SVM) and Decision Tree classifiers
work at predicting flooding based on different environmental and infrastructure factors. Data collection had been done
through gathering 140 samples with a total of 21 variables where analysis had been done in order to identify significant
contributory factors: topography, urbanization, and climate change. The results show that an SVM model was capable
of achieving an accuracy of 91%, while the Decision Tree classifier did much better at an accuracy of 95%. The
decision tree model was also more precise in flood prediction (1.00) and recall for non-flood cases (1.00), while for
both models, the recall for flood cases was the same (0.88). This indicates that both the models had some false
negatives for floods. The current study focuses more on machine learning applications and disaster readiness in flood
risk assessment for better and more effective mitigation.

Medical image segmentation is an integral part of computer-aided treatment planning and diagnosis,
enabling accurate analysis of abnormalities and anatomical structures. In this paper, a novel Hybrid U-Net and
Transformer encoder (HUT) -based deep learning architecture is developed to advance precision and speed in medical
image segmentation. The U-Net architecture, for its remarkable capability to capture local details, is integrated in
constructive collaboration with a transformer encoder, whose power is in capturing data's long-range dependencies.
The constructive interaction between such capabilities makes the resultant hybrid architecture robust for segmentation
in diverse medical imaging modalities. The approach is rigorously compared on publicly available datasets, including
brain MRI, Chest X-Rays, and ISIC, and is shown to have better dice coefficients than the current state-of-the-art.
Our model achieves a Dice score of 0.86 on BraTS, 0.90 on ISIC 2024, and 0.96 on Chest X-Ray representing a 3.5%
improvement over TransUNet 0.83 on BraTS, 0.87 on ISIC 2024, and on Chest X-Ray 0.95, as shown in Table 1.
The constructive collaboration between U-Net and transformer components, in addition to better segmentation, is
shown to result in computational savings, thus becoming efficient for clinical application. The result proves the
capability of such a hybrid architecture to advance diagnostic precision and contribute to enhanced treatment planning
in clinical settings. HUT sets a new benchmark for efficient, high-precision segmentation, with direct applicability to
resource-constrained clinical workflows

A global public health concern with a substantial economic impact is antimicrobial resistance, which is the capacity
of microorganisms to alter and adapt their behavior, rendering medications typically employed against them useless.
The rise of drug-resistant infections, driven by the overuse and misuse of antibiotics in healthcare, agriculture, and
other sectors, has led to the emergence of dangerous superbugs, such as MRSA and MDR-TB. These resistant bacteria
evade traditional treatments through various mechanisms, including horizontal gene transfer, biofilm formation, and
the production of resistance enzymes. Antimicrobial Resistance spreads primarily through human-to-human
interaction and environmental sources, including contaminated water and food. Addressing this crisis requires a
multifaceted approach, including antimicrobial stewardship, better hygiene practices, more effective infection control,
and the development of new treatments. Artificial intelligence is emerging as a promising tool to identify resistance
patterns and optimize treatment regimens, offering hope in the fight against AMR. Efforts to combat AMR must be
collaborative, involving governments, healthcare providers, researchers, and communities to ensure sustainable
solutions to this global health challenge.

The current study aimed to determine the morphological description of the pancreas in the Indian
Roller Coracias benghalensis (Linnaeus, 1758). In this study 15-10 adult live birds were used, which were
anesthetized with chloroform and then dissected and the pancreatic gland was removed, then the samples were fixed
with fixative solutions. The morphological examination revealed that the Indian roller bird\'s pancreas is rounded and
long, and is a pale pink color. It is found on the lower right side of the body cavity and it has three lobes: the ventral
lobe, the dorsal lobe, and the splenic lobe. The ventral lobe was the longest and largest cylindrical lobe and extends
within the mesenteric tissue of the duodenal loop. While the dorsal lobe appears flat and lay parallel to the descending
arm of the duodenum, the splenic lobe appears in the form of a small volume pear-shaped lobe extending from the
end of the dorsal lobe of towards the spleen.

This study investigates the role of the ADH4 gene expression and hematological biomarkers in the
pathogenesis of Immune Thrombocytopenic Purpura (ITP) in pediatric patients. The study involved 100 children, 50
with ITP and 50 healthy controls, to evaluate blood parameters and ADH4 gene expression when Real-Time PCR.
Results showed the mean white blood cell (WBC) count was significantly lower in ITP patients (8.11 ± 0.73)
compared to controls (11.94 ± 0.69), Similarly, lymphocyte counts were significantly reduced in ITP patients (3.29 ±
0.43) versus controls (5.65 ± 0.42), The platelet count was markedly lower in ITP patients (26.18 ± 2.77) compared
to controls (277.40 ± 10.74), On the contrary, The results show there was a non-significant difference between ITP
patients and healthy individuals according to both neutrophil count and hemoglobin (Hb) levels (P < 0.05), while,
ADH4 gene expression was significantly higher in ITP patients shown The mean 13.29, 1.00 between the ITP patients
and Control respectively, suggesting its involvement in immune dysregulation. These findings highlight the potential
of the ADH4 gene and some hematological parameter as a biomarker and therapeutic target in ITP, emphasizing the
need for personalized treatment approaches

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that can severely impair speech,
and therefore its early detection is important for the management of patients. Conventional diagnostics are often
invasive, language-dependent, and are not sensitive in the early phase. Hence, there is an urgent need for a robust,
non-invasive, and language-agnostic solution. To improve the classification performance, this study introduces a
novel diagnostic framework based on the analysis of speech signals using a Long Short-Term Memory (LSTM) neural
network optimized by the programming of the Grasshopper Optimization Algorithm (GOA). As a measure to decrease
complexity by maximizing class separability, the proposed framework employs Linear Discriminant Analysis (LDA)
for dimensionality reduction. At the same time the LSTM is the main deep learning model, which is trained by using
speech samples collected from healthy controls and PD patients to recognize the temporal speech patterns of PD.
GOA optimizes the hyperparameters and guarantees better performance while the training convergence is rapid and
very efficient. Two publicly available speech datasets were used to evaluate the model, NeuroVoz (Castilian Spanish)
and EWA-DB (Slovak). It obtained accuracies of 99.45% and 99.71%, respectively, outperforming existing methods
in both precision and generalizability. These findings emphasize the potential of emerging hybrid techniques such as
the integration of deep learning and swarm intelligence and using techniques such as feature engineering for swift,
accurate, and scalable diagnosis of Parkinson’s in multiple languages.