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检索条件"主题词=Machine Learning Algorithms"

共 26022 条记录，以下是4641-4650 订阅

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Determination of Fault Location in Active Distribution System Using machine learning with HT Data Extraction Method

Determination of Fault Location in Active Distribution Syste...

引用

Sustainable Energy and Future Electric Transportation (SEFET), International Conference on

作者： Archana Tamrakar Arpit Shivam Mishra Saumendra Sarangi Electrical Engineering Department MNNIT Allahabad Prayagraj India

ISBN: (数字)9798350383997

ISBN: (纸本)9798350384000

Reliable power supply in active distribution system demands restoration of power within minimum time following an outage due to fault. However, recent changes in the DS with penetration of renewables, use of underground cables, smart loads and power quality improvement using smart controllers, limits the application of conventional methods to locate the fault. Further, to minimize the losses and enhance the energy management, smart intelligent meters are added which provides large sets of data and better observability is achieved even during fault. In this work, an intelligent fault location algorithm is proposed to handle huge data sets and deriving sequence components during the fault from these data. Hilbert Transform is applied to the raw voltage and current data collected from smart meters to compute the sequence components which is considered as input to the machine learning based intelligent Random Forest (RF) algorithm. The efficacy of this method is verified through simulation of modified IEEE 33 bus system with renewables in MATLAB/ SIMULINK platform and the RF algorithm is realized through JUPITER NOTEBOOK. The results reveal, the method accurately identifies the faulty section and fault location for different types of faults with and with without high resistance and DGs even with unbalance in the system.

关键词： Radio frequency Meters Renewable energy sources machine learning algorithms Software packages Transportation Transforms Voltage Fault location Smart meters

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Improvements in Target Detection Using machine learning

Improvements in Target Detection Using Machine Learning

引用

IEEE Research and Applications of Photonics In Defense Conference (RAPID)

作者： S.P. Bragdon V.H. Truong J.L. Clausen M.I. Bishop Cold Regions Research and Engineering Laboratory U.S. Army Engineer Research and Development Center Hanover NH USA

ISBN: (数字)9798350373097

ISBN: (纸本)9798350373103

A new approach using computer vision/machine learning (ML) algorithms to improve thermal target detection performance is discussed. ERDC's prototype two-step environment informed automatic target detection and recognition (ATD/R) algorithm shows a greater than 50% improvement in sub-surface target detection as compared to other ML ATD/R algorithms.

关键词： machine learning algorithms Image recognition Target recognition Prototypes Object detection machine learning Classification algorithms

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Multifunctional Radar Jamming Effect Evaluation based on Optimized Limit machine learning

Multifunctional Radar Jamming Effect Evaluation based on Opt...

引用

Electronic Engineering and Information Systems (EEISS), International Conference on

作者： Yanwen Shi Jianyu Yu Yi Huang Yi Liang Xi’an Electronic Engineering Research Institute Xi’an China

ISBN: (数字)9798350351033

ISBN: (纸本)9798350351040

Taking multifunctional radar as the object of interference research, according to the radar working characteristics, the signal return rate and signal parameters are analyzed in the radar working state change, and the radar takes anti-jamming measures to change. According to the signal return rate and radar signal parameters to establish a sample library, the algorithm based on genetic algorithm (GA) optimization of the extreme learning machine (ELM) is proposed for the evaluation of the interference effect, and compared with the unoptimized ELM algorithm. The average accuracy of the algorithm before and after optimization is 78.75% and 96%, respectively.

关键词： Accuracy machine learning algorithms Radar measurements Radar Interference Radar tracking Robustness

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Fetal Health Classification Using machine learning: Impact of Data Transformation and Balancing Methods

Fetal Health Classification Using Machine Learning: Impact o...

引用

International Conference on Computer and Information Technology (ICCIT)

作者： Sumaiya Tabassum Tasmiah Tahrim Jannatul Farzana Tandra Somak Saha Chamak Saha Sadia Sobhana Ridi Muhammad Iqbal Hossain Department of Computer Science and Engineering Brac University Dhaka Bangladesh Department of Animal and Dairy Science University of Georgia Athens GA USA

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Health issues regarding mothers and fetuses have long been an important concern worldwide, especially in middle and low-income countries. Both the fetus and mother may experience severe health issues, which often lead to maternal mortality if the conditions for the fetus inside the mother’s womb are not suitable. Many medical experts use Cardiotocography (CTG) technology to anticipate the fetus’s heart rate to minimize those health problems. This study classifies fetal health from the CTG dataset using two ensemble classifiers, Random Forest (Bagging) and AdaBoost (Boosting) and two baseline classifiers, Gaussian Naive Bayes and Logistic Regression. This research further investigates the performance of these different models on three data transformation methods Discretization, Rank and Z-Score combined with PCA. Repeatedly, this study addresses the class imbalance issues using SMOTE and SMOTE-Tomek methods. In performance analysis, evaluating through 10-fold cross-validation it was found that Random Forest provided the best performance (97.58% accuracy) when both Z-Score (data transformation) and SMOTE-Tomek (data balancing technique) were used.

关键词： Accuracy machine learning algorithms Refining Standardization Fetus Data models Performance analysis Cardiography Random forests Principal component analysis

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Predicting and Forecasting Metal Stock Prices Using machine learning

Predicting and Forecasting Metal Stock Prices Using Machine ...

引用

Cybernetics, Cognition and machine learning Applications (ICCCMLA), IEEE International Conference on

作者： Katakam Bhavana Indukuri Raghu Varma Kalluri Rohitha G Suryanarayana Kachi Anvesh Sama Kruthika Department of Information Technology Vardhaman College of Engineering (Autonomous) Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering (Autonomous) Hyderabad India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

machine learning algorithms analyze past market data to forecast future trends in metal stock prices. Precise estimation of metal stock values is essential for industry stakeholders, policy makers, and investors. Correctly predicting market trends can be challenging as there are large number of underlying factors that affect shifts in the price of stocks. Autoregressive Integrated Moving Average (ARIMA) and Random Forest algorithm are two techniques used to predict prices of different metal genres. Non-linear correlations between features and underlying temporal patterns in the data can both be captured using ARIMA and Random Forest. A preliminary process is taken to guarantee the quality of the historical stock data, which includes transaction volume, opening and closing prices, and technical indicators. Random Forest model use both historical data and supplementary features, ARIMA models are trained on time-series data to extract temporal patterns. The models are able to learn from important aspects influencing metal equity prices in addition to the intrinsic time-series characteristics. To improve forecast accuracy, this includes outside variables such as geopolitical events, economic indicators, and market structures. The above technique offers a thorough grip of the market dynamics affecting metal genres prices by combining technical and fundamental analysis. The initiative intends to give investors with the skills and knowledge required to make good decisions in the ever-changing metal stock market environment by combining cutting-edge machine learning algorithms with domain expertise and outside data sources. These models aim to capture the tangled relationships between market variables and historical price data to detect patterns and trends that direct future price fluctuations.

关键词： machine learning algorithms Accuracy Metals Predictive models Market research Feature extraction Data models Stakeholders Forecasting Random forests

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Survey of Quantum machine learning in IoT Application Field

Survey of Quantum Machine Learning in IoT Application Field

引用

IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Moumita Pradhan IT Dr. B.C. Roy Engineering college Durgapur India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Due to massive interest in emerging technology for different types of computing and storing data, Quantum Computing plays a significant role. Physics based strategy like superposition, entanglement, inference, decoherence make this technology more efficient. Well structured quantum hardware provides time efficiency for different types of operation. machine learning model, a subset of artificial intelligence, furnish predictive model for unknown data exploring training and testing mechanism. Quantum machine learning may upgrade classical machine learning algorithms. Running computationally expensive algorithms efficiently on a quantum computer using concepts from physics, open a new aspect. IoT provides effective data (air quality, temperature etc.), gathering from physical environment through sensor. Digital India, smart agriculture, smart city, health care, smart home, remote monitoring etc., are benefited by emerging technology, called IoT. This research is preliminary lay the cornerstone of making novice researcher interested to learn about QML in different types of IoT field.

关键词： Training Temperature sensors Temperature distribution Quantum computing machine learning algorithms machine learning Predictive models Internet of Things Physics Testing

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Human Activity Recognition with Smartphones using machine learning Algorithm

Human Activity Recognition with Smartphones using Machine Le...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： K Ghamya K Prema D Abhinay Reddy K Varaprasad K Navyasri Sai S Tejesh Department of CSE(AI&ML) School of Computing Mohan Babu University Tirupati India Department of CSE Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Human Activity Recognition with Smartphones (HAR) leverages the ubiquitous nature of smartphones equipped with sensor technology to identify and classify daily human activities from the generated data. This paper explores the field of HAR, focusing on the use of smartphone sensors like magnetometers, gyroscopes, and accelerometers to capture movement patterns. This provides a comprehensive exploration of HAR, delving into methodologies such as feature extraction data acquisition, and the application of various machine learning algorithms for activity classification. The discussion encompasses challenges and limitations intrinsic to smartphone-based HAR, addressing issues like data variability and device heterogeneity. Promising research directions are highlighted, with an emphasis on context awareness, multi sensor fusion, and deep learning approaches, aiming to enhance the robustness and accuracy of activity recognition systems. The abstract concludes by emphasizing the potential applications of HAR in healthcare, where it could revolutionize patient monitoring, fitness monitoring, optimizing routines, and personalized user experiences, showeasing its role in shaping the future of health-related technologies. This model contributes valuable insights into the dynamic field of HAR, considering the synergy between smartphones, sensor technologies, and machine learning algorithms to propel innovative applications and personalized user experiences.

关键词： Knowledge engineering Patient monitoring machine learning algorithms Magnetometers Medical services Sensor fusion Propulsion

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machine learning and Neural Network based Early Prediction of Corona Virus and Diagnosing the Status of heart diseases

Machine Learning and Neural Network based Early Prediction o...

引用

Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Nagavarapu Sowmya Jitendra Kumar Pradyut Kumar Biswal Department of Electronics and Communication Engineering GIETU Odisha Gandhi Institute of Engineering and Technology India Department of Electronics and Communication Engineering Indian Institute of Information and Technology Bhubaneswar Odisha IIIT Bhubaneswar India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

The prediction of several viruses and biomedical signal classifications in recent times have evolved tremendously with the help of machine learning models and neural networks. This paper studies coronavirus, and ECG datasets which are further trained and tested by machine learning algorithms and neural networks such as Linear Regression (LR), Logistic Regression, Extreme Gradient Boosting (XGBoost), Random Forest (RF) Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN) to predict the possibility of confirmed, cured and death cases including the symptoms related to virus attack, heart and brain disease falling under each subject post training and validation. This paper demonstrates a comparative analysis among our work and parallel research work using machine learning models and neural networks with respect to accuracy. An accuracy of (90-95)% has been achieved by machine learning algorithms during the prediction of various diseases with an error rate of approximately 6%. Furthermore, neural networks produced an accuracy of (90-92)% for the same datasets with an error rate of nearly 3%. The resultant model produces high accuracy and may be used to in embedded and IoT devices to improvise the outcome for intended diseases.

关键词： COVID-19 Heart Logistic regression Accuracy machine learning algorithms Computer viruses Electrocardiography Biological neural networks Random forests Diseases

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Harnessing Deep learning for Fault Detection in Industry 4.0: A Multimodal Approach

Harnessing Deep Learning for Fault Detection in Industry 4.0...

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IEEE International Conference on Cognitive machine Intelligence (CogMI)

作者： Jialie Shen Marie Morrison Haiyan Miao Fengshou Gu Department of Computer Science City St Georgia’s University of London London United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

The advent of Industry 4.0 has reshaped the modern industries (e.g., Manufacturing, Automotive, Aerospace and Defense), driven by the rapid development of artificial intelligence, smart sensing technologies, and interconnected cyber-physical systems. One of the most important goal for Industry 4.0 is the improvement and enhancement in productivity, reduction of production losses, and operational efficiency. High quality fault detection within different environments is the foundation to achieve these goals. Industry 4.0 emphasizes the use of interconnected systems, smart sensors, and advanced analytics, which generate large volumes of data from production environments. The data complexity and scale, product by the widespread deployment of sensors and IoT devices, demands sophisticated machine learning (ML) and data-driven methods. In this paper, we argue that the future of accurate and reliable fault detection lies in the seamless fusion of edge-cloud computing, explainable AI, and adaptive machine learning algorithms capable of processing high-frequency, multimodal data streams in real time. Different kinds of advantages for multimondal learning and core enabling techniques are also discussed and revise. This vision paper outlines the future trajectory of multimodal learning based fault detection, via identifying critical research gaps and promising directions for future study.

关键词： Productivity machine learning algorithms Fault detection Real-time systems Fourth Industrial Revolution Trajectory Internet of Things Intelligent sensors Streams machine intelligence

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A machine learning Approach to Model Counting

A Machine Learning Approach to Model Counting

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Marco Dalla Andrea Visentin Barry O'Sullivan SFI Centre for Research Training in AI School of Computer Science & IT University College Cork Ireland Insight Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

Model counting (#SAT) is the problem of computing the number of satisfying assignments for a given Boolean formula. It has a significant theoretical and practical interest. Tackling it can be challenging since the number of potential solution grows exponentially with the number of variables. Due to the inherent complexity of the problem, approaches to approximate model counting have been developed as a practical alternative. These methods extract the number of solutions within user-specified tolerance and confidence levels and in a fraction of the time required by exact model counters. However, even these methods require extensive computations, restricting their applicability to relatively small instances. In this paper, we propose a new approximate machine learning model counter that overcome this limitation. Predicting the number of solutions can be seen as a regression problem. We deploy an array of machine learning techniques trained to infer the approximate number of solutions based on statistical features extracted from a SAT propositional formula. Extensive numerical experiments performed on synthetic crafted and benchmark datasets show that learning approaches can provide a good approximation of the number of solutions with a much lower computational time and resource cost than the state-of-the-art approximate and exact model counters, making it possible to approximate the model count of instances previously out of reach. We then investigated the structural factors that lead to a high model count using AI explainability approaches.

关键词： Deep learning Measurement Accuracy machine learning algorithms Computational modeling Estimation learning (artificial intelligence) Predictive models Feature extraction Numerical models

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