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Öğe Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic-Random Forest(2025) Uçan, Gülfem Özlü; Gwassi, Omar Abboosh Hussein; Apaydın, Burak Kerem; Uçan, BahadırBackground/Objectives: Dental age estimation is a vital component of forensic science, helping to determine the identity and actual age of an individual. However, its effectiveness is challenged by methodological variability and biological differences between individuals. Therefore, to overcome the drawbacks such as the dependence on manual measurements, requiring a lot of time and effort, and the difficulty of routine clinical application due to large sample sizes, we aimed to automatically estimate tooth age from panoramic radiographs (OPGs) using artificial intelligence (AI) algorithms. Methods: Two-Dimensional Deep Convolutional Neural Network (2D-DCNN) and One-Dimensional Deep Convolutional Neural Network (1D-DCNN) techniques were used to extract features from panoramic radiographs and patient records. To perform age estimation using feature information, Genetic algorithm (GA) and Random Forest algorithm (RF) were modified, combined, and defined as Modified Genetic-Random Forest Algorithm (MG-RF). The performance of the system used in our study was analyzed based on the MSE, MAE, RMSE, and R2 values calculated during the implementation of the code. Results: As a result of the applied algorithms, the MSE value was 0.00027, MAE value was 0.0079, RMSE was 0.0888, and R2 score was 0.999. Conclusions: The findings of our study indicate that the AI-based system employed herein is an effective tool for age detection. Consequently, we propose that this technology could be utilized in forensic sciences in the future.Öğe Performance analysis of input power variations in high data rate DWDM-FSO systems under various rain conditions(Springer, 2025) Abdulwahid, Maan Muataz; Kurnaz, Sefer; Kurnaz Türkben, Ayça; Hayal, Mohammed R.; Elsayed, Ebrahim E.; Juraev, Davron AslonqulovichThis paper investigates the performance of a 32-channel Dense Wavelength Division Multiplexing Free-Space Optical (DWDM-FSO) system under various rain conditions and transmission distances ranging from 5 to 20 km. The study aims to identify optimal input power levels across different rain scenarios (-10 dBm, -5 dBm, 0 dBm, 5 dBm, and 10 dBm) to enhance the reliability and efficiency of optical communication in adverse weather. Findings indicate that for light rain conditions, input power levels of -10 dBm are suitable for distances up to 15 km. In moderate rain scenarios, -5 dBm is optimal for reliable communication up to 10 km, while higher input powers of 5 dBm are necessary to maintain performance in heavy rain conditions beyond 5 km. This study highlights the critical relationship between input power and atmospheric conditions, confirming that higher power levels can effectively mitigate the effects of rain-induced attenuation and scattering. Key parameters such as transmitter and receiver configurations, atmospheric attenuation, scattering, and turbulence were analyzed, demonstrating the importance of selecting appropriate power levels to ensure successful data transmission. Additionally, the research suggests future explorations into adaptive modulation techniques and quantum applications to further enhance system resilience and performance. The results provide valuable insights for system designers, enabling the adaptation of FSO systems to meet the challenges posed by varying environmental conditions and guiding developments in robust optical communication technologies.Öğe A hybrid model using 1D-CNN with Bi-LSTM, GRU, and various ML regressors for forecasting the conception of electrical energy(World Scientific Publishing, 2025) Abdulameer, Yahya Hafedh; Ibrahim, Abdullahi AbduTo solve power consumption challenges by using the power of Artificial Intelligence (AI) techniques, this research presents an innovative hybrid time series forecasting approach. The suggested model combines GRU-BiLSTM with several regressors and is benchmarked against three other models to guarantee optimum reliability. It uses a specialized dataset from the Ministry of Electricity in Baghdad, Iraq. For every model architecture, three optimizers are tested: Adam, RMSprop and Nadam. Performance assessments show that the hybrid model is highly reliable, offering a practical option for model-based sequence applications that need fast computation and comprehensive context knowledge. Notably, the Adam optimizer works better than the others by promoting faster convergence and obstructing the establishment of local minima. Adam modifies the learning rate according to estimates of each parameter's first and second moments of the gradients separately. Furthermore, because of its tolerance for outliers and emphasis on fitting within a certain margin, the SVR regressor performs better than stepwise and polynomial regressors, obtaining a lower MSE of 0.008481 using the Adam optimizer. The SVR's regularization also reduces overfitting, especially when paired with Adam's flexible learning rates. The research concludes that the properties of the targeted dataset, processing demands and job complexity should all be considered when selecting a model and optimizer.Öğe Enhancing Content-Based Image Retrieval with a Stacked Ensemble of Deep Learning Models(Institute of Electrical and Electronics Engineers Inc., 2024) Zeain, Abdulrahman; Ibrahim, Abdullahi AbduOur research paper delves into an innovative exploration of content-based image retrieval (CBIR), harnessing the capabilities of deep learning models to transform the way images are searched and accessed in extensive databases. The primary goal of our study is to create an ensemble stacking model that synergizes the strengths of various deep learning architectures, thereby boosting the accuracy and efficiency of image retrieval processes. We utilize the Corel Images dataset, rich in diverse visual themes, to test and validate our model's efficacy. Our research methodology encompasses four key stages: data preprocessing, model training with DenseNet, MobileNet, and Inception-ResNet, followed by an in-depth evaluation of the model. The approach demonstrates the effectiveness of our ensemble model as it achieves a high accurate rate of 97 % exceeds the benchmarks set by the individual models in our compare and differential analyses. Moreover, the manuscript investigates the model's technical detail, such as the feature extraction, runtime with different images, and scalability to more massive datasets. The eulogy for the model's performance in the performance evaluation section encapsulates the functional performance and the practicality of CBIR efficacy.Öğe Enhancing IoT Security Through Hardware Security Modules (HSMs)(Institute of Electrical and Electronics Engineers Inc., 2024) Khan, Mansoor; Ilyas, Muhammad; Bayat, OguzStrong security measures must be integrated in an era where data security is critical, particularly for sensitive data handled by IoT devices. In order to strengthen Internet of Things security, the use of Hardware Security Modules (HSMs) is investigated in this research. We examine the development and effectiveness of HSMs in boosting IoT security through a thorough study of the literature. Our results demonstrate the vital role that HSMs play in protecting cryptographic keys and thwarting any attacks. We explore the difficulties of incorporating HSMs into IoT environments and suggest practical approaches. This research concludes by highlighting the role that HSMs play in strengthening IoT security architecture. © 2024 IEEE.Öğe Smart Energy Management in Green Cloud Computing using Machine Learning Algorithms(American Scientific Publishing Group (ASPG), 2025) Salman, Assel Hashim; Ibrahim, Abdullahi AbduCloud computing has many advantages as well as some disadvantages. An internet connection is required to use Cloud Computing. In other words, it is not possible to access the data in cases without internet. Cloud Computing can provide infrastructure services, platform services and software services to individuals with any device connected to the internet. If the connection speed is low when there is internet, the data transmission is also slower. In this context, it may not be practical for individuals or institutions to benefit from Cloud Computing in places where internet connection is low, limited, or absent. A new technology was obtained in this study; this method depends on deep learning and machine learning techniques applied to detect the attacks in the cloud computing-based systems. The suggested method compared with many traditional machine learning techniques. © 2025, American Scientific Publishing Group (ASPG). All rights reserved.Öğe Automation of traditional networks : a mini-review(Institute of Electrical and Electronics Engineers Inc., 2024) Altalebi, Omar; Ibrahim, Abdullahi AbduIn an era characterized by the fast advancement of networks, the challenge of effectively monitoring and regulating network devices has also escalated. Undoubtedly, the increase in the number of devices has resulted in elevated expenses, heightened vulnerability to human mistakes, extended timeframes for implementing modifications to these network devices, and a larger workforce. This work offers an in-depth analysis of conventional networks and their automation. Furthermore, including the statistical analysis conducted within this domain, the prospective trajectory of network automation, the imperatives necessitating its automation, and the inherent constraints associated with these networks. moreover, provides an analysis of the proposed gaps and solutions for these networks, which involve the automation of conventional networks. As well as it's included highlighting their advantages, disadvantages and examines prior research that has focused on the automation of conventional networks. The work aims to encompass a wide range of models that automate such networks. Additional ideas and recommendations for future research endeavors are provided as part of this work's exhaustive analysis of the findings.Öğe A fuzzy logic-based quality model for identifying microservices with low maintainability(Elsevier Inc., 2024) Yılmaz, Rahime; Buzluca, FezaMicroservice Architecture (MSA) is a popular architectural style that offers many advantages regarding quality attributes, including maintainability and scalability. Developing a system as a set of microservices with expected benefits requires a quality assessment strategy that is established on the measurements of the system's properties. This paper proposes a hierarchical quality model based on fuzzy logic to measure and evaluate the maintainability of MSAs considering ISO/IEC 250xy SQuaRE (System and Software Quality Requirements and Evaluation) standards. Since the qualitative bounds of low-level quality attributes are inherently ambiguous, we use a fuzzification technique to transform crisp values of code metrics into fuzzy levels and apply them as inputs to our quality model. The model generates fuzzy values for the quality sub-characteristics of the maintainability, i.e., modifiability and testability, converted to numerical values through defuzzification. In the last step, using the values of the sub-characteristics, we calculate numerical scores indicating the maintainability level of each microservice in the examined software system. This score was used to assess the quality of the microservices and decide whether they need refactoring. We evaluated our approach by creating a test set with the assistance of three developers, who reviewed and categorized the maintainability levels of the microservices in an open-source project based on their knowledge and experience. They labeled microservices as low, medium, or high, with low indicating the need for refactoring. Our method for identifying low-labeled microservices in the given test set achieved 94% accuracy, 78% precision, and 100% recall. These results indicate that our approach can assist designers in evaluating the maintainability quality of microservices.Öğe Effective audio and video-based learning for youngest age(2024) Akram, Basmal Omer; Koyuncu, HakanThe research project is an interactive educational book and it is a game, but in reality it is a measure of intelligence through which intelligence breakthroughs are achieved through the accuracy of answering questions with the speed of answering. The focus was on children under the age of six, but it included a number of those who are older. Discussing through research on the types of education, focusing on interactive and electronic education, explaining the difference between the two, explaining the types of human intelligence, how to build a school system through the use of a calculator, and what are the requirements and types for that, leading to the importance of the impact of the diversity of educational media, the importance of learning through play, and the benefits and harms of each case. Reaching results indicating that students prefer studying in class to studying electronically, it was noted that there are mental mutations for every age, but the age that responded most and most quickly was the age of youth and advanced youth. The reasons for this result were explained through the research and what we reached, and some methods were presented to increase mental efficiency.Öğe Using deep learning technology to optimize VPN networks based on security performance(2024) Mahdi, Rana Abdul Kadhim; Ilyas, Muhammad- In recent years, network developments and user growth have increased network security problems and techniques. The trend in network security is towards web-based networks, given Internet users' diverse origins, unpredictable persons are more likely to participate in malevolent activities. Security and privacy safeguards are implemented using many technologies. This paper proposes using a virtual private network (VPN) to secure particular communications across vast networks. VPNs restrict unauthorised connections, benefiting secured hosts. Through a VPN network, connections can be kept hidden and external connections prohibited. The influence of a virtual private network (VPN) on a standard network's performance is studied by producing and assessing CBR, HTTP, and FTP payloads. The evaluation used throughput and time delay as performance measures after analysis of the finding, deep learning (DL) can predict attacks. Because that learns attack patterns during training to effectively forecast attacks. To detect attacks, deep learning-based attack prevention model was created This method uses Nave Bayes and FFNN to enhace network performance. The results show that VPNs affect packet latency and performance differently depending on the data type. The FFNN algorithm detects intrusions with 98% accuracy .Öğe Modelling and analysis of high performance for solar power injection with distribution networks(2024) Assaf, Abdullah Sami; Kurnaz, SeferGiven the growing emphasis on environmental consciousness, there is a spike in the integration of solar photovoltaic energy into contemporary distribution networks. Interestingly, there is a non-linear relationship between the energy production of solar modules and changing external environmental conditions. In response, this study aims to increase the effectiveness of solar photovoltaic systems (SPVS) by putting out a careful analysis to ascertain the ideal performance criteria for the integration of renewable energy sources into distribution networks. The use of a Maximum Power Point Tracker is crucial to this assessment (MPPT). The model provides important insights into the possible increases in energy efficiency, making it a strong tool in this optimization process. In addition, the research conducts a comparison analysis, examining the distribution system properties in relation to different levels of solar PV system penetration. This aspect of the research illuminates the consequences of harmonic-induced distortions in current and voltage on the feeder networks in the distribution system. The simulation findings clearly show that as the penetration capacity of the PV system increases, so does the amount of harmonic dispersion that is introduced into the network. This suggests that it would be wise to incorporate the photovoltaic (PV) array only as far as the network can support it in order to prevent any possible performance reduction. Using MATLAB/SIMULINK as a computational tool, the research carefully examines the important characteristics from the technical data in order to examine the overall model. Further testing of the model's flexibility and resilience under a range of weather scenarios and partial shade conditions offers a thorough assessment of the model's performance dynamics. Positively, the investigation concluded with results that were satisfactory and confirmed the system's exceptional performance capabilities, which are supported by the MPPT. This result reflects a positive trend toward maximizing the integration of renewable energy sources into distribution networks, which will help to create a more sustainable and environmentally friendly energy landscape.Öğe Advancements in telehealth: enhancing breast cancer detection and health automation through smart integration of IoT and CNN deep learning in residential and healthcare settings(Semarak Ilmu Publishing, 2025) Duodu, Nana Yaw; Patel, Warish D.; Koyuncu, HakanThe rapid evolution of telehealth, or telemedicine, has spurred crucial technological advancements aimed at addressing the early stages of complex cancer conditions, where conventional diagnostic methods face challenges. This research introduces a cancer detection system that utilizes Internet of Things (IoT)-based patient records and machine learning. The primary objective is to automate real-time breast cancer monitoring and detection in residential institutions and smart hospitals, thus enhancing the delivery of quality cancer healthcare. Background: Traditional diagnostic methods, particularly physical inspection, exhibit inherent limitations in identifying breast cancer at early stages. This research responds to this challenge by leveraging innovative technologies, such as IoT and deep learning-based techniques, to overcome the constraints of conventional approaches. Objective: The primary goal of this study is to develop and implement a cancer detection system that integrates IoT-based patient records and machine learning for real-time breast cancer monitoring in residential and healthcare settings. Method: The research employs a synergistic combination of IoT technology for collecting images of residential users and Convolutional Neural Network (CNN), a deep learning technique, for early cancer prediction. The focus lies on contributing to the overall well-being of individuals who may unknowingly be living with cancer. Result: Simulated outcomes after 25 epochs are presented, emphasizing the training accuracy of the model and its validation accuracy using the proposed VGG16 classifier. Graphical representations of the results indicate consistent performance metrics, with both validation and training accuracy exceeding 99%. Specifically, the training accuracy measures at an impressive 99.64%, while the validation accuracy stands at 99.12%. Main Findings: The study demonstrates the effectiveness of the integrated IoT and deep learning techniques in achieving high accuracy rates for early breast cancer prediction. The findings affirm the potential of this approach to assist dermatologists in identifying breast malignancies at treatable stages. Conclusion: This research establishes a foundational framework for the integration of IoT and deep learning techniques, presenting a promising avenue for advancing early cancer detection in smart healthcare systems. The proposed cancer detection system holds significant potential for improving healthcare outcomes and contributing to the overall well-being of individuals at risk of breast cancer.Öğe Security threat analysis and countermeasure using ML in cloud(American Institute of Physics, 2024) Kushwaha, Akhilesh; Patel, Warish; Koyuncu, Hakan; Parikh, Swapnil; Chauhan, AnkitThe rise of cyber-attacks such as Distributed Denial of Service (DDoS) and SQL injection has become a significant concern for organizations and individuals who rely on the internet. Traditional detection methods have become increasingly ineffective in addressing these challenges, necessitating the development of new and innovative solutions. This paper proposes using Convolutional Neural Networks (CNNs) to detect DDoS and SQL injection attacks. Our paper proposes a Convolutional Neural Network model with a multi-layer neural and relu activation function optimizer that reaches a higher degree of precision than previous Deep Learning models. In this study, we tested the relatively new dataset CIC-IDS-2018, which contains different types of attacks. With this dataset, our model achieves an unprecedented accuracy of>96%, minimizing computational time.Öğe A Comprehensive Survey of Predicting stock market prices: an analysis of traditional statistical models and machine-learning techniques(American Institute of Physics, 2024) Patel, Divy; Patel, Warish; Koyuncu, HakanThe stock market has witnessed a remarkable surge in popularity in recent years, attracting investors from all walks of life. However, predicting stock values remains a daunting task due to financial markets' inherent unpredictability and complexity. Despite these challenges, the stock market offers a dynamic and ever-changing platform for traders to invest in shares, with the potential for significant gains and losses. For investors, accurate forecasting of stock prices is crucial as it provides invaluable insights into a company's financial health and growth prospects. With this information, investors can make informed decisions, mitigate risks, and capitalize on lucrative opportunities in the market. As a result, extensive research has been dedicated to developing effective prediction methods, leveraging various mathematical models and machine-learning techniques. This research paper delves into the realm of stock market prediction, explicitly focusing on evaluating different machine-learning styles. The primary objective is to comprehensively analyze and compare the performance of these techniques in forecasting stock market behavior. By understanding the strengths and limitations of each method, investors, financial analysts, and market participants can gain critical knowledge to optimize their trading strategies and decision-making processes. To achieve this goal, the study explores an array of machine-learning algorithms, ranging from traditional linear regression models to sophisticated deep-learning approaches. These algorithms leverage historical stock market data, macroeconomic indicators, company financials, and sentiment analysis, among other factors, to predict future price movements and market trends. In addition to performance comparison, the research paper examines the impact of various factors that influence the effectiveness of these machine-learning techniques. Factors such as data quality, feature engineering, model selection, hyperparameter tuning, and market conditions play pivotal roles in the accuracy of predictions. Understanding these factors will aid in refining the model-building process and enhancing overall forecasting capabilities. The study encompasses an extensive dataset spanning multiple stock markets and periods, ensuring robustness and reliability in the findings. Performance evaluation metrics, including mean squared error, accuracy, precision, recall, and F1 score, will be employed to assess the predictive power of the machine-learning techniques objectively. Furthermore, the paper investigates the potential of ensemble methods, combining the strengths of multiple models to achieve enhanced prediction accuracy. Ensemble techniques, such as bagging, boosting, and stacking, have proven effective in diverse domains and are expected to demonstrate their value in stock market prediction. By the end of this research, readers will have a comprehensive understanding of the landscape of machine-learning techniques applied to stock market prediction. The findings will offer insights into which methods are most suitable for different market conditions and will aid in establishing best practices for effective and reliable stock market forecasting. In conclusion, this research paper serves as a valuable resource for investors, financial analysts, and researchers, thoroughly assessing machine-learning techniques' efficacy in predicting stock market behavior. It contributes to the growing body of knowledge in financial technology. It underscores the critical role of data-driven decision-making in navigating the complexities of the modern stock market.Öğe Empowering healthcare innovation: IoT-enabled smart systems and deep learning for enhanced diabetic retinopathy in the telehealth landscape(Taru Publications, 2024) Shah, Arpit; Patel, Warish; Koyuncu, HakanBackground: Among prevalent medical complications, Diabetic Eye Disease (DED) stands as a significant contributor to vision loss. To forecast its progression and accurately assess the various stages, diverse methodologies have emerged. Machine Learning (ML) and Deep Learning (DL) algorithms have become essential tools in this endeavor, primarily through their adept analysis of Diabetic Retinopathy (DR) images. However, there is still a need for a more efficient and accurate method to predict DR performance. Method: We have developed an innovative method for classifying and predicting diabetic retinopathy. The novel idea in this research is to combine several techniques, including ensemble learning and a 2D convolutional neural network; we utilized transfer learning and a correlation method in our approach. Initially, the Stochastic Gradient Boosting process was employed for predicting diabetic retinopathy. We then used a boosting-based Ensemble Learning method for predicting images of diabetic retinopathy. Next, we applied a 2D Convolutional Neural Network. We successfully employed Transfer Learning to classify different stages of diabetic retinopathy images accurately. This research explores the role of artificial intelligence in identifying and categorizing diabetic retinopathy at an early stage, using techniques such as machine learning and deep learning. It also use techniques like transfer learning, domain adaptation, multitask learning, and explainable AI to accurately classify different stages of diabetic retinopathy images. Our proposed technique achieves impressive results through experiments, with a 97.9% accuracy in forecasting DR images and a 98.1% accuracy in image grading. Additionally, sensitivity and specificity metrics measure 99.3% and 97.6%, respectively. Comparative analysis with existing methods underscores the high predictive accuracy achieved by our proposed approach.Öğe A hybrid ensemble learning approach for efficient diabetic retinopathy prediction and classification using machine learning and deep learning techniques(Ismail Saritas, 2024) Shah, Arpit; Patel, Warish; Koyuncu, HakanAI is a crucial tool in early detection and classification of diabetic retinopathy, which is a leading cause of visual impairment globally. Transfer Learning (TL) was used to improve the accuracy of predictions and classifications within training datasets, surpassing existing methodologies. The study provides comprehensive insights into current databases, screening programs, performance evaluation metrics, relevant biomarkers, and challenges encountered in ophthalmology. The findings underscore the potential of AI-based approaches in enhancing diagnostic precision and offer a promising direction for future studies. The paper concludes by delineating opportunities for further research and development in integrating AI advancements in the field. Conclusion: The findings underscore the efficacy of Transfer Learning in significantly improving the accuracy of diabetic retinopathy image predictions. This research highlights the potential of AI-based approaches in enhancing diagnostic precision and offers a promising direction for future studies. The paper concludes by delineating opportunities for further research and development, emphasizing the continued integration of advanced AI methodologies in ophthalmology to advance diabetic retinopathy detection and management.Öğe High-frequency waves of the fifth generation and their effects on the head and their consequences for new perspectives(Institute of Electrical and Electronics Engineers Inc., 2023) Al-Sabti, Saif Mohamed Baraa; Mohammed, Alaa Hamid; Mustafa, Ali; Saadi, Abdulrahman Maadh; Abdulateef, Aqeel Nafea; Atilla, Doğu ÇağdasThis research focuses on the efficacy of electromagnetic field RF at high frequency 27.74 GHz on the human head, which includes modelling 5G communication networks to compute the SAR value via the head human layers' tissues and which layers the SAR is attained. Locating the source of the reflected power or electromagnetic signal. Most of the available power from the source is reflected under these conditions. The head serves as a parasitic element, and as a result, the reflected light has a radiation pattern that is 180 degrees out of phase with the original light. In the long run, it leads to being able to steer the reflected pattern. The radiation pattern of reflected power becomes similar to the head geometry. From this point on, estimating power density on the scalp will be less of a hassle. Energy may be collected by harvesting from two sources: reflected energy and sustainable energy.Öğe Using optical character recognition techniques, classification of documents extracted from images(Springer Science and Business Media Deutschland GmbH, 2023) Ibrahim, Abdullahi Abdu; Safaa Salim, Ahmed; Ameer Abd Almaged, HusamWe now have autonomous cars, speech recognition, efficient online search, and a far greater grasp of the human genome thanks to machine learning techniques developed during the previous ten years. Today, machine learning is employed so often that many times are mistakenly made. It is possible to educate a computer to anticipate outcomes that are challenging for the human brain by trying to teach it certain processes or scenarios. Additionally, these techniques enable us to quickly do various tasks that are frequently impractical or challenging for humans to complete. These factors make machine learning so crucial in today's world. There are two distinct machine learning techniques that were employed in this study. The manuscript materials were moved to the computer and then categorized to address a real-world issue. To complete the procedure, we relied on three fundamental techniques. A scanner or digital camera has converted handwriting or printed materials to digital format. Two alternative optical character recognition (OCR) operations have been used to process these papers. Next, the Naive Bayes method is used to categorize the sentences that were created. The entire project was created using the Windows operating system and Microsoft Visual Studio 12. All components of the study were written in the C# programming language. DLLs and prepared codes were also employed. Exploited.Öğe Evaluation DDoS attack detection through the application of machine learning techniques on the CICIDS2017 dataset in the field of information security(International Information and Engineering Technology Association, 2023) Ahmed, Ali Saadoon; Kurnaz, Sefer; Khaleel, Arshad M.Amongst network and Intrusion Detection System (IDS) threats, Distributed Denial of Service (DDoS) attacks often take precedence due to their significant potential to disrupt services, leading to financial and reputational damages for organizations. This study employs eight advanced machine learning techniques to distinguish between two types of DDoS attacks: DoS Hulk and DoS Slow HTTP Test. The applied algorithms include Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), AdaBoost, Naive Bayes (NB), Extreme Gradient Boosting (XGB), Ridge regression, and Multilayer Perceptron (MLP). Utilizing a Python environment, these methods were applied to the DDoS attacks in the CICIDS2017 dataset for classification into benign or DoS categories across two distinct experiments. The results were highly encouraging: The first experiment achieved an accuracy rate exceeding 99%, while the second experiment achieved a perfect success rate of 100%. These findings outperform those of previous studies in terms of their efficiency, demonstrating the potential of these machine learning techniques in enhancing DDoS attack detection.Öğe Various simulation tools for wireless sensor networks(CRC Press, 2023) Koyuncu, Hakan; Bagwari, AshishNetworks of specialized, geographically scattered wireless sensors that track and record physical environmental conditions and transmit the collected information to a central point are called wireless sensor networks. They are formed by a large number of wireless sensor nodes. WSNs are capable of measuring environmental factors such as sound, wind, temperature, humidity, and pollution levels. It is difficult to model a WSN analytically because it is complex and infeasible, resulting in simplified analysis and low confidence. Therefore, simulation plays a crucial role in the study of WSNs. It requires a good model built on reliable premises and a suitable framework that facilitates implementation. Simulation results also depend on assumptions about the environment, physical layer, and hardware, which are often too inaccurate to accurately reflect the actual behavior of a WSN, calling into question their validity. However, due to the enormous number of nodes that need to be simulated depending on the application, advanced models have scalability and performance issues. Thus, in modeling WSNs, the trade-off between scalability and accuracy becomes a significant problem. In this chapter, we present several simulation tools for wireless sensor networks that are widely used in the industry.
