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检索条件"主题词=Machine Learning Algorithms"

共 26022 条记录，以下是4711-4720 订阅

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Quantum Positive Unlabeled learning algorithms with Applications to Energy

Quantum Positive Unlabeled Learning Algorithms with Applicat...

引用

IEEE Workshop on machine learning for Signal Processing

作者： Salil Naik Glen Uehara Kristen Jaskie Leslie Miller Andreas Spanias SCAI at Arizona State University SenSIP Center School of ECEE at Arizona State University

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

Positive unlabeled (PU) learning is a semi-supervised machine learning (ML) approach to a binary classification problem where a small subset of the data has positive labels, and the rest of the data is unlabeled. PU learning algorithms can be very efficient at reducing the computational cost of training ML classifiers because the labeling of data for supervised machine learning algorithms is time intensive. In this study, we develop new quantum PU (QPU) learning methods for photovoltaic (PV) fault detection and perform comparative analysis with classical PU learning methods. Quantum circuits for PU learning are designed and evaluated in this study. In addition, we examine various quantum machine learning (QML) and PU learning challenges including quantum measurement noise. As with many QML simulations, the presence of quantum noise and the heavy computational and memory requirements affect classification accuracy. Nevertheless, regardless of quantum noise effects and limited computational resources, our initial designs of quantum PU learning circuits achieved an accuracy score of 79.6%. This was achieved by implementing QPU learning with a quantum-classical hybrid neural network (QNN) architecture. In our study, we present simulation results with and without quantum noise models for comparison purposes.

关键词： learning systems Training machine learning algorithms Accuracy Computational modeling Simulation Noise Signal processing algorithms machine learning Integrated circuit modeling

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Automated Hyperparameter Tuning and Ensemble machine learning Approach for Network Traffic Classification

Automated Hyperparameter Tuning and Ensemble Machine Learnin...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Liwei Chen Xiu Sun Yuchan Li Muhammad Jaseemuddin Baha Uddin Kazi Computer Networks Toronto Metropolitan University Toronto ON Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada Faculty of Applied Science & Technology Humber College Toronto ON Canada

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

Network traffic analysis and forecasting have become indispensable to dynamically allocate bandwidth, congestion control, security, and planning in today’s complex, heterogeneous, and traffic-intensive networks. This study focuses on leveraging and optimizing machine learning techniques to perform network traffic analysis and classification for decision-making. The study initiated with a thorough exploration of machine learning algorithms. Various algorithms, including Logistic Regression, SVM, KNN, Decision Trees, Random Forests, Extra Trees, Bagging, and AdaBoost, are applied for multi-class classification in network traffic analysis. To improve accuracy, we first employed automated hyperparameter tuning for each of the algorithms. After that we implemented the ensemble method with unanimous voting. In terms of unanimous voting, increasing the number of algorithms with certain criteria correlates with a decrease in false positives and false negatives. The results of our approach clearly improve the accuracy of the network traffic classification over fundamental machine learning models for better management of complex heterogeneous networks.

关键词： Support vector machines Logistic regression Accuracy machine learning algorithms Telecommunication traffic Nearest neighbor methods Classification algorithms

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machine learning-Enabled Smart Transit: Real-Time Bus Tracking System for Enhanced Urban Mobility

Machine Learning-Enabled Smart Transit: Real-Time Bus Tracki...

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Intelligent Systems and machine learning Conference (ISML)

作者： Nadimpalli Madana Kailash Varma Md. Irfan Ahmed G. Madhusudhan G. Rishab Babu T. Shalini Fatima Unnisa Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad Telangana India Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Hyderabad-Panatella India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

In bustling urban cities, the efficiency of public transportation stands as a critical factor in alleviating traffic congestion and enhancing the daily commuter experience. This paper introduces a state-of-the-art Bus Tracking System that is meticulously designed to empower passengers by providing real-time updates on bus locations, availability, and estimated arrival and departure times. By leveraging cutting-edge technologies, this innovative system offers a myriad of advantages, notably including the reduction of passenger wait times, the optimization of route planning, and an overall enhancement of the public transportation journey. Through a comprehensive exploration of the significance of bus tracking technology and its potential to revolutionize urban mobility, this research underscores its pivotal role in promoting sustainable, convenient, and seamless transportation solutions tailored to the needs of modern cities. Our model uses machine learning techniques like regression, Kalman filter algorithm, and support vector machines to analyze the accuracy of arrival and departure of buses and further help in time-efficient and demand-based management of buses. A real-time location of buses can be provided by Google Maps API and Google Geolocation API.

关键词： Support vector machines machine learning algorithms Accuracy Web services Urban areas machine learning Real-time systems Kalman filters Public transportation Traffic congestion

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NASFLY: On-Device Split Federated learning with Neural Architecture Search

NASFLY: On-Device Split Federated Learning with Neural Archi...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Chao Huo Juncheng Jia Tao Deng Mianxiong Dong Zhanwei Yu Di Yuan School of Computer Science and Technology Soochow University Suzhou China Muroran Institute of Technology Muroran Japan Department of Information Technology Uppsala University Sweden

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The integration of Artificial Intelligence (AI) and Internet of Things (IoT) devices has given rise to IoAT, promising transformative applications across various domains. Federated learning (FL) and Split learning (SL) are pivotal in harnessing the potential of IoAT, enabling decentralized model training while preserving data privacy. However, the heterogeneity and scalability challenges in IoAT environments necessitate advanced frameworks. In this paper, we introduce an integration of block-wise Neural Architecture Search (NAS) with a multi-partition SFL framework, called NASFLY. This approach uses only devices for actual model training and offers flexible scaling of model fragments to accommodate a wide range of device capabilities. In particular, NASFLY allows devices to utilize idle periods during the lengthy SFL forward and backward propagation phases. This is achieved by employing auxiliary model components dispatched from the server to conduct local supernet elastification using the device’s local dataset. Our method alternates between SFL for backbone network optimization and the local supernet elastification within NAS, where knowledge from the backbone network is transferred to the local supernet branches using distillation techniques. We also propose a device clustering algorithm to further improve training efficiency. Our experimental results demonstrate that this methodology significantly enhances device utilization and improves training efficiency compared with the conventional SFL.

关键词： Training machine learning algorithms Federated learning Scalability Clustering algorithms Neural architecture search Service-oriented architecture Internet of Things Servers Optimization

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Optimizing Cardiovascular Disease Prediction with Data Mining and machine learning

Optimizing Cardiovascular Disease Prediction with Data Minin...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Garima Joshi Namita Joshi Himanshu Pant Preeti Malara Himanshu Rai Goyal Department of Zoology MBPG College Haldwani Nainital India College of Pharmacy y Graphic Era Hill University Bhimtal India School of Computing Graphic Era Hill University Bhimtal India College of Nursing Graphic Era Hill University Bhimtal India Computer Science & Engineering Graphic Era University Dehradun Uttarakhand

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Heart-related diseases, or CVDs for short, are cardiovascular ailments that are now the most dangerous sickness in the world and the leading cause of mortality globally in recent decades, not just in India. Because of this, a dependable, precise, and efficient system is needed to recognize these disorders and provide prompt, appropriate treatment. Utilizing machine learning methods and algorithms on a range of medical datasets has automated large-scale and sophisticated data processing. Scientists have recently begun to use a range of machine learning methods to help doctors and other medical professionals identify heart-related conditions. Numerous machine learning and data mining approaches are utilized for the forecasting of cardiac disease, including Support Vector machine (SVM), Random Forest, and Artificial Neural Network (ANN). Cardiovascular disease diagnosis and prognosis have become difficult issues for medical professionals. The enormous number of deaths from heart disease must be decreased, hence a rapid and effective diagnosis method must be found. In this field, data mining methods and machine learning algorithms are crucial. Researchers are working more quickly to create software that uses machine learning algorithms to assist physicians in identifying and predicting cardiac problems. In order to lower the death rate from cardiovascular disorders, this research creates a model that can accurately forecast these conditions.

关键词： Support vector machines Heart machine learning algorithms Mortality Cardiac disease machine learning Medical services Data mining Decision trees Medical diagnostic imaging

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Yoga Pose Detection and Correction Using 3D Pose Estimation and machine learning

Yoga Pose Detection and Correction Using 3D Pose Estimation ...

引用

International Conference on Communication and Electronics Systems (ICCES)

作者： Anusha N Shreya S Prabhu Shruthi Shekhar Poojari Department of Information Science and Engineering NMAM Institute of Technology (NMAMIT) Nitte (Deemed to be University) India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Yoga is an ancient Indian practice that nurtures and enhances both physical and mental well-being. However, performing yoga poses incorrectly can lead to adverse health effects. Many practitioners lack sufficient knowledge of the poses or fail to follow proper instructions, creating a need for a system that can accurately classify yoga poses and offer corrective guidance. This research aims to integrate technology with yoga to assist individuals in performing poses correctly, thus reducing the risk of injury and enhancing the effectiveness of their practice. Specifically, the objective is to detect live, performed yoga poses and provide real-time assistance using the Mediapipe library and deep learning techniques, such as the multilayer perceptron model. Recent advancements in computer vision and deep learning, including pose estimation algorithms and convolutional neural networks (CNNs), have shown promise in yoga pose recognition. However, challenges persist, such as accurately identifying complex and dynamic poses, dealing with variations in body shapes and postures, and ensuring real-time feedback. The proposed system seeks to overcome these challenges by leveraging robust pose detection methods and providing corrective suggestions to improve performance. This research marks a significant advancement in the fusion of technology with wellness, offering innovative solutions in the evolving field of mind-body fitness.

关键词： Deep learning Three-dimensional displays machine learning algorithms Shape Heuristic algorithms Pose estimation Multilayer perceptrons Real-time systems Libraries Injuries

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Object Color Identification and Classification using CNN Algorithm and machine learning Technique

Object Color Identification and Classification using CNN Alg...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： R. Kiruba V. Sneha S. Anuragha S. K. Vardhini V. Vismaya Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798350386349

ISBN: (纸本)9798350386356

The need for automation in the textile industry is growing rapidly today. Color based object sorting is a highly challenging process to be considered and needs to be addressed. It involves an automated material handling system. It synchronizes the movement of robotic arm to pick up objects moving on a mobile robot. It aims in classifying the colored objects then picking and placing the objects in its respective pre-programmed place. Thereby eliminating the monotonous work done by human, achieving accuracy and speed in the work. The core objective of the project is to propose an intelligent color-based object sorting system using deep learning technique like Convolution Neural Network for extraction of feature embedded with the machine learning algorithm. The two classifiers Random Forest and K-NN algorithm were implemented and studied for better classification. Based on the performance metrics, the Radom Forest algorithm out performs in classification. The project module involves cameras that captures the object’s color through the computer vision Library and sends the signal to the controller. The dataset of the captured images has been uploaded and compared with the trained data set. The ESP 32 Module transmit a signal to relay circuit, which then drives the robotic arm’s multiple motors to grip the object and position it in the given area. Based on the color observed, the robotic arm goes to the given point, releases the object, and returns to its original position.

关键词： Computer vision machine learning algorithms Image color analysis Neural networks Forestry Manipulators Feature extraction

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machine learning-Based Power Allocation Optimization Algorithm for Enhanced CR-NOMA Network

Machine Learning-Based Power Allocation Optimization Algorit...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yu Fu Bingcai Chen Qian Ning Kai Lin College of Computer Science and Technology Dalian University of Technology Dalian China College of Computer Science and Technology Xinjiang Normal University Urumq China College of Electronics and Information Engineering Sichuan University Chengdu China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Hybrid non-orthogonal multiple access (NOMA) is a promising scheme to significantly improve communication performance within a 5G mobile network. In this article, a NOMA network is established in the context of underlay cognitive radio (CR) firstly. In addition, the combination of artificial noise (AN) and decode-and-forward (DF) technology is applied at the unmanned aerial vehicle (UAV) relay. We then derive the secrecy outage probability (SOP), which is affected by power allocation factors, to measure the system secrecy performance. Finally, a machine learning (ML) power allocation optimization algorithm is proposed considering both network security and user fairness. To be specific, we employ a revised decision tree (DT) embedded multilayer perceptron (MLP) in the leaf nodes, aiming to utilize the strengths of both ML algorithms effectively and find the optimal groups of systems with various parameters rapidly. The data results demonstrate that the introduced algorithm provides satisfactory predictions and obtains more accurate coefficients compared to other ML algorithms.

关键词： NOMA machine learning algorithms machine learning Probability Prediction algorithms Autonomous aerial vehicles Power system reliability Resource management Cognitive radio Optimization

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Enhancing Power System Protection with machine learning: A Smart and Reliable Approach for Insulator Inspection

Enhancing Power System Protection with Machine Learning: A S...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Hamid Gulzar Saif Ur Rehman Adnan Yousaf Electrical Department Superior University Lahore Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Increasing demand for electricity due to technological advancement in every aspect of human life has increased the importance of the smooth operation of distribution lines. Distribution lines pass through mountains, deserts, forests and many other inaccessible zones. Therefore, the detection of present and future hazards to transmission lines due to the presence of anything on the line, human towns adjacent to the distribution lines, trees of forests, new plants nearer to the lines, and birds nest is an important concern. Conventionally, these lines were inspected by line men which consumes too much time, increases inspection cost because of the huge workforce, increases human life and safety risks, compromises the reliability of the data and take too much delay in the desired information. In order to cope with the aforementioned problems, an unmanned autonomous vehicle (UAV) is proposed in this study with an instant camera is proposed for distribution lines insulator inspection. This system can monitor the physical conditions of the insulator. This UAV-based system is capable to replace humans which reduces workforce costs and increases human life security and data reliability. In this study, a technician or any other expert maintains a distance and operates UAV to observe the lines. Further machine learning-Based algorithms help in fault classifications. It's an easy, safe, and long-lasting way to analyze transmission and distribution lines. In the local market of Pakistan, the proposed system of inspection and fault detection through imaging of distribution lines can be used by WAPDA and all DISCOs (distribution companies). It can inspect distribution lines that are unreachable where manual labor cannot inspect.

关键词： machine learning algorithms Costs Forests machine learning Inspection Insulators Autonomous aerial vehicles Power system reliability Maintenance Classification algorithms

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Topological machine learning: Integrating Topological Data Analysis with machine learning to Enhance Breast Cancer Classification

Topological Machine Learning: Integrating Topological Data A...

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Sustainable Technology and Engineering (i-COSTE), International Conference on

作者： Rabelani Netshifhire Maria Vivien Visaya Department of Electrical and Electronic Engineering Science University of Johannesburg Johannesburg South Africa Department of Mathematics and Applied Mathematics University of Johannesburg and DSI-NRF CoE-MaSS Johannesburg South Africa

ISBN: (数字)9798331517335

ISBN: (纸本)9798331517342

Accurate classification tools are essential for improving patient outcomes in breast cancer diagnosis and treatment. Oncologists face the challenge of interpreting diverse patientspecific datasets such as mammograms, ultrasounds, and biopsy reports. Manual assessment of datasets is time-consuming and error-prone, complicating breast cancer classification. To address these issues, we explore the integration of Topological Data Analysis (TDA) with machine learning (ML), known as Topological machine learning (TML). TDA provides a systematic framework to uncover topological structures in highdimensional datasets, while ML offers powerful classification tools to analyze complex data. We present a comprehensive overview of TDA and ML, highlighting their individual strengths and the synergistic benefits of their integration. Topological features extracted through TDA are fed into ML models, including K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Gaussian Naive Bayes (GNB), Logistic Regression (LR), and Support Vector machine (SVM), to improve classification accuracy. Results show significant classification accuracy compared to traditional ML methods.

关键词： Support vector machines Accuracy Data analysis machine learning algorithms Clustering algorithms machine learning Nearest neighbor methods Feature extraction Breast cancer Classification algorithms

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