SU Journal of Engineering and Information Technology Innovations
The scope of Sohar University Journal of Engineering & Information Technology Innovations (SUJEITI) journal includes all fields of research encompassing various specializations related to Engineering and Information Technology. SUJEITI will publish: • Original communications (Research articles) focusing on recent and important discoveries or further developments in important fields. • Review articles on recent high-priority areas in Engineering and Information Technology Innovations.
Authors who publish with an applied computing and applications publisher agree to the following terms: Authors of this work retain copyright and grant the journal the right to a publishing license of first publication, with the work simultaneously open access licensed under Creative Commons Attribution International License (CC BY 4.0). Author(s) and ACAA permit unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Proactive Microservice Orchestration in Space–Air–Ground Integrated Networks Using Scout Containers
This work explores the problem of microservice orchestration in integrated networks based on efficient deployment methods. A set of methods for orchestrating microservices in dynamic distributed environments with limited and intermittent communication channels was developed and evaluated. After analyzing the architecture of integrated networks and URLLC requirements, an approach using scout containers for predicting and proactively initiating migration was proposed. Using hardware-virtual and virtual rigs emulating real-world conditions, results were obtained on deployment time, migration success, the impact of losses, latency, and jitter, and the resource intensity of the scout containers was assessed. The proposed approach reduced downtime to a few seconds and significantly reduced the load on computing nodes. Based on a comparative analysis of reactive migration, proactive scheduled migration, a scout container, and a hybrid strategy, practical recommendations were developed for selecting and configuring orchestration parameters to ensure stable and rapid service delivery under limited communication conditions.
Copyright (c) 2026 Author(s) of this work retain copyright and grant the journal right, publishing license of first publication with the work simultaneously open access licensed under Creative Commons Attribution International License (CC BY 4.0). https://creativecommons.org/licenses/by-nc/4.0
Conceptual Design and Prototyping of an IoT-Based Camel Tracking and Perimeter Monitoring System
The main aim of this study is to provide conceptual design elements for tracking and monitoring animals, specifically camels, for the purpose of complete prototyping, considering ensuring precise location tracking and enhancing security. The primary innovative monitoring approaches in our system implementation are the utilization of GSM and GPS technologies. The laser sensor, which is integrated with farm gates, is a critical component of the system that ensures the protection of camels and real-time surveillance through a mobile platform. The Internet of Things (IoT) successfully receive the information concerning physical environments of the camels via an integrated security gate alarm sensor, then transmit it to a cloud network using a GSM network. The purpose of the security system allocation is to implement real-time monitoring and access control measures. Various components have been utilized in the design consideration process throughout the deployment of the system. The robust components of this technology (geographical location using Arduino and SIM800L GSM and Global Positioning System (GPS) modules, aside from geographical capabilities) have been specifically chosen for the purpose of efficiently and effectively tracking animals in diverse environments. The tracking devices will be made to be worn for a long period of time because of these devices which also provide accurate and updated position data. The proposed prototyping has several features to provide a unique, reliable performance over long-life usage in terms of user-friendliness and sustainability through economic and cost considerations. In short, the system that was installed in place gives a latitude and longitude that is closely with the real location in Google Maps. The latest IOT technologies have enabled the camels' real-time monitoring and safety management.
Towards Smart Cities: Practical Swarm Optimization and Long Short-Term Memory for Short-Term Traffic Prediction and Management in Oman
Traffic congestion forecasting is a serious challenge to Omani cities, especially Muscat, due to population growth, rapid urbanization, and heavy reliance on private vehicles. Despite government efforts to improve infrastructure, the congestion problem persists. Furthermore, few studies have addressed the issue of congestion in Oman, as this is due to the lack of a national dataset that can be used for prediction studies. Therefore, this study aims to generate a notional dataset and propose a hybrid model that combines a deep learning model with an optimization algorithm to provide accurate predictions for the short term. This work is expected to contribute to bridging an important knowledge gap and establishing a scientific basis for developing smart transportation applications in Oman. Furthermore, in the future, the model can be enhanced to use IoT techniques to collect data and extend the model to predict for the long term on multiple roads. The proposed hybrid PSO-LSTM model provides accurate predictions, with MAE of 1.7185 vehicles, an RMSE of 1.3109 vehicles, R2 of 0.9460 and an MAPE of 1.75%. It provides superior performance compared to the standalone LSTM.
Machine Learning Approach to Analyze the Impact of Seismic Force
The study investigates the impact of seismic forces on structural response using a machine learning (ML)–based approach. The study has utilized scaled structural models and subjected them to control shaking table excitations representing low, medium, and high earthquake intensities. A linear regression–based ML model was developed using multiple input parameters to predict the structural response characteristics. The proposed model successfully captured the overall behavioral trends of the structures, achieving high validation accuracy. However, prediction deviations increased at higher peak ground acceleration (PGA) levels due to nonlinear seismic effects. Residual and root mean square error (RMSE) analyses indicate that, although the ML model slightly underestimated peak responses, it remained effective in identifying general response patterns. Comparative validation demonstrated a reasonable variation between lab observations and ML predictions, supporting the use of ML as a complementary predictive tool. The findings highlight the effectiveness of ML frameworks in achieving high prediction accuracy and their potential contribution to seismic design optimization.
• Seismic waves • Lead rubber bearing • Base isolation • Structural response • Experimental methods • Machine learning
Investigation of flow velocity distribution in open channels according to specific characteristics (under the effect of bed materials: sand, gravel, and concrete)
This study presents an in-depth experimental investigation into the effects of varying bed materials on flow velocity distribution in open channels—a critical consideration in hydraulic engineering and sustainable water resource management (Ab Ghani ,2018). Conducted within a controlled laboratory flume setup, the research examined how different sediment types, including vegetation (at varying lengths and arrangements), gravel (of multiple grain sizes), and sand (of different densities), influence hydraulic parameters such as flow velocity, discharge, water depth, hydraulic radius, and Reynolds number. Using flow current meters and hydraulic modelling principles, velocity profiles were produced for each bed condition using flow current meters and the concepts of hydraulic modeling. The results show that bed material dramatically changes sediment transport dynamics, turbulence properties, and flow resistance. Finer sands improved flow velocity because of their smoother surfaces and lower roughness coefficients, but vegetation increased flow resistance because of drag effects. These realizations lay the groundwork for creating channel systems that are more effective and resistant to erosion. These findings offer valuable insights for agricultural irrigation, urban drainage planning, and environmental hydraulics, with applications in sustainable water management and civil infrastructure development (Ohmoto, T. 2020). . The results showed that sea sand achieved the highest flow velocity (0.284 m/s), while dense vegetation (25 cm spacing) and coarse gravel (14 mm) significantly reduced flow due to increased resistance. The Reynolds number ranged between 552 and 5371 depending on the material, indicating transitional to turbulent flow. Manning’s coefficient was highest for coarse gravel and dense grass, reinforcing the strong effect of roughness on flow characteristics.
Effects of Stainless-Steel Waste Metal Strips and Recycled Aggregate on the Structural Behavior of Reinforced Concrete Beam: Experimental Case Study
This study investigates the structural performance of reinforced concrete (RC) beams incorporating recycled aggregates (RA) and stainless-steel fibers (SSF) derived from commercial metal waste. A total of nine RC beams (1.5 m × 0.15 m × 0.1 m) were cast using varying RA contents (0%, 25%, and 50%) sourced from hollow blocks, along with SSF dosages of 0%, 0.25%, and 0.5% by volume. Additionally, 27 cubes and 9 cylinders were prepared to evaluate compressive and tensile strengths for each mix design. The beams were tested under two-point loading using a 1500 kN frame. Experimental results demonstrated that the inclusion of SSF significantly enhanced the flexural behavior and tensile strength of the concrete matrix, while the use of RA led to a general reduction in mechanical behavior. However, the strength loss due to RA was partially offset by the presence of SSF. Compressive and tensile strength increased consistently with higher SSF content across all RA levels, with the most notable gains observed at 0.5% SSF. The increased density of the specimens was attributed to the high specific gravity of SSF (8000 kg/m³) compared to conventional concrete (2400 kg/m³). Overall, the incorporation of SSF improved the mechanical performance of RC beams and mitigated the adverse effects associated with recycled aggregates. All tests and evaluations were conducted by BS 8110-1:1997.
Copyright (c) 2025 Sohar University Journal of Engineering and Information Technology Innovations https://creativecommons.org/licenses/by-nc/4.0
CFD Simulation of Sohar Ambient Temperature Variation Effects on the Performance of a Wind Tunnel with a Fixed-Blade Wind Accelerator
Ambient temperature plays an important role in influencing the aerodynamic behaviour and energy efficiency of wind energy systems, particularly valid in arid and semi-arid regions such as Sohar. The present study shows a computational investigation into the effects of temperature variations on the performance of a wind tunnel integrated with a fixed-blade wind accelerator, using climatical conditions of Sohar. The Computational Fluid Dynamics (CFD) simulations were performed using ANSYS Software, through a temperature variety from 15°C to 50°C in 5°C increments. The aerodynamic response was assessed through the velocity distribution, pressure profile, and turbulence intensity, at six distinct points within the tunnel. The results of this novel study indicate that increasing ambient temperature leads to a decrease in the pressure profile and then an increase in the wind velocity. The turbulence intensity rises significantly due to a reduced air density and changed flow stability. These aerodynamic properties degrade the efficiency of the wind accelerator. They impact the downstream turbine performance. The findings of this research highlight the importance of incorporating temperature sensitivity into the aerodynamic design of wind energy systems in hot climate regions. This research gives a great insight into enhancing the resilience and efficiency of wind power infrastructure under varying environmental conditions.
Copyright (c) 2025 Sohar University Journal of Engineering and Information Technology Innovations
UAV-Based IoT System for Public Temperature Surveillance Using Thermal Imaging
Elevated body temperature is often an early indicator of potential COVID-19 infection, making its timely detection crucial for controlling the spread of the virus. Resent research underscores the versatility and advantages of unmanned aerial vehicles (UAVs) in various civilian applications. Building on these strengths, this study presents the design and implementation of a UAV-based temperature monitoring system for public areas to be used by the authorities. The design and development of this UAV based system, equipped with a thermal imaging sensor, autonomously patrols a defined area to continuously monitor individuals’ body temperatures. When a person with temperature above the normal threshold is detected, the UAV captures an image of the individual and stores it locally, while simultaneously sending an alert to a central control unit to initiate appropriate action. The system's successful development demonstrates the practicality and effectiveness of the proposed solution for real-time public health surveillance.
Rainfall Forecasting in Muscat Governorate Using Artificial Neural Networks and Hybrid Modeling Approaches
This study presents an advanced method for forecasting seasonal and annual rainfall in the Muscat Governorate of Oman, using artificial neural network (ANN) models and a hybrid approach combining wavelet decomposition with neural learning. Historical rainfall data from 1872 to 2017, sourced from Oman’s meteorological records, were analyzed to uncover long-term patterns, seasonal variability, and drought trends using the Standardized Precipitation Index (SPI). Initial statistical evaluations revealed high interannual variability and a slight declining trend in total annual rainfall, with the majority of precipitation concentrated in winter months. Artificial neural networks were developed to predict both annual and monthly rainfall based on autoregressive inputs and prior-month rainfall values. While the ANN models demonstrated moderate skill, limitations were observed in capturing extremely wet or dry years. To address this, a hybrid Wavelet-ANN model was constructed, enabling decomposition of rainfall signals into low- and high-frequency components for more targeted forecasting. The hybrid model showed improved performance, offering a more nuanced understanding of rainfall dynamics. Despite promising results, the models underscore the need for incorporating global climate predictors such as ENSO and IOD to improve forecast accuracy. The study concludes that ANN and hybrid methods provide a practical and scalable framework for enhancing regional rainfall forecasting capabilities, with significant implications for water resource planning and climate resilience in arid regions like Muscat.
Real-Time Glucose Level Interpretation Using a Fuzzy Logic Framework for Diabetes Management
Successful control of diabetes necessitates continuous observation of blood glucose levels and timely intervention to prevent acute complications. Traditional threshold-based systems often fail to capture subtle glucose fluctuations, particularly in real time. This paper presents a fuzzy logic-based system for dynamically assessing diabetes status and determining insulin doses using real-time glucose data from wearable or handheld sensors. Using expert-defined linguistic variables and fuzzy membership functions, the model categorizes glucose levels into clinically meaningful states, such as hypoglycemia, normoglycemia, and hyperglycemia, with graded severity. The fuzzy inference engine generates personalized alerts and dose recommendations based on American Diabetes Association (ADA) guidelines, ensuring medical relevance. The system was implemented using Python and tested across a wide glucose range (40–310 mg/dL). Simulation results showed that the model accurately recommended 0 units at low glucose levels (50–65 mg/dL), small doses at borderline values, and aggressive dosing at critical levels, with smooth transitions between categories. Compared to traditional PID control, the fuzzy logic model offered safer, more conservative dose adjustments and reduced risk of overcorrection. Designed for integration into mobile health platforms and intelligent agents like Furhat, this model represents a major step forward in delivering autonomous, interpretable, and patient-centric diabetes care.
Investigation of flow velocity distribution in open channels according to specific characteristics (under the effect of bed materials: sand, gravel, and concrete
This study presents an in-depth experimental investigation into the effects of varying bed materials on flow velocity distribution in open channels—a critical consideration in hydraulic engineering and sustainable water resource management (Ab Ghani ,2018). Conducted within a controlled laboratory flume setup, the research examined how different sediment types, including vegetation (at varying lengths and arrangements), gravel (of multiple grain sizes), and sand (of different densities), influence hydraulic parameters such as flow velocity, discharge, water depth, hydraulic radius, and Reynolds number. Using flow current meters and hydraulic modelling principles, velocity profiles were produced for each bed condition using flow current meters and the concepts of hydraulic modeling. The results show that bed material dramatically changes sediment transport dynamics, turbulence properties, and flow resistance. Finer sands improved flow velocity because of their smoother surfaces and lower roughness coefficients, but vegetation increased flow resistance because of drag effects. These realizations lay the groundwork for creating channel systems that are more effective and resistant to erosion. These findings offer valuable insights for agricultural irrigation, urban drainage planning, and environmental hydraulics, with applications in sustainable water management and civil infrastructure development (Ohmoto, T. 2020). . The results showed that sea sand achieved the highest flow velocity (0.284 m/s), while dense vegetation (25 cm spacing) and coarse gravel (14 mm) significantly reduced flow due to increased resistance. The Reynolds number ranged between 552 and 5371 depending on the material, indicating transitional to turbulent flow.
Open channel, velocity distribution, bed materials, discharge, rough coefficients, Reynolds number.
A Systematic Review of Performance Determinants in UAV-Assisted Cooperative Communication Systems
Unmanned aerial vehicles (UAVs)-assisted communication systems present a cost-effective solution for ad-hoc network operations. In mission-critical applications like surveillance, UAVs can function as aerial relays, particularly when multiple UAVs collaborate. This collaborative approach proves to be more efficient and economical in accomplishing assigned tasks compared to the use of a single UAV. Achieving reliable operation, however, requires addressing significant issues associated with deploying UAVs in mission areas. This article explores the opportunities offered by a cooperative relaying topology in a multi-UAV system, shedding light on specific challenges related to its implementation and providing communication reliability. Additionally, the article delves into the system evaluation metrics essential for measuring the performance of such network and discusses simulation tools suitable for this purpose.
Artificial Intelligence-Driven Enhancement of X-Ray Images for Medical Detection
Early disease prediction and diagnosis models have piqued the interest of medical professionals. A simple medical examination, such as a chest X-ray, is essential for diagnosing lung conditions such as lung cancer, COVID-19, pneumonia, tuberculosis, and pneumoconiosis. However, the advancements in medical imaging have practical implications for accurate diagnosis and patient treatment that extend beyond lab settings. Recently, image analysis capabilities have improved by integrating artificial intelligence (AI) with computer vision processing. The objective of this research is to improve X-ray images using AI techniques, thereby contributing to the developing medical scene. This research integrates image processing and machine learning techniques to enhance diagnostic accuracy in the medical imaging sector. It outlines the methods for analyzing and enhancing the stored X-ray images to identify potential diseases separating them from normal ones. A software Programme is developed for processing and classifying X-ray images using MATLAB. Median filtering and edge detection techniques of image processing are employed to significantly improve image clarity. These pre-processing steps ensure accurate classification of the images using a trained deep convolutional neural network (CNN) based model. This model is trained and validated on a labelled dataset of X-ray images, and implementation results exhibit high accuracy in detecting and classifying diseases. AI and image processing facilitate the prompt and accurate diagnosis, highlighting the critical role that AI plays in the medical industry.
Evaluating Machine Learning Models for PM2.5 Prediction: A Case Study on Air Pollution in Beijing
Large pollution impacts on human, animal, and plant health, along with advanced computing technologies capable of managing big data, create new opportunities for applying ML to improve air quality observation. Questions also continue to increase as more are created about how the performance of newer, hybrid ML models is matched to a particular application for the most suitable ML model. This paper presents a systematic review of state-of-the-art studies that implement ML techniques in the context of PM2.5 concentration prediction, focusing on analyzing dataset size, hyperparameters, and preprocessing techniques to answer these questions. This review investigates some proposed ML techniques and models applied in Beijing by highlighting their main characteristics and relevant results. They then pointed out that hybrid models are capable of uncovering the hidden features of data, which was not possible by single approaches with high dimensions. Another conclusion was drawn that air pollution prediction models have to be compared under the same conditions with the same future characteristics.
Evaluating the Wind Potential of Coastal and Inland Locations in Oman for Power Generation
The potential for wind energy in Oman is promising as a power generation option. Different locations in Oman demonstrate varying characteristics that indicate varying energy potentials for wind energy. The geographical features, wind speed variations, and wind directions all impact the potential for wind power generation. To explore the wind characteristics for power generation in Oman, seven different locations (coastal and inland) were selected for this study. Weather data sets collected from meteorological stations within Oman for the year 2023 were used to analyze wind speed variations, and directions, and to predict the amount of wind power generation. The results show that coastal locations such as Sur and Masirah, along with inland locations like Marmul, Qairoon Hairiti, and Thumrait, exhibit promising average wind speeds of 4.99 m/s, 5.50 m/s, 4.33 m/s, 5.22 m/s, and 5.83 m/s respectively. Conversely, Qalhat and Raysut demonstrate lower potentials with average speeds of 2.82 m/s and 2.34 m/s. The results highlight that the predominant wind directions are in line with seasonal patterns. The power generation for the high-potential locations can generate power ranging from 1.00 to 1.61MW, signifying substantial energy generation capabilities. The findings of this research study underscore the strategic importance of location-specific wind assessments in advancing Oman’s renewable energy agenda and highlighting the significant contribution of wind power to sustainable energy goals.
Graph Structure and Correlation Coefficient for Face Detection
Human face detection has been the subject of extensive research for several decades. Face detection has become increasingly important with the advent of video surveillance, security access control, and content-based information retrieval applications. Due to the non-rigid nature of faces and their high degree of variability, face detection remains challenging. There have been many studies that examined various face detection methods under different conditions, including illumination, facial expressions, head rotations, occlusions, and aging. A novel approach to face detection is described in this paper using Local Graph Structure and Correlation Coefficient (LGS-CC). Through LGS-CC, texture information is enhanced by considering both texture and local shape, rather than relying solely on grayscale information. The proposed method has been shown to achieve encouraging results in extensive experiments.
A Standardized Textual Language for UML Modeling: Review and Suggestions
Using Unified Modeling Language (UML) notations and tools to represent structure, behavior, architecture, and business processes is crucial when specifying newly developing systems. UML has become the de facto standard in software engineering and development. Numerous tools support UML modeling, helping project managers, system analysts, business analysts, and architects create UML diagrams through both text-based instructions and visual drawing tools. These tools often have their own unique notations for text-based instructions. In this paper, we review a selection of popular UML textual notations and introduce a new formalized textual language called Standard Textual Unified Modeling Language (STUML). STUML is designed to standardize the way UML notations are expressed in text-based tools. It can be used directly or mapped to the notations of existing textual and graphical tools. This new language is intended for various types of diagrams, including class and use case diagrams, and is crafted to be easy to write, easy to read, and usable without requiring special editors. STUML has the potential to become the standard for textual UML notations.
Ensemble Machine Learning for COVID-19 Forecasting: Enhancing Resource Planning and Pandemic Response in Oman
The COVID-19 pandemic, declared a global health tragedy by the WHO, has profoundly impacted the world economy, leading to recession, unemployment, and unprecedented debt. This manuscript introduces an innovative approach to outbreak analytics for Novel COVID-19, aiming to move beyond the conventional assessment of confirmed cases and fatalities. The primary focus is forecasting the number of individuals likely to be infected by the novel coronavirus, utilizing an ensemble machine-learning algorithm to predict infection and death rates. The study's specific goal is to assist government entities in anticipating the impact of Novel COVID-19 across the Sultanate states. The proposed machine learning-based model will forecast future cases and guide private and government institutions' needed resource and response planning. The contributions of this work encompass the development of a machine learning-based method to predict the spread of COVID-19 among the governorates of the Sultanate. It optimizes the current detection method by introducing new features for swift and accurate disease detection. Additionally, a deep learning method is proposed to determine COVID-19 status, issues, and issue alerts rapidly. The introduced approach is designed to function seamlessly in both offline and online systems, easily integrating with any framework. The decision Tree model has a high training score of 99.41% but a low testing score of 53.39%, indicating overfitting. The Random Forest model has a lower training score of 94.66% but a higher testing score of 61.32%, suggesting better generalization performance. The Gradient Boost model has a training score of 92.96% and a testing score of 59.44%, placing it between Decision Tree and Random Forest models in terms of accuracy. The heat map results analysis reveals a robust positive correlation between the variable representing Daily New Cases and the variable denoting Active Cases. This suggests that instances of elevated new cases align with an augmentation in the number of active cases. Conversely, negative correlations between the variables representing Total Coronavirus Cases and Total Coronavirus Deaths might infer that the mortality rate diminishes as the aggregate number of cases increases. This negative correlation could be attributed to efficient healthcare services and vaccination efforts. Nonetheless, it is imperative to conduct further statistical analyses to validate this hypothesis rigorously.
Sustainable Asphalt Mix DesignForExploring the Effects of Industrial Waste Materials and Bitumen Proportions on Mix Performance
In our modern world, where sustainability and environmental consciousness are becoming increasingly crucial, the use of waste materials in pavement layers has emerged as a groundbreaking approach to address both infrastructure needs and environmental concerns. Traditional road construction methods rely heavily on virgin materials, which can deplete finite resources and generate significant waste. However, the incorporation of recycled and waste materials into pavement layers not only mitigates the environmental impact but also presents innovative solutions to improve the durability and performance of road infrastructure. This paradigm shift in pavement construction reflects a forward-thinking approach that not only conserves natural resources but also addresses the challenges of waste management. In this exploration, we will delve into the various waste materials that can be harnessed for use in the Asphalt mix, highlighting the benefits, challenges, and promising advancements in this eco-friendly and sustainable construction practice. The purpose of this research is to explore the potential use of three different types of waste materials generated by the Intaj Sohar company in the Asphalt mix. The main parameters were studied: stability and the flow. The effect of the waste material on the Asphalt mix is evaluated using the Marshall Test, which is an important laboratory test used in civil engineering and road construction to evaluate the quality and performance of bituminous mixes. In this project, three different types of waste materials have been prepared with different percentages of (3, 5, 8, and 10%) and mixed with natural aggregate (Grade 2). In addition, bitumen of (5, 6, 7, and 8 %) have been added to find the optimum bitumen content, which gives acceptable load and flow by using Marshall Test.