University of Information Technology and Communications

The high mortality rate around the world can be attributed to Coronary Artery Disease (CAD). Therefore, in order to prevent extreme outcomes (including coronary heart attacks and sudden death), early detection is essential. A diagnostic (non-invasive) tool commonly used to identify early symptoms of CAD is Electrocardiography (ECG). However, the interpreter's knowledge may overlook diffuse patterns, thus affecting the accuracy. Artificial Intelligence (AI) is a promising solution that has emerged recently. That is because it can utilize advanced algorithms to analyze large ECG datasets and identify previously hidden indicators of early-stage disease. This research demonstrates the significance of ECG in CAD diagnosis and investigates how AI can enhance diagnostic accuracy to improve preventive measures for cardiac disease.

The ship's event-triggered tracking algorithm, which combines game theory and remote sensing. The technique addresses issues with conventional continuous and discrete control techniques, which consume excessive amounts of energy and prematurely wear out actuators due to frequent updates. In order to locate ships, this paper first employs the YOLOv8 deep neural network and radar-based remote sensing. After determining the distances and connections between ships, the target trajectory is altered using a game-theoretic framework. In order to reduce needless computations and communications, the controller only activates when significant events take place. This is not the same as conventional time-triggered techniques. Good results were obtained from tests on two benchmark datasets: the Synthetic Aperture Radar Ship Dataset (SAR-Ship-Dataset) and the SAR Ship Detection Dataset (SSDD). On SSDD, the proposed algorithm achieved 92% accuracy and 93% sensitivity, while on SAR-Ship-Dataset, it achieved 93% accuracy and 91% sensitivity. These results lay the foundation for the development of more efficient marine tracking systems by demonstrating that event-triggered tracking can maintain high tracking accuracy while using less energy.

In disaster scenarios where traditional communication infrastructure is unavailable or damaged, the rapid deployment of efficient ad hoc wireless networks is critical. These networks must be adaptable, self-organizing, and capable of maintaining reliable communication links under uncertain and dynamic environmental conditions. However, obtaining optimal ad hoc network design and accurate node localization remains a challenging task, especially in disaster scenarios where time is critical. This work addresses these issues by introducing a novel Multi-Conditioning Variational Autoencoder (MCVAE) model for generating optimized ad hoc node localization conditioned on multiple factors, such as network area size, node count, and success criteria. The proposed generative model not only addresses the localization problem but also enables on-demand generation of emergency ad hoc networks, making it a practical solution for real-time deployments in disaster-stricken areas. Furthermore, a synthetic dataset incorporating realistic features that effectively describe the ad hoc node distribution environment was created. The MCVAE model was trained on this dataset to learn complex dependencies between input conditions and network design. Experimental results demonstrate that the model effectively generalizes beyond the training area sizes, accurately generating node layouts for previously unseen dimensions with efficient distribution and low localization error (RMSE 0.125 for a network size of 450 m2 with 285 nodes) and an R2 score of 0.9999. Additionally, the proposed methodology achieves excellent scalability and adaptability for generative ad hoc networks, allowing users to specify different area dimensions and automatically receive efficient node distributions with minimal localization error. The findings highlight the model's potential for real-time, data-driven deployment of resilient ad hoc networks in disaster-stricken or infrastructure-limited regions. This is the first work to contribute a practical and scalable use of a generative AI model that can significantly enhance wireless communication readiness in emergency situations.

Abstract It is widely acknowledged that the increase in energy use, particularly from fossil fuels, is the main reason behind the present environmental issues in the world, including climate change. To mitigate and adapt to this anthropogenic effect, a range of innovative, new low carbon and renewable technologies were needed, and these are gradually replacing the traditional, largely fossil fuel-driven construction systems. This gives emerge to a research problem: Evident ignorance of the techniques for creating zero-energy structures in Iraq: Utilising thermal thrust, it can assist in the creation of thermally comfortable and user-friendly building environments in order to prevent rising carbon emissions and the financial load on users. This research intends to address the evident ignorance regarding the techniques for creating zero-energy structures in Iraq: Utilising thermal thrust, and find innovative tools and methods for building design that can produce structures that optimise thermal comfort for users while conserving energy and improving the efficiency of the interior environment.

In this paper, the evaluation of drone deployment strategies within wireless communications networks is evaluated through the development of a simulation framework done using Python Tkinter, and Matplotlib libraries. The modelling system utilizes a 1000x1000 meter area for both random and optimized drone deployments. Performance metrics, including signal strength, average data rate, latency, coverage ratio, battery consumption are computed using distance based mathematical models. The findings show that optimized drone deployment increases the point-to-point signal strength by 25–35% and prevents average data rates from degradation over time when compared to passive deployment. Latency is stable at the levels of 25.5–26.5 ms in the optimized case. User coverage also increased from 75%-85% up to almost 100% in all the intervals, battery usage also enhanced over the drone swarm. These enhancements to ensure strategic positioning do indeed greatly improve the reliability of a network, resource efficiency, and continuity of service. The results provide implementable understanding in a practical-world UAV assisted communication network that gives scalable solutions for future 5G/6G and emergency communications.

Control charts are widely used in statistical process control (SPC) to detect small, gradual shifts in process behavior, although effective at mitigating noise, such as the exponentially weighted moving average (EWMA). Traditional EWMAs, however, face significant challenges and limited adaptability in complex and dynamic environments. In this paper, we propose an improved hybrid approach that integrates EWMAs with artificial intelligence algorithms, such as anomaly detection models, deep learning networks, and unsupervised learning, to enhance the early detection of non-random variations and subtle process trends. Simulations and real-world datasets were used to validate the effectiveness of the integrated model in identifying slow-developing faults.

The primary factor influencing road performance is pavement deterioration. Pavement cracking, a prevalent form of road deterioration, is a significant challenge in road maintenance. This paper proposes a method utilizing deep convolutional neural network models for precise crack detection, segmentation, and geometric parameter calculation in pavement crack identification. The system operates through three primary stages: Commencement, crack identification employs YOLOv10, a rapid and efficient object detection model. Secondly, crack segmentation employs a modified Unet 3+ variant known as Residual-Attention UNet 3+, which effectively distinguishes crack pixels from the background by utilizing attention mechanisms and residual connections to enhance accuracy. Finally, crack quantification, wherein the system computes the crack's geometric parameters, including width, length, angle, and orientation. We assessed performance using two datasets: SUT-Crack, a publicly accessible dataset, and IRD-Crack, a new real-world dataset compiled by the authors from roads in Diyala, Iraq, with diverse lighting conditions and surface complexities. The suggested technique attained an accuracy of 98.96% on the SUT-Crack dataset. It showed superior performance on the IRD-Crack dataset under actual situations, therefore validating its efficacy and generalization capability. This method offers a pragmatic and computationally efficient instrument for monitoring pavement cracks and can facilitate road repair choices.

This study presents the design, simulation, and experimental validation of a high-frequency hybrid coupler employing Substrate Integrated Waveguide (SIW) technology, targeting operation at 26 GHz and 28 GHz within the Ka-band spectrum. Conventional planar structures at millimetre-wave frequencies often exhibit substantial insertion losses due to their limited confinement and the reduced wavelength. To mitigate these limitations, a coupler architecture based on the direct coupling vias technique is introduced. The proposed structure integrates two rows of metallized via holes along the SIW’s narrow wall, with a strategically engineered aperture enabling controlled electromagnetic coupling between adjacent waveguides. This configuration offers enhanced control over both the coupling coefficient and the phase difference between output ports. The design was rigorously analysed using CST Microwave Studio and subsequently fabricated using metal additive manufacturing techniques. Measured results demonstrate reflection and isolation levels below –20 dB, transmission coefficients between –2.9 dB and –3.6 dB, and an output phase imbalance of approximately –87.2°, confirming the design’s efficacy. The proposed SIW-based hybrid coupler exhibits strong potential for integration in high-performance Ka-band systems, including next-generation satellite communications and radar applications.