Lightweight Federated Intrusion Detection System for Resource Constrained IoT Networks Using Edge-Assisted Learning
The rapid growth of the Internet of Things (IoT) has expanded the attack surface, while many endpoints operate on microcontroller-class hardware with tight limits on CPU, memory and energy, making conventional intrusion detection systems (IDS) difficult to deploy. This work proposes and evaluates a lightweight federated intrusion detection system (LF-IDS) that enables accurate, privacy-preserving intrusion detection on resource-constrained IoT devices through edge-assisted learning. The architecture proposed will be an integration of a small 1D-CNN student on the IoT node and a more profound teacher on the edge. Pruning and 8-bit quantization of the student model are done in a structured manner to the original and result in 132 kB of the student model but with the same discriminative capacity. Gradient sparsification (top-10%) and hierarchical edge aggregation is a federated learning that has a proximal goal to train the IDS on non-IID Bot-IoT-like, NSL-KDD-like and TON_IoT-like datasets. The latency, communication cost and energy are tested on an emulated ARM Cortex-M4-based platform.LF-IDS obtained an accuracy of 98.3, 96.8 and 95.5 and macro F1-scores of up to 0.979 and a false positive of less than 3 percent in the three datasets. Latency on-device inference was 3.4 ms/flow and energy was 0.26 mJ/inference, which were both well within 256kB RAM. The compression and sparsification pipeline proposed in comparison with dense 32-bit federated updates decreased uplink communication per client per round, by up to 640 kB to 52 kB (91.9% reduction) without significantly affecting the accuracy.The findings indicate that co-designed lightweight models and edge-assisted federated learning can deliver high-quality intrusion detection while remaining compatible with the stringent resource budgets of large-scale IoT deployments.
Internet of Things, Intrusion detection system, Federated learning, edge computing, lightweight deep learning, model compression, non-IID data, IoT security.
Copyright (c) 2026 Journal of Al-Turath University College https://creativecommons.org/licenses/by/4.0
The law applicable to international trademarks
A trademark is considered one of the most important elements of intellectual property, as it plays a fundamental role in distinguishing goods and services and indicating their source, in addition to its growing economic value in the context of globalization. The research examines the legal nature of trademarks by presenting their legislative and doctrinal definitions, highlighting the modern trend toward broadening the concept of the trademark and not limiting it to visual perception. The study also addresses the distinction between international and national trademarks in terms of the scope and duration of protection and their economic value, while emphasizing the legal independence of trademarks from other commercial designations. Furthermore, the research discusses the conflict of laws in the protection of international trademarks, reviewing the main approaches to determining the applicable law, as well as the rules governing contracts related to trademarks. The research concludes by emphasizing the importance of unifying the legal rules governing international trademarks, due to their role in enhancing legal protection and achieving stability in international commercial transactions.
Trademark; Intellectual Property; Conflict of Laws
The Characteristics of Representational Performance in Iraqi Political Theatre Performances: The Play “Cartoon Dreams” as a Model
The characteristics of the acting performance change according to the concepts that the actor is subject to, namely the actor's ability to embody characters according to his creative abilities, as well as the directions he receives in order for the employment to be in its closest aesthetic form. Therefore, the researcher poses a question: (Do the characteristics of the acting performance differ in political theater performances?). As for the second chapter, it included the theoretical framework, which included two topics as follows: The first topic: The performance development of the theatrical actor. The second topic: The acting performance of political theater in global experiences. This chapter also contains the most important indicators that resulted from the theoretical framework. Chapter Three: It was devoted to the research procedures in which the researcher included the research community and the research sample, and determined the research performance and its methodology. Then the researcher analyzed the research sample, the play Ahlam Cartoon, written by: Karim Shaghedel and directed by Kazem Al-Nassar. The researcher used the descriptive method in analyzing the sample. Chapter Four: It included the results that the analysis reached, including: The theatrical performance was directed to the elites more than to the masses because it contained symbolism and codes. The theatrical performance dominated the event, and priority was supposed to be given to revolutionary events. The actors' movements revolved around circular plots. The play began with movements and ended with the same movements.
Traits, performance, acting, political theatre.
Adaptive Brute Force Attack Detection Based on Behavioral Profiling And Machine Learning
In addition to the growing use of digital services, brute force accesses are now popular ways of breaching the security of the information and integrity of the system. Traditional detection systems, such as simple threshold-based systems, have a large false positive rate and will not react to legitimate user behavior, particularly to low-rate stealthy attacks. The paper proposes an intelligent and dynamic brute force login attempt identification system that is composed of behavioral analytics, machine learning (Isolation Forest and LSTM), and adaptive thresholding. As experimental results on synthetic and real authentication log datasets show, the proposed framework works: In synthetic datasets (60 attacks injected), the hybrid framework was able to identify all attacks with Recall = 1.0, Precision = 0.92 and F1-Score = 0.96, with a larger decrease in false positives than the approaches of using only static thresholds. The model obtained Recall = 1.0, Precision = 0.92 and F1-Score = 0.96 on 10,000-login attempt logs with 34 known attacks and high-frequency and low-rate stealthy attacks. Overfitting was also prevented using cross-validation, feature normalization, and dropout of LSTM, and early stopping and strong generalization was demonstrated to unseen data. The proposed framework contributes an efficient, scalable, and context-sensitive framework for the real-time detection of brute force attacks, and it demonstrates a significant enhancement over the traditional techniques and a foundation for an intelligent system in intrusion detection in the future.
KEYWORDS Adaptive threshold; Anomaly detection; Behavioral analysis; Brute force Attack; cybersecurity
Evidence-Based Human Resource Management and Its Role in Proactive Decision-Making
This study aims to examine Evidence-Based Human Resource Management (EB-HRM) and its role in enhancing proactive decision-making within modern organizations. The research focuses on analyzing how scientific data, internal organizational records, and practical expertise are utilized to support administrative decisions based on forecasting and scientific analysis. Relevant Arabic and international studies were reviewed to establish the theoretical foundations and identify research gaps, followed by a conceptual model illustrating the relationship between evidence, predictive analysis, and proactive decision-making. Findings indicate that organizations adopting evidence-based HRM exhibit higher capabilities in predicting behavioral and organizational trends, improving decision quality, and reducing operational risks. The study also emphasizes the crucial role of leaders and HR managers in promoting an evidence-based culture and leveraging statistical analysis and machine learning to support proactive decisions. The research concludes that applying these practices contributes to achieving a sustainable competitive advantage and enhances organizational resilience against environmental changes and managerial challenges.
Evidence-Based Human Resource Management
A Hybrid Alternating Direction Method of Multipliers Based on Structural Guided Filtering for the Reconstruction of Robust Low Dose CT
Reduced radiation exposure in the process of computed tomography (CT) needs to be kept down in order to protect the health of patients. Automated and deep learning methods offer rapid, efficient reconstructions of higher-quality low-dose CT images. But these control images increasingly show that, due to undesired artifacts, quantum noise and streak interference in large quantities are then inflicted upon their visual content as well. Filtered Back-Projection (FBP), a classical reconstruction method in low-dose diagnostics, simply does not work very well. Pure deep learning algorithms, however, always result in pictures with poor visibility that destroy complex structural features. Here, we provide a systematic solution that combines physical data regularity and deep learning priors. The Alternating Direction Method of Multipliers (ADMM) is used to define the reconstruction problem. Then, as a regularization term, the trained Denoising Convolutional Neural Network (CDNN) can further enhance the images (remember our main goal is reducing noise). At this point, an adaptive Guided Filtering step is further included that, based on the structures learned in the initial reconstruction, preserves edges and low-frequency information in order to ensure that the deep denoising does not completely lose texture. Experimental results on a clinical dataset show that the proposed approach attains a PSNR (average peak signal-to-noise ratio) of 31.04 dB, with rich texture, which is used for diagnosis (quality SSIM). In conclusion, the net reduction in noise combined with improved chances for a diagnosis will be a boon for hospital personnel compared with existing approaches. These results demonstrate the proposed hybrid method's ability to successfully eliminate noise while maintaining crucial diagnostic data.
A Comprehensive Review of Deep Learning Methods for Detection and Tracking of Multiple Objects: A Review
In recent years, the advancement of deep learning techniques has significantly transformed the fields of computer vision, particularly in object detection and tracking. This paper presents a comprehensive review of state-of-the-art deep learning methods utilized for the detection and tracking of multiple objects in various environments. We categorize the techniques based on their underlying architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid approaches, highlighting their strengths and limitations. The survey covers traditional algorithms as well as emerging deep learning models, examining their performance across different benchmarks and datasets. Furthermore, we address the challenges faced in real-time applications, such as occlusion, scale variation, and computational efficiency. By synthesizing current research trends, this review aims to provide insights for future developments in the domain, guiding researchers and practitioners toward effective solutions for multi-object detection and tracking tasks.
Deep Learning, Methods, Algorithms, Challenges, Object
Consumer Protection in the Face of the Challenges of Digital Transformation in Commercial Companies
This study examines digital transformation and its impact on the activities of commercial companies, as well as the legal challenges related to consumer protection, following the shift of commercial transactions from the traditional environment to an electronic one. This shift has led to the development of new methods of marketing, contracting, and electronic payment. This transformation has also resulted in the emergence of new forms of deception and fraud, particularly misleading digital advertising, unfair terms in electronic contracts, and violations of consumer data privacy. Despite the economic advantages and facilitation of commercial transactions offered by digital transformation, it has created new legal challenges that affect consumer rights, especially in the absence of a legal framework compatible with electronic commerce. The study further emphasizes that reliance by the judiciary and regulatory authorities on general legal provisions represents a partial and temporary solution that fails to provide adequate protection for consumers in the digital environment. The research concludes by stressing the necessity of developing a comprehensive legislative framework to regulate digital contractual relationships, strengthen oversight of unlawful digital commercial practices, and ensure effective protection of consumers’ personal data, thereby achieving contractual balance.
Digital commerce, Digital commercial companies, Consumer protection, Personal data protection.
Using the Hybrid GARCH–STARIMA Model to Model the Potential Spread of Measles in Diyala Governorate
This study aims to model and analyze the spatio-temporal dynamics of measles incidence in Diyala Governorate using a hybrid framework that combines the Space–Time Autoregressive Integrated Moving Average (STARIMA) model with the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, where the parameters of STARIMA are estimated using the Cat Swarm Optimization (CSO) algorithm. Weekly measles case data from eight health sectors in Diyala were used over a period of 130 weeks from January 1, 2023, to July 1, 2025. Preliminary analysis using the Mann–Kendall test revealed the presence of significant temporal trends in all series, indicating non-stationarity, which was corrected by applying first-order differencing. The Moran’s I test confirmed the existence of significant spatial autocorrelation among the sectors, implying that measles transmission follows a spatially dependent pattern. Several STARIMA models with different orders were estimated using CSO and evaluated based on the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Hannan–Quinn Criterion (HQ). The results showed that the STARIMA model with first-order spatial lag (Lag = 1) provided the best fit. The residuals of the selected STARIMA model were further modeled using a GARCH(1,1) specification, which revealed significant conditional heteroskedasticity, reflecting the volatility associated with epidemic outbreaks. The integration of STARIMA and GARCH enabled a more accurate representation of both the conditional mean and the conditional variance of the measles incidence series, leading to improved short- and medium-term forecasting performance. The findings demonstrate that the proposed STARIMA–GARCH hybrid model provides a robust and efficient statistical framework for analyzing and forecasting spatio-temporal epidemic data, offering valuable support for public health planning and disease control strategies.
Spatio-temporal modelling, STARIMA, GARCH, Cat Swarm Optimization, Measles, The hybrid model, Epidemic forecasting
Detection of Lung Affected by the COVID-19 Using New CNN Architecture
Accurate and early detection of COVID-19-related lung infection is crucial for effective clinical decision-making. This paper presents a novel four-stage convolutional neural network (4CNN) architecture for classifying chest X-ray images into three clinically consistent categories: COVID-19 infected, non-COVID abnormal, and normal lungs. The proposed model addresses dataset imbalance using class-weighted learning and evaluates performance using comprehensive medical metrics. Experimental results demonstrate strong classification performance with an overall accuracy of 92% and improved sensitivity for minority classes. 4CNN architecture is employed to provide visual explainability.
COVID-19 virus, Four-stage peripheral neural network, Lung, Final lesson for categories, Early detection
Enhanced Ransomware Detection via 1D Convolutional Neural Network Architecture
Ransomware has become one of the most common and devastating types of computer attacks that can not only encrypt important information but also interrupt business activities in multiple organizations across the globe. The conventional detection systems that are based on signature-based or rule-based mechanisms have not been effective in keeping pace with the sophisticated and evasive character of the contemporary ransomware. In turn, the given study suggests an improved one-dimensional Convolutional Neural Network (1D-CNN) network exclusively optimized to detect ransomware on the network using traffic analysis. The given model operates on flow-based characteristics of packets in a network in sequence, learning distinctive characteristics of behaviour through which the difference between ransomware and harmless traffic is identified implicitly. The architecture consists of a series of convolutional blocks that perform the hierarchical and then the fully connected layers that perform binary classification. The model evaluated on benchmark ransomware data had an overall detection accuracy of 98.1 with a precision and recall rate of over 96 and an AUC of 0.984. The 1D-CNN model has proven to be more efficient with much less computational cost than the current hybrid and recurrent methods, and thus it can be used in computational machines that detect intrusions in large-scale network applications in real-time. The results validate the notion that one-dimensional convolutional architectures can provide a lightweight and very efficient solution to early ransomware detection as a potentially helpful path towards the creation of the decades to come AI-driven cybersecurity systems.
Ransomware Detection, 1D Convolutional Neural Network, Network Traffic Analysis; Cybersecurity; Intrusion Detection
The Impact of Cybersecurity Dimensions on Enhancing Tourism Security: An Analytical Study of the Opinions of A Sample of Administrative Leaders and Employees at Najaf International Airport
The current study aimed to identify the dimensions of cybersecurity, which include confidentiality, privacy, and enhancement, and their role in strengthening security, represented by reliability, tangibility, trust, and safety. The study also aimed to assess the extent to which the organization under investigation understands the concept of cybersecurity. A descriptive-analytical approach was used, with a sample of 160 employees from Najaf International Airport selected using a random sampling method. A questionnaire was designed to collect data, and its items were analyzed to test the study hypotheses using statistical programs. The study reached several conclusions, the most prominent of which is that cybersecurity at the studied airport was at a good level, with a suitable degree of consistency in the opinions of the sample participants. This indicates a clear administrative interest in applying the requirements for protecting systems and sensitive data. The study concluded with a set of recommendations, the most important of which is the need to enhance the dimension of cybersecurity through continuous investment in updating digital protection systems and adopting early detection techniques for breaches, which would contribute to supporting tourism security at the airport.
Cybersecurity, confidentiality, privacy, tourism security, reliability, tangibility, safety.
Thermal Conductivity Assessment and Mechanical Strength of Braze-Welded AA1050 Aluminum in Cooling Duct Applications
This study evaluates the mechanical integrity and heat-transfer performance of braze-welded AA1050 lap joints for cooling-duct applications using Al–Si 4047 filler and NOCOLOK® potassium-fluor aluminate flux. A two-factor, three-level Taguchi L9 design varied wire diameter (1.6, 2.4, 3.2 mm) and hole diameter (5, 7, 9 mm) in a plug-brazing configuration. This research is focused on the mechanical and thermal evaluation of the joint. Therefore, it gives a clear indication of the characteristic behavior of the welded joints. Mechanical testing showed a clear optimum at 2.4 mm wire with a 7 mm hole, achieving the highest shear load (≈3150 N) and the most stable response to noise factors. Thermal-conductivity measurements indicated that well-filled, low-porosity joints at the same condition retained conductivity near the upper end observed for brazed joints (≈200 W·m⁻¹·K⁻¹) compared with the base AA1050 (≈222 W·m⁻¹·K⁻¹), confirming that interfacial porosity and Si-rich eutectic phases are the primary causes of the modest reduction. ANOVA from the Taguchi analysis identified wire diameter as the dominant factor, followed by hole diameter, highlighting the importance of balancing filler volume with joint geometry to suppress shrinkage porosity and ensure complete wetting. Overall, the optimized condition simultaneously maximizes shear strength and thermal conductance, meeting the dual requirements of structural robustness and efficient heat dissipation in aluminum cooling-duct assemblies.
AA1050 thermal properties/datasheet; 4047 filler specification; NOCOLOK® flux brazing process; ASTM E1461 (flash method) and E1225 (guarded comparative) for thermal testing.
An Analytical Study of Vision Transformer Models for Medical Image Classification and Segmentation
This study aimed to demonstrate the importance of analyzing medical images through precise segmentation and pinpointing locations to obtain the finest details. It presented a model for image classification and data segmentation within an integrated framework. This is because traditional deep learning methods perform image segmentation, analysis, and classification separately. The unified model, based on vision converter technologies, features an innovative architecture that relies on a shared encoder and dual decoding. A single unified vision encoder creates a rich, integrated representation of the input image, which is then used concurrently by dedicated decoding specialists. This involves graded sampling for segmentation and a simple header for classification. This approach leverages the common features of the tasks to enhance efficiency and specificity while reducing structural redundancy. After extensive testing on a standard set of medical imaging data, the model demonstrated superior, accurate, and integrated performance compared to currently used hybrid structures and single-tasking models in terms of accuracy and efficiency. By integrating segmentation and classification functions into a single system, the model represents significant progress towards developing more efficient, user-friendly, and effective AI systems, thereby improving workflow and achieving optimal diagnostic results for the patients.
Vision Transformer (ViT), Medical Image Analysis, Image Classification, Image Segmentation, Unified Models.
Accurate and Robust Tuberculosis Detection Using Transferred Deep Learning Model
Tuberculosis (TB) is an infectious illness that is extremely dangerous to people's health. Early detection can help with therapy and reduce disease spread. Automatic TB detection is essential when there is a lack of diagnostic resources and a requirement for rapid detection. Automatic TB detection is a computer-based diagnostic system that may generate a rapid and precise diagnostic report as an output with less error than a human diagnosis report. This will give the ability to produce more effective mass screening with less resources. The major goal of this research paper is to create an effective computer-aided detection system that will help clinicians make more informed Tuberculosis diagnoses. The paper adopts a deep learning model to detect Tuberculosis infection. To lessen the need for data and hyperparameter adjustment, transfer learning was used. The transfer learning model was VGG-19, and a total of 4200 images were used to evaluate the model's performance. The test results show an accuracy 99.444%, which is achieved, and the recall is 97.143%. This suggests that the proposed model is accurate and robust and can be adopted as a means for TB detection in the future through real-time implementation.
Tuberculosis Detection, Transfer Learning, Convolutional Neural Networks, VGG-19, Deep Learning, Medical Image Processing.
Applicable Law in International Armed Conflicts
Examines the legal framework governing the resolution of international conflicts, drawing on international law that regulates the behavior of conflicting parties. This research also highlights the law of armed conflict (international humanitarian law) as the fundamental building block for resolving international armed conflicts. This research also addresses the international law that applies in the event of an international conflict, outlining the mechanisms for regulating such conflict in accordance with the law. The research also highlights the role of international humanitarian law, which aims to protect civilians and individuals not participating in the conflict, regulate methods of killing, and punish anyone who violates any of these boundaries.
International armed conflict, International humanitarian law, Legal protection during war, International aggression
Artificial Intelligence and Digitalization (Mechanisms of Economic Transformation): The 2023 AI Index as a Model
In a world where digital transformation is accelerating at an unprecedented pace, automation in government work is no longer an optional extra or a cosmetic project, but rather a strategic necessity that touches the very core of public performance and is directly linked to service quality, spending efficiency, and citizen trust. A state that postpones automation postpones its ability to achieve results and accumulates administrative complexity, waste, and slow decision-making. Today, automation represents the true bridge between governmental vision and actual implementation on the ground. A following question, which expresses the research problem, can be raised: To what extent do decision-makers in Iraq understand the importance of transforming paper-based procedures and manual processes into smart digital operating systems that rely on databases, systems integration, and real-time data analysis? This transformation not only affects the speed of completing transactions but also reshapes the public administration, from the way citizens are received to the decision-making process and even the mechanisms of oversight and follow-up. Data from international reports specializing in government digital transformation indicates that approximately 60 to 70 percent of administrative transaction times can be shortened through full automation, and that the average service time in some countries that have adopted digital services has decreased by approximately 20 to 40 percent over the past five years. These figures reflect the significant difference between traditional and digital administration, not only in terms of time but also in terms of public satisfaction, performance accuracy, and procedural efficiency. The importance of this research is highlighted by demonstrating how automation enhances the performance of government institutions because it reduces reliance on direct human intervention in routine tasks and minimizes errors resulting from manual data entry, duplicate transactions, or lost files. In an automated environment, the government employee transitions from a paper-based execution role to that of a monitor, analyst, and value creator. According to studies on job transformation in the public sector, individual employee productivity increases by as much as 30 to 35 percent after the widespread implementation of smart digital systems. One of the research objectives is to increase the focus on automation as the most powerful tool for reducing operational costs. Paper-based transactions consume significant time, storage space, human resources, and a series of repetitive approvals. With digital transformation, printing, archiving, and internal correspondence costs decrease, and the need for in-person transactions diminishes. Economic reports indicate that some governments have managed to reduce administrative operating costs by 15 to 25 percent after transitioning to comprehensive automated systems. Automation also strikes at the heart of corruption because it eliminates the ambiguity associated with multiple layers of bureaucracy and subjects every transaction to a clear digital path that can be tracked both chronologically and procedurally. Every step is recorded, every modification is documented, and every decision is linked to a specific user and timeframe. This level of transparency reduces opportunities for manipulation, enhances accountability, and rebuilds trust between citizens and state institutions. Furthermore, automation brings about a qualitative shift in decision-making. When citizen data, resources, and services are transformed into interconnected digital data, they become amenable to immediate analysis through performance dashboards, business intelligence systems, and predictive models. Government decisions are no longer based on impressions or individual judgment, but rather on precise figures and real-time indicators. This directly impacts the quality of economic, social, and service policies. Furthermore, in terms of achieving government objectives, automation represents the most effective tool for translating strategic plans into measurable results. Every government goal in education, health, investment, or development can be linked to a digital system that measures the level of achievement, progress rates, gaps, and shortcomings. This allows the government to shift from reactive to proactive management, relying on anticipation and early risk analysis.
Artificial intelligence, digitalization, AI index, Iraq
Perceptions of Iraqi EFL students towards Formative and Summative Testing
The Iraqi EFL students’ attitudes to testing in the English course (formative and summative) at the university level. The study adopted a descriptive research design. The structured questionnaire was administered to a sample of 30 students to assess their attitudes towards both tests. The findings showed that the students had a positive perception of formative assessment, which was seen as supportive and motivating. Further, it was found to be an aid in improving their language skills. In contrast, students’ views about summative assessment were neither high nor low, as they find their stressful finals limited in reflecting their actual level of skill. Average scores indicative of formative preference over summative. The study's conclusion indicates that types of summative assessment should not be used solely in EFL classrooms in Iraq. Including formative assessment types can further help in reducing learning anxiety. The researcher will provide recommendations for teachers, institutions, and future researchers regarding assessment strategies in language learning contexts.
Iraqi EFL students, formative assessment, summative assessment, perceptions, language learning, classroom evaluation, feedback, assessment practices
Comparison of Poisson Regression Model Estimation for Longitudinal Data Using the Maximum Likelihood (MLE) Method and the Generalized Equations (GEE) Method
This study aims to estimate the Poisson regression model for longitudinal data and to compare the efficiency of two estimation methods: Maximum Likelihood Estimation (MLE) and Generalized Estimating Equations (GEE). Longitudinal data represent a special type of statistical data collected from the same subjects over multiple time periods, leading to within-subject correlation that complicates analysis compared to cross-sectional data. The study utilized Poisson-distributed longitudinal data, where model parameters were estimated using both methods, and their performance was compared based on accuracy measures such as the Mean Squared Error (Mean Squared Error - MSE)and the Mean Absolute Error (Mean Absolute Error - MAE). Results indicated that the GEE method provides more stable estimates in the presence of temporal correlation, whereas the MLE method yielded higher precision when within-subject correlation was weak. This comparison highlights the suitability of each estimation approach depending on the data structure and analytical goals, thereby enhancing the reliability of statistical modeling for longitudinal data.
Longitudinal data, Poisson regression, Maximum Likelihood Estimation (MLE), Generalized Estimating Equations (GEE), statistical comparison.
Nationality of a Baby Born from Artificial Insemination (A Comparative Study)
This study addresses the problem of establishing nationality in cases of artificial fertilization in light of scientific progress and the legal complexities it has created in determining parentage, and the resulting implications for the determination of nationality. The study is divided into two main sections. The first examines cases of artificial fertilization within the framework of the marital relationship, whether ongoing or terminated, and analyzes their impact on the establishment of parentage and nationality. The second section is devoted to the study of artificial fertilization outside the marital framework, including the multiplicity of parties involved in the reproductive process, as well as virtual fertilization and the legal issues it raises. The study aims to emphasize the need for a legal framework that ensures the protection of the child’s legal status
Nationality of the newborn, IVF, surrogate mother, proving nationality through IVF
Hybrid K-means and CNN Refinement for Kidney Images Segmentation
This study aims to present a two-stage framework combining artificial intelligence algorithms, including the efficient K-means algorithm and a lightweight convolutional neural network (CNN) algorithm, which provides high accuracy for precise segmentation of Ultrasound images and obtaining more accurate data. Image segmentation is a fundamental process for image analysis and obtaining more precise information; it is one of the best methods used in image processing. The K-means algorithm generates an initial mask (an initial image segmentation). The CNN algorithm, a neural network, then refines this mask to recover the precise boundaries of objects within the overall image, thus identifying the image's edges without loss of features and pinpointing the location for diagnosis. The results obtained from the Ultrasound images used on the BSD500 and PASCAL VOC 2012 datasets showed a clear improvement compared to using K-means alone, as the CNN algorithm proved its worth in obtaining more accurate and clear data, and achieving performance that rivals the latest deep learning-based methods, while maintaining real-time execution speed at the same time.
Image segmentation, K‑means clustering, CNN refinement, hybrid methods, real‑time processing.
A Critical Review of Low-Cost Internet of Things (IoT) Architectures for Water Quality Monitoring: Hardware Trends, Communication Protocols, and Performance Challenges (2020–2025)
Providing a safe and permanently monitored source of water is considered a major challenge the world is still grappling with, especially in developing countries where laboratory facilities are very limited. The traditional way of determining the quality of water, though highly precise, has been dependent on laborious sampling and analysis in laboratories, which is not ideal for real-time monitoring in different applications. The availability of microcontrollers has led to a rise in the development of Internet of Things (IoT)-assisted solutions that facilitate the autonomous collection and transmission of essential information on water quality.
Water Quality Monitoring; IoT, Raspberry Pi, ESP32, Arduino, Wireless Communication, Real-Time Monitoring, Sensor Calibration, Environmental Sensing
lightweight image authentication and encryption in the Internet of Things
The Internet of Things (IoT) is dramatically increasing and, at the same time, is facing significant growth of security issues because of the very limited resources of connected devices. This study introduces Lightweight Authentication Encryption (LAE), an algorithm optimized for resource-constrained IoT systems. IoT connects different devices for smart homes, healthcare, and manufacturing. However, these systems have huge security challenges because of the limited resources, for example, power, memory, and processing power, among others. Old encryption algorithms tend to consume too many resources, and this is not good for IoT devices. As a result, lightweight cryptography has emerged as a remedy that strikes a balance between the security measures and resources employed. The LAE algorithm involves the use of two critical cryptographic techniques, which are combined. The IoT is dramatically increasing and, at the same time, is facing significant growth of security issues because of the very limited resources of connected devices. Stream ciphers for confidentiality and hash functions for the integrity of data. The stream cipher is used to provide confidentiality by the translation of plaintext to ciphertext, whilst hash functions are used to provide data integrity by producing a unique value to detect changes that occur to data. The performance of the LAE algorithm is benchmarked against the ChaCha20Poly1305 encryption method. Results display competitive execution times and low memory consumption to ChaCha20Poly1305, therefore making it more compatible with IoT devices. The proposed algorithm can be applied to ensure the integrity of encrypted data, which resolves the security issue of data in transit or at rest.
Lightweight Authentication Encryption (LAE), Internet of Things (IoT), Image encryption, Stream cipher, Hash function
A Comprehensive Satellite Review of Deep Learning Methods for Road Extraction from Satellite Images
Given the increase in demand for the use of satellite imagery in automated and accurate extraction of roads, high-definition satellite imagery has recently created an increase in the need for automated and accurate techniques for extracting road networks from remote sensing images using modern techniques. Due to the increase in the accuracy and capability of creating strong representations within a deep learning architecture that can automate many of the manual tasks of creating and maintaining maps, deep learning has become the most prevalent method to extract roads from remote satellite images. In this paper, we have provided a complete overview of methods that utilize deep learning architectures to extract roads from satellite images. The papers that we discussed in this overview are organized by the architecture of the network or models, such as CNN’s, U-Net models, attention/implementation models, and transformer models. Performance comparisons for the extracted road data were performed based on the benchmarking datasets of DeepGlobe, SpaceNet, and the Massachusetts Roads Dataset using established performance metrics of Intersection Over Union (IoU), Precision, Recall, and F1 Score. Moreover, we discuss the strengths and weaknesses of the current techniques and methods used for extracting road data from these four types of models. We highlight common issues with extraction, such as road discontinuities, occlusion of roads, low contrast surfaces, and a lack of generalizability across datasets when using the former three types of networks, which cause similar performance issues. Finally, we propose several future research directions to develop better-performing systems that do not just rely on using deep learning with the aforementioned architectures but also on hybrid architectures, fusing multiple sensor data types and limiting the need to rely on collecting large sets of manually annotated satellite images in order to improve the performance of road extraction systems.
Road extraction and detection, High-resolution remote sensing images, Satellite images, Deep learning.
Impact of Carbon Emissions Disclosure on Firm Value: An Empirical Study of Iraqi Oil Companies According to IFRS S2
Iraqi oil companies are among the most important economic institutions in the country, representing the main source of public revenue and reflecting Iraq's vital role in global energy markets through production and export operations. Given the rapid developments in sustainability and environmental disclosure standards, the need has arisen to study the impact of implementing the International Financial Reporting Standard on Sustainability (IFRS S2), which pertains to disclosing climate change risks, particularly carbon emissions, on the value of oil companies. This research aims to analyse the level of IFRS S2 implementation in Iraqi oil companies and measure the impact of carbon emissions disclosure on enhancing the value of these companies from the perspective of investors and stakeholders. To achieve this objective, a sample of managers and executives from ten oil companies was selected for the period from 2019 to 2024. The study adopted a comprehensive analytical approach, including descriptive statistics, correlation analysis, simple and multiple regression, and analysis of variance (ANOVA), in addition to testing the validity and reliability of the responses of 60 participants, all of whom were managers and decision-makers. Trend analysis was also used to examine the evolution of environmental disclosure policies over the years. The results showed that disclosing carbon emissions is significantly and effectively linked to the value of oil companies, contributing to increased transparency and higher investor valuations. The study also revealed that an effective regulatory framework for managing emissions and monitoring their environmental impact plays a crucial role in explaining the relationship between environmental disclosure and company value. The research concluded that implementing IFRS S2 not only improves the quality of financial reporting but also enhances investor and stakeholder confidence, supporting companies' ability to meet global sustainability requirements. The study affirms that Iraqi oil companies' commitment to environmental disclosure according to IFRS S2 standards represents a strategic step towards improving their market value, strengthening their competitiveness, and achieving long-term business sustainability.
Comparison of Some Calibration Estimators for Estimating the Average Wheat Production in Iraqi Governorates Based on the Cultivated Area
This study presents a set of calibration estimators to estimate the population mean under stratified random sampling using a single auxiliary variable. A numerical illustration is provided to demonstrate the implementation procedure and computational aspects of these estimators. Furthermore, their performance is evaluated and compared with that of the traditional estimator. The empirical results indicate that applying the calibration technique in estimating the population mean significantly enhances the precision of the estimates and reduces the variance compared to the conventional approach. Among the four estimators examined, the Koyuncu and Kadilar method yielded the lowest estimation variance, making it the most efficient estimator. Meanwhile, the Alam and Singh estimators exhibited comparable accuracy, showing substantial improvement over the traditional estimator while maintaining stable adjusted weights close to the original ones. These findings provide strong evidence of the effectiveness of the calibration approach and highlight its importance in improving the reliability of statistical data used for planning and decision-making processes
Calibration estimators, stratified random sampling, auxiliary information, distance functions
The Employment of Artificial Intelligence Technology in Enhancing Cybersecurity: An in-depth Analysis of Future Challenges and Opportunities
Placing AI at the heart of cybersecurity operations acts as a catalyst for transformation, reshaping how firms detect and handle digital threats. Machine‑learning tools boost security functions in multiple dimensions: they speed up the identification of attacks, lower the incidence of false positives, streamline the steps taken during an incident, and allow for forward‑looking risk evaluations before any breach materialises. This melding also strengthens identity‑verification processes and curtails fraudulent activity. Even with these advantages, embedding AI within security frameworks encounters major obstacles, such as scarce training datasets, bias inherent in algorithms, susceptibility to adversarial manipulation, emerging moral dilemmas, a shortage of qualified professionals, and problems aligning with legacy systems. Looking ahead, researchers foretell an expanded presence of generative AI, the need for blended human‑AI approaches, the creation of all‑encompassing global regulatory schemes, the design of AI that is transparent and trustworthy, and deeper cooperation between academia, industry, and government. Using AI responsibly is crucial to reap its upside while mitigating its dangers, ultimately forging a secure and dependable digital landscape for all.
Artificial Intelligence, Cybersecurity, Threat Detection, Machine Learning, Adversarial Attacks, Predictive Security, Human-AI Collaboration, Generative AI
The impact of banking knowledge risk management on information systems
This research aims to study the relationship between knowledge risk management as an independent variable and the effectiveness of banking information systems as a dependent variable. This relationship was expressed through a research question designed to encompass both its theoretical and practical dimensions, specifically at the application level. The question sought to determine the extent to which knowledge risk management can explain the varying effectiveness of information systems in the banks included in the study sample. In other words, it explored the degree of influence of knowledge risk management integration on the varying effectiveness of management information systems in the banks under study،A questionnaire was used as the primary data collection tool. It was distributed to 77 respondents from the Middle East Bank and its branches in Baghdad. The data was then analyzed, described, and the associated hypotheses tested using appropriate statistical software programs (Excel, SPSS, and AMOS). The overall results of the test indicated that it was significant and fully accepted at the level of the dimensions (the risk of knowledge transfer and dissemination) in its impact on the effectiveness of the management information systems of the banks studied, and that it was not significant and rejected at the level of the partial dimension of the risk of knowledge continuity, which calls on the bank's management to exert more effort in supporting knowledge risk management and employing it correctly to avoid potential risks.
Knowledge risk management, knowledge continuity risk, knowledge transfer risk, knowledge dissemination risk, banking information systems, decision-making speed, user satisfaction, information security.
The impact of the marketing mix on sales volume of the national product in local markets: A case study at the General Company for Food Products
This research aims to study the impact of marketing mix elements on the sales volume of national products in local markets, with a focus on the General Company for Food Products. The impact of each element of the marketing mix (product, price, promotion, and distribution) on enhancing sales and competitiveness of national products against imported products was analyzed. Data were collected using a questionnaire based on a five-point Likert scale, and distributed to a purposive sample of (88) employees, including officials and specialists in the company. The research adopted the descriptive analytical approach, and the data were analyzed using the statistical program (SPSS). The results showed that improving product quality and offering competitive prices, along with enhancing promotional campaigns and using effective distribution channels, contribute to increasing sales volume and enhancing consumer confidence.
Statistical Prediction of Diabetes Prevalence in Iraq using a Hybrid ARIMA–LSTM Model
This study concerns the emerging challenge of diabetes in Iraq and aims to forecast its prevalence with advanced statistical methodologies . Diabetes is a rising health problem in Iraq attributed to variables from obesity to hypertension and an outdated healthcare system. Conventional linear models fail to well illustrate the complicated and non-linear nature of the history of the disease, thus we use a two-stage method in this work. The linear trend is extracted from the data with ARIMA and then it is used to predict for non-linear analysis using LSTM. Data were gathered, refined and analyzed from 7 Iraqi governorates. The diabetes prevalence decline identified by using the ARIMA-LSTM hybrid model in this data suggests that the integrated model showed better prediction of diabetes prevalence than individual BPMs. The hybrid model was found to be the best way to reduce the prediction error, noise and incomplete data processing. Given the robustness of the model in various locations and sample sizes, it could be considered as a decision support instrument for health policy planning, early detection strategies, prevention and resource allocation. The paper suggests more in-depth data analysis and exploring other sociodemographic factors, as well as training healthcare software developers in advanced data science. This method may be suitable for other chronic diseases as well.