Univesity of Thi-Qar Journal

Federated Learning (FL) is a model training scheme with guaranteed preserved data privacy, but model poison and manipulation attack susceptibility, and model performance degradation through adversary attack. In this work, a Blockchain-Based Verification with Fusion Mechanism (BVFM) is designed to enhance FL’s security and robustness. With a tamper-evidence model update guaranteed through a blockchain layer, and a trust-dependent weighted fusion mechanism, trust values are granted to participating nodes, dynamically weighing them in contributing to a global model. Experimental evaluation in terms of performance under Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Carlini & Wagner (C&W) attack scenarios in medical imaging tasks confirms efficacy of BVFM and, in comparison with baseline FL techniques, its accuracy improvement to 94.3%, outperforming local training nodes (88.7%–90.1%). Under adversarial conditions, BVFM reduces the Adversarial Success Rate (ASR) from 59.3% to 25.0% (C&W attack) and from 49.8% to 20.1% (PGD attack), significantly enhancing model robustness. Furthermore, t-SNE visualizations illustrate BVFM’s ability to maintain the separability of benign and malignant classifications despite adversarial perturbations. Compared to existing FL approaches, BVFM achieves the highest accuracy (94.4%), precision (92.5%), recall (93.1%), and F1-score (92.8%), while requiring only 85 seconds of training time—29% faster than leading methods. These results highlight BVFM as a scalable and secure FL solution for adversarial resilience in medical imaging, autonomous systems, and cybersecurity applications.

The discovery of veins in the forearm continues to be a significant obstacle, especially in susceptible patient groups such as the elderly, youngsters, and those who are obese. Conventional approaches typically fail to find veins effectively on the very first try. Not only does this make the patient uncomfortable, but it also reduces the effectiveness of the therapeutic procedure. To solve this ongoing problem, we suggest a cutting-edge method that uses a U-Net model coupled with ResNet (Residual Network). This technique is intended to considerably improve the accuracy of vein recognition. Because of the addition of ResNet, the model's capability to learn detailed elements in medical pictures is much enhanced, which results in a significant improvement in its performance. The performance of the proposed method achieved 96% accuracy when applied to a dataset of forearm medical photos, an 80% precision rate, and a minimum loss function of 0.06. These results were achieved through the application of the Enhanced Forearm Vein Detection method. By demonstrating that the model can outperform conventional approaches, these findings indicate that the model represents a significant advancement in the field of medical picture segmentation and vein recognition. The use of this cutting-edge method provides medical practitioners with a revolutionary instrument that guarantees a venous access procedure that is quick, accurate, and less invasive.

Breast cancer is the leading cause of cancer-related mortality among women. Mammography surveillance plans have been proposed to decrease breast cancer mortality. However, the sensitivity of mammography is suboptimal, especially in women with “dense breasts.” In the analysis of breast images for cancer detection, lesion multiplicity and complexity may occur due to dense tissue interactions, making it challenging for radiologists to precisely detect and analyze masses. The present paper introduces a new computer-aided detection (CAD) system for breast cancer prediction. This system consists of three major steps: i) Segmentation strategies: The first strategy involves manually assigning the region of interest (ROI), while the second strategy uses a threshold- and region-based method, ii) Feature extraction: Features are extracted using a novel convolutional neural network (CNN), iii) Tumor classification: A decision tree is employed for classifying tumors. The performance of the proposed method is evaluated using the Digital Database for Screening Mammography (DDSM) dataset. The simulation results indicate that the proposed method outperforms existing methods, with an accuracy improvement of approximately 1.29%.

Bacteria strain H7, which produces flocculating substances, was isolated from the soil of
corn field at the College of Agriculture in Abu-Ghrib/Iraq, and identified as Bacillus subtilis
by its biochemical /physiological characteristics. The biochemical analysis of the partially
purified bioflocculant revealed that it was a proteoglycan composed of 93.2 % carbohydrate
and 6.1 % protein. The effects of bioflocculant dosage, temperature, pH, and different salts on
the flocculation activity were evaluated. The maximum flocculation activity was observed at
an optimum bioflocculant dosage of 0.2 mL /10 mL (49.6%). The bioflocculant had strong
thermal stability within the range of 30-80 °C, and the flocculating activity was over 50 %.
The bioflocculant had the highest flocculating activity at alkaline conditions pH 10 (71%), and
when many salts were used as cations, ZnSO4.7H2O, MnCl2, and CuSO4 enhanced
flocculation activity at 89%, 80%, and 73% respectively.

Middleware is a key enabler of advanced digital transformation, simplifying the process of creating and managing smart applications, although its development poses significant challenges. The adoption of cloud software solutions has seen a significant increase, driven by their scalability, cost-effectiveness, and ease of deployment. As companies adopt these solutions to meet their software needs, the importance of customization within these cloud solutions becomes very important but somewhat complex and expensive. Customization may allow applications to be designed according to the specific requirements of the organization, enhancing user experiences and overall efficiency, but it does not enhance the capabilities of devices and personal use. NAS systems include user management capabilities that allow administrators to create user accounts, set up groups, and grant permissions on a per-file or per-share basis. This ensures that sensitive data can be kept secure while still allowing essential access to authorized users. Hence, the need for dedicated storage that facilitates cloud software-as-a- service solutions and a comprehensive examination of the benefits and challenges of customization within the cloud comes into play. As is well known, real-time resource-intensive applications require low latency, large data rates, and high computing and storage resources. Mobile cloud computing systems cannot meet the requirements of these applications due to high communication latency. For this reason, newly acquired edge computing applications are in dire need of a new design or a fundamental redesign. In addition, edge computing does not use the legacy capabilities of cloud computing. However, despite the many benefits, customization also poses challenges that must be addressed. The problem that will be discussed in this research paper is to enable the capabilities of mobile devices through more flexible and convenient uses of cloud resources through privacy in the process of information storage. This paper presents the motivation for using private networks.To overcome the challenges of cloud computing and mobile edge systems, propose a private mobile edge cloud in which mobile and fixed devices such as sensors, robots, home appliances, and smartphones are interconnected via wireless local area networks, based on the related infrastructure, creating a cloud data center in a small area with network storage space for ease of use. Also provides a general background and reviews with previous papers. Finally, the research is guided by a systematic exploration of interconnected private network storage solutions.