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检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, SPML 2020"

共 732 条记录，以下是291-300 订阅

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A machine learning Approach for Anomaly Detection in Power Mixing Equipment Intelligent Bearing Fault Diagnosis

A Machine Learning Approach for Anomaly Detection in Power M...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Ce Fang Qian Wang Binbin Huang Zhejiang Canaan Technology Limited China International College Krirk University Bangkok Thailand Zhejiang Canaan Technology Limited Wenzhou China

Bearing and Gear are the essential and most significant pieces of pivoting machinery utilized in ventures. Normal applications incorporate planes, autos, power turbines, and steel plants. In the event of flaws happening in any bearing or gear, of these machines during working circumstances, serious outcomes are to happen. The issue determination utilizes the vibration signal at the beginning phase of shortcoming event. Vibration-based investigation is taken as a result of its simplicity of estimation. The viability of the astute shortcoming finding strategy is approved utilizing different datasets gathered from turning bearing and gear parts from an only made test rig in the research center through planned plans. For the robotized bearing shortcoming finding framework, three distinct and novel plans were figured out, and it is assessed. Also, for gear issue conclusion, another three unique and novel plans were modeled and assessed independently. In bearing issue determination, contain wavelet parcel change for highlight extraction, half and half hereditary calculation and particle swarm optimization for highlight choice and for coordinated ANFIS for navigation. In plot 2, Feature Extraction process did from the time and recurrence area of the gained signal, Hereditary Calculation and Particle Swarm optimization strategies with various wellness capabilities were applied. The outcomes from gear and bearing shortcoming analysis exhibit that the proposed smart shortcoming finding philosophy is exceptionally exact and successful in separating the issue examples of the two bearings and gears.

关键词： Vibrations Fault diagnosis Gears signal processing algorithms signal processing Feature extraction Turning

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machine learning Equipped Web-Based Disease Prediction and Recommender System 3rd

Machine Learning Equipped Web-Based Disease Prediction and R...

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3rd international conference on machine Intelligence and signal processing, MISP 2021

作者： Rajora, Harish Punn, Narinder Singh Sonbhadra, Sanjay Kumar Agarwal, Sonali Indian Institute of Information Technology Gujarath Allahabad India

ISBN: (纸本)9789811908392

Worldwide, several cases go undiagnosed due to poor healthcare support in remote areas. In this context, a centralized system is needed for effective monitoring and analysis of the medical records. A web-based patient diagnostic system is a central platform to store the medical history and predict the possible disease based on the current symptoms experienced by a patient to ensure faster and accurate diagnosis. Early disease prediction can help the users determine the severity of the disease and take quick action. The proposed web-based disease prediction system utilizes machine learning-based classification techniques on a data set acquired from the National Centre of Disease Control (NCDC). K-nearest neighbor (K-NN), random forest and Naive Bayes classification approaches are utilized, and an ensemble voting algorithm is also proposed where each classifier is assigned weights dynamically based on the prediction confidence. The proposed system is also equipped with a recommendation scheme to recommend the type of tests based on the existing symptoms of the patient, so that necessary precautions can be taken. A centralized database ensures that the medical data is preserved and there is transparency in the system. The tampering into the system is prevented by giving the no "updation" rights once the diagnosis is created. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Semantic Computing Enhancement of Industrial Augmented Reality Solutions with machine learning 2021

Semantic Computing Enhancement of Industrial Augmented Reali...

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2021 3rd international conference on Video, signal and Image processing

作者： Juan Izquierdo-Domenech Jordi Linares-Pellicer Jorge Orta-Lopez Valencian Research Institute for Artificial Intelligence Polytechnic University of Valencia Spain

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Time Series Pattern Matching Technique for Low-Energy Arrhythmia Classification in Implantable Cardiac Devices

Time Series Pattern Matching Technique for Low-Energy Arrhyt...

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Computing and machine Intelligence (ICMI), international conference on

作者： Prianka Sengupta Eugene B. John Dept. of Electrical and Computer Engineering Texas A&M University College Satation TX Dept. of Electrical and Computer Engineering University of Texas at San Antonio San Antonio TX

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Implantable cardiac devices (ICDs) are often used as an effective treatment for arrhythmia. Although these devices have access to a live Electrocardiogram (ECG) stream, currently they do not offer on-device classification of arrhythmia due to the limited computing capability and severe power constraints. In this paper we propose a low-energy computing method for extracting shape-based features from ECG in combination with machine learning techniques for classifying nine different cases of arrhythmia. This is achieved by using a Localized Longest Common Subsequence (LLCS) algorithm which has low computational requirements that allows on-device execution. The proposed method strongly focuses on maintaining minimal energy and computational footprint, in line with the operating constraints of implantable devices. To demonstrate the energy efficiency and low computation load of the proposed method we implement the classification pipeline on a low-power RISC microcontroller and compare its performance with existing classification techniques. The classification accuracy and energy of the proposed method is compared with state-of-the art research in arrhythmia classification.

关键词： Performance evaluation Accuracy Arrhythmia Time series analysis Pipelines signal processing algorithms Implants

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Skin Cancer Detection using modified CNN with GLCME Features

Skin Cancer Detection using modified CNN with GLCME Features

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Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha international conference on

作者： Subhadarshini Das Goutami Das Mihir Narayan Mohanty Applied Signal Processing Lab ITER Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

Skin cancer detection is a critical application of computer vision in the medical field. Early detection of skin cancer, including melanoma and non-melanoma types, significantly improves treatment outcomes. Automated detection methods can assist dermatologists by providing consistent and objective analysis. Traditional methods rely on clinical expertise and manual examination, which can be subjective and error-prone. machine learning is the subset of AI that offers promising results for automatic skin cancer detection using image analysis. This report explores a hybrid approach combining Convolutional Neural Networks (CNNs) with GLCM and Energy features as one set of feature to enhance diagnostic accuracy. It is standard clinical criteria for evaluating skin lesions. The combination of CNNs with GLCME features enhances diagnostic accuracy by leveraging both learned and handcrafted features.

关键词： Hair Power engineering Accuracy Adaptive filters Melanoma Skin Convolutional neural networks Medical diagnosis Medical diagnostic imaging Standards

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machine learning-based Spectrum Sensing Techniques for Optimized Spectrum Utilization in Cognitive Radio Networks

Machine Learning-based Spectrum Sensing Techniques for Optim...

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Advancement in Technology (ICONAT), international conference for

作者： Pavan Chaudhary Rajesh Gupta H Malathi Maharishi University of Information Technology Uttar Pradesh India Department of Management Sanskriti University Mathura Uttar Pradesh India Department of Biotechnology and Genetics School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

the usage of cognitive radios has enabled dynamic spectrum to get the right of entry to spectrum-congested regions. It has revolutionized how spectrum is allotted with the aid of turning the conventional static spectrum allocation into a dynamic and opportunistic method. Gadget studying-based totally spectrum sensing techniques have been advanced to permit optimized spectrum usage with better accuracy in cognitive radio networks. Those strategies use machine learning algorithms to learn the behaviors of spectrum assets and adaptively replace sensing strategies by intelligently deciding on the maximum suitable spectrum. This manner permits faster and more correct sensing in comparison to traditional spectrum sensing strategies. Furthermore, various fashions were proposed for specific spectrum sensing functionalities together with signal category, spectrum prediction, and function extraction. Those fashions are employed that allow you to maximize the spectrum assets utilization in cognitive radio networks. Furthermore, diverse processes have been proposed with the purpose of improving the overall performance of spectrum sensing techniques. Those consist of the usage of deep mastering fashions, exploiting heterogeneous facts sources, and using ensemble getting-to-know strategies. These approaches remember the complexity of the environment and permit extra correct and reliable choices. To conclude, device learning-based spectrum sensing techniques preserve a fantastic capacity for optimized spectrum utilization in cognitive radio networks. Consequently, they constitute a vital step forward closer to the improvement of green and reliable verbal exchange structures.

关键词： Performance evaluation Accuracy Noise signal processing algorithms Turning Sensors Complexity theory Cognitive radio Reliability Resource management

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An Overview of Deep learning in MRI and CT Medical Image processing 2021

An Overview of Deep Learning in MRI and CT Medical Image Pro...

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3rd international Symposium on signal processing Systems, SSPS 2021

作者： Shomirov, Ahliddin Zhang, Jing School of Information Science and Engineering Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Shandong Jinan250022 China

ISBN: (纸本)9781450389587

The medical image is a set of all organizations, institutions, and resources whose primary goal is to improve health. The extensive growth of medical data increases the utility of machine learning and deep learning in the healthcare domains. Nowadays, the use of in-depth training to process medical images has received particular attention. In recent years, medical instruments have developed rapidly with the help of artificial intelligence and are widely used to process medical images. Artificial intelligence is numerous sources of medical imaging processing such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). CT and MRI image processing tasks with a high computation time requirement and computation speed. Nowadays, one of the most critical trends in the development of computer technology in neuroscience is the processing of medical images and digital images, which are used to improve image quality, restore damaged images, identify individual elements and diagnose various diseases. In this paper, we briefly review the progress and challenges associated with in-deep learning in the processing of CT and MRI medical images. © 2021 ACM.

关键词： Magnetic resonance imaging

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OCR and Speech Recognition System Using machine learning 3

OCR and Speech Recognition System Using Machine Learning

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3rd IEEE international Virtual conference on Innovations in Power and Advanced Computing Technologies, i-PACT 2021

作者： Jamtsho, Thubten Powdyel, Krishna Powrel, Reshan Kumar Bhujel, Rakesh Muramatsu, Kazuhiro Royal University of Bhutan College of Science and Technology Electronics and Communication Engineering Department Phuentsholing Bhutan

ISBN: (纸本)9781665426916

Image processing and Speech synthesis is a complex and growing field of computer science and is being developed by many researchers. An image is a map of several pixels with values assigned according to what they are representing. These images are captured through various methods and for the common people it is easy to identify the characters in the images. For the visually impaired and illiterate people, identification of character in the image is impossible. Therefore, this paper presents a model for a system where the characters/text in the images can be read out loud by the system. The system is composed of two main subsystems: a model for Optical Character Recognition and a model for speech synthesis. The Optical Character Recognition model recognizes the characters/text in the images and converts into editable text through various process such as preprocessing, segmentation and classification. The editable text is fed as input to the speech synthesis model, which generates speech signal from the input text. Both the models are trained with machine learning and deep learning neural networks. The models have an accuracy of about 90% and the speech generate sounds similar to the speech used to train the model. © 2021 IEEE.

关键词： Speech synthesis

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Development and Design of Surface Quality Online Inspection System Based on machine Vision 1

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3rd international conference on 3D Imaging Technologies - Multidimensional signal processing and Deep learning, 3DIT-MSP and DL 2021

作者： Huang, Min Leshan Vocational and Technical College Leshan China

ISBN: (数字)9789811924521

ISBN: (纸本)9789811924514

The important quality assurance of the use value of the workpiece can be to ensure the overall surface quality of the workpiece, but also can be a part of the main factors that affect the overall quality of the workpiece. Now artificial quality testing and other inefficient quality testing techniques have been the main technical methods for the surface quality and safety testing of workpiece products. As a modern road detection technology means of robot visual road detection equipment technology, it has three characteristics of seamless damage resistance, high precision, and high efficiency, so that it plays a prominent role in the highway detection equipment industry. The in-depth research of this paper has an important guiding influence on the application of machine learning vision in workpiece surface quality inspection practice for high-speed online quality inspection. According to the method of on-line vibration testing probe of visual high-speed machine vibration testing, a method suitable for high-speed on-line vibration testing has been developed. Based on the whole on-line vibration testing method of workpiece surface vibration in machine vision scenario, the main technical characteristics of high-speed on-line vibration testing method are comprehensively searched. A key research includes precision online inspection, on-site vibration size, work efficiency and defined motion, and internal positioning light detection and analysis of the workpiece. An online detection method based on vibration detection error compensation is proposed, which can optimally detect the overall size of the workpiece and greatly improve the detection accuracy. The detection method can make the image acquisition of the moving workpiece more accurate, ensure the clear positioning accuracy of the image and provide the original image for the follow-up, and improve the precision detection of the surface quality of the workpiece, which provides a reference for the surface quality detecti

关键词： Surface properties

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machine learning and the Legal Framework for the Use of Passenger Name Record Data 3rd

Machine Learning and the Legal Framework for the Use of Pass...

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3rd international conference on Intelligent Technologies and Applications, INTAP 2020

作者： Kostov, Iva Faculty of Law Hamburg University Hamburg20148 Germany

ISBN: (纸本)9783030717100

The processing of passenger name records (PNR) for security purposes on a European level was officially announced in 2016. Since then, ongoing legal discussions about PNR data focus mainly on its collection and the impact on data protection. The focus of this paper lies on a less-discussed aspect of the legal framework: the processing of PNR data and the different technological approaches it allows for. The following analysis of the German implementation of the European Directive on PNR data processing shows that it is open to the use of two different technological approaches: theory-based and machine learning methods. Taking this into account can provide a perspective that is mostly lacking in current legal debates about PNR data but can be an important addition since different technological approaches might shift the focus from data protection concerns to some aspects of technologies like machine learning. The paper also addresses a need for more technical research on the topic. © 2021, Springer Nature Switzerland AG.

关键词： machine learning

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