• م.د السيد ابراهيم محمد
  • Alsayed Ibrahim mohammed
  • تدريسي : قسم علوم الامن السيبراني
  • Teaching : Department of Cybersecurity Sciences
  • دكتوراه علوم الحاسوب
  • computer science
  • alsayed@esraa.edu.iq
  • sayeddmb@gmail.com
  • السيرة الذاتية
  • Google Scholar
  • ORCId
  • scopus
  • 68

    • نشاطات التدريسي

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    المقالات داخل المجلة

    نشاطات التدريسي

    نشاطات التدريسي

    شهادات تدريبية بالأمن السيبراني
    م.د السيد ابراهيم محمد

    شهادات تدريبية بالأمن السيبراني

    2024-11-11
    البحوث

    البحوث

    2024 Multimedia Tools and Applications
    Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Early detection of CVD reduces the risk of a heart attack and increases the chance of recovery. The use of angiography to detect CVD is expensive and has negative side effects. In addition, existing CVD diagnostic methods usually achieve low detection rates and reach the best decision after many iterations with low convergence speeds. Therefore, a novel heart disease detection model based on the quantum-behaved particle swarm optimization (QPSO) algorithm and support vector machine (SVM) classification model, namely, QPSO-SVM, was proposed to analyze and predict heart disease risk. First, the data preprocessing was performed by transforming nominal data into numerical data and applying effective scaling techniques. Next, the SVM fitness equation is expressed as an optimization problem and solved using the QPSO to determine the optimal features. Finally, a self-adaptive threshold method for tuning the QPSO-SVM parameters is proposed, which permits it to drop into local minima, and balances between exploration and exploitation in the solution search space. The proposed model is applied to the Cleveland heart disease dataset and compared with state-of-the-art models. The experimental results show that the proposed QPSO-SVM model achieved the best heart-disease-prediction accuracies of 96.31% on the Cleveland heart data set. Furthermore, QPSO-SVM outperforms other state-of-the-art prediction models considered in this research in terms of sensitivity (96.13%), specificity (93.56%), precision (94.23%), and F1 score (0.95%).

    2025 Neural Computing and Applications
    Virtualization technology enables cloud providers to abstract, hide, and manage the underlying physical resources of cloud data centers in a flexible and scalable manner. It allows placing multiple independent virtual machines (VMs) on a single server in order to improve resource utilization and energy efficiency. However, determining the optimal VM placement is crucial as it directly impacts load balancing, energy consumption, and performance degradation within the data center. Furthermore, deciding on VM placement based on a single factor is usually insufficient to improve data center performance because many factors must be considered, and ignoring them may be too expensive. This paper improves a new multi-objective VM placement (MVMP) algorithm using a quantum particle swarm optimization (QPSO) technique. We call it QPSO-MOVMP, and its objective is to find the Pareto optimal solution for the VM placement problem by balancing different goals. This algorithm generates Pareto optimal solutions that save power by minimizing the number of running physical machines, avoid performance degradation by maintaining service level agreement (SLA), and improve load balancing by keeping server loads at optimal utilization. The experimental results show that QPSO-MOVMP had superior performance in terms of power consumption and performance degradation compared to three other multi-objective algorithms and three conventional single-objective algorithms. Simulation results show that the proposed QPSO-MOVMP achieves a consumption of 2.4 × 104 watts in power. Furthermore, it outperformed the others, achieving a minimum of 12% SLA breaches while experiencing a significant surge in requests from VMs. Moreover, the proposed model generated Pareto solutions that had a better distribution than those derived from a comparative method.

    2025 Scientific Reports
    The Internet of Things (IoTs) has revolutionized cities, enabling them to become smarter. IoTs play an important role in monitoring the traffic cameras, roads, smart farming, connected vehicles, air quality, water level, humidity, and carbon dioxide pollution levels in city buildings. One of the major challenges of smart cities is the cyber threat to sensitive data. This paper presents an intelligent approach for detecting cyberattacks and mitigating malicious events in IoT-based smart systems. The proposed approach, known as FCM-SWA, hybridizes a fuzzy C-mean (FCM) with a sperm whale algorithm (SWA). In the first step, we use a novel SWA optimization algorithm to enhance the FCM performance and provide effective defenses against various types of smart city attacks. Next, we propose an adaptive threshold strategy to enhance the global search capability of SWA and prevent the algorithm from settling into local optima. Lastly, we present an efficient scaling approach that solves the clustering problem and finds the optimal cluster center, striking a balance between exploration and exploration in the search space. The proposed FCM-SWA model does better than related and state-of-the-art methods in terms of accuracy, detection rate, precision rate, and F1-scores, as shown by experiments on the NSL-KDD, AWID, and BoT-IoT datasets.

    2025 International Journal of Electrical and Computer Engineering (IJECE)
    Liver diseases rank among the most prevalent health issues globally, causing significant morbidity and mortality. Early detection of liver diseases allows for timely intervention, which can prevent the progression of such diseases to more severe stages such as cirrhosis or liver cancer. To this end, many machine learning models have been previously developed to early predict liver diseases among potential patients. However, each model has its accuracy and performance limitations. In this paper, we present a comprehensive comparison of three different machine learning models that can be employed to enhance the prediction and management of liver diseases. We utilize a big data set of 32,000 records to evaluate the performance of each model. First, we implement a preprocessing technique to rectify missing or corrupt data in liver disease datasets, ensuring data integrity. Afterwards, we compare the performance of three machine models: k-nearest neighbors (KNN), gaussian naive Bayes (Gaussian NB) and random forest (RF). We concluded that the RF algorithm demonstrates superior performance in our evaluation, excelling in both predictive accuracy and the ability to classify patients accurately regarding the presence of liver disease. Our results show that RF outperforms other models based on several performance metrics including accuracy: 97.3%, precision: 97%, recall: 96%, and f1-score: 95%.

    2024 Neutrosophic Sets and Systems
    The ever-growing volume and complexity of Big Data pose challenges for traditional classification tasks. This paper explores the potential of Neutrosophic Sets (NS), a powerful framework for handling uncertainty, in building robust classification models for Big Data prediction using Machine Learning (ML) techniques. We provide a detailed background on NS and discuss its advantages over Fuzzy Sets. We then propose a methodology that integrates NS with relevant ML algorithms for classification. We evaluate the performance of our Neutrosophic-based model on a Big Data source. The results are analyzed to assess the effectiveness of the Neutrosophic approach for Big Data prediction. This research contributes to the advancement of uncertainty management in Big Data classification and paves the way for further exploration of Neutrosophic-based ML models for various prediction tasks. Results show that the Neutrosophic Neural Networks (NNs) model achieved commendable performance across various metrics, with an accuracy of 79.08%, precision of 74.58%, recall of 77.64%, and an F1-score of 75.63%. These metrics indicate that the Neutrosophic NNs model effectively balances the trade-offs between precision and recall, providing a robust classification performance in the context of the evaluated datase
    المؤلفات

    المؤلفات

    2025 Scientific Reports

    2025 Neural Computing and Applications

    2024 Multimedia Tools and Applications