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)
Intelligent Network Slicing Management Using Microservice-Based Software Architecture for 5G and Beyond Communication Systems
Network slicing is a key enabling technology for flexible resource management in 5G and beyond communication networks. However, maintaining strict service level agreements (SLAs) while efficiently allocating resources across heterogeneous services remains a major challenge. This paper proposes an intelligent network slicing framework that integrates microservice-based architecture with an AI-Driven Slice Orchestration Engine (ASOE) for dynamic and SLA-aware resource management. The novelty of the proposed framework lies in combining AI-driven orchestration, microservice-based slicing architecture, and automated SLA monitoring within a unified management system. The framework was evaluated using a 5G-oriented simulation environment and compared with conventional monolithic and static slicing approaches. Experimental results show that the proposed method improves SLA compliance to 98.9% (compared to 88.4%), reduces URLLC latency from 9.4 ms to 3.9 ms, and increases resource utilization from 64.8% to 83.7%. These results demonstrate that intelligent orchestration significantly enhances scalability, efficiency, and service reliability in next-generation programmable network infrastructures.
Copyright (c) 2026 Karar Talal Hamzah http://creativecommons.org/licenses/by-nc-nd/4.0
A Review: Context-Aware Hate Speech Detection using Feature Fusion
The way people communicate has changed as a result of the proliferation of digital platforms and smart devices, which provide individuals with a means to interact, voice their opinions, and share their ideas. This trend has led to an increase in posts and comments. Some individuals have resorted to posting abusive content, including hate speech, which aims to insult or incite violence and crimes against a group or individual based on a set of protected characteristics, such as religion, race, disability, religious orientation, gender, and others. The number of hate speech posts has increased recently, creating a hostile and unsafe online environment for vulnerable groups, in addition to its psychological effects on individuals within these groups. Social media platforms have resorted to using automated methods and techniques to detect and mitigate this phenomenon. This review aims to offer an in-depth understanding of previous studies in the field of hate speech detection, describing the methodologies, frameworks, and models developed using a range of standard databases while highlighting their strengths and limitations. The paper also seeks to learn how feature fusion can be used to promote the text representation with the addition of information that would advance the model to make a distinction between the categories whether in multi- or binary classification and particularly in the complex environment. What is more, the paper explains the role of context-aware models which consider texts as a unified grammatical unit in order to enhance semantic interpretation since this strategy is essential in hate speech since meaning can be context-dependent.
Copyright (c) 2026 Marwa Adnan Ahmad, Ali Obied, Ali Hamzah Najim http://creativecommons.org/licenses/by-nc-nd/4.0
Fourier Neural Operators with Shock-Aware Loss for the Burgers Equation
The viscous Burgers equation is a benchmark for nonlinear conservation laws with shock formation, and poses a difficulty in obtaining accurate numerical solutions of non-smooth problems. We propose an operator-learning neural network to solve the one-dimensional viscous Burgers equation using Fourier transforms, which we term shock-aware Fourier neural operator (SA-FNO). The SA-FNO model employs a mixed loss function combining a mean squared error associated with the model output with a component that incorporates the effects of shocks via a shock-aware weighted loss function derived from the spatial gradient magnitude of the reference solution at each spatial location. The model learns an operator mapping from the initial conditions and time to the solution field. The operator allows for direct predictions across the entire spatio-temporal solution field. Our numerical results demonstrate that the SA-FNO model achieves a relative L2​ error of 0.0248 for the entire time interval, an improvement of approximately 22% over a baseline FNO model trained with only shock-aware loss. We conclude that by training with a mixed shock-aware loss, the SA-FNO model can provide improved representation of non-smooth solutions, without modifying the underlying neural network architecture.
Copyright (c) 2026 Dhuha Amer jaleel http://creativecommons.org/licenses/by-nc-nd/4.0
Design and Implementation of a Real-Time IoT Monitoring System for Smart Manufacturing Environments
The success of smart manufacturing, as promised high and low by the industry 4.0 revolution, depends on the integration of physical manufacturing systems with digital monitoring and control technologies. This research proposes a smart factory solution for retrofitting conventional manufacturing equipment into a real-time Internet of Things (IoT) monitoring system. The main objective is to enable continuous monitoring of critical production parameters, particularly moisture levels in raw plastic materials, to improve product quality and operational efficiency. The proposed system integrates industrial machines with IoT sensors and a smart gateway for in situ data acquisition and remote management. The system can also be used in time response by case study in a plastic manufacturing facility. The system was deployed in a real plastic manufacturing facility (using dehumidifying dryers for raw materials) to evaluate performance under normal and abnormal conditions (e.g., abnormal moisture levels). The architecture consists of a cloud-connected monitoring server, a web dashboard for real-time data visualization, and a caching system to process high-frequency sensor data efficiently. Unlike existing approaches, the proposed system emphasizes low-cost and non-intrusive integration with legacy equipment without requiring full system replacement. We show that the use of this IoT-based monitoring approach improves operational insight and responsiveness. Experimental results indicate a reduction in defective product rates and faster response to process anomalies, with data latency maintained within 1–2 seconds as results indicate a marked improvement of defective product rates via timely interventions and data processing performance via an optimized cache. This work provides a practical framework to upgrade legacy production systems using IoT technology, enhancing production reliability, reducing downtime, and enabling data-driven decision-making, so as to implement innovations improving manufacturing efficiency and pave the way to further improvements of smart factories in the future.
Copyright (c) 2026 Osama Abdulmalek Ahmed Al-Flayyh http://creativecommons.org/licenses/by-nc-nd/4.0
Software Defect Prediction with a Stacking Ensemble Model
Software plays a critical role in modern systems, making defect prediction an essential task in software engineering. in all aspects of life, including in the industrial and medical fields, as designing reliable and high-quality software is considered one of the most important aspects of daily life to avoid potential risks, for example, financial and human, resulting from software errors, as they are considered a major challenge in terms of time and budget. Therefore, many companies have adopted and paid attention to developing their own software in terms of employing computer science, including its own algorithms, including algorithms that use machine learning to predict and detect these errors by using previous project records that contain the required data. This approach makes decision-making more accurate and effective in improving software performance and security. In our study, the use of ensemble machine learning was proposed, the purpose of which is to build a model to predict errors, as this approach helps in increasing the accuracy of prediction due to the diversity of data used... as well as improving the performance of defect classification models by using ensemble learning techniques based on the principle of stacking. In our work, we used the process of a combination among base learning algorithms (Bagging, Gradient Boosting, and CatBoost) and then fed into a meta learning algorithm such as XGBoost, in each dataset loaded containing a set of features and a target column called "Defective" that specifies whether the sample is defective or not. All possible combinations of these models (duplexes, triples) are generated and combined into a higher-order ensemble model known as a stacking classifier. In this context, NASA's MDP data warehouse, which specializes in defect prediction, was used, for feature selection and dataset balancing. The defect prediction was then evaluated, and evaluation metrics (accuracy, precision, recall, and F1 score) were calculated. The proposed model was executed with the help of the stacking method on the dataset that was used.
Copyright (c) 2026 Maher F. Ismael http://creativecommons.org/licenses/by-nc-nd/4.0
A Comprehensive Review of Compressive Sensing Frameworks for Internet of Things-Enabled Wireless Sensor Networks
Compressive sensing (CS) is important in applications as diverse as magnetic resonance imaging (MRI), high-speed communications and high-speed video imaging because it allows signals to be sampled at rates significantly lower than the Nyquist rate thanks to the sparsity of the signals. The study of CS from a signal processing, imaging, and mathematical perspective has led to many studies to go deeper into this topic and understand its pros and cons. We investigate power consumption, communication load, memory requirements, computational complexity, scalability and peripheral feasibility in intensive manner for the technologies. These are more important issues than in the conventional CS research in IoT applications, where wireless sensor networks are employed. Moreover, most existing research works are conducted on a single method for CS, such as sensing arrays, sparse representation, reconstruction, compression, or reconstruction based on deep learning techniques without any prior knowledge. In this review, another factor considered is that a complete framework for compressed sensing must include three key aspects – measurement (sensing) methodologies, sparse representation methodologies, and reconstruction methodologies. In the paper the interactions of compressed sensing techniques have been discussed in the context of the IoT and wireless sensor networks. It makes a full assessment of the latest strategies which have been developed between 2021 and 2025. Besides, we go over existing practical examples of compressed sensing and applications with special focus on the wireless sensor networks (WSN) in IoT. The use of modern methods such as deep learning and hybrid methods is very efficient, useful, and precise; however, it has its practical limitations, especially with high training costs, large memory requirements and power limitations. Finally, the synthesis of the latest literature shows that the current adaptive CS schemes offer a substantial saving in energy consumption (up to 40-50%) and communication overhead, with higher reconstruction quality (PSNR=38-48dB), than the classical iterative deep learning schemes for low sampling frame rate and low computation cost. Finally, this review emphasizes the importance of future research on lightweight, hardware-friendly co-design strategies that will maintain a balance between power efficiency, accuracy, and scalability issues and make the application of compressed sensing to practice a viable future.
Copyright (c) 2026 Noor Salah Mehdi, Hadeel Saad Noori http://creativecommons.org/licenses/by-nc-nd/4.0
Prediction of the Surface Air Temperature in Iraq based on LSTM With Random Forest Regressor
A B S T R A C T The increasing temperature in the world in this recent century convert to one of the most important challenges in the literatures. It affects many aspects of life in economy, productivity, income, health and environments. In this regard, the appropriate forecasting of temperature is the main task of this research. The annual average mean surface air temperature from 1900 to 2022 were gathered for 18 provinces in Iraq. The annual observation and five-year smoothing data were imported into the LSTM models with the Random Forest Regressor. Finally, the 10 years forecast are provided intro two periods: 1) 2023-2027 and 2) 2028-2032 for each province. The top 3 provinces with the lowest and highest temperature in all over the time are: The Duhok, Erbil and Sulaymaniyah have the lowest average 15.30°, 17.26° and 18.00° respectively. The Basrah, Dhi Qar and Muthanna have the highest average with 25.17°, 24.83° and 24.71°, respectively. The average and standard deviation of five-year forecast for Iraq with smooth data are 23.781 (0.013) for 2023-2027 and 23.767 (0.012) for 2028-2032.The LSTM models select the best model for temperature forecast and they estimate the positive increment on each province.
Copyright (c) 2026 Nadhim Azeez Sayel, Mohammed Ali Mohammed http://creativecommons.org/licenses/by-nc-nd/4.0
A Hybrid CNN–LSTM Framework for Behavioral Malware Detection and Dynamic YARA Rule Generation
The rapid evolution of malware through the use of obfuscation techniques and continuous runtime behavior mutation has made traditional signature-based detection mechanisms much less effective, making there is a dire need for adaptive and deployable malware detection solutions. In response, behavioral analysis based on API call sequences has received more and more attention, especially with the use of deep learning models, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTMs) networks. Although these models are shown to have a strong ability in modeling the sequential execution behavior, a lot of the existing approaches are limited to API level features and are not strongly linked to the practical detection tools used in real-world environments. This paper proposes a hybrid malware detection framework using CNN-LSTM-based behavioral modelling and contextual intelligence using the Hybrid Analysis platform. The system takes execution level API call sequences and augments them with light-weight external features such as threat score and antivirus detection counts to make the classification robust and reduce ambiguity in decision making. Furthermore, the learned behavioral patterns are then translated into dynamically generated YARA rules for interpretable and practical deployment, not limited to black box classification. The proposed framework is evaluated with a well-established academic data set created by combining the MalBehavD-V1 and Oliveira API call sequence datasets with 3500 samples. Experimental results show that the accuracy of hybrid CNN and LSTM reach 95.43% with only API sequences, and reach 97.49% when incorporating Hybrid Analysis features and combining the two sets of accuracy will be clearly improved, the discriminative effect will be improved as shown in the AUC metric. These results show that the fusion of deep learning-based behavioral analysis with external contextual intelligence is an effective and deployable malware detection solution which supports dynamic YARA rule generation.
Copyright (c) 2026 Hamid Talib Zaidan, Jumana Waleed, Ruaa Azzah Suhail http://creativecommons.org/licenses/by-nc-nd/4.0
An Interpretable Classical Machine Learning Framework for EEG-Based Brain Abnormality Detection
Although electroencephalography (EEG) provides an excellent, non-invasive method for monitoring brain activity, the complexity and density of these signals present a significant challenge for automated analysis. Therefore, this paper proposes an intelligent, machine learning-based framework that is both understandable and computationally efficient for classifying these signals and detecting Brain Abnormalities. The study adopted traditional machine learning techniques as a practical alternative to deep learning models, which typically consume enormous amounts of data and computing resources. It started with TUH recordings being processed with frequency filtering and subdivision into overlapping windows in order to deal with signal instability. The research then meticulously extracted the time and frequency domain features manually with emphasis on statistics and power density and used PCA to simplify the data and still maintain its central features. To improve the accuracy of the results and improve the data analysis process, the study used a number of machine learning algorithms. Such algorithms are Support Vector machine (SVM), random forest (RF), k-Nearest neighbors and extreme gradient boosting (XGboost). With experiments on these algorithms, the Supporting Vector Machine (SVM) proved to be the most successful with a fine balance between performance and an accuracy of 88.6 per cent. This shows that the traditional approaches, where they are backed by effective feature extraction, are still very competitive in the area, particularly because the suggested system focuses on clarity, modifiability, and low complexity, thus suit best the intelligent software and decision-making systems.
Copyright (c) 2026 Asam Almohamed http://creativecommons.org/licenses/by-nc-nd/4.0
An Empirical Study of Machine Learning Algorithms for Network Flow Anomaly Detection
Cyberattacks have increased significantly, while networks are becoming more complex and difficult to defend; therefore, there is a pressing need to find intelligent ways to identify and respond to abnormal activity threatening information security. Current systems can identify abnormalities based on static and repetitive network behavior that do not apply to the constantly changing nature of the volatile Information Technology (IT) environment we operate in today. This study uses Artificial Intelligence (AI) and Machine Learning (ML) to develop an anomaly-detection model from Network Log Data to help bridge this gap. This study utilized a complete dataset with several important network-flow characteristics (ports, protocols, duration of flows, number of packets in each flow, length of packets, labels of flows (normal or malicious)) that are commonly found in network log data. Preprocessing of data included filling-in missing values, converting categorical variables into numeric format, and eliminating all illogical records. In order to test the ability to evaluate the performance of the developed model, the total dataset was split equally between training (70%) and testing (30%). Four Machine Learning algorithms (Random Forest, Support Vector Machine, K-Nearest Neighbors and Decision Trees) were applied to distinguish between normal and abnormal network behaviors. The results of the experiments indicated that the aforementioned models provide good accuracy and efficiency in dealing with large and diverse datasets. Overall, this research aims to provide a systematic comparison of machine language models within the context of a network dataset for analyzing network logs and identifying anomalies, thereby contributing to the improvement of various cybersecurity systems and supporting proactive defense strategies for networks. The results will offer a real-world example of the performance tradeoffs associated with different models to assist organizations in making informed decisions when selecting an appropriate model for their specific network environment.
Copyright (c) 2026 Hasanain Mohammed Manji al-Rzoky http://creativecommons.org/licenses/by-nc-nd/4.0
A Survey of Machine Learning for Bladder Tumor Detection in Medical Imaging
bladder cancer at an early stage and treating it successfully is extremely difficult due to its recurrence and spread. With the use of artificial intelligence and medical imaging tools to identify the disease, modern systems face many difficulties in applying them to different data sets; thus, they lack classification accuracy. Other problems with these models include a lack of spatial information and high computational complexity. On the other hand, although VIT models have shown promising results in medical image analysis, their performance still depends heavily on the quality of hyperparameter tuning, making them less efficient. This supports the early detection of bladder cancer, emphasizing the need to create a complete VIT-based system that is then enhanced in a meta-heuristic manner to increase detection accuracy while reducing computational complexity. In the Radiomics study, machine learning (ML) methods were systematically applied to identify sophisticated features and create analytical patterns for cancer classification and medical outcome prediction. Deep learning (DL) has significantly improved cancer detection, segmentation, and classification with convolutional neural networks and U-Net. Recently, vision transformer models have become well known for their ability to detect long-range connections in medical images. This proves that histological examination and magnetic resonance imaging allow for a new approach to bladder cancer treatment. Even if these different ideas have evolved, there are not enough routine tests to prove how well they work together. Based on bladder tumor detection techniques, This survey provides a systematic classification and diagnostic evaluation of current artificial intelligence, thereby emphasizing its methods, benefits, limitations, and potential directions for further investigation.
Copyright (c) 2026 Hind Fleah AL-Salihy, Alaa Taima Albu-Salih http://creativecommons.org/licenses/by-nc-nd/4.0
Development and Comparative Evaluation of Hybrid EfficientNet-CapsNet Architecture for Glaucoma Diagnosis in Resource-Limited Settings
Glaucoma is a serious eye disease that causes significant damage to the optic nerve without pain or symptoms. Its long-term effects can lead to blindness. In this study, we designed a hybrid model, EfficientNet-CapsNet, which has shown significant improvements and superiority over both traditional CNN and CapsNet models. The model was developed using EyePACS-AIROGS data, which includes images of the healthy eye (NRG) and the eye affected by glaucoma (RG). Based on the results of our study, the test accuracy was 80.7%, 64.2%, and 60.8% for the EfficientNet-CapsNet, CNN, and CapsNet models, respectively. The balance achieved by the hybrid model between accuracy and efficiency makes it suitable for routine clinical environments with limited computing resources that do not require advanced graphics processing (GPUs).
Copyright (c) 2026 Nibras Yousif-Algburi http://creativecommons.org/licenses/by-nc-nd/4.0
A Control-Oriented Information System for Low-Light Object Tracking Based on Multi-Level Image Enhancement and YOLOv12
Tracking and object detection in low-light conditions is one of the most complicated problems of computer vision, as the conditions of poor light, low contrast, and the disappearance of valuable structural image characteristics in photographed images make it difficult to detect objects. These are some of the restrictions that reduce the output of detection and tracking algorithms, especially in the cases of surveillance and monitoring. To deal with this issue, it is proposed in this paper that an integrated framework can be created to improve the enhancement of low-light images and object detection through a multi-level image fusion strategy in conjunction with the YOLOv12 detector model. The suggested approach will improve low-light frames by a multi-stage algorithm including enhancement of base illumination, amplification of the details layer, and amplification of contrast with the help of Contrast Limited Adaptive Histogram Equalization (CLAHE). Noise-sensitive mask is also included in such a way that the strength of improvements in the dark area can be controlled to avoid unnecessary increase in noise. The refined frames are then used by the YOLOv12 model as input to detect and track objects. A dataset of 79 images in seven groups that depict the scenes of different crowds was evaluated experimentally. Conditions of low light were artificially produced to mimic difficult light conditions. The performance of the enhancement was measured in Structural similarity index (SSIM) and peak signal to noise ratio (PSNR) and the performance of the detection process was measured in confidence scores and the number of detected objects. The findings of the experimental study show that the suggested framework contributes to the increase of the visual quality and object detection throughput during the low-light conditions, especially in dense crowd scenarios. The findings affirm the fact that the combination of image enhancement, combined with detection models, can be an effective means of enhancing tracking reliability and the visibility of the object in problematic settings.
Copyright (c) 2026 Laith Hakem Malak http://creativecommons.org/licenses/by-nc-nd/4.0
A Review of Intelligent System for Predicting Complications in Hemodialysis Patients Using Machine Learning Techniques
Anesthesiology patients have CKD/ESRD as one of the comorbid condition and burdened with associated morbidity; despite continuous evolution in dialysis technology including mechanics, blood purification principle, membrane science, fluid therapy with but advances in long-term outcomes however remains meager especially during intra-dialysis period where patient may experience few abnormalities like hypotension/hypertension/arrhythmias/fluid overload/anemia instability due to vascular access damage potential contributory for morbidly predicable state and thus affecting outcome. Machine learning (ML) has the potential to enhance early detection of hemodynamic and biochemical deterioration with multidimensional clinical, laboratory, and statistical data from foreign agencies. The evidence from published machine learning research on dialysis-related complications, including blood pressure swings, dry weight monitoring, quality-of-life prediction, hospitalization risk, cardiac arrest, anemia management, and mortality prediction, is compiled in this review. In more than 30 studies, gradient boosting models (XGBoost/LightGBM) and deep learning architectures performed best, especially in predicting low and high systolic blood pressure [50–56]; simple_ linear statistical models performed worse, indicating that it is unrealistic to expect these models to capture complex nonlinear physiological relationships. Despite this compelling evidence, most studies have methodological shortcomings, such as retrospective single-center training sets. variable feature sets, a lack of external validation, poor interpretability, and underrepresentation of time-series signals. Multicenter cohorts, explainable AI, real-time streaming analytics, federated learning, and the integration of the Internet of Things in Healthcare. Platforms for providing Individualized adaptive hemodialysis therapy should be the focus of future research.
Copyright (c) 2026 Alyaa Kamash Wthaij, Adala Mahdi Chyad http://creativecommons.org/licenses/by-nc-nd/4.0
Adaptive and Secure IAM Policy Optimization in AWS Using Reinforcement Learning
Cloud computing systems like The use of cloud computing environments like Amazon Web Services (AWS) requires setting up of accurate and dependable Identity and Access Management (IAM) policies that can ensure the secure and uninterrupted functioning of the service. Traditional inflexible IAM management practices are not very flexible to evolving workloads, and learning-based practices often breed uncertainty and system risk.In this research, a flexible IAM policy optimization framework has been suggested based on a reinforcement learning method, namely Proximal Policy Optimization (PPO) in combination with deterministic safety guardrails to secure business continuity. The framework was stringently tested with 1,378 real IAM policy files and 23, 125 real CloudTrail logs that were obtained in a managed AWS environment.Empirical findings provide that the proposed methodology has a 96.5% precision in the identification of high-risk permissions, a 100 per cent recall among essential services, and lessens unnecessary privileges by 78.9 per cent as measured by the Least Privilege Reduction Score (LPRS). These results support the view that adaptive IAM optimization is sufficiently safe to run in production cloud in the presence of deterministic safety enforcements.
Copyright (c) 2026 abdualrahman mohammed Talib, Ghassan Sabeeh Mahmood, Hazim Noman Abed http://creativecommons.org/licenses/by-nc-nd/4.0
Tracing Luggage Carrier Robot Vehicle: Design and implement an accurate self-control robot system
Nowadays, travelers have been faced with a common problem when carrying heavy luggage, especially when airports do not meet the passengers' needs to some extent. Thus, the need to reduce the luggage carried is one of the obstacles that the traveler faces due to the lack necessitated by the fact that the vehicles provided by the airports. Therefore, to overcome this issue, several luggage carrier robots have been developed to provide user convenience and automation, which significant advancement in introducing mobility. Carrying luggage, following users, and elimination, common challenges faced by travelers. Therefore, a self-controlled luggage carrier robot is proposed to provide enhanced mobility, follow the user, avoid obstacles, and carry luggage effectively. A tracing person algorithm-based self-control robot and an ultrasonic sensor (HC-SR04) based avoiding obstacle avoidance, respectively. The simulation results have shown that the proposed system achieved in effectively demonstrates its ability to switch between tracing, stop, and collision avoidance modes autonomously by achieving a Mean absolute error (MAE) of 3.88 cm in distance tracking. Besides, it provides accurate logical tracking under different loads which is best for light-to-medium luggage loads (4-5 kg approximately) to maintaining optimal walking of 0.36 m/s.
Copyright (c) 2026 Hakim Adil Kadhim http://creativecommons.org/licenses/by-nc-nd/4.0
A Machine Learning Framework for Anticipating Ferritin Deficiency Utilizing Clinical Biomarkers and Gradient Boosting Models
Ferritin deficiency is a public health problem that causes. It means people are iron deficient and that can have serious health consequences. We need to catch people at risk early and make sure that these problems do not happen. This study takes these insights and uses a machine learning framework to predict whether an individual will have ferritin levels. We use data including who people are, what their blood contains and their diets. We extracted this information from NHANES data. We ran three different machine learning systems. XGBoost, LightGBM and CatBoost. A sample of 1318 people were tested. The main problem was that there were no subjects with ferritin deficiency in the group. We used person measures such as area under the curve and accuracy to take a look on how the systems worked. CatBoost did the best of the bunch. It was good at detecting whom had deficiency and whom not. The area under curve of CatBoost system was 0.946 which’s very good. It also scored with a recall of 64.77% and an F1-score of 0.60 So that it was not bad at identifying deficiency. CatBoost system reduced the number of negatives by 38% compared to basic system. LightGBM system did well too. The Catboost system performed better than the others in identifying individuals with ferritin deficiency. The results indicate that machine learning systems can help us identification of the people who are at risk of health problems. To make sense of results, we also need to view them in the context of something that some health problemsre not common
Copyright (c) 2026 Zahraa Kadhim AlSendi http://creativecommons.org/licenses/by-nc-nd/4.0
A hybrid of Grey Wolf (GWO) and Particle Swarm Optimization (PSO) to predict chronic kidney disease
Chronic kidney illness represents one of the greatest chronic ailments, with a considerable rate of mortality around the globe. It is also one of the most critical illnesses nowadays, where an accurate diagnosis must be made as soon as feasible. Several studies have recently focused on creating computer-aided techniques to identify CKD in the early stages. In medical science, machine learning methods are now a valuable tool, contributing significantly to disease predictions. The primary purpose of the present research is to construct a reliable prognostic model using a hybrid of Grey Wolf and Particle Swarm Optimization for the choice of features to support classifiers on the CKD dataset. This approach helps reduce dimensionality and enhance model performance. In CKD data analysis, four ML algorithms are applied: KNN, NB, DT, and SVM. The experimental findings were implemented on the CKD dataset, and the results show that the suggested approach achieved exceptional matching performance (100%, 98.75%, 100%, and 100%), respectively. In this aspect, machine learning algorithms have proven to be promising and point towards the future of disease diagnosis.
Copyright (c) 2026 Nisreen Mizeheir AL-kardhi http://creativecommons.org/licenses/by-nc-nd/4.0
An Enhanced Image Encryption Framework Integrating Hyper-Chaotic Maps with the Serpent Block Cipher for Secure Medical and Biometric Data Transmission
The secure transmission of sensitive e-images, especially medical records and biometric templates, remains a vital challenge because of the characteristics of images, including high adjacency correlation between the pixels, large volumes of data, and vulnerability to statistical attacks. This study presents an enhanced multi stage confusion–diffusion image encryption framework employing a four-dimensional (4D) hyper-chaotic map with the Serpent block cipher (via Cipher Block Chaining (CBC) mode). The confusion stage employs a 4D hyperchaotic system that permutes pixels through a variation at the pixel-level to destabilize the spatial temporal structure of the original image, subsequently leading to a diffusion stage where 32-rounds of the Serpent 192 -bit cipher homogenizes the distribution of gray-levels over the entire image. The framework is wrapped in a modular three-layer software architecture: a User Interface Layer, a Logic Layer and a Cryptographic Core, and coded for implementation in Python with CustomTkinter-based graphical user interface. Experimental evaluation on standard test images and representative medical and biometric datasets shows excellent security performance: information entropy of 7.9996 (close to the theoretical maximum of 8.0), Number of Pixels Change Rate (NPCR) > 99.6%, a Unified Average Changing Intensity (UACI) ≈ 33.47%, and nearzero horizontal and vertical correlation coefficients. These results confirm that the proposed framework provides robust protection for clinical and biometric authentication contexts.
Copyright (c) 2026 Abdulhadi Nadhim Mohsin Alzamili http://creativecommons.org/licenses/by-nc-nd/4.0
Cataract classification based on traditional data augmentation methods: Review study
Cataract is a major cause of visual impairment and blindness worldwide. This has led to the design of automated systems for early detection through retinal fundus imaging. Recently, deep learning techniques with a focus on CNNs have shown good results for cataract classification. However, the results obtained from these models are often limited due to the inherent class imbalance problem in most medical image classification problems. To overcome the class imbalance problem, several researchers have adopted traditional data augmentation techniques to increase the number of the minority class. This review aims to highlight the recent studies that have adopted traditional data augmentation techniques for the classification of cataracts. It presents an overview of the recent research trends for the classification of cataracts using traditional data augmentation techniques.
Copyright (c) 2026 Zainab Naseer Hadeed, Ahmad Shaker Abdalrada, Hasanain Hazim Azeez http://creativecommons.org/licenses/by-nc-nd/4.0
Modify the Speck algorithm-SHA3 to enhance data integrity and authentication in WoT environments
The rapid evolution of the Web of Things (WoT) demands cryptography solutions to ensure a high level of security with minimal usage and to work efficiently. In large scale WoT environments, standard cryptographic primaries can be of great energy and computational cost. The following paper is a proposal of a hybrid cryptographic system that will combine the lightweight SPECK block cipher and the Secure Hash Algorithm-3 (SHA-3) to provide improved data integrity, authentication, and efficiency in operations. The proposed hybrid cryptography can reduce the complexity of computing and still have a high level of security since it adds SPECK to the SHA-3 sponge design. Experimental results show significant gains in execution time and throughput compared to SHA3-512, especially for large data sets. Furthermore, NIST Statistical Test Suite findings show that the proposed hybrid approach has randomness properties comparable to SHA3-512, with all p-values exceeding the acceptable significance threshold. These findings show that the suggested hybrid cryptography is a scalable and efficient security solution for resource-constrained WoT.
Copyright (c) 2026 Baraa Mohammed Hasan, Samar Kareem Tuama http://creativecommons.org/licenses/by-nc-nd/4.0
Integrating GAN-Generated Data into Real-Time IDS Workflows: A Practical Study
Class imbalance occurs in intrusion detection systems (IDS) when the level of benign traffic is oversampled, whereas rare attacks, such as U2R and R2L, are undersampled, causing biased classifiers to generate high false-negative rates. This study proposes a novel architecture that enables real-time synthetic data generation and augmentation with integration into existing IDS of an additional module based on Generative Adversarial Networks (GANs). The generator proposed in the GAN variant consists of sequential generative layers with linear activations and reaches (ReLU), with a discriminator whose activation is LeakyReLU and binary cross-entropy loss. Data on minority classes is synthesized with the help of the datasets such as NSL-KDD, CIC-IDS-2017, and CIC-IDS-2018. These features are performed by the architecture to monitor asynchronous traffic using tools (for example, Suricata and tcpdump), GAN activation in response to anomalies, and incremental updates on classifiers (for example, Random Forest) to enable minimal operations. The accuracy of prototypical validation reached up to 98%, and F1 score improvements between 3-5% on minority classes of low latency level of less than 2 s per batch under the acceleration of a graphics card. The early assessments show positive progress regarding the detection performance, cost-effectiveness of the computations, and possible scalability to more modern types of threat, which can be further expanded by developing conditional GANs and federated learning in various areas of cybersecurity.
Copyright (c) 2026 Zainab A. Abdulazeez, Israa Abdulkadhim Jabbar Al Ali, Basma Mustafa M. H. http://creativecommons.org/licenses/by-nc-nd/4.0
Lightweight Heterogeneous Signcryption for SDN-IoT Authentication and Access Control
In modern software defined networking environments, the number of Internet of Things (IoT) devices is rapidly increasing, rendering critical security threats such as authentication and secure communication between heterogeneous cryptographic domains. Most of the existing signcryption schemes are designed in a homogenous cryptographic environment and do not fulfill the needs of interoperability between resource-constraint IoT devices and powerful SDN controllers. This paper presents a new lightweight heterogeneous signcryption scheme facilitating secure bidirectional communication between CLC based IoT devices and IBC based SDN controllers. Our scheme achieves 128-bit security based on elliptic curve cryptography over the secp256r1 curve, and has no computational overhead of bilinear pairings. We provide a complete implementation for Ryu SDN framework, interfaced with Mininet network emulation, illustrating the feasibility of practical deployment. We conduct a comprehensive performance evaluation to demonstrate a throughput of 226.9 ops/s (Signcryption) and up to 2,156.4 ops/s for Optimized Unsigncryption, with linear scalability for more than 100 IoT devices. Formal security analysis through ProVerif confirms that the scheme fulfills confidentiality, authentication, and integrity properties in accordance with the Dolev-Yao attacker model. We also provide mathematical security proofs of IND-CCA2 security and EUF-CMA unforgeability with respect to the Elliptic Curve Discrete Logarithm Problem (ECDLP) and Computational Diffie-Hellman (CDH) assumptions in the Random Oracle Model.
Copyright (c) 2026 Fahad Naim Nife, Bilal Majeed Abdulridha Al-Latteef http://creativecommons.org/licenses/by-nc-nd/4.0
An Intelligent Decipher System to Examine Cipher Algorithms
The high rate of cryptographic primitive development requires improved, automatic assessment structures that guarantee effective data security. The traditional cryptanalysis, which is mostly based on special linear and differential approaches, is both computationally infeasible and needs substantial domain knowledge. To overcome technical deficiencies like the Curse of Dimensionality and limitations under high-degree algebraic transformations, this paper presents the Intelligent Decipher System (IDS). The IDS architecture combines a Deep Residual Network (ResNet) and Attention-based Long Short-Term Memory (LSTM) to extract both spatial and sequential features, capturing differential properties and long-range dependencies. We compare the given system with reduced-round SPECK32/64 and AES-128, and classical polyalphabetic systems. The experimental findings indicate the IDS has a distinguishing accuracy of 92.4% on 5-round SPECK and that its bias detection is effective up to 7 rounds (61.5%), which is remarkably better than traditional differential distinguishers and baseline machine learning models. The framework offers a scalable black-box auditing device for detecting structural weaknesses in current lightweight cryptography.
Copyright (c) 2026 Ayad osama Jalal http://creativecommons.org/licenses/by-nc-nd/4.0
Design and Security Evaluation of BSP-128: A Hybrid Blowfish and Serpent Inspired 128-Bit Block Cipher
In this work, a new 128-bit block cipher (namely, Blowfish–Serpent Protocol 128-bit (BSP-128)) has been introduced that allows to improve the performance and implementation of an optimal compromise between security, performance, and implementation efficiency. The key size for the input and output for BSP-128 is also scalable at 128-bit blocks and keys with a length of up to 256 bytes, and the design conforms to the underlying scheme for AES. The BSP-128 algorithm uses a 16-round Feistel network similar to Blowfish and Serpent with multiple architectural improvements that aid the diffusion, so this cipher is much more robust against modern cryptanalysis techniques. The primary goal of BSP-128's design is to be resistant to both differential and linear cryptanalysis. Finally, the round structure was made even more powerful to better handle identified potential security vulnerabilities and targeted attack scenarios discovered in a subsequent internal analysis. By mixing substitution and permutation in the structure of the F–function, BSP-128 transforms from standard SP to a Substitution–Permutation–Substitution (SPS) type of model which boosts the avalanche effect. And we use in-place integer multiplication as a diffusion intermediate for additional mixing details to each round and to maintain the stable quality of the speed as well as security. While Blowfish's fixed-factor algorithm runs on 64-bit blocks and takes up a lot of memory for large lookup tables, BSP-128 automatically supports 128-bit blocks to avoid matching ciphertext attacks and achieve low memory consumption. This attribute of it adds a large value to its ability to work in limited hardware settings such as smart cards. The key expansion mechanism yields 786 bytes of subkeys, which are made from a P-array having 32 subkeys of 64 bits each, and 32 S-boxes with 64 fields per box. It ensures a strong key-dependent transformation and protects against brute-force attacks, thanks to this long key length. While the performance of BSP-128 is highly promising with regard to core security metrics, experimental findings show that BSP-128 surpasses expectations through computational efficiency. The final design will be submitted for public survey and comparison in academia, resulting in a secure and high throughput candidate tailored for contemporary cryptographic uses.
Copyright (c) 2026 Ammar Abdul Ameer, Qusay F. Al-Doori, ِNoor A Yousif http://creativecommons.org/licenses/by-nc-nd/4.0
Improving Ancient Language Classification with Deep Learning and Traditional Models for Class Imbalance
The paper suggests a hybrid framework of the ancient language image classification, which combines deep transfer learning with ensemble machine learning to overcome the issue of imbalance in the classes in low-resource settings. They use a pre-trained Deep Visual Feature Extractor (DVFE) that is built on the ResNet-50 architecture to produce the high-level visual representations of the Ancient Language Images (ALI) dataset that consists of eight historical scripts. In order to reduce moderate levels of imbalance in the deep feature space, imbalance-aware resampling algorithms, such as SMOTE and ADASYN are only used on the training set. They are then used to extract features which are then further classified with the help of Random Forest and LightGBM models with hyperparameters being optimized via cross-validation. On a held-out test set, experimental results show strong and balanced performance with an overall accuracy of 0.89, macro-precision of 0.88, macro-recall of 0.87, macro-F1 of 0.88 and per-class values of AUC of 0.88 to 0.97. The results show that the ability to correct the imbalance on the feature level allows to increase the fairness between the majority and minority language classes and retain good discriminative power. This comparison also supports the fact that the proposed DVFE-ensemble system yields better performance as compared to the traditional methods used to deal with imbalance on the raw images level. On the whole, the research project demonstrates the usefulness of the deep feature extraction and imbalance-conscious ensemble learning as a tool of ensuring a high level of reliability in the ancient languages classification, which will further the development of the digital heritage preservation systems in the conditions of limited amounts of information.
Copyright (c) 2026 Hadeel M Saleh http://creativecommons.org/licenses/by-nc-nd/4.0
Evaluating the Impact of Data Volume on Deep Learning Performance: A Cloud-Based Experimental Study
The considered study discusses the impact of the data size on the efficiency of the deep learning in the framework of cloud-based computing with the specific emphasis on the significance of big data as one of the primary providers of the artificial intelligence (AI) efficiency. This paper takes into account the role of the augmentation of data as an essential digital resource in enhancing the learning and the generalization of the performance of the models and the precision of the decision-making of the modern AI systems. The paper looks at structured, semi structured and unstructured data stored in cloud environment and how the same can be used in scalable and efficient train AI pipelines. A detailed experimental framework is designed to determine the correlation between the dataset size and the model performance and the deep learning architecture that is deployed on the cloud. The results show the positive relationship that exists between the size of data and the model accuracy and the training and the validation accuracy is 84.55 and 84.38 with the size of data respectively. These findings confirm that the data scalability can have a dramatic impact on the convergence behavior, overfitting, and the generalization performance. In addition, the study also reveals the significance of cloud computing in supporting the high-performance AI workflow, which can include the aspects of distributed training, elastic resource management, and real-time information processing. The practical implications are exemplified to applications that contain autonomous systems and data-driven intelligent systems where cloud-based infrastructures allow in enacting superior perception, decision-making and automation processes. In general, this piece confirms the fact that the volume of data, when properly handled by using cloud computing, serves as a core facilitator of the contemporary deep learning systems. The results offer an empirical basis in favor of the combination of cloud technologies and massive data analytics as the supporting elements of scalable, intelligent, and high-performance AI solutions.
Copyright (c) 2026 Shaymaa kaseb Layus http://creativecommons.org/licenses/by-nc-nd/4.0
A Dual Hybrid Approach for Log Anomaly Detection using Deep Learning
The growing complexity of computer software and the explosive growth in the log data have made the detection of anomalies and the identification of problems in the system from the massive logs a major research field. However, existing log anomaly detection techniques cannot identify context-dependent semantic connections in unstructured logs and do not have explicit decision making processes and therefore they cannot be used in very dynamic systems. In this paper, we propose a dual hybrid technique for the anomaly detection task including the feature representation and the classification stages. During the feature representation stage, the statistical TF-IDF technique is used to extract features and merge them with the semantic representations of SBERT to obtain comprehensive representations with both statistical importance and semantic significance. In the classification stage, the hybrid deep learning strategy is used consisting of Convolutional Neural Networks (CNNs) for local pattern extraction and Bidirectional Long Short-Term Memory (Bi-LSTM) network for dealing with temporal relations in the data. This helps to improve the accuracy and effectiveness of the model in detecting anomalies in system logs. The approach has been trained and tested on the commonly used HDFS and BGL datasets. Experimental results showed that the proposed approach achieved an anomaly detection accuracy of up to 0.9989 and therefore demonstrated the potential and effectiveness of this approach compared with previous methodologies.
Copyright (c) 2026 Harbi Mahmood Abas, Ziyad Tariq Mustafa Al-Ta'i, Shumoos jamal Rashid http://creativecommons.org/licenses/by-nc-nd/4.0
A Hybrid Deep Learning and Color Space Model for Digital Image Enhancement
Image contrast enhancement is crucial in digital image processing. Enhancing contrast highlights image details, improves lighting, and focuses on key features that help maintain image quality and enhance visual perception in various fields such as computer vision, medical imaging, and surveillance systems. Deep learning models are applied to improve the contrast and brightness of digital images while preserving fine details and minimizing distortion. This article presents a method for improving image appearance using a hybrid application of deep learning and digital image processing techniques with different color systems to obtain an enhanced image from the original. Three color models and four deep learning techniques are used with contour transformations to capture the finest details of the digital image, in addition to applying contrast enhancement techniques to increase image clarity. The color system of a color image is converted from RGB to other color systems. The color system is then divided into its original layers, which are further deconstructed using contour transformations. Contrast enhancement techniques are then applied to increase the contrast in the digital image, thus training the model to optimize image colors. A range of metrics were also used to measure the generalizability of the proposed system (PSNR, SSIM, accuracy). The results showed that the best color system was HSV, when combined with the ResNet50 deep learning model. The results were as follows: PSNR = 44.2, SSIM = 0.9952.
Copyright (c) 2026 Sarah M. Mahdi, Ahmed S. Abdullah http://creativecommons.org/licenses/by-nc-nd/4.0
Deep Learning for Multi-Class Gastrointestinal Endoscopy: A Survey of Recent Advances and Reliability Challenges
Deep learning has become a cornerstone of computer-aided diagnosis (CAD) for gastrointestinal (GI) diseases from endoscopic imagery, enabling automated recognition of complex and subtle visual patterns that often challenge clinical interpretation. While recent years have witnessed a rapid growth of deep learning-based approaches for multi-class GI disease classification, the existing literature remains highly fragmented across heterogeneous datasets, architectural choices, and evaluation protocols. This fragmentation is particularly problematic in realistic multi-class settings, where severe class imbalance, fine-grained inter-class similarity, and distributional shifts substantially undermine clinical reliability. Unlike prior surveys that primarily emphasize architectural performance or accuracy-centric comparisons, this work provides a reliability-aware analytical review of recent deep learning methods for multi-class GI disease classification from endoscopic images. We critically examine how contemporary studies address-or overlook-key reliability dimensions, including class imbalance handling, patient-level data separation, macro-level evaluation metrics, and robustness under distribution shifts. Furthermore, we identify recurring methodological limitations that may lead to hopeful performance reporting while offering limited translational value in real clinical environments. By organizing recent advances within a unified reliability-focused taxonomy, this survey highlights unresolved challenges and emerging research directions toward dependable and clinically deployable GI CAD systems. The analysis aims to support researchers and practitioners in designing evaluation pipelines and modeling strategies that move beyond optimizing accuracy toward trustworthy decision support for endoscopic diagnosis.
Copyright (c) 2026 Asaad Kadhim Abd Tamimi, Zena H.Khalil, Ali Mohsin Aljuboori http://creativecommons.org/licenses/by-nc-nd/4.0
A Transformer-Enhanced Human-Centric Unsupervised Framework for Multi-Person Video Anomaly Detection
Video anomaly detection in surveillance environments is still difficult. This is because abnormal events do not happen often, take different forms, and depend on the complex nature of real scenes. In addition, methods that depend on visual appearance are affected by changes in lighting, camera angles, and background conditions. These issues can reduce detection accuracy and also cause privacy problems. For this reason, recent studies focus more on motion-based representations that describe human behavior and reduce the effect of unnecessary visual details. In this work, a framework for video anomaly detection is proposed. Spatial motion features are extracted using a 2D convolutional neural network. These features are then passed to a GRU network to model motion over time. A Transformer module is also used to help capture longer temporal relationships in motion sequences. The proposed framework is able to handle scenes that include more than one person. During training and testing, all detected persons are considered within fixed-length temporal windows. Information from each person is then combined to produce an anomaly score that represents the overall scene behavior. This helps the model detect abnormal activities even in crowded surveillance scenes. The proposed model uses an unsupervised one class learning approach. Training is performed using normal motion data only, Abnormal events are identified by observing deviations from the learned patterns of normal behavior. The experiments were carried out on real surveillance datasets using standard evaluation metrics. The model achieved an AUC-ROC of 93% at the frame level, indicating stable and consistent performance across different cases. The integration of spatial and temporal features contributed to a more accurate representation of complex motion patterns and reduced the likelihood of confusing abnormal behavior with normal activity.
Copyright (c) 2026 Hiba Mohsin Abdulameer, Ali Mohsin Al-juboori http://creativecommons.org/licenses/by-nc-nd/4.0
Quality of Experience in Multimedia Streaming: Objective Metrics, Subjective Assessment, and Trends for Research and Practice
Quality of Experience (QoE) has become the dominant success criterion for modern multimedia streaming, yet the literature is fragmented across network QoS studies, perceptual quality models, subjective user experiments, and data-driven prediction. This survey consolidates these strands and explains how to translate QoE concepts into measurable, comparable, and actionable engineering signals. Specifically, we (i) propose a taxonomy linking network-, application-, and perceptual-level metrics to user-perceived outcomes; (ii) synthesize evidence on how objective indicators relate to subjective ratings (MOS/ACR) in adaptive streaming scenarios (startup delay, rebuffering, and quality switching); (iii) compare widely used models and standards (e.g., ITU-T P.1203/P.1204 and VMAF) with their assumptions and limitations; and (iv) summarize datasets, protocols, and practical guidance for reproducible evaluation and machine learning-based QoE prediction. Finally, we highlight open gaps—live-streaming QoE, cross-device perception, and model generalization under distribution shift—and outline research directions for 5G/6G, edge computing, and immersive media.
Copyright (c) 2026 Ahmed M. Kareem, Muntadher Khamees, Alaa Taima Albu-Slaih http://creativecommons.org/licenses/by-nc-nd/4.0
A Systematic Review of Multi-Class Malware Classification: Techniques, Challenges, and Future Directions
This study examines the evolution of malware classification from binary detection (malicious/good) to multifamily classification, addressing the challenges of behavioral overlap between families, data imbalances, and advanced obfuscation techniques. The systematic review (2020–2025) aimed to analyze and evaluate the performance of multifamily classification methodologies. It examined 45 studies out of 170 searched major scientific databases, classifying them by feature type (static, dynamic, in-memory, hybrid) and learning algorithm type. The results revealed four main strategies: traditional machine learning (76–88%), deep learning (85–97.9%), particularly with in-memory data, hybrid models (87–99%), and specialized obfuscation techniques. The study also highlighted the challenges of feature overlap (reduced reliability by 10–15%), class imbalances (reduced recall by up to 40%), and obfuscation (reduced reliability by 15–25%). The study concludes that more interpretable models are needed, zero-day families should be addressed, and evaluation criteria should be standardized in the future.
Copyright (c) 2026 Athraa Abdulkarim Matlub, Alaa Abdulhussein Daleh http://creativecommons.org/licenses/by-nc-nd/4.0
Data-Driven Information Technology Systems: Evaluating Machine Learning Algorithms on the Iris Dataset
Machine learning is the core of the present-day Information Technology (IT) systems. It allows processing the data intelligently and allowing the decision making to be automated. The present study uses four classification algorithms, namely Naive Bayes, Decision Trees, Support Vector Machines (SVM) and a Deep Learning model that is built upon an Artificial Neural Network (ANN), as a benchmark to run on the Iris dataset. The experiment replicates a classic IT setting where data has been preprocessed, divided into training and testing data, and it has been utilized to create predictive models. The experimental findings prove the fact that both Decision Trees and SVM have 100 percent accuracy, which proves their suitability as the means of working with structured data. Naive Bayes had an accuracy of 98% and it is a fast and computationally efficient problem solver that is applicable in real time IT applications. ANN model has an 97% accuracy which shows that it is capable of representing complex patterns, but this is affected by the fact that the dataset is relatively small. These results demonstrate why proper choice of machine learning methods is important in developing intelligent IT systems, including decision support system, data analytics platform, and automated classification applications. All in all, this paper provides a viable basis to implementing machine learning algorithms into the modern information systems and improving efficiency, scalability, and decision-making processes.
Copyright (c) 2026 Maha Khalil Ibrahim http://creativecommons.org/licenses/by-nc-nd/4.0
Neutrosophic Domain for Image Zero Watermarking
In our time, the use of various technological application systems has become an accepted and indispensable necessity. Naturally, the use of images in these applications is a priority due to the significant impact of image circulation, whether for medical, industrial, economic, or, of course, social and personal purposes. Therefore, the ease with which this vast amount of data is shared has led to significant challenges in terms of security, intellectual property rights protection, and other areas. Consequently, watermarking technologies, especially zero-based watermarking, have become an ideal solution for preserving images, provided that ownership is securely verified without any alteration to the original images. This research aims to propose a developed zero-watermarking model using: 1- A neutrosophic domain, which is used to extract important image features and use them to generate new features. 2- Calculating the average of each block to create a new, more stable feature matrix. Initially, the image is converted to a neutrosophic domain, and the component T is selected and divided into non-overlapping blocks, each of size 4x4. Accordingly, a feature matrix is ​​created whose elements are the average values ​​of each block and converted to binary form, where the watermark is merged with it through XOR to build the zero watermark.
Copyright (c) 2026 Hussein Kadhim Abdali, Areej M. Abduldaima, Matheel E. Abdulmunim http://creativecommons.org/licenses/by-nc-nd/4.0
Review Of Intrusion Detection System Architectures in IoT-WSN‏ Network
The rapid evolution of the Internet and the increased reliance on computerized services have exacerbated concerns about security, privacy, and confidentiality. Intrusion Detection Systems (IDSs) work with preventive controls by monitoring hosts and network traffic for suspicious activities or policy violations, and reporting alerts to operators or SIEM platforms. This work explores the deployment model (host-based vs. network-based) and the application domain (web applications, cloud environments, Internet of Things, wireless sensor networks, mobile ad hoc networks, and Voice over IP). We review representative research topics (e.g., alert reduction, honeypot-aided detection, botnet command-and-control discovery, SDN/OpenFlow-based IDS, and hybrid cloud-based IDS architecture), and summarize evaluation caveats such as the false positives/false negatives trade-off, data/feature bias, and resource-overhead challenges. 2.p4p3 Recommendations The paper offers some recommendations for aligning IDS design to threat models and deployment realities.
Copyright (c) 2026 Sara Mahdi Abboud http://creativecommons.org/licenses/by-nc-nd/4.0
Spatio-Temporal Meta-Learning for Patient Trajectory in Longitudinal Electronic Health Records: A Review
Research on spatiotemporal hyper-learning applied to longitudinal electronic health records (EHRs) provides a concise yet helpful summary. The research problems, innovative methodology, datasets (with a focus on MIMIC-IV), expected findings, and implications of spatiotemporal hyper-learning for patient pathway modelling are all covered in detail in this study. With more and more electronic health record data being available, predictive analytics have grown, particularly in the areas of patient pathways and clinical outcomes. Different problems arise due to the complexity caused by changes in time and the accompanying clinical characteristics. We require state-of-the-art modelling approaches that incorporate spatiotemporal dimensions. By monitoring the development of clinical features and intricate spatial connections, spatiotemporal hyper-learning frameworks have the potential to enhance patient route modelling. The approach is novel because it can adapt to new patient groups and clinical conditions through hyper-learning. Though it is briefly mentioned in the abstract, the MIMIC-IV dataset confirms the framework; however, the method section explains the approach, strategies, and algorithms. Most courses address data processing, feature extraction, learning to represent spatial and temporal components, and hyper-learning for fast adaptation to new patient data. Stochastic hyper-learning has the potential to outperform more conventional methods of prediction in terms of accuracy and flexibility. This method has the potential to enhance clinical decision-making since it can detect patient trajectories.
Copyright (c) 2026 Qassim A. Hadi 1, Zainab N. Nemer 2 http://creativecommons.org/licenses/by-nc-nd/4.0
Development of a machine learning-based online test system with a hierarchical structure for students
A significant data amount is being exchanged, turning internet to a contemporary Silk Road for information. Machine learning (ML) is a burgeoning discipline that is progressively employed for diverse purposes. Artificial intelligence (AI) is the area of study that grants machines the capacity to demonstrate intelligent behavior. In recent decades, there have been notable developments in the fields of ML and deep learning. Devices with low processing capacity are being equipped with advanced algorithms and technology. The machine learning-powered online student testing system frees students from the limitations of conventional paper-based testing and leads to the improvement of the effectiveness of testing procedure. At the same time, it results in maintaining fairness in evaluating students' performance while improving the efficiency of grading. The objective of the present work is creating an internet-based assessment system for students that utilizes machine learning to improve the evaluation of college courses. The main focus of research and design lies in functional modules, fundamental technologies, and implementation of the on-line testing system. The advanced educational software and on-line evaluation system can be helpful for the schools in developing a more systematic and rigorous administration. The conclusion highlights that the online examination system, which is an advanced and reliable educational software, can aid schools in implementing systematic and efficient management procedures
Copyright (c) 2026 Ruqaia Jwad Kadhim http://creativecommons.org/licenses/by-nc-nd/4.0
Context-Aware Loss Scheduling for Responsible Image Enhancement
While modern image enhancement and restoration systems are widely used in safety-critical settings such as clinical imaging and humanitarian documentation, they tend to be context-agnostic, which is a major limitation: global quality objectives tend to only look at the picture as a whole. It does not take account of local-structure preservation and larger fidelity goals Recent works in medical enhancement put emphasis on structure preservation and non-reference evaluation to ensure that improvement does not mislead evaluations, while studies in fairness focus on differing restoration quality between groups or content types. The Context-Aware Loss Scheduling framework is an image enhancement proposal we make. CALS uses a semantic context predictor and alterations to the optimization base plate, along with an algorithm on loss scheduling aimed at contexts (e.g. medical, facial recognition/regeneration), that are iteratively executed many times to figure out how image outputs can be made higher-quality according to each context in the most pleasant or successful manner. This framework insights advance previous work on semantic-guided enhancement; preserving medical constraints and fairness-inspired evaluation techniques. Instead of making up arbitrary rules, CALS accords with these goals by contextualising the significance of objectives (retention structures, representation quality) through a weighting strategy that is dependent on context. Unlike conventional context-agnostic enhancement systems, the proposed framework dynamically adapts objective weighting according to semantic context. Experimental results demonstrate that context-aware scheduling reduces structural distortion, improves perceptual realism (LPIPS), and preserves identity consistency in face-sensitive scenarios. These findings support the importance of context-conditioned optimization in responsible image enhancement systems.
Copyright (c) 2026 Hamsa M Ahmed, Shokhan M. Al-Barzinjia http://creativecommons.org/licenses/by-nc-nd/4.0
Feature Engineering for High-Accuracy Discrimination Between TCP SYN and Modbus Query Flooding Attacks in Industrial Cloud Environments
In this study, we present an innovative methodology for distinguishing between TCP SYN flood attacks and Modbus query flood attacks in industrial cloud computing environments. We employed advanced feature engineering techniques that focus on the relationship between read and write operations in industrial protocols. A total of 3,528 attack scenarios were analyzed, and 25 distinctive features were extracted, the most prominent being the write packet ratio (67.9% importance in gradient enhancement models). The binary classifier demonstrated 97.45% accuracy in the new test data, showing balanced performance for both types of attacks. Comparisons between Random Forest and XGBoost algorithms showed similar effectiveness, despite the different feature importance distributions. The results indicate that protocol operation ratios provide higher discrimination power than traditional motion metrics. These findings provide a practical framework for detecting real-world attacks in industrial cybersecurity systems, while also allowing for the expansion of the feature engineering methodology to other industrial protocols and additional types of cyberattacks.
Copyright (c) 2026 Murooj Fadhil Zaiter, Ahmed Saadi Abdullah, Mohammed Mahde Mahmood, Majid Hamid Ali http://creativecommons.org/licenses/by-nc-nd/4.0
The Core of Inner Ideals of Four Dimensional Real Lie Algebra With Tow Dimensional Derived
Suppose that L is a 4-dimensional Lie algebra with a 2-dimensional derived, and E is an arbitrary non-trivial inner ideal of L. The core of E is an inner ideal of L. This paper presents a proof. If L is a 4-dimensional Lie algebra with a 2-dimensional derived algebra. Then the core of each inner ideal E of L is zero.
Copyright (c) 2026 Firdous Kareem Mohammed, Hasan Mohammed Ali http://creativecommons.org/licenses/by-nc-nd/4.0
Comparing the Solution of the Equations of Integral Volterra by the Method of Adomian Decomposition and Method of Series Solution and Method of Successive Approximations Numerically
In this article, we try three a numerical techniques to solve nonlinear Volterra crucial equations of the second type, that is primarily based on the use of The Method of Series Solution (SSM), The Method of Adomian Decomposition (ADM) and The Method of Successive Approximations approach (SAM). They are very beneficial to obtain the solutions. We additionally show list a few numerical examples to reveal the effectiveness of the numerical methods. Our studies highlights the significance of the numerical techniques as these strategies are quick convergence of solutions. Furthermore, our evaluation led us to the willpower that any imperative equations may be solved by using those techniques without problems and all of the techniques led us about at one factor. The results obtained through strategies had been compared with the other solution. Hence, ADM is observed to be a terrific device for the answer of Volterra necessary equations and additionally minimizes the volume of calculations.
Copyright (c) 2026 Dheyaa Hamid Hatif http://creativecommons.org/licenses/by-nc-nd/4.0
Integration of Fractional Complex Transform with A-Transform and Adomian Decomposition Method for Solving Nonlinear Fractional Differential Equations
In this study, we investigated the possibility of integrating the fractional complex transform ( ) with the Aman transform This methodology was applied to several nonlinear fractional-order differential equations The illustrative examples, including the fractional relaxation equation and the fractional Riccati equation using the Adomian Decomposition Method The approach yielded accurate numerical solutions in a concise and efficient manner, highlighting the effectiveness of this integration in addressing complex mathematical models.
Copyright (c) 2026 Duaa Mohammed Hamid https://creativecommons.org/licenses/by-nc-nd/4.0/
Analysis the oscillatory sutter by Fluid flow Through a flexible inclined channel
In this research paper, we examined a type of fluid known as the Sutterby fluid, while accounting for the elasticity present in the channel walls. The governing equations were solved using techniques for solving differential equations, and the resulting plots were interpreted using the Mathematica software
Copyright (c) 2026 Zain Al-abdeen Abbas Al-safi http://creativecommons.org/licenses/by-nc-nd/4.0
We will outline the idea of semi annihilator large small submodules. It was one of generalizations of annihilator small submodules. an ℜ-module with is named semi-annihilator large small submodule of for is submodule in imply is large small in , where . In addition, investigate properties and characterizations of semi annihilator large small submodules. Additionally, we introduce many the notion which serve our concept. These concepts extend existing ideas and offer new perspectives on the relationships between different submodule and module types within ℜ-modules.
Copyright (c) 2026 Rasha Najah Mirza http://creativecommons.org/licenses/by-nc-nd/4.0
This paper introduces a new generalization of Rickart modules, namely the Supplement-Rickart module. Let be a module over a ring with identity, and let denotes the endomorphism ring of . The module is called Supplement-Rickart if for all is a supplement sub-module of . Moreover, several remarks and examples are included, and various properties together with connections to other related notions are analyzed.
Copyright (c) 2026 Hussein Abdullah Jaafar, Sahira Mahmood Yaseen http://creativecommons.org/licenses/by-nc-nd/4.0
Formulas of General Solution for linear System from Ordinary Differential Equations by Using Young transformation
In this paper we applied a new integral transformation called the Yang transformation to solve a system of linear differential equations , while homogeneous and non- homogeneous . We found general formula of the set solution of systems of first-order equations.
Copyright (c) 2026 Maha Salah Jaheed http://creativecommons.org/licenses/by-nc-nd/4.0
Ideal convergence and ideal equivalence of bounded linear functions in an n-normed linear space
An exploratory study of advanced convergence structures in linear n-norm spaces is presented in this paper with specific emphasis on examining bounded b-linear functionals. Building upon the classical theory of convergence, we develop both l-convergence and l-Cauchy sequences in linear n-normed spaces. An important result of our research is the concept of ideal equivalent sequences for bounded b-linear functionals which provides a strong theoretical framework for analyzing the equivalence of sequences of bounded b-linear functionals using ideals as a basis of comparison. Thus, using aspects of ideals, we characterize limiting behaviors in multidimensional normed structures more accurately.
Copyright (c) 2026 Dunia .A. Al-hikany http://creativecommons.org/licenses/by-nc-nd/4.0
Adjacency and Distance Characteristic Soft Topology on Soft Graphs: Properties and Fundamental Results
In this paper, we study the Adjacency and Distance Characteristic Soft Topology on soft graphs. This topology is constructed by combining adjacency and distance relations between vertices within the framework of soft sets. We reformulate the notions of closure and interior in a manner consistent with classical topology and investigate their behavior in the soft graph setting. It is shown that both operators are completely determined by adjacency relations. Several fundamental properties are established and supported by illustrative examples. In addition, the connectedness of the proposed topology is examined, highlighting the relationship between graph structure and topological properties.
Copyright (c) 2026 Iman Abbas Ali, Asmhan Flieh Hassan http://creativecommons.org/licenses/by-nc-nd/4.0
On Differential Sandwich Results for Analytic p-valent Meromorphic Functions Defined by Generalized Rafid Operator
In the paper, we obtain some differential sandwich results for analytic meromorphic p-valent functions using results of differential subordinations and superordinations defined by generalized Rafid operator in the punctured open unit disk .
Copyright (c) 2026 Salam Abbas Khlabas , Waggas Galib Atshan http://creativecommons.org/licenses/by-nc-nd/4.0
Numerical Analysis of Thermal Radiation and Viscous Dissipation Effects on Heat Transfer in a Newtonian Boundary Layer Flow between Porous Plates
In the present study, the boundary layer flow of a Newtonian fluid between two parallel porous plates separated by a fixed distance is examined numerically. The influence of viscous dissipation and thermal radiation on the flow field and heat transfer characteristics is specifically considered. The mathematical model of the problem is developed using the governing equations of mass, momentum, and energy for a Newtonian fluid. Suitable nondimensional variables are introduced to convert the governing partial differential equations into coupled ordinary differential equations, which are solved using the finite difference method. A graphical discussion is used to discuss the physical properties of the Reynolds number, Darcy number, Hartmann parameter, Eckert number, Radiation parameter, Rayleigh number and Prandtl number. The variation of a velocity and temperature profiles for various relevant parameters is investigated using graphs.
Copyright (c) 2026 Maha Mohammed Ali Thanon, Alaa Abdul Rahim Ahmed http://creativecommons.org/licenses/by-nc-nd/4.0
Introducing the Half-Logistic Marshall–Olkin XGamma (HLMOXG) Distribution with Enhanced Flexibility
Over the past few years, some new probability distributions founded on composite and generalized frameworks have been suggested to offer more flexible statistical models that are able to capture a wide range of data behaviors. Even though the XGamma distribution is effective in the modeling of positively skewed lifetime information, it is not very effective in the analysis of heavy-tailed behavior and intricate pattern of hazard rates. In order to address these weaknesses, we propose a new distribution, built by the successive use of Half-Logistic generator and the extension with the Marshall-Olkin to XGamma distribution base, which transforms into Half-Logistic-Marshall-Olkin XGamma (HLMOXG) distribution. The proposed model offers the flexibility in shape offered by the MarshallOlkin transformation, even more shape control offered by the HalfLogistic generator, and the desirable characteristics of the XGamma base model. Statistical properties of the new distribution, such as PDF, CDF, survival and hazard rate functions, and major mathematical properties such as moments, moment-generating function, quantile function and order statistics are calculated and discussed. The estimation of parameters is done using the maximum likelihood estimation (MLE) technique. To test the performance of the proposed model, a simulation study and real data applications in the fields of reliability and environmental are employed. The relative performance shows that the HLMOXG distribution offers a better goodness-of-fit and more flexibility than the baseline and competing models that prove that the HLMOXG can be an effective instrument to analyze lifetime and reliability data
Copyright (c) 2026 Haitham Hassoon Majed, Wasan Hakim Abd Al-Shemmari http://creativecommons.org/licenses/by-nc-nd/4.0
Integrating Statistical Depth Functions with Deep Learning for Explainable Multivariate Outlier Detection
The process of identifying outliers in multiple variables stays as a major obstacle for data-driven modeling because researchers must handle expanding data dimensions and growing data complexities. The paper presents a new framework called Statistical Outlier Detection with Depth (SODD) which combines Statistical Depth Functions (SDFs) with Deep Learning systems to create an explainable multivariate outlier detection system that shows strong performance. The statistical depth functions establish a formal system which determines how far multivariate data points exist from their center point while maintaining their resistance to extreme values and their ability to show geometric characteristics. The proposed scheme utilizes a combination of four major approaches where depth scores are incorporated within deep neural networks to generate depth-enhanced feature extraction processes, depth-modulated losses, depth-guided regularizations, and depth-dependent architectures that generate better detection results along with higher levels of interpretability in models. The SODD framework utilizes the application of Tukey depth, Mahalanobis depth, Projection Depth, and Spatial depth in order to measure the extent to which an observation is an outlier under different data distributions. Experiments have been carried out on synthetic data as well as some standard real-world datasets, and the framework was implemented through a Python development environment. The results obtained are quite promising, with scores for AUC, Precision, and Recall of 0.94, 0.89, and 0.87, respectively, making the model perform comparably well relative to the baselines examined. Moreover, the inclusion of depth-based explanations enhances the interpretability aspect of the model.
Copyright (c) 2026 Hedeel Kamil Habeeb http://creativecommons.org/licenses/by-nc-nd/4.0