A Cybersecurity graduate from the International University of Technology Twintech (IUTT), class of 2026. Combines defensive expertise in Machine Learning-based Intrusion Detection and Prevention Systems (IDS/IPS) with offensive hands-on skills in penetration testing and vulnerability assessment. Participated in the development of the graduation project “Deep Sentinel,” a hybrid Real-Time Network Intrusion Prevention System (RT-NIPS) based on Decision Tree and Random Forest algorithms. Possesses extensive practical experience within the Kali Linux environment, including vulnerability scanning, ARP Spoofing, and utilizing frameworks such as Metasploit and Burp Suite for network and web application security assessment. Aspires to work in Vulnerability Analysis or Security Operations Centers (SOC), leveraging a comprehensive view of both offensive and defensive security.
A Cybersecurity graduate (Class of 2026) with a focus on identifying vulnerabilities and strengthening digital defenses. I have hands-on experience in malware analysis and incident response, complemented by a solid foundation in PHP programming and secure development practices.
An integrated HR management system that combines data management, attendance, payroll, and recruitment.
An interior design image editing website, integrates two artificial intelligence models: one for image generation and the other a linguistic model for design modification.
Breast cancer is a primary cause of cancer-associated mortality among women globally, and early detection and personalized treatment are critical for improving patient outcomes. In this study, we propose an optimal framework for predicting breast cancer patient survivability using the GentleBoost algorithm and Bayesian optimization. The proposed framework combines the strengths of the GentleBoost algorithm, which is a powerful machine-learning algorithm for classification, and Bayesian optimization, which is a powerful optimization technique for hyperparameter tuning. We evaluated the proposed framework using the publicly available breast cancer dataset provided by The Surveillance, Epidemiology, and End Results (SEER) program and compared its performance with several popular single algorithms, including support vector machine (SVM), artificial neural network (ANN), and k-nearest neighbors (KNN). The experimental results demonstrate that the proposed framework outperforms these methods in terms of accuracy (mean= 95.16%, best = 95.35, worst = 95.1%, and SD = 0.008). The values of precision, recall, and f1-score of the best experiment were 92.3 %, 98.2 %, and 95.2 %, respectively, with hyperparameters of (number of learners = 246, learning rate = 0.0011, and maximum number of splits = 1240). The proposed framework has the potential to improve breast cancer patient survival predictions and personalized treatment plans, leading to the improved patient outcomes and reduced healthcare costs.
Breast cancer (BC) is a major global health concern. Detecting BC at an early stage gives more treatment options and can help avoid more aggressive treatments. The use of machine learning (ML) in BC prediction offers significant potential for improving the accuracy and speed of diagnosis, personalizing treatment, and identifying high-risk patients. However, there are significant challenges associated with the use of ML, including the need for high-quality data and more flexible models with optimal parameters to achieve high efficiency. In this paper, we propose an optimized framework based on multi-stage data exploration. This framework is designed to provide a comprehensive approach to data exploration, ensuring that the data is well-prepared for ML. In addition, the framework includes dynamic ensemble-based classifiers, which combine multiple independent classifiers to improve accuracy and mitigate the risk of overfitting in conjunction with the cross-validation techniques. These classifiers are optimized using Bayesian hyperparameter tuning, which involves selecting the optimal values for the various hyperparameters of the model. This approach can significantly improve the prediction accuracy of the resulting model. The study evaluates the framework using the publicly available Wisconsin Diagnostic Breast Cancer (WDBC) dataset and compares our results with other state-of-the-art models. The experimental results show that the best result is 100% for accuracy and recall with hyperparameters of (Ensemble Method = AdaBoost, Number of learners = 322, learning rate = 0.9350, and the Maximum number of splits = 1). The highest performance has been achieved with the proposed framework compared with the other models in terms of accuracy (mean = 99.35%, best = 100%, worst = 98.7%, and Standard Deviation = 0.325). The framework can potentially improve the accuracy and efficiency of BC prediction, ultimately leading to better outcomes for patients.
evising a protocol for localization in a wireless sensor network is a formidable task. In this paper, we present a localization protocol for a wireless sensor network. In our protocol, a sensor computes its location using the locations of either anchors or sensors whose locations are already computed, and their distance estimates. Our protocol is distributed and does not need the availability of the topological information of the whole network at a centralized sensor before starting the computation. Our protocol is asynchronous as it does not need the clocks of sensors to be synchronized. Each sensor relies on its local clock. Our protocol is scalable and can be applied to a network irrespective of its size. We evaluate the performance of the proposed protocol by carrying out simulations. We study the effect of the number of anchors and the transmission range of sensors on the localizability and the error of localization. Further, we provide an expression for computing the probability that a sensor is localized in a sensor network.