Journal of Al-Qadisiyah for Computer Science and Mathematics
How to publish in Al-Qadisiyah Journal of Computer Science and Mathematics The Journal of Al-Qadisiyah for Computer Science and Mathematics (JQCM) is a scientific journal published by the College of Computer Science and Information Technology at the University of Al-Qadisiyah. It has been published since 2009, initially two issues a year, and since 2019, four issues a year. To date, the journal has published 15 issues. The articles published in Volume 5 and above have the submission and acceptance dates indicated, whereas the articles published in Volume 10 and higher have the online availability date indicated. ISSN 2521-3504 (Online), ISSN 2074-0204 (Print)
A Review of Smart Drone Technologies for Security Surveillance and Search and Rescue
Drones are increasingly being used in search and rescue operations due to their ease of use, wide coverage, and cost. Therefore, this research has faced challenges, including identifying human figures in aerial photographs, as these figures may be small and unclear, and are also affected by several factors, such as darkness or bad weather conditions, such as fog and dust, or because of debris resulting from disasters. One of the most important areas of research currently in vogue is the integration of drones with cloud computing systems and attackable devices. This integration leads to increased efficiency in emergency response. Furthermore, small, lightweight, and power-efficient embedded devices such as the Jetson Nano are powered by advanced portable AI systems that offer real-time analysis with the necessary precision and speed. This is an encouraging development in the field of application. In the field of computer vision, advancements have been made in detection models, such as the introduction of context enrichment modules to enhance the accuracy of small target detection. Efforts have also been made to create new databases, such as thermal imaging of partially occluded individuals, which contributes to filling a clear gap in available resources. In the field of multi-sensing, thermal and optical imaging are combined using transformer techniques to overcome the limitations of traditional convolutional networks, and acoustic sensing is used to identify human cries and characteristic signals in disaster environments. The novelty of these studies lies in the construction of new databases that support challenging rescue scenarios, the optimization of lightweight models to suit capacity-limited devices, and the potential for integrating drones with multiple sensing and communication channels (optical, thermal, acoustic, and wearable devices). These contributions also form the basis for developing practical frameworks that support future surveillance and search and rescue missions.
Copyright (c) 2026 Noof Ali Khashush http://creativecommons.org/licenses/by-nc-nd/4.0
Cybersecurity in the Age of the Internet of Things (Threats, Vulnerabilities, Attacks, and Countermeasures): A review
The technology of connecting physical objects and devices to the Internet is called the Internet of Things[IoT]. With this technology, it has become possible to live within a smart environment that provides users with comfort, entertainment, and ease of doing business in various fields, including industry, medicine, energy, and even urban settings. Although this technology is a breakthrough in the field of communication and data exchange, it is not isolated from the challenges of cybersecurity. It is necessary to not forget that everything connected to the internet is vulnerable to hacking. Therefore, it is essential to rely on cybersecurity technologies and apply them to IoT systems to protect infrastructure and sensitive data from hacking. It's important to note that traditional security measures don't work effectively with IoT systems. Therefore, such systems require highly accurate and efficient protection systems, as security risks can put companies, governments, and even individuals at risk. Therefore, it's essential to develop strategies to combat cybersecurity crimes. To understand the most important challenges and opportunities within cybersecurity management, the paper discussed them in this article.
Copyright (c) 2025 Nada Khaleel Kareem http://creativecommons.org/licenses/by-nc-nd/4.0
Addressing Linguistic Challenges in Arabic NLP: A Comprehensive Study on Content-Based and Collaborative Filtering Techniques for
Efficient algorithms need to detect Arabic-speaking users and provide them with content that promotes cultural exchange and knowledge acquisition. This study proposes an enhanced Arabic Article Recommendation System (AARS) grounded on semantic analysis techniques strengthened by the BERT model. The system aims to promote recommendation relevance and accuracy, a process frustrated by the complexity of the Arabic language brought about by dialectical variations and diacritical presence. To mitigate these challenges, the method takes a light normalization step for removing noise and redundant characters, followed by a morphological dialect stemmer to extract root forms and improve semantic representation. In addition, the proposed system integrates cultural, personal, and contextual dimensions—such as user interests and preferences—to make context-aware recommendations. Empirical results confirm the effectiveness of the approach in recalling and presenting relevant content and thereby enhancing user satisfaction and involvement. In addition to its theoretical significance, the system also offers economic benefits to content providers and advertisers by enabling personalized, targeted suggestions. Future work will entail model refinement and exploring advanced methods to further promote stability and adaptability in the face of future computational demands and challenges
Copyright (c) 2025 Rasha Falah Kadhem http://creativecommons.org/licenses/by-nc-nd/4.0
A secure banking transaction system for special needs people
This paper synthesizes previous research efforts focusing on the development of innovative banking transaction systems designed to empower individuals with special needs through secure and accessible financial management tools. These systems leverage advanced technological solutions and sophisticated authentication strategies to guarantee the confidentiality and integrity of sensitive financial data. The creating a customizable interfaces and settings tailored to a variety of cognitive and physical abilities are also integrated by User-centered design principles. [10] Testing and User feedback elucidate the significant positive impact of these systems on the banking experiences of individuals with special needs, offering a reliable, secure, and accessible means for managing their financial well-being.[1],[4],[6]
Copyright (c) 2025 Rooa K. Uraiby http://creativecommons.org/licenses/by-nc-nd/4.0
Enhancing Energy Efficiency in IoT Wireless Sensor Networks: AI-Driven Clustering and Routing Protocols
The Internet of Things (IoT) is now a disruptive technology that gives electronic devices the capacity for communication and cooperation in multiple fields. This research aims to enhance the efficiency of WSNs by including IoT and sophisticated energy-saving methods. The study is based on the “Modern Cluster Supervisor-Based Cluster Head (MCSBCH)” selection algorithm and Token Broker- Based Routing (TBBR), which applies Reinforcement Learning (RL) to improve energy management, clustering, and routing. The simulation results show that the RL-enhanced MCSBCH protocol has a packet delivery ratio of 66%, which outperforms ABR and 3LHHBTD protocols with PDRs of 42% and 22% respectively. The application of RL makes a significant improvement in the energy consumption as well, and saves energy for 25% more in MCSBCH + RL and prolongs network lifetime till 90%, while MCSBCH only consumes 50% of energy and has 55% network lifetime. It is shown that the proposed algorithms provide significant gains in network performance, energy consumption, and scalability, which are well-suited for many wireless applications such as disaster monitoring, health monitoring, and smart cities. In the future, integration of AI with developing technologies such as edge computing and blockchain may be investigated for improved scalability, security, and energy efficiency in WSNs with the IoT.
Copyright (c) 2025 Mohammed Younus Mohammed http://creativecommons.org/licenses/by-nc-nd/4.0
Non-Invasive Glioma vs. Meningioma Discrimination on MRI: A Comparative Study of Baseline CNN, Transfer-Learned AlexNet and Custom FF-CNN Architectures
Brain tumoruzrds are a group of very aggressive and frequently fatal neurological diseases and early and accurate diagnosis are required for successful therapy and for increased patient survival. Traditional diagnostic methods, including histopathological examination using surgical biopsies, are the only gold standard tests for diagnosis, but are invasive, time-consuming, and risky—associated with a high risk of infection, hemorrhage and neurological complications. The emergence of machine learning, in particularly deep learning has provided an unprecedented non-invasive tumour detection/classification tools which has transformed the medical imaging analysis field. Here we investigate and benchmark three deep learning architectures for classifying brain tumors on magnetic resonance images (MRI): a baseline CNN, a modified pre-trained AlexNet via transfer learning, and a custom FF-CNN tailored for our problem. The models were trained and tested on a curated Brain Tumor MRI dataset containing annotated images of two major tumour types: glioma and meningioma. For CNN, AlexNet, FF-CNN, the classification accuracies were 97.1%, 98.6%, and 95.1%, respectively. The better performance of the AlexNet is due to its higher depth and its possibility to be used as a transfer learning model able to perform strong feature extraction on small medical datasets. Taken together, they demonstrate the substantial potential of DL methods for quick, accurate and non-invasive detection of brain tumors. Incorporating such technologies into clinical decision support systems can greatly improve radiological workflow efficiency, reduce diagnostic delay, and eventually improve the quality of patient care and prognostication.
Copyright (c) 2025 Bahaa Salih Mandeel, Diyaa Kareem Abd-Alhmza, Waad Saad Hindil, Manar Raad Abdalhassan, Muntadher adnan waheed http://creativecommons.org/licenses/by-nc-nd/4.0
Deep Learning for Chest X-Ray Classification: A Comprehensive Review of Methods, Advances, and Clinical Integration
Deep learning transformed medical imaging and enabled the accurate and scalable diagnosis of respiratory infections such as COVID-19 and pneumonia. Unlike traditional radiology's weaknesses owing to overlapping characteristics and observer variability, CNNs are able to identify intricate visual patterns and achieve near-radiologist performance in multi-class CXR classification. Methodological innovations such as transfer learning, data augmentation, attention, and ensemble learning continue to enhance performance, with techniques to enhance interpretability like Grad-CAM advancing clinical trust. With significant advances already achieved, key challenges remain dataset imbalance, domain generalization, and computational cost. Directions for future research include the creation of standardized large-scale datasets, efficient model design for low-resource settings, and the fusion of imaging with clinical metadata. This review highlights recent achievements, current limitations, and potential directions in capitalizing on deep learning innovations into clinically reliable diagnostic tools.
Copyright (c) 2025 Kawther Samer Ali , Ahmad Shaker Abdalrada http://creativecommons.org/licenses/by-nc-nd/4.0
A Hybrid CNN–LSTM Framework for Network Intrusion Detection with SMOTE Balancing
In order to improve intrusion detection in network traffic analysis, this research presents a novel hybrid deep learning approach for feature extraction and classification· The complexity, volume, and sequential nature of contemporary network data frequently pose challenges for traditional machine learning techniques, which has a negative impact on anomaly detection's speed and effectiveness· In order to tackle this issue, our approach utilizes a hybrid model that initially automatically extracts significant temporal patterns and spatial features from unprocessed network traffic data using a Convolutional Neural Network (CNN)· Following that, a Long Short-Term Memory (LSTM) network receives these learnt properties and uses its capacity to process sequences to accurately classify traffic and identify interference· Our model outperforms traditional machine learning classifiers like Support Vector Machines and Random Forests by a considerable margin, achieving an accuracy of 98·7% and an F1-score of 97·4% on the publicly accessible CICIDS2023 dataset· The findings show that our hybrid CNN-LSTM model offers a promising, data-driven solution to a significant network security concern by having the ability to greatly improve the speed and efficacy of current intrusion detection systems.
Copyright (c) 2026 Methaq Shaker Mahmood http://creativecommons.org/licenses/by-nc-nd/4.0
Integrating Satellite and Climate Data for Crop Yield Prediction: Spatiotemporal Analysis and Neural Network based Model
In Diwaniyah-Iraq province, which its economy considerably depends on crop production, the accurate crop harvest prediction is an elementary requirement. The present study has faced some challenges represented as 1) scarce available agricultural data such as the complete absence of cropland maps and also fewer crop health and crop yields statistics; and 2) interannual variation in climatic factors in such semi-arid regions. To overcome these impediments, a novel methodology that concentrates on data fusion and Artificial Neural Networks (ANN) was proposed in this work. The time-series satellite-based Normalized Difference Vegetation Index (NDVI-MODIS) was integrated with a multi-source climate database, which showed extra and unique information across the whole season. The strong relation of NDVI with the time of the critical period of the crop growth and its sensitivity to the variability of yield during the growing season provides a powerful feature for prediction. Exploratory data analysis was applied to reveal the temporal patterns of correlations between fused data streams with crop yield. Artificial Neural Network (ANN) was used to construct a winter yield prediction model at the province level, due to the predictive capability of neural networks that come from the hierarchical/multi-layered structure. Several ANN models were tested on 18 years of data for Diwaniyah-Iraq province. Critically, the suggested model enables a reliable yield prediction nine weeks before wheat harvest and seven weeks before barley harvest. Through the testing phase, the best models achieved the results of R² > 0.9 for both wheat and barley, with 0.01<RMSE<0.1 for wheat and 0.06<RMSE<0.1 for barley. The proposed methodology presents a robust and generalizable framework for yield prediction in circumstances of data-scarce, semi-arid environments and small-scale cultivation areas, offering early decision-making and resource management tools across Iraq.
Copyright (c) 2025 Zena H. Khalil http://creativecommons.org/licenses/by-nc-nd/4.0
A Lightweight Hybrid Framework for Secure Communication in Low-Resource IoT Devices
The spread of Internet of Things (IoT) devices has brought about imposing security issues especially because of their nature of limited processing capacity, memory, and power. Most traditional cryptographic algorithms are usually resource-consuming such that they render them susceptible to attacks. The present paper suggests a new lightweight hybrid cryptography system that can serve resource-restrained IoT devices in particular. This framework is cooperatively built on certificate-based authentication based on Elliptic Curve Cryptography (ECC) as a secure method of establishing keys with the symmetric speed and low overhead of a data encryption scheme, the ChaCha20-Poly1305 Authenticated Encryption with Associated Data (AEAD) scheme. The full architecture design showed with a higher level of security measures and simulate it on a representative model of an IoT hardware (ARM Cortex-M4). An overall comparative analysis with standard algorithms (RSA, AES) and other lightweight schemes show that the proposed framework shortens the execution time by up to 82% and reduces energy consumption by more than 60% and offers strong security to all types of common IoT attacks such as a man-in-the-middle, replay, and timing attacks. In the framework, there is also a reduction of 34 percent in RAM and 47 percent in Flash memory consumption as compared to conventional methods.
Copyright (c) 2025 Alyaa Hasan Zwiad http://creativecommons.org/licenses/by-nc-nd/4.0
Enhanced Twitter Bot Detection via Static and Temporal Feature Integration
This study presents a robust supervised learning framework tailored for the precise identification of Twitter bots. The central contribution lies in the integration of both static and temporal features to establish a comprehensive representation of user behavior. Using the benchmark Cresci-2017 dataset, the framework constructs a rich feature space encompassing profile-level metadata, detailed posting frequency statistics, and fine-grained temporal activity features, enabling effective discrimination between sophisticated bots and genuine human users. To fully exploit this diverse information, an advanced stacking ensemble is employed. This architecture strategically combines the predictive strengths of multiple base learners Random Forest, LightGBM, XGBoost, and Support Vector Machine while a Logistic Regression meta-learner is trained to optimally integrate their outputs. The resulting model delivers state-of-the-art performance, achieving 98.71% accuracy, 99.26% precision, 99.04% recall, and a 99.15% F1-score, substantially outperforming individual classifiers. These findings underscore the value of unifying heterogeneous feature types with stacked generalization, highlighting that strategically diverse feature engineering coupled with advanced ensemble methods is essential for building resilient defenses against the rapidly evolving landscape of automated bot behavior.
Copyright (c) 2025 Zahraa E. H. Al-Khersan, Nahla A. Flayh http://creativecommons.org/licenses/by-nc-nd/4.0
A dynamic structure for designing business models strategy in smart cities
Smart cities look to supply more dynamic and sustainable services via utilizing digital technologies and innovative designs business models. This paper work toward to test the SC-BMC framework in the context of an Iraqi city (Baghdad) via evaluating a smart service and measuring its economic, social, and environmental dimensions. The work adopted a mixed-method proceed toward merging quantitative and qualitative tools. The UTAUT model was utilize via a questionnaire of (100) participants to measure adoption intentions, in addition to semi-structured online interviews with experts and local officials. Data were analyzed utilizing structural equation modeling (SEM) and qualitative content analysis, with outcomes compared to benchmark cities such as Dubai and Istanbul. Quantitative outcomes offer that performance expectancy (PE) was the most effective factor on behavioral intention (β=0.48, p<0.01), come behind by supportive conditions (FC) (β=0.32, p<0.05), however effort anticipation and social effect had no notable action. The qualitative outcomes appeared the framework's strengths in explaining profitable value and institutional communication, as contrasted with its flaws in addressing the informal economy and low environmental data. Comparing Baghdad with the reference cities, the economic value was similar (C/B ≈ 1.9), but it lagged behind in environmental and social indicators. These results confirm that applying the adapted framework (SC-BMC-Localized) provides a practical tool for understanding and developing business models in developing cities, enabling decision-makers to achieve a balance between economic, social, and environmental dimensions in the transformation towards smarter and more sustainable cities.
Copyright (c) 2025 Rusul A.Salman AlMansoori http://creativecommons.org/licenses/by-nc-nd/4.0
Employing Brakerski/Fan-Vercauteren's for secure data classification in GRU networks for sentiment analysis
Due to the widespread use of technology and the resulting vast amounts of big data, which are beneficial for product and application development, sentiment analysis is one such benefit. It allows users to understand their opinions and guide development and growth in the right direction. However, the use of sensitive textual data remains a matter of privacy and requires careful handling. Therefore, users must be provided with the necessary guarantees regarding the security of their privacy. This work introduces a privacy-preserving binary sentiment analysis system that categorizes text as positive or negative through comprehensive contextual understanding while safeguarding user data using homomorphic encryption. The Gated Recurrent Unit (GRU) model is trained on unencrypted data of type IMDB and performs inference on encrypted inputs with the BFV graded homomorphic encryption technique. To facilitate efficient encrypted inference, it was replaced non-polynomial activations with low-degree polynomial approximations, reduce the depth of multiplication. The accuracy attained by the proposed technique is 90.1%.
Copyright (c) 2025 Nibras Hadi Jawad http://creativecommons.org/licenses/by-nc-nd/4.0
An intelligent system for detecting and recognizing human faces
Detecting human faces in any image remains a challenging problem in computer vision. The use of artificial intelligence and deep learning techniques has significantly enhanced performance in the areas of face detection and analysis. The progress is supported by both standard and complex datasets, which enable models to be trained across various scenarios. The advancement of face detection technologies is being utilized across various fields, including security systems, marketing, and healthcare systems, with innovations to enhance the system’s speed and accuracy. Local database (LDB) models were used for the two models: a training set that employs a convolutional neural network (CNN) and an accompanying test set which assesses its performance using a confusion matrix (CM), and three image sizes (32,64 and 128). The findings indicate that model performance improved with the use of local data and across differing image sizes, with the pinnacle being an image size of 64 with the use of an adaptive network layer, achieving a result with an accuracy of: Accuracy (ACC) = 0.94375, Precision (P) = 0.90149, True Positive Rate (TPR) = 0.8875.
Copyright (c) 2025 Nawar Nihad Hasobe, Zaid Othman Ahmed , Anwar Hasan Mahdi http://creativecommons.org/licenses/by-nc-nd/4.0
Deep Learning Models for Ocular Disease Detection
Early and precise identification of ocular diseases using fundus imaging is essential for effective ophthalmic care, yet it remains a non-trivial challenge due to the complexity of visual patterns and inter-observer variability. Conventional manual diagnosis, while widely practiced, is labor-intensive and susceptible to subjective bias, thereby limiting scalability and consistency in clinical settings. To address these limitations, automated diagnostic systems based on deep learning have emerged as a promising alternative, offering improved efficiency, reproducibility, and diagnostic accuracy. In this work, we propose a robust deep learning framework for automated detection of ocular diseases, with a specific focus on distinguishing between normal and cataract cases. This was performed through two deep learning models, an InceptionV3 structure and a pre-trained GoogLeNet model, which was developed and extensively validated. Both models were trained and evaluated on the Ocular Disease Intelligent Recognition (ODIR-2019) dataset in case of binary classification tasks. The InceptionV3 model had a relatively high accuracy of 97.9\% and the GoogLeNet model also achieved a high accuracy of 97.6\%. These results demonstrate the possibility of professional DL algorithms to provide comprehensible, precise, and efficient solutions for automation diagnosing ocular diseases, and to be a significantly innovative breakthrough to ophthalmic disease detection and clinical decision supporting.
Copyright (c) 2026 Bahaa Salih Mandeel http://creativecommons.org/licenses/by-nc-nd/4.0
Automated Detection of Diabetic Retinopathy Using conventional neural network
Convolutional neural networks (CNNs) have emerged as a powerful tool in medical image analysis, enabling automated disease detection with high accuracy. In this study, a CNN-based approach was applied to retinal images to detect diabetic retinopathy, a leading cause of vision impairment in diabetic patients. Traditional detection methods rely on manually defined image features, such as blood vessels or exudates, which can limit diagnostic accuracy and require significant human intervention. These approaches also face challenges in identifying subtle pathological variations due to the complex and diverse visual patterns in retinal images. The proposed CNN model automatically extracts relevant visual features and classifies retinal images without manual intervention, achieving robust performance in distinguishing between Moderate and No Diabetic Retinopathy cases. Furthermore, the system is designed for potential integration into Internet of Things (IoT) environments, allowing real-time, remote diagnostics and supporting improved healthcare delivery. These results demonstrate the potential of CNNs to enhance automated screening and contribute to more efficient, accurate diabetic retinopathy detection.
Copyright (c) 2025 Anaam A. Muhamed http://creativecommons.org/licenses/by-nc-nd/4.0
A Systematic Review and Experimental Evaluation of Classical and Transformer-Based Models for Arabic Extractive Text Summarization
Arabic text summarization has become an active research area due to the rapid growth of Arabic digital content. developing effective summarization models has many challenges result from the linguistic richness, the complex morphology, flexible syntax, and diverse writing styles. This study looks at Arabic extractive summarization, where the goal is to select the most relevant sentences from a text to create a concise version that still captures the original meaning. Many techniques were analyzed and synthesized with the existing data sets in addition to identifying the problems and gaps to understand the history of the given area and determine the direction of the research. Also, the metrics that used to evaluate the outcomes of the Arabic text summarization are mentioned. The review pointed out an evident development of classical statistical and graph-based extractive procedures to current transformer-based procedures. Transformer architectures have been quickly embraced by the field and Arabic-specific pre-trained models have proven to perform better than multilingual counterparts. Nevertheless, there are still great gaps in multi-document summarization, dialect management and formalized evaluation systems. The research work that is to be done in the future is the creation of bigger Arabic corpora, better dialectal coverage, and creation of full-fledged evaluation standards.
Copyright (c) 2025 Hind R. Almayyali http://creativecommons.org/licenses/by-nc-nd/4.0
Modeling Cholesterol Levels Using Machine Learning: A Study with the Framingham Heart Study Dataset
Cholesterol levels are associated with many health risks, especially cardiovascular disease. Therefore, predicting an individual's cholesterol levels is important to avoid such complications. This paper explores the determinants of blood cholesterol level and builds a machine learning model to predict cholesterol levels using the Framingham Heart Study dataset. Factors such as age, body mass index, and glucose levels were analyzed. The results showed that these factors are the most influential in determining cholesterol levels. Random Forest achieved the highest accuracy in predicting cholesterol levels in the three-level case (78%) and the binary case (88%). These findings indicate that machine learning can effectively identify individuals at risk of elevated cholesterol and highlight the usefulness of focusing on the main influential factors in preventive healthcare and early risk assessment.
Copyright (c) 2025 Yahya Albugg http://creativecommons.org/licenses/by-nc-nd/4.0
Energy Conserving Communication in WSN based on static data prediction by using SEKF
Wireless Sensors Networks (WSNs) recently, have drawn a lot of attention. Despite its potential applications in a wide range of fields, wireless sensor nodes' restricted communication bandwidth, inadequate processing units, little memory, and power limitations severely limit their capabilities. One of the main challenges in this area is extending the life of battery-powered sensors in WSNs by reducing energy usage. This problem is addressed using a variety of techniques, such as deep learning, machine learning techniques, statistical techniques, and time series forecasting. One strategy is to utilize data prediction to reduce the volume of transmitted data without sacrificing its quality. This paper presents a model for wireless sensor networks energy saving using the Static Extended Kalman Filter (SEKF). The technique is used to accurately dual predict. The plan consists of two stages. In the first stage, the transmission from the sensor node to the sink node is reduced based on four steps (data equality, data deviation computation, faulty data detection, data reduction based on prediction). In the second stage, the data is reconstructed at the sink node to maintain system reliability. The proposed model demonstrated superior performance compared to other methods, reducing data throughput in the first phase by 60.72%. In the second phase, data was reconstructed with 97.86% accuracy at a data reduction rate of 62–60%, with an energy consumption of 3.928 J. These results were achieved by SEKE for single-node reconstruction. Furthermore, the proposed model performed well when applied to data containing negative values, achieving acceptable data reduction with accuracy ranging from (94-95%) in several experiments. The Intel Berkeley Research Lab (IBRL) dataset was used for all experiments.
Copyright (c) 2025 Furqan Muhammad Abbas, Hadeel Noori Saad http://creativecommons.org/licenses/by-nc-nd/4.0
Enhancing Routing Performance in Software-Defined Networks through the Random Forest Algorithm
Computer networks are growing fast in both their size and the number of applications that use them. Because of that, managing the flow of data has become more difficult than before. Software-Defined Networking (SDN) makes it possible to manage and monitor the network from one place. SDN relies on three main principles: the separation of the control plane from the data plane, centralized control of the whole network, and providing a global programmable view of network states. These features allow SDN controllers to make dynamic and intelligent routing decisions. However, the controller still deals with a very large amount of traffic data. In the last few years, researchers started to use Machine Learning (ML) methods to handle this data and make routing decisions more efficient. In this study, the Random Forest method was used within an SDN setup to classify and predict traffic patterns. The experiments showed that the model accuracy ranged from about 0.85 to 0.94 in selecting routes. It also helped lower the average delay by nearly 30 to 35 percent compared with OSPF and kept the system stable even when the traffic load changed.
Copyright (c) 2025 Samah Fakhri Aziz http://creativecommons.org/licenses/by-nc-nd/4.0
Low-Light Image Enhancement Techniques: A Review
Low-light images from low-quality environments suffer from noise, contrast, and visibility problems, and occur in both computer vision and human perception. Conventional image enhancement methods, including Retinex-based algorithms and histogram equalization, are insufficient for suppressing noise and preserving enhancement in low-light conditions. In recent years, many deep learning-based methods (both hybrids, GANs, and CNNs) have been proposed and have exhibited promising results in enhancing the quality of low-light images. And finally, in the frame of this work, a detailed description of well-known low-light image enhancement algorithms is presented. Their advantages, disadvantages, and applications are discussed. The paper also examines how well these techniques generalize to other domains such as autonomy, surveillance, and medical imaging, and outlines several future directions for research on low-light enhancement.
Copyright (c) 2025 Israa Mohammed Hassoon http://creativecommons.org/licenses/by-nc-nd/4.0
Optimization Algorithms with Improved Convergence for Minimax in Strongly Convex and Nonconvex Settings
The aim of this paper, is to address important issues in smooth minimax optimization by creating fast algorithms that can be used in strongly convex-concave and nonconvex-concave situations. For the strongly convex-concave scenario, a new approach was presented that cleverly combined the momentum of Nesterov's accelerated gradient descent (AGD) with the stability of the Mirror-Prox method. This hybrid technique significantly outperformed the previous benchmark, achieving a near-optimal convergence rate of . For complicated nonconvex-concave problems, an imprecise proximal point framework was created, which greatly outperformed the earlier result with a stationary point convergence rate of . This framework improved efficiency in high-dimensional settings by producing a rate of log m when applied to finite-sum problems. The adoption of adaptive error-control approaches to handle nonconvexity and the successful integration of acceleration techniques were identified as the primary advancements. The additions offer useful advantages for applications such as adversarial training, game equilibrium computing, and robust learning systems, and they successfully fill important theoretical gaps in minimax optimization. Extensive analyses demonstrated how well acceleration tactics were incorporated with fundamental issue features, resulting in enhanced approaches for intricate optimization scenarios.
Copyright (c) 2026 Raghad hasan Radhi http://creativecommons.org/licenses/by-nc-nd/4.0
Adversarial Robustness of Network IDS (Structured Data)
Network intrusion detection systems (NIDS) trained on tabular flows are vulnerable to constrained evasion, where an attacker perturbs few features while preserving protocol semantics and valid ranges. This paper addresses two gaps: (i) the absence of a standardized, constraint-aware robustness evaluation for tabular NIDS, and (ii) the lack of defenses that remain effective under such realistic, semantics-preserving attacks. We propose a measurement framework that formalizes attacker budgets and constraint sets, instantiates reproducible attacks, and benchmarks models on UNSW-NB15 and BoT-IoT. As a defense, we train a TabTransformer with constraint-respecting adversarial examples and feature tokenization that groups mixed-type attributes. Across both datasets and multiple attack budgets, the adversarially trained TabTransformer consistently outperforms tuned tree-based ensembles under constrained attacks while maintaining competitive clean accuracy. Ablations show robust optimization and tokenization jointly reduce attack success and transferability. Our findings provide practitioners with a concrete, reproducible pathway to deploy attack-aware tabular NIDS and establish a baseline for future robustness studies in operational network environments
Copyright (c) 2025 Haedar Ahmed Mukhef http://creativecommons.org/licenses/by-nc-nd/4.0
A Reinforcement Learning–Driven Scheduler for Minimizing Uplink Delay in 5G Networks
Uplink scheduling is a core challenge in 5G New Radio (NR), where diverse services—enhanced Mobile Broadband (eMBB), ultra-Reliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC)—compete for shared spectrum under stringent delay and reliability constraints. Traditional policies (Round Robin, Best-CQI, Proportional Fairness) are simple and effective in limited regimes, but they expose well-known drawbacks in the uplink: RR preserves opportunity fairness yet struggles to suppress backlog under load; Best-CQI maximizes instantaneous rate but can starve cell-edge users; and PF lacks the agility to re-prioritize when traffic mixes or deadlines shift, leading to elevated tail latencies and delay-budget violations. This paper proposes an adaptive reinforcement-learning (RL) scheduler based on on-policy SARSA to minimize uplink delay while maintaining efficient spectrum use. The state encodes per-UE buffer status reports (BSR) and achievable rates (quantized for tractability), actions select a UE per resource-block group (RBG) at each slot, and a delay-aware reward (negative sum of BSRs) directly penalizes aggregate backlog. We implement the scheduler in a slot-driven 5G NR simulator with asynchronous HARQ and compare against RR, Best-CQI, and backpressure. Beyond average BSR, we evaluate end-to-end (E2E) delay, 95th/99th-percentile latency, URLLC delay-violation ratio (DVR), eMBB throughput, and mMTC delivery ratio. To address scalability and realism, we extend experiments from 4 UEs to 16–64 UEs and include mixed eMBB/URLLC/mMTC traffic. Results show that SARSA nearly matches RR on mean and tail delay while substantially reducing URLLC DVR relative to Best-CQI; under mixed traffic it preserves URLLC reliability close to RR yet improves eMBB throughput via opportunistic allocations. Stability analyses under high offered load and fast fading indicate bounded queues and improved robustness compared with Best-CQI and backpressure. These findings demonstrate a practical path to learning-enhanced, delay-conscious uplink scheduling within standards-conformant 5G stacks.
Copyright (c) 2025 Ali Haider Abbas http://creativecommons.org/licenses/by-nc-nd/4.0
A Swarm- Optimized Hybrid Approach to Feature Selection in IDS
The network intrusion detection (IDS) strategies are important to securing our systems and networks from unauthorized behaviors. An IDS routinely deals with large amounts of data transmission that contain non-informative and duplicate features, which implication the performance of the machine learnind model negatively. In this paper, we proposed on hybrid two optimization techniques Artificial Bee Colony (ABC) and Genetic Algorithm (GA) to feature selected data. we used Random Forest (RF) and XGBoost classifiers to evaluate how well they perform with the reduced features. Experiments are conducted on three datasets: NSL-KDD, UNSW-NB15, and CIC-IDS2017. Experimental results show that the ABC-GA algorithm reduces the number of features (up to 70-88%) while maintaining detection accuracy, with accuracy reaching 97% in NSL-KDD, 92% in UNSW-NB15, and 94% in CIC-IDS2017.
Copyright (c) 2025 Maan Y Anad Alsaleem http://creativecommons.org/licenses/by-nc-nd/4.0
Internet of Things Security Based on Incremental XGBoost
As the Internet of Things (IoT) has become more common in recent years, security vulnerabilities and attacks on the networks associated with it have also multiplied, increasing the urgency for improved attack detection, deterrence, and response methods. As a countermeasure to IoT attacks, this paper proposes incremental XGBoost-learning and studied the case of creating an incrementally-learning model and how to batch-train the model to leverage the data for full analysis. To validate the proposals, three datasets were chosen, the NSL-KDD dataset, the CICIDS2017 dataset, and the BoT-IoT dataset, in which each of these datasets also included different categories in their definitions of Internet-of-Things attacks. Upon obtaining the dataset, the datasets were then subset into training batches that would continuously assess the model's ability to learn and adapt to new data planning without a retraining process from scratch being necessary. Finally, after all batches were trained, the proposed model attained a classification accuracy of 96.5% on the NSL-KDD dataset, 97.3% on the CICIDS2017 dataset, and 96.8% on the BoT-IoT dataset with improvements in training accuracy while still maintaining consistency across the training subset batches.
Copyright (c) 2025 Omar Shakir Hasan http://creativecommons.org/licenses/by-nc-nd/4.0
Malware classification using SVM and XGBoost: A study of the influence of features
Zero-day malware shows a significant cybersecurity threat due to its ability to avoid traditional antivirus detection. This study investigates the application of machine learning techniques to detect and classify such advanced threats. Specifically, two well-known machine learning classifiers: Support Vector Machine (SVM) and XGBoost, to classify malware in a static analysis environment. The data was collected from the Kaggle platform and includes 5212 samples containing 70 features extracted from PE (Portable Executable) headers, including features from the DOS Header, PE Header, and Optional Header. The samples include both malicious and healthy files, and ten-fold cross-validation was used. The performance of the classifiers was evaluated using metrics such as accuracy, recall, F1 score, Roc curve and positive precision. The results demonstrate XGBoost's performance was superior with an accuracy of about 99%, while SVM's accuracy was about 96%. XGBoost shows its high effectiveness and low error rate, which enhances its potential to be used as an effective tool in detecting zero-day malware and improving cyber security capabilities to detect unknown threats.
Copyright (c) 2025 Mohamed Fadhil Imran http://creativecommons.org/licenses/by-nc-nd/4.0
Deep Spoof Face Detection Techniques in React Native
The rapid rise in the use of artificially generated faces has significantly increased the risk of identity theft in biometric authentication systems. Modern facial recognition technologies are now vulnerable to sophisticated attacks using printed images, replayed videos, and highly realistic 3D masks. This creates an urgent need for advanced, reliable, and mobile-compatible fake face detection systems. Research indicates that while deep learning models have demonstrated strong performance in detecting artificially generated faces, deploying these models on consumer mobile devices remains challenging due to limitations in computing power, memory, privacy, and processing speed. This paper highlights several key challenges: (1) optimizing deep learning models to operate efficiently on mobile devices, (2) ensuring real-time inference without compromising accuracy, (3) maintaining user privacy when processing sensitive facial data, and (4) addressing the variability in mobile phone cameras, input resolution, and platform limitations across Android and iOS. Furthermore, the increasing sophistication of identity spoofing attacks—such as 3D masks and AI-generated faces—demands more sophisticated, robust, and multimodal detection technologies. The research findings provide a clear roadmap toward practical solutions. By evaluating the latest deep learning architectures, datasets, and anti-spoofing metrics, the study proposes a comprehensive React Native deployment path using TensorFlow Lite and TensorFlow.js to ensure cross-platform compatibility. The proposed system offers a unified classification of identity spoofing attacks and defense mechanisms, along with a structured evaluation framework that compares on-device processing with server-side detection. The results demonstrate that optimized models can achieve high accuracy, low false accept/rejection rates, and sub-second processing speeds on mobile devices. Ultimately, the study provides practical design guidelines for building robust, privacy-preserving, efficient, and real-world consumer-grade fake face detection systems.
Copyright (c) 2025 Saud, Jamila H., Shoobi, Liqaa M., Wurood A. Jbara, Reiam Abd Alkarsim Abd http://creativecommons.org/licenses/by-nc-nd/4.0
A Hybrid Genetic-Chaos Algorithm for Energy-Efficient Cluster Head Selection and Reduced Energy Consumption in Wireless Sensor Networks
WSNs are an essential part of the current data-intensive technology, including environmental monitoring and industrial automation. The operational lifetime of a WSN and energy usage is a key difficulty to maximize throughput or minimize energy consumption. In this study, we explore one such novel way of using supervised learning machine algorithms in dealing with this problem. The research opens the capability of designing and executing supervised machine learning algorithms in the WSNs in order to maximize energy usage. These intelligent routing algorithms allow the decision to be made at the node level reducing state on the network using historical data and network parameters. Using the supervised learning, the ability to change the operating parameters and routing strategies of the nodes dynamically to save energy is satisfied. The research proves the effectiveness of supervised machine learning by minimizing energy consumption in WSNs based on extensive simulations and real world experiments. Results indicate a considerable increase in network lifetime, and more efficient data transferring. The use of such algorithms not only helps in minimizing the amount of energy wasted but also lengthens the lifetime of the network thus it becomes long lasting and more viable to use over a long period of deployment. The research elevates work towards WSNs by becoming more energy- efficient, resilient, and adaptable. These findings are important to several spheres including smart cities or precision agriculture, since it allows developing reliable and sustainable wireless sensors networks that are capable of surviving in the data-rich realms of the future.
Copyright (c) 2025 Ilham Huusein Ali Alawaad http://creativecommons.org/licenses/by-nc-nd/4.0
Predicting Consumer Sentiment from Social Media by Text Mining
Currently, social media has expanded and disseminated, and a vast quantity of information is available to individuals of all ages and is being disseminated over the Internet. This information is not only vast, but also rapidly disseminated and diverse. Consequently, conventional tools and methodologies are inadequate for managing this information. Given the rapid advancement of this field, it is imperative to cultivate capabilities and investigate solutions that enable the extraction of specific values from data sets and their accurate analysis. Data analysis is one of these solutions. For example, the classification of emotions through data mining, which employs machine learning, involves the treatment of information that is transmitted through a variety of communication channels as emotions and the development of analytical models. Using three text mining and machine learning techniques: k-means, Decision Tree, and Classification and Regression Tree (CART), a significant amount of information was collected in this study and transmitted to individuals via Twitter from the Amazon website (comments). The results indicated that the utmost accuracy was achieved by employing two methods to extract properties from non-structural data and convert them into usable numerical representations. It is obtained at a rate of 95% through the use of Bag of Word feature extraction in conjunction with CART. So, it outperformed to Decision Tree, while K-means, which the desired outcome did not get.
Copyright (c) 2026 Fatima Hassan Fadel http://creativecommons.org/licenses/by-nc-nd/4.0
Explainable and Automated Pneumonia Detection from Chest X-Rays using CNNs
Chest X-ray is the most popular examination type for thoracic diseases, but its interpretation exhibits error rates which are still subject to inter-observer variability and economic workload constraints. This work can be found in this paper: "A reproducible deep learning pipeline for pneumonia identification vs normal based on DenseNet-121 from chest X-ray". The dataset originated from the NIH ChestX-ray14 corpus and was downsampled to 8,500 frontal radiographs (1,050 pneumonia-positive, 7,450 normal) and split at the patient level into training, validation and testing sets. Preprocessing: Grayscale normalization, Resize, Targeted Augmentation and Training (with) Early Stopping, Learning Rate Scheduling, Class Weighting and Post-Hoc Probability Calibration. In the held-out test set, the model achieved ROC-AUC: 0.87, PR-AUC: 0.72, as well as a general accuracy of 93.2%, sensitivity: 82.8% and specificity: 94.6%. Calibration analysis contributed to improving the Brier score from 0.042 to 0.019 and led to good-fitting reliability curves. Interpretability was built into the inference using Grad-CAM and Integrated Gradients, with explanation faithfulness quantitatively checked (deletion AUC = 0.84, insertion AUC = 0.87, sanity check pass rate = 98%, pointing-game hit rate = 76%). Based on the above results, it can be seen that CNN-based diagnosis is promising to achieve a good accuracy as well as interpretability and reproducibility simultaneously. Hence, the proposed framework provides a white-box baseline for clinical examination and future multi-label thoracic disease detection extensions
Copyright (c) 2026 Manaaf Abdulredha Yassen http://creativecommons.org/licenses/by-nc-nd/4.0
Neuro-Fuzzy Optimization Technique for Enhancing Accuracy of Predication Hepatitis Disease
Hepatitis remains one of the most widespread infectious diseases globally, posing significant challenges to public health systems and the WHO(WHO). Its inherent ambiguity, clinical variability, and data heterogeneity make accurate diagnosis a complex task. This study presents a hybrid optimization model that integrates Particle Swarm Optimization (PSO) with the Dragonfly Algorithm (DA) to improve the performance of an Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed hybrid algorithm, referred to as PSODA, is designed to optimize two critical components in the ANFIS structure: the number of membership functions in the initial layer and the model’s learning rate. By balancing the exploration behavior of DA and the exploitation capabilities of PSO, the PSODA framework enhances both the stability and accuracy of the diagnostic system. Experimental results suggest that the PSODA-ANFIS model achieves a classification accuracy of 90%, outperforming standard ANFIS as well as models optimized individually by PSO or Grey Wolf Optimizer (GWO). This hybrid model demonstrates promising potential for clinical decision support in the diagnosis of hepatitis and similar medical conditions characterized by uncertainty and complexity.
Copyright (c) 2025 Rasol H. Rweily, Qusay O. Mosa, Alaa Hussein Hammadi http://creativecommons.org/licenses/by-nc-nd/4.0
A Comprehensive Review of Thermal-Aware Face Recognition Systems: Progress and Challenges
Modern biometric systems have relied on face recognition as it is precise, convenient and unobtrusive. However, standard visible-spectrum face recognition systems are very much sensitive to changes in lighting, facial expressions, and surroundings and at the same time have no ability to conduct physiological measurements in tandem. Within the framework of access control and overall safety needs in the post-pandemic context, these restrictions have indicated a gap in research that is urgent: the lack of integrated frameworks that carry out the functions of identity verification and health-related screening in tandem with each other. This article is a critical survey of the state-of-the-art in the field of thermal-sensitive face recognition, which integrates the visible RGB imaging with biometric recognition and long-wave infrared (LWIR) thermal sensing with the purpose of estimating body temperature. We syntactically examine the available architectures, sensing configurations, fusion strategies, datasets, and evaluation protocols found in the literature. The review emphasizes the fact that dual-modal systems have the potential to allow real-time and contactless identity verification and support a large-scale approach in high-traffic settings. Moreover, this paper addresses the main technical and practical issues that are limiting the large-scale implementation, such as sensor calibration, cross-modular data alignment, environmental bias, data privacy, as well as ethical factors. Last but not the least, we describe the new research directions and future outlooks of the unified biometric and health-conscious access control systems through a critical lens that analyzes performance trade-offs, computational costs, and ethical implications, with the aim of informing the researchers and practitioners of the creation of effective, scalable, and privacy-aware solutions.
Copyright (c) 2025 Samar S. Mahdi, Ebtesam N. AlShemmary http://creativecommons.org/licenses/by-nc-nd/4.0
Intelligent Data Mining Techniques for Big Data in Cloud Computing: A Review
The rapid growth of data production across diverse fields created big data as an essential area for both academic study and practical application during recent times. The spread of cloud computing provides businesses with an adaptable infrastructure that enables economical and scalable processing and storage of enormous data collections. Popular use of modern data mining methods requires dealing with major technical obstacles while analyzing massive and complicated information collections. Data mining as an intelligent methodology emerged because it unites artificial intelligence methods with machine learning techniques together with statistical approaches and database operations to resolve these difficulties. In this review paper demonstrates extensive details about Intelligent data mining solutions that operate within cloud infrastructure while handling big data. The paper presents essential details about the basic principles followed by a classification scheme which includes techniques like classification and clustering alongside association rule mining with deep learning methods and their applications for big data needs. This study analyzes data mining system integration models for clouds while it assesses their operational abilities and scalability and makes note of essential difficulties including heterogeneity and real-time processing requirements.
Copyright (c) 2025 Baraa Mohammed Hasan, Fatima Shaker http://creativecommons.org/licenses/by-nc-nd/4.0
The Properties (Raw) and (gRaw) as a Generalization of The Weyl's Theorem
In this paper, we introduce a novel generalization of Weyl's theorem of bounded linear operators, which dented by ( ) and ( ). Moreover, we demonstrate that this generalization encompasses and extends the properties ( ) and ( ) by the researchers in [1]. Our approach utilizes key characteristics of the relatively regular alongside the properties of constrained operators to explore and verify the proposed generalization. Several illustrative examples are provided to support the theoretical results and to highlight the applicability of the presented theorems.
Copyright (c) 2025 Mohammed J Fari, Fatimah S. Hussein http://creativecommons.org/licenses/by-nc-nd/4.0
Spectral Approaches with Alternative Polynomial Bases for Quadratic Integral Systems: An Expanded Investigation
This work presents a detailed exploration of spectral methods tailored to systems of quadratic integral equations (SQIEs). While conventional approaches often rely on Legendre, Jacobi, or the classical Chebyshev families, here we emphasize three less conventional polynomial bases: the Chebyshev polynomials of the eighth kind (CP8K), the Boubaker sequence, and Bernoulli polynomials. We establish a rigorous operator framework, analyze conditions for existence and uniqueness, and provide stability bounds. The paper further elaborates on discretization strategies—collocation, Galerkin, and Tau. Also we discuss quadrature adjustments for both smooth and weakly singular kernels. Error estimates, conditioning concerns, and preconditioning remedies are also studied. Finally, we present algorithmic templates and conceptual numerical experiments to illustrate comparative performance. The emphasis is on providing not only thoretical assurances but also practical insights that make these alternative bases viable in real computations.
Copyright (c) 2025 Muntadher Hussein Oudah Alabbooda http://creativecommons.org/licenses/by-nc-nd/4.0
This paper investigates fundamental and advanced concepts in rough topology by extending classical topological notions through the lens of rough set theory. We define and analyze rough-open and rough-closed sets, rough convergence of nets and filters, rough cluster and limit points, and separation axioms in rough spaces. The study introduces new forms of continuity such as rough-continuity, rough-irresoluteness, and rough-proper functions, and explores their interactions with compactness and convergence. Several illustrative examples and theorems are provided to demonstrate how rough approximations influence topological structure and behavior. The results generalize classical convergence theory and offer a unified framework for studying indiscernibility-based topology.
Copyright (c) 2025 Ruqaya Nameer Abd Ali , Sattar Hameed Hamzah http://creativecommons.org/licenses/by-nc-nd/4.0
Igi-induced Fuzzy Topology on Undirected Fuzzy Graphs
In this paper Associating a fuzzy tritopological space with an undirected fuzzy graph that is, three distinct fuzzy topologies derived from either three distinct fuzzy graphs or the same fuzzy graph is the goal of this article. The tritopologies have been proposed to link fuzzy topological spaces with fuzzy graphs
Copyright (c) 2025 Zainab Irhayyim Abed, Asmhan Flieh Hassan http://creativecommons.org/licenses/by-nc-nd/4.0
Using A Linear Programming Method for an Approximate Solution of Linear Fractional Volterra–Fredholm Integro-Differential Equations
This research presents a new algorithm for approximating the linear fractional Volterra-Fredholm integro-differential equation (LFVFIDE) of fractional order α, where (0 < α < 1). The strategy requires that the equation be transformed into a Linear Programming Problem (LPP), allowing the coefficients of the approximate solution to be obtained through an optimization process. The fractional order (α) is depicted in Caputo's sense. Some test examples with exact solutions are solved using the proposed approach, where the results demonstrate the high accuracy and low relative errors of the presented method. Furthermore, different values of α are assigned to each example to enhance the convergence of the proposed technique for fractional-order integro-differential systems.
Copyright (c) 2025 Samah Mohammed Ali http://creativecommons.org/licenses/by-nc-nd/4.0
Darboux Integrability of a 3D Symmetric Chaotic System with Non-Isolated Equilibria
In this paper, we study the Darboux-type first integrals of the three-dimensional polynomial dynamical system defined by the equations This system exhibits chaotic behavior for suitably selected values of the real parameters α and β. We demonstrate that the system has no polynomial, rational, or Darboux first integrals for any values of α and β. Furthermore, we derive all Darboux polynomials associated with the system, in conjunction with their corresponding exponential factors
Copyright (c) 2025 Wirya Jawhar Jameel, Adnan Ali Jalal http://creativecommons.org/licenses/by-nc-nd/4.0
Chronological Analysis of the Development of the Fourth Hankel Determinant in Analytic Function Classes
In the geometric study of analytic functions with a single derivative, the fourth Hankel determinant, H₄(1), is essential. This article provides a systematic overview of the historical and current evolution of research on H₄(1), beginning with basic studies, moving on to more complex methods involving symmetry and subordination, and ending with current applications utilising special polynomials like Chebyshev within extended subclasses
Copyright (c) 2025 Bassim K. Mihsin, Zainab H. Mahmood, Reem O. Rasheed, Waggas Galib Atshan http://creativecommons.org/licenses/by-nc-nd/4.0
Application of Factorial Experiments in Health Data Analysis: Exploring Interactions and the Impact of Factors on Clinical Outcomes
This paper will use a 2x2x2 factorial experiment design to evaluate the impacts and interactions of type of treatment, diet, and exercise activities on glycated hemoglobin (HbA1c) in subjects who have type 2 diabetes. Factorial ANOVA, simple effects Analysis, and Tukey post hoc test were used to analyze the data of 240 participants. The Marginal Means (EMM) were calculated to correct the imbalanced data. The findings showed that all factors had significant main effects (p < 0.001) and large-scale two-way interactions. The simple effects analysis revealed that only under low physical activity, the interaction between treatment and diet was significant, which means that dietary change increases the effectiveness of drugs in sedentary patients. Factorial model explicated approximately 95 percent of the variance in change of HbA1c justifying the excellent position of integrated therapeutic and lifestyle interventions in glycemic regulation
Copyright (c) 2025 Wisam Wadullah Saleem http://creativecommons.org/licenses/by-nc-nd/4.0