Yazar "Ahmed, Ali Saadoon" seçeneğine göre listele

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    Data mining management system optimization using swarm intelligence
    (Institute of Electrical and Electronics Engineers Inc., 2023) Hasan Al Mashhadani, Asraa Ahmed; İnan, Timur; Ahmed, Ali Saadoon
    Because of a phenomenon known as the 'curs e of dimensionality,' standard machine learning algorithms have difficulty dealing with high-dimensional data. There are more possible examples in the data space as the number of dimensions increases; however, as the number of dimensions increases, the amount of data that can be accessed decreases. There are a greater number of potential instances in the data space when there are more dimensions. The amount of data required by machine learning algorithms to address problems with such a high dimension increases exponentially with the number of problem-related characteristics. In this paper, we examine the suggested algorithms' methods for selecting features and their relationship to the data representation.
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    Detection of COVID-19 using classification of an x-ray image using a local binary pattern and k-nearest neighbors
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ahmed, Ali Saadoon; Kurnaz, Sefer; Hamdi, Mustafa Maad; Khaleel, Arshad Mohammed; Jabbar, Amenah Nazar; Seno, Mohammed E.
    The recently identified coronavirus pneumonia, which was later given the name COVID-19, is a virus that can be fatal and has affected more than 300,000 individuals around the world. Because there is currently no antiviral therapy or vaccine that has been granted approval by the FDA to cure or prevent this sickness, an automatic method for disease identification is required because of the fast global distribution of this exceedingly contagious and lethal virus. A unique machine learning strategy for automatically detecting this ailment was discovered. Machine learning approaches should be applied in essential jobs in infectious illnesses. As a result, our major aim is to use computer vision algorithms to identify COVID-19 without the need for human interaction. This paper suggested using image processing to classify objects and make early detections using X-ray pictures. Features are extracted for this region using a variety of techniques, including (LBP), (HOG), and use K-Nearest Neighbor algorithm (KNN) for classification, with training percentages of 50%, 60%, 70%, 80%, and 90%. Experiments indicated that using the suggested approach to identify X-ray photos of corona patients, it is feasible to diagnose the disease using X-ray images by training the device on the image data set (about 2,400 photos). The results were tested on the average of the samples taken (random 2000 images) each time and the measurement of multiple training ratios (50%, 60%, 70%, 80%, and 90%). The experimental findings revealed remarkable prediction accuracy in all investigated scenarios, ranging from 85% to 99%.
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    Energy efficient routing protocol for IoT networks using Ns2 simulation
    (Engineering and Technology Publishing, 2022) Ahmed, Ali Saadoon; Kurnaz, Sefer
    Limited power, poor memory, and low computing capabilities make the Internet of Things (IoT) devices resource restricted. Extending the network lifetime is one of the critical objectives in IoT. When it comes to preserving energy in a smart city, routing is one of the most critical factors. Designing an energy-efficient routing protocol boosts energy utilization to a significant extent. Networks with Reduced Power and Loss, the Energy Aware Routing Protocol for the IoT, decreases network traffic and extends network lifespan. As a result, routing variables such as load, remaining energy, and predicted transmission count identify the optimum parent for data transfer. The data traffic is dispersed throughout the network because it considers the load routing measure during route construction. As a result, it enhances network lifetime and achieves a high packet delivery ratio.
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    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.
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    Image filtering of impulsive noise using biologically inspired algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2022) Gharraf, Hadeel Sabti; Cansever, Galip; Ahmed, Ali Saadoon
    A digital image is a two-dimensional representation of an image in the form of a numerical matrix. In grayscale images, pixels are represented by an integer numeric value that is between 0 and 255. One of the main problems encountered today is the appearance of noise in digital images. The main sources of noise appear during the image acquisition and transmission phases. There are many types of noise and among the best known are some such as Gaussian or impulsive. The main goals of applying filters are to smooth the image, remove noise, enhancement, and edge detection, in this paper we propose the combining of the powerful set of Fuzzy rules with the genetic algorithm pattering recognition features to filter the image from impulsive noise yet retaining the image features unfiltered thus performing a powerful noise filtering on the noise without and destruction to the image itself.
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    Internet of things: Security threats and proposed solutions
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ahmed, Ali Saadoon
    Internet of Things is a very comprehensive topic, which does not exist a single definition for it, however, according to the IoT definition, the Internet of Things is an infrastructure of the information society, which allows the realization of services advanced, by connecting (physically and virtually) 'things', based on existing and evolving interoperable information and communication technologies. This study aims to address the security and privacy challenges related to IoT services, which have been accelerated by the introduction of IoT innovations. The main objectives are to collect best practices to ensure IoT security while mapping the relevant security and privacy challenges, threats, risks, and attack scenarios.
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    LAN based GIS optimization for coverage in wireless networks
    (Institute of Electrical and Electronics Engineers Inc., 2023) Atiyah, Israa Salman; Cansever, Galip; Ahmed, Ali Saadoon
    Machine learning is a branch of artificial intelligence based on the idea that systems can learn to identify patterns and make decisions with a minimum of human intervention. In this Paper, demonstration learning will be used, using neural networks in a prototype of a drone built to perform trajectories in controlled environments. To accelerate the training convergence process, a new training data selection approach has been introduced, which picks data from the experience pool based on priority instead of randomness. An autonomous maneuver strategy for dual-UAV olive formation air warfare is provided, which makes use of UAV capabilities such as obstacle avoidance, formation, and confrontation to maximize the effectiveness of the attack.
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    Study for buildings with IoT system for energy management
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ahmed, Ali Saadoon; Kurnaz, Sefer; Hamdi, Mustafa Maad; Khaleel, Arshad Mohammed; Khaleel, Amjed Mohammed; Seno, Mohammed E.
    A smart city makes use of sustainable information and communication technology to upgrade the effectiveness and quality of municipal services for inhabitants and government officials while conserving resources. Buildings with energy-efficient control are an essential part of this. The IoT may be able to help. Its goal is to link many heterogeneous devices over the internet, which necessitates a flexible layered architecture that combines things, people, and web services to simplify an application's work. Also, this Paper will offer a customizable Internet of things, hierarchical architectural model, with an overview of each important component enabling intelligent energy regulation in buildings of smart cities. This paper describes an advanced IoT-based energy management system for buildings. A semantics framework is proposed to unify and standardize the modelling of the things that make up the built environment. Appropriate rules are created with the goal of intelligent energy management and the overall Smart Building mode of operation.