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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

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Induction Motor Stator inter-turn Fault Diagnosis Approach based on Machine learning Models

Induction Motor Stator inter-turn Fault Diagnosis Approach b...

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international Middle-East Power System conference, MEPCON

作者： Tamer A. Kawady Wagdy M. Khater Hagar G. Abu-Faty Mohamed A. Izzularab Mohamed E. Ibrahim Electrical Engineering Department. Faculty of Engineering Menoufia University Shebin El_Kom Egypt Dredging Department Suez Canal Authority Ismailia Egypt Data Science Department Faculty of Artificial Intelligence Menoufia University Shebin El_Kom Egypt High Voltage and Dielectric Materials Lab. Faculty of Engineering Menoufia University Shebin El_Kom Egypt Faculty of Technological Industry and Energy Delta Technological University Quweisna Egypt

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

Induction motors (IMs) are essential components in industrial applications due to their widespread use and critical importance. However, stator inter-turn faults (ITFs) can significantly impede the operation of IMs, leading to potential disruptions in industrial processes. Extensive research has been conducted to develop methods for detecting ITFs and evaluating their severity. In this paper, a machine-learning (ML) based approach is utilized for the detection and severity assessment of ITFs in IMs. the training data are generated through simulation tests based on an accurate mathematical model of the IM under ITFs. When ITFs occur, they directly affect the motor's balance and consequently modify the motor impedance matrix. this serves as the key feature for detecting such faults. By using the impedance matrix, the voltage measurements are incorporated into the ITF detecting feature, which helps to distinguish between ITF-induced imbalances and those caused by voltage unbalances. After feature extraction, different ML models are trained and validated using simulation measurements, and their performance is further tested on a separate dataset to evaluate their generalization capabilities. the results demonstrated that k-Nearest Neighbors (kNN) and Gradient Boosting models achieved high accuracy in fault detection, with estimation errors remaining low across various fault ratios and voltage conditions. Both models effectively distinguish between ITFs and SVU-induced anomalies, maintaining consistent detection accuracy.

关键词： Estimation error Accuracy Voltage measurement Symmetric matrices Power system stability Feature extraction Boosting Stability analysis Impedance Testing

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Using Graph theory for Improving Machine learning-based Detection of Cyber Attacks

Using Graph Theory for Improving Machine Learning-based Dete...

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IEEE Workshop on High Performance Switching and Routing

作者： Giacomo Zonneveld Lorenzo Principi Marco Baldi Department of Information Engineering Università Politecnica delle Marche Ancona Italy

ISBN: (数字)9798350363852

ISBN: (纸本)9798350363869

Early detection of network intrusion attempts is one of the main pillars of cybersecurity. An effective approach in this regard consists in analyzing network traffic with the help of artificial intelligence algorithms, with the aim of detecting the possible presence of an attacker by distinguishing it from a legitimate user. this is commonly done by collecting the traffic exchanged between terminals in a network and analyzing it on a per-packet or per-connection basis. In this paper, we propose instead to perform pre-processing of network traffic under analysis with the aim of extracting some new metrics, on which we can perform detection more efficiently and overcome some limitations of classical approaches. these new metrics are based on graph theory, and consider the network as a whole, rather than focusing on individual packets or connections. Our approach is validated through experiments performed on publicly available data sets, from which it turns out not only to overcome some of the limitations of classical approaches, but also achieve an improved detection capability of intrusion attempts.

关键词： Measurement Focusing Telecommunication traffic Switches Routing Graph theory Cryptography

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Voltage Stability Enrichment in DC Microgrids with UPFC Compensator and Artificial Neural Networks

Voltage Stability Enrichment in DC Microgrids with UPFC Comp...

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international Middle-East Power System conference, MEPCON

作者： Aya M.A. Heikal Shady H. E. Abdel Aleem Ragab A. El-Sehiemy Almoataz Y. Abdelaziz Department of Electrical Engineering Science Valley Academy Faculty of Engineering Future City Egypt Department of Electrical Engineering Institute of Aviation Engineering and Technology Giza Egypt Department of Electrical Engineering Faculty of Engineering Kafrelsheikh University Kafr ElSheikh Egypt Electrical Power & Machines Department Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

Integrating renewable energy sources (RESs) presents several challenges in power systems, such as power management, voltage stability, and power quality issues. this paper aims to maintain voltage stability within microgrids by introducing a unique approach that utilizes an artificial neural network (ANN) in conjunction with approximating dynamic programming integrated with the unified power flow controller (UPFC) for rapid fault detection and voltage stabilization in DC microgrids. the UPFC injects compensating current to counter disruptions and uphold voltage stability. A comparative study validates the effectiveness of the ANN-based UPFC in comparison to Fuzzy-PI and Fuzzy-PID controller-based distribution static synchronous compensator systems under faulty conditions in a DC microgrid connected to various RESs and the grid. this comparison aims to maintain microgrid stability and achieve a more reliable system. the ANN’s ability to adapt to predicting and learning from historical data enhances the UPFC’s performance, enabling a rapid and precise response to faults. this methodology significantly enhances the reliable operation and sustainability of the DC microgrid, mitigating the impacts of power fluctuations and faults. Simulation results demonstrate that the proposed ANN-based UPFC outperforms conventional controllers.

关键词： Simulation Power quality Microgrids Artificial neural networks Power system stability Stability analysis Power system reliability Time factors Vehicle dynamics Load flow

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Prediction of Hypertension Disease Using Machine learning Techniques: Case Study from Jordan

Prediction of Hypertension Disease Using Machine Learning Te...

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Information Technology (ACIT)

作者： Raed Alazaidah Ghassan Samara Anwar Katrawi Wael Hadi Maram Y. Al-Safarini Farah Al-Mamoori Ali Elrashidi Muhyeeddin Alqaraleh Haneen Alzoubi Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan Information Security Department Faculty of IT Petra University Amman Jordan Department of Pharmaceutical Faculty of Pharmacy Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Software Engineering Faculty of IT Zarqa University Zarqa Jordan Department of Computer Science Facultty of Information Technology Applied Science Private University Amman Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Hypertension is one of the most common diseases in Jordan. It is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. these classification models have been trained on a primary dataset that has been collected for the purpose of the research. the results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan.

关键词： Hypertension Measurement Predictive models Information technology Random forests Diseases

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intelligent Prediction of Multiple Defects in Rolling Element Bearing using ANN Algorithm

Intelligent Prediction of Multiple Defects in Rolling Elemen...

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international conference on Multimedia, Signal Processing and Communication Technologies, (IMPACT)

作者： Md Asif Ali Khan Mohd Atif Jamil Sidra Khanam Department of Mechanical Engineering Aligarh Muslim University Aligarh India

ISBN: (纸本)9781665476485

Rolling Element Bearing (REB) failure in heavy rotating machines or assembly lines might cause the machine to shut down, affecting the total cost and quality of the product. Bearing condition monitoring prevents failure and reduces the time and expense of preventative maintenance. With the advent of machine learning in the area of condition monitoring of mechanical systems, Artificial Neural Network (ANN) is becoming more widely used, redefining the state-of-the-art in defect diagnosis and classification. this work provides a real-time online fault detection approach for rolling bearings based on the ANN algorithm in order to realize the predictive maintenance of rolling element bearings in the industry. the training of the fault diagnostic model and the real-time fault diagnosis are the two primary steps of the procedure. the vibration signal is first preprocessed, which includes data categorization, data segmentation, and feature extraction before the defect diagnosis model is trained and optimized. the extraction of the characteristic features of the vibration signal is utilized to implement the online fault diagnosis through the fault diagnostic model. the fault data for analysis is accessed from the open-source Paderborn university bearing data. the fault categories taken are of multiple faults on the inner and the outer raceways. Optimization is carried out in the ANN model with regard to its number of hidden layers, size of the layers and the activation function to seek for a set of these parameters, which result in the best performance in diagnosing the bearing defects for the fault categories under consideration. the ANN approach is proved to be a useful technique for detecting faults and improving classification accuracy, making it more suitable for rolling bearing fault diagnosis. A maximum fault classification accuracy of 98.5% was achieved with a single hidden layer of size 25 and the Tanh activation function. the corresponding training time was 3.9831

关键词： Training Fault diagnosis Vibrations Condition monitoring Rolling bearings Artificial neural networks Feature extraction

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Multi Label Classification Based on Ensemble learning: Investigative Study

Multi Label Classification Based on Ensemble Learning: Inves...

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Information Technology (ACIT)

作者： Ola Nasir Raed Alazaidah Wael Hadi Ghassan Samara Essam Al Daoud Suhaila Abu Owaida Ali Elrashidi Esraa Abu Elsoud Haneen Alzoubi Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Security Faculty of Information Technology Petra University Amman Jordan Department of Computer Science Faculty of Information Technolog Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Al-al-Bayt University Mafraq Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Computer Science Faculty of IT Zarqa University Department of Computer Science Faculty of IT Applied Science Private University Amman Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

In the big data technology nowadays, activities associated with Multi-Label Classification (MLC) pose big and complex challenges, receiving terrific interest in diverse fields. Existing MLC algorithms suffer from low overall performance. this paper considers four well-known Problem Transformation Methods (PTMs). Two of these PTMs are based on the frequency of the labels (MFL and LFL) while the last two PTMs are based on the positive correlations among labels. these PTMs have been implemented and compared against four multi label datasets of various sizes to identify the best one that fits the nature of MLC datasets. Moreover, six popular ensemble learning models have been carried out and compared on the four datasets with respect to several evaluation metrics such as Accuracy, Hamming loss, and Exact Match and using SMO, DecisionStump, Random Forest, and Random Commission classifiers. the results revealed that MFL accomplished the quality performance amongst the four PTMs, and Stacking SVM is the best ensemble learning technique.

关键词： Support vector machines Measurement Correlation Accuracy Stacking Multi label classification Big data Ensemble learning Information technology Random forests

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Automating the Diagnosis of Cucumber Plant Diseases Using Machine learning

Automating the Diagnosis of Cucumber Plant Diseases Using Ma...

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Information Technology (ACIT)

作者： Ahmed BaniMustafa Hazem Qattouz Sattam Almatarneh Khalid Mansour Raed Alazaidah Olla Bulkrock Computer Information Science Dept Higher Colleges of Technology Abu Dhabi UAE Software Engineering Dept Princess Sumaya University for Technology Amman Jordan Data Science & Artificial Intelligence Dept Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah KSA Faculty of Computer Studies Arab-Open University Bahrain Bahrain

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Farmers all over the world struggle to fight plant diseases. However, the incorrect use of pesticides can waste time, effort, and money and can also be very harmful to the environment and human health. In this work, we present an empirical study that involves identifying two pathogens related to Cucumbers plant diseases: Powdery Mildew and Alternaria Cucumerina. the study aims to develop a computer-based solution based on deep learning and machine learning algorithms to detect these diseases. To achieve this, a dataset of hundreds of samples was collected from ordinary farms in Beqaa, an agricultural region 15 KM north of Amman, Jordan. three machine learning models were created in this study using Support Vector Machines (SVM), Logistic Regression (LR), and Multi-layer perceptron algorithms. the constructed models were applied to the dataset after embedding the dataset images using the Convolutional Neural Networks (CNN) InceptionV3 pre-trained model. the model was tested and evaluated using cross-evaluation and F1-Score, CA, AUC, Precision, Recall, and Specificity, which all showed excellent results, particularly using the SVM algorithm. the constructed methods are intended to be deployed to farms in their field condition environments using a CCTV system that can capture images of plants during growth, which can help identify plant diseases at that right stage, which would save time, effort, and money and reduce the impact of wrong and overuse of pesticides on consumers and the environment.

关键词： Support vector machines Plant diseases Pathogens Machine learning algorithms Pesticides Soil Cameras Real-time systems Mobile applications Convolutional neural networks

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Real-Time Traffic Density Monitoring and Adaptive Signal Control Using YOLOv8 and Arduino-Based LED System

Real-Time Traffic Density Monitoring and Adaptive Signal Con...

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international conference on Communication and Electronics Systems (ICCES)

作者： Ms. J. Nulyn Punitha Markavathi Saravanaselva N Viswam M.R Veeramani M Pandikannan M Information technology Kalasalingam Academy of Research and Education Krishnankovil Virudhunagar India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankovil Virudhunagar India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

this research paper introduces a real-time adaptive traffic management system designed to improve urban transportation efficiency and emergency vehicle prioritization. the system integrates advanced machine learning and IoT technologies, using the YOLOv8 object detection model and an Arduino-based microcontroller to monitor vehicle density, detect emergency vehicles, and dynamically manage traffic signals. the proposed solution incorporates multiple features, including software-controlled signals and real-time ambulance detection, to address the limitations of conventional fixed-timer traffic systems. Sensors and cameras capture live traffic data, which is processed to categorize density levels—Easy, Medium, and Heavy—for each direction (North, South, East, West). the integration of real-time data with dynamic signalling reduces traffic congestion while ensuring uninterrupted passage for emergency vehicles. Key innovations include an ambulance detection feature, which overrides normal traffic logic to immediately provide green-light passage, and centralized software management for greater flexibility and scalability. the YOLOv8 model, trained on a dataset of 10,000 annotated images, achieves a detection accuracy of 97.8%, ensuring precise and reliable operation. Experimental results demonstrate a 30% reduction in congestion wait times and a 40% improvement in ambulance response times. By leveraging real-time data and automated decision-making, this intelligent traffic management system addresses critical urban traffic challenges, offering a scalable and cost-effective solution. the system enhances operational efficiency and emergency response, marking a significant advancement in modern transportation networks.

关键词： Visualization Adaptive systems Accuracy Computational modeling Transportation Object detection Feature extraction Real-time systems data models Context modeling

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Advancing Stroke Prediction: Explainable Machine learning Models and data Augmentation Strategies

Advancing Stroke Prediction: Explainable Machine Learning Mo...

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Information Technology (ACIT)

作者： Rahma Abu Salma Ahmad al-Qerem Abedalhakeam Iessa Mohammed Rajab Mohammad Alauthman Mohammad Jeadi Ali Elrashidi Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan University Headquarter University of Anbar Ramadi Iraq Department of Information Security University of Petra Amman Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Stroke, a leading global disability cause, affects 12 million people annually, with 6.5 million dying. Preventing at least half of strokes requires public policy action and increased awareness. Early diagnosis is crucial and enhances patients' opportunities to survive a stroke. this research looks into how well machine learning (ML) models can predict when strokes will happen and how data augmentation affects those predictions.. Five ML models (Logistic Regression, Decision Tree, K-Nearest Neighbors, XGBoost, and Random Forest) are assessed and their performance is compared using an imbalanced and augmented dataset. data augmentation techniques, including Random Oversampling, SMOTE, and Cluster Centroids, are employed to address class imbalance. the results demonstrate that some models achieve relatively high accuracy without data augmentation (XGBoost: 0.95). However, data augmentation techniques further improve performance across all models. Random Oversampling leads to the highest Accuracy for Random Forest (0.99), while SMOTE achieves the best Precision for Logistic Regression (1.00). the results found that age, occupation type, BMI, blood glucose level, and history of cardiovascular disease are risk factors for stroke. this study highlights the effectiveness of ML models, particularly when combined with data augmentation, for stroke prediction and emphasizes the importance of specific features in identifying stroke risk.

关键词： Logistic regression Accuracy Predictive models Stroke (medical condition) Nearest neighbor methods data augmentation data models Public policy Information technology Random forests

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Hilbert-encoded Cooperative Coevolutionary Genetic Algorithm for Few-shot learning

Hilbert-encoded Cooperative Coevolutionary Genetic Algorithm...

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Machine Intelligence theory and Applications (MiTA), international conference on

作者： Xinyuan Zhang Boda Ou Yang Yue Hu School of Intelligent Systems Sciences and Engineering Jinan University Guang Zhou China School of Humanities Jinan University Guang Zhou China

ISBN: (数字)9798350385670

ISBN: (纸本)9798350385687

In recent years, the advancement of artificial intelligence algorithms, particularly in the domain of semantic segmentation within computer vision, has been noteworthy. this progress is attributed to the availability of diverse large-scale datasets and the continual improvement of computational resources, with deep learning serving as a representative paradigm. Consequently, numerous sophisticated segmentation models have emerged for visual tasks. However, the efficacy of these models hinges on the availability of substantial annotated datasets for training. A notable challenge arises in scenarios where few-shot datasets are prevalent due to inherent limitations. the sparse data volume in these instances often stems from difficulties in obtaining a sufficiently large and high-quality dataset. the process of image annotation is resource-intensive, demanding considerable time and human effort. Addressing the consequential shortage of sample size in few-shot datasets becomes a paramount concern. this paper tackles the aforementioned issues related to few-shot datasets and introduces the Hilbert encoded cooperative coevolutionary genetic algorithm (HCCGA), a novel image enhancement algorithm designed for the automated generation of 3D-Lookup Tables (3D LUTs). HCCGA leverages 3D LUTs generated automatically through the genetic algorithm to map the original few-shot dataset, thereby obtaining a more extensive set of high-quality samples. the methodology employed in this research encompasses the generation and optimization of 3D LUTs using a divide-and-conquer strategy based on genetic algorithms. Additionally, it involves the mapping of new images using 3D LUTs in conjunction with sampling and interpolation. the evaluation of the proposed approach encompasses comparisons based on various image binary classification evaluation metrics applied to images before and after mapping through the results of model inference. Experimental results obtained using HCCGA on diverse publ

关键词： Measurement Training Visualization three-dimensional displays Semantic segmentation Computational modeling Table lookup Optimization Standards Genetic algorithms

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