Yazar "Ibrahim, Abdullahi" seçeneğine göre listele

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    Design of electricity theft detection system based on supervised learning
    (Institute of Electrical and Electronics Engineers Inc., 2022) Mohammed Alnaftchi, Shaymaa Mustafa; Ibrahim, Abdullahi
    Power grids are critical assets, not limited to the theft, disrupting or defective meters and the arrangement of false meter readings of the infrastructure faced with non-technical losses (NTLs). In emergent markets, NTL is a main concern and up to 10% of the total distribution of electricity. NTL's estimated annual global cost to utilities is around 100 billion USD. Therefore, it is crucial for utilities and authorities to reduce NTL to increase revenue, profit and reliability of the grid. The result of electricity theft, broken electronic meters or billing errors is non-technological losses (NTD) in grids. In this paper, we present a novel frame work called ETD (Electricity Theft Detection), which comprises of an intelligent algorithms such as ETD and SVM, RF, XGBoost and Neural Network classifiers to detect fraudulent consumer from the normal consumer based upon the consumer's consumption pattern. simulation result shows that the proposed system is efficient in identifying the suspects with high accuracy.
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    Efficient Fault Detection in Photovoltaic Systems Using Machine Learning: A Comparative Analysis of Tree-Based Models
    (Institute of Electrical and Electronics Engineers Inc., 2025) Alqaraghuli, Omar; Ibrahim, Abdullahi
    Using the most recent machine learning techniques, this article examines several defect detection algorithms that may be utilised in photovoltaic (PV) systems. Maintaining energy efficiency and economic stability requires accurate defect detection, which is becoming increasingly important as the use of renewable energy and large-scale photovoltaic systems continue to expand. Faults that are common in photovoltaic systems, such as string, string-to-ground, and string-to-string faults, can have a significant negative influence on production and result in financial losses. The research presents a flaw detection technique that makes use of classifiers such as Decision Tree, Random Forest, XGBoost, Gradient Boosting, and Extra Trees. We used data from a simulated photovoltaic power plant with a capacity of 250 kW. This data included 600 training instances, 50 testing instances, and 30 characteristics. Among the defects that were examined were those that occurred during normal operation (16.67%), string faults (25.5%), string-to-ground faults (24.83%), and string-to-string mistakes (33%). Accuracy, F1 score, recall, precision, and training duration were the aspects that were considered while evaluating the models. Due to the fact that the Decision Tree model earned the maximum accuracy of 95%, an F1-score of 0.949, recall of 0.95, and precision of 0.958, all while requiring just 0.0083 seconds for training, it is an excellent choice for applications that need real-time processing. The next best model was Random Forest, which achieved an accuracy of 88%, an F1-score of 0.879, and a training time of 0.4856 seconds. The accuracies of XGBoost and Gradient Boosting were below average, coming in at 79% and 78%, respectively. Additionally, the training durations for Gradient Boosting were significantly longer, requiring 4.732 seconds. Extra Trees demonstrated the lowest accuracy, with a score of 74%, an F1-score of 0.723, and a training time of 0.3199 seconds.
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    Fog and cloud load balancing using regression based reccurent deep learning algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2022) Jameel, Eftekhar Jumaah; Ibrahim, Abdullahi
    Fog computing has been studied by a variety of academics, and they have identified concerns which need to be solved. It is the primary goal of this project to build and execute an energy conscious load balancer that will reduce energy usage and help in the distribution of loads. In order to test the method that was created, establishing a fog computing platform By assigning a job and assessing the outcomes, the energy-aware load balancing method was found to be effective when it was tested.
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    Robot pathplanning by image segmentation using the fuzzy C-means algoritm and KNN algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2020) Al-Khayyat, Abdulrahman Tareq Ali; Ibrahim, Abdullahi
    Mobile robotics is a valuable tool for exploring environments inaccessible to humans due to their remoteness, cost or danger, and for performing unpleasant or laborious tasks. It is a relatively new field, until recently experimental, but it is already being applied to real problems with satisfactory results, 3D simulation plays a relevant role in both the design and control of mobile robots. It allows to reflect the situation of the real robot or simulate hypothetical scenarios. At the same time, it provides a graphical interface for robot control, in this paper, we present a scheme for guiding a robot into an uncharted area by using the fuzzy c-means segmentation algorithm and k-NN classification to classify the safe and unsafe areas, our method proves to be fast with satisfactory results in terms of both segmentation, classification, and correct path planning. © 2020 IEEE.