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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 769 条记录，以下是561-570 订阅

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Reconstruction and Denoising of EEG signal Using Alternating Direction Method of Multipliers

Reconstruction and Denoising of EEG Signal Using Alternating...

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international conference on Imaging, signal processing and Communication (ICISPC)

作者： Revant Lala Dehit Trivedi Department of Electronics and Communications Institue of Technology Nirma University Ahmedabad India

ISBN: (纸本)9781665454810

the tremendous progress of big data acquisition and processing in the field of neural engineering has enabled a better understanding of the patient’s brain disorders with their neural rehabilitation, restoration, detection, and diagnosis. Electroencephalography (EEG) is a method to record an electrogram of the electrical activity on the scalp that has been shown to represent the macroscopic activity of the surface layer of the brain underneath. However, EEG signals are contained by different noises. this report presents efficient reconstruction and denoising of EEG signal based on Alternating Direction Method of Multipliers. the alternating direction method of multipliers (ADMM) has been widely explored due to its broad applications, and its convergence has been gotten in the real field. this report discusses the alternating direction method of multipliers (ADMM), a simple but powerful algorithm that is well suited to distributed convex optimization, and in particular to problems arising in applied statistics and machine learning. It takes the form of a decomposition-coordination procedure, in which the solutions to small local subproblems are coordinated to find a solution to a large global problem. the algorithm in the report show that the proposed ADMM-based method performs better in EEG-denoising than Compress Sensing (CS) method which is simple and traditional method. Furthermore, the proposed algorithm keeps the detail of the EEG-signal in reconstruction and achieves smaller root means square error (RMSE) for small-iteration.

关键词： Surface reconstruction machine learning algorithms Noise reduction Scalp signal processing algorithms signal processing Electroencephalography

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FNMD: An Evaluation of machine learning and Deep learning Techniques for Fake News Detection 6th

FNMD: An Evaluation of Machine Learning and Deep Learning Te...

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6th international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2023

作者： Daneshvar, Bahareh Abdi, Asad Hosseini, Seyyed Mahmoud Department of Computing and Mathematics Faculty of Science and Engineering University of Derby Derby United Kingdom Department of Finance and Accounting Faculty of Westminster Business School University of Westminster Westminster United Kingdom

ISBN: (纸本)9783031530814

Fake news proliferation on social media platforms has become alarming because it poses threats to various aspects of society. Fake news encompasses intentionally falsified information designed to mislead readers and manipulate public perception, resulting in low-quality and misleading content. this paper presents a machine learning-based approach for fake news detection using: 1) Sentiment knowledge to calculate a sentence sentiment score as one of the features for sentence-level classification. 2) Statistical and linguistic knowledge to extract required features. the proposed method combines several types of features into a unified feature set to design a more accurate classification system ("True": the fake news;"False": otherwise). thus, to achieve better performance scores, we carried out a performance study of five well-known feature selection techniques and several most famous classifiers to select the most relevant set of features and find an efficient machine learning (ML) classifier, respectively. Alongside traditional machine learning models, we also applied different Deep learning (DL)-based models. Different machine learning and deep learning-based methods are applied to three different datasets including Liar, GossipCop, and PolitiFact, and the results show the integration of machine learning-based classification method and feature selection technique can significantly improve the performance and make the method comparable to other existing methods. Furthermore, DL models, in particular LSTM, exhibited remarkable accuracy thresholds of up to 88% in capturing intricate patterns within textual data. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fake detection

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2021 international conference on Wireless Communications, signal processing and Networking, WiSPNET 2021

2021 International Conference on Wireless Communications, Si...

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6th international conference on Wireless Communications, signal processing and Networking, WiSPNET 2021

ISBN: (纸本)9781665440868

the proceedings contain 94 papers. the topics discussed include: secrecy performance of uplink-downlink NOMA network at physical layer;an efficient modified distributed arithmetic architecture suitable for FIR filter;prototype design of intelligent traffic signal control using Haar cascade classifier;implementation and analysis of couple suppress current sense amplifier at 45nm and 65nm regime;low-cost BLE based indoor localization using RSSI fingerprinting and machine learning;design of polynomial function based blind equalizer using particle swarm optimization;implementation of HUB and spoke topology in VPN using EIGRP;deployment of sensor nodes for connectivity restoration and coverage maximization in WSNs;study of hardware imperfections in SWIPT power splitting architecture;and multi-class classification of Alzheimer’s disease using 3DCNN features and multilayer perceptron.

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A Semantic Approach to Negation Detection and Word Disambiguation with Natural Language processing 22

A Semantic Approach to Negation Detection and Word Disambigu...

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Proceedings of the 2022 6th international conference on Natural Language processing and Information Retrieval

作者： Izunna Okpala Guillermo Romera Rodriguez Andrea Tapia Shane Halse Jess Kropczynski Information Technology University of Cincinnati USA Information Science and Technology Penn State University USA

ISBN: (纸本)9781450397629

this study aims to demonstrate the methods for detecting negations in a sentence by uniquely evaluating the lexical structure of the text via word-sense disambiguation. the proposed framework examines all the unique features in the various expressions within a text to resolve the contextual usage of all tokens and decipher the effect of negation on sentiment analysis. the application of popular expression detectors skips this important step, thereby neglecting the root words caught in the web of negation and making text classification difficult for machine learning and sentiment analysis. this study adopts the Natural Language processing (NLP) approach to discover and antonimize words that were negated for better accuracy in text classification using a knowledge base provided by an NLP library called WordHoard. Early results show that our initial analysis improved on traditional sentiment analysis, which sometimes neglects negations or assigns an inverse polarity score. the SentiWordNet analyzer was improved by 35%, the Vader analyzer by 20% and the TextBlob by 6%.

关键词： Artificial Intelligence machine learning Negation Detection Text analysis Word Sense Disambiguation

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A Real-Time System for Monitoring and Managing Neuropsychiatric Symptoms in Dementia Patients

A Real-Time System for Monitoring and Managing Neuropsychiat...

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international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Abeer Badawi Ahmed Badr Somayya Elmoghazy Sara Elgazzar Khalid Elgazzar Amer M. Burhan Electrical and Computer Engineering Ontario Tech University Oshawa ON Canada Ontario Shores Centre for Mental Health Sciences Whitby ON Canada Science Department Ontario Tech University Oshawa ON Canada Temerty Faculty of Medicine University of Toronto Toronto ON Canada

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Dementia is a range of neurological disorders that affect the brain and are associated with neuropsychiatric symptoms (NPS). Among these symptoms, agitation and aggressive behavior are common, as they can significantly impact an individual's quality of life. Monitoring NPS using wearable sensors can help detect the behavior of people with dementia (PwD). this paper proposes a real-time remote healthcare monitoring system with scalable event processing and analytics platforms for healthcare applications using open-source components. We studied the system with sample data from PwD at the Ontario Shores Mental Health Institute. the system can collect data from wearable devices such as wristbands, rings, or patches and uses machine learning to classify agitation in PwD. the proposed system also provides valuable insights into other health problems. Moreover, it handles continuous data in real-time while performing classification with high accuracy using the Extra Trees model. Furthermore, we design the system featuring a horizontally scalable architecture to adapt to the growing number of devices from different sensors over time. this research introduces an end-to-end real-time system capable of rapidly identifying aggressive behavior and promptly notifying healthcare providers in 3.5 seconds using a customized mobile app and dashboard. the results highlight the potential of this system in enhancing the early detection of NPS in PwD and ultimately reducing the risks faced by PwD and caregivers.

关键词： Hospitals Mental health signal processing Real-time systems Sensor systems Mobile applications Wearable devices Monitoring Wearable sensors Dementia

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Study on Students' emotional concentration in online classroom based on convolutional Neural Network 22

Study on Students' emotional concentration in online classro...

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Proceedings of the 2022 6th international conference on Electronic Information Technology and Computer Engineering

作者： Baoxian Jia Yingying Cui Liaocheng University China

ISBN: (纸本)9781450397148

In view of the lack of supervision methods for students in online education, convolutional neural networks have unique advantages in face recognition. this paper establishes a machine learning sentiment analysis model support vector machine, a deep learning model convolutional neural network, long-term memory and long-term memory. network, bidirectional long-term and short-term memory network, and proposed a new BERT-C-BiLSTM model, using real data sets for model training, and comparing the five established models to verify that the accuracy of the new model has indeed improved. the learning concentration detection system of the online education platform designed based on this algorithm has been tested in simulated scenarios, which can effectively evaluate the students' classroom concentration according to the face detection results, and improve the classroom quality and students' learning effect.

关键词： Emotion analysis convolutional neural network Bert. the image processing Deep learning

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Using LSTM to Accurately Predict the Ammonia Concentration Detected by Graphene Sensors Affected by Temperature and Humidity

Using LSTM to Accurately Predict the Ammonia Concentration D...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Keyifan Yang Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

In response to the issue of graphene ammonia gas sensors being highly susceptible to temperature and humidity interference, this paper proposes a novel method for detecting ammonia gas concentration. this method incorporates both a temperature and humidity compensation algorithm and a Long Short Term Memory network (LSTM) to achieve highly accurate predictions of ammonia gas concentration under temperature and humidity interference. Firstly, collect the response data of a 4-unit Pt NPs/GO/graphene gas sensor array to different concentrations of ammonia in different testing environments (50% RH-100% RH, 20 °C to 60 °C, 0-100ppm). Subsequently, temperature and humidity compensation is applied to adjust the sensor response data, mitigating the impact of temperature and humidity variations. Additionally, dimensionality reduction is employed through downsampling. Finally, LSTM is constructed to train and predict the response data collected by the sensor array. the experimental results show that LSTM has high accuracy in predicting the ammonia concentration detected by the sensor array, with an average absolute error (MAE) value of 0.916. In addition, a decrease in the number of sensors will reduce the prediction accuracy, and different materials modified with graphene also have an impact on the prediction accuracy. the research in this work provides an effective solution for graphene sensors to resist temperature and humidity interference and has high application potential.

关键词： Temperature sensors Ammonia Accuracy machine learning algorithms Graphene Humidity Interference Long short term memory Gas detectors Sensor arrays

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Enhancing Text Extraction Using OCR with AWS Textract Service 6th

Enhancing Text Extraction Using OCR with AWS Textract Servic...

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6th international conference on Advanced Informatics for Computing Research, ICAICR 2023

作者： Bawasker, Aakash Dedania, Yash Karia, Vrushti Bhatt, Nirav Prajapati, Purvi Bhatt, Nikita Chandubhai S. Patel Institue of Technology Charotar University of Science and Technology Gujarat Anand India

ISBN: (纸本)9783031840616

the advent of Optical Character Recognition (OCR) technology has revolutionized document handling for businesses, enabling the conversion of handwritten text into machine-readable content. this project explores Amazon Web Services Textract, a cutting-edge OCR service integrating advanced machine learning and deep learning models. Emphasis is placed on dissecting Textract's architecture, features, and practical applications, showcasing its proficiency in extracting text, forms, and tables from diverse document types such as scanned papers, PDFs, and images. Real-world examples demonstrate Textract's seamless integration with document management systems, facilitating data analysis and ***, the article conducts a comprehensive evaluation of the efficiency and accuracy of three OCR solutions: Tesseract (English text), Amazon Textract (Arabic text), and Google DocumentAI (English text). through a comparative analysis of their performance and identification of distinctions in handling common text imperfections, the study aids researchers in informed decision-making for OCR solutions aligned with specific research ***, the project underscores the utility and benefits of incorporating AWS Textract into OCR applications. It highlights how Textract enhances document processing efficiency, improves data accessibility, and ensures compliance adherence. Valuable insights are provided for organizations seeking to optimize document management workflows and leverage OCR capabilities through AWS Textract. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical data processing

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Ensuring Ethical, Transparent, and Auditable Use of Education Data and Algorithms on AutoML 23

Ensuring Ethical, Transparent, and Auditable Use of Educatio...

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Proceedings of the 2023 6th international conference on machine learning and Natural Language processing

作者： Kylie Griep Leah Stone Stephen Edwards Malik Hill Jialin Cui Chengyuan Liu Qinjin Jia Yang Song Department of Computer Science University of North Carolina Wilmington USA Master of Data Science Program University of North Carolina Wilmington USA Department of Computer Science NC State University USA

ISBN: (纸本)9798400709241

Automated machine learning (AutoML) creates additional opportunities for less advanced users to build and test their own data mining models. Even though AutoML creates the models for the user, there is still technical knowledge and tools needed to evaluate those models, and due to the black-box nature of the machine learning models, problems can arise with regard to algorithmic biases and fairness. Such biases can escalate in future applications, necessitating a structured approach for fairness evaluation in AutoML. this involves defining fairness criteria, selecting appropriate metrics, assessing fairness across groups, and addressing biases. In the realm of educational data mining, where AutoML is prevalent, biases related to attributes like gender or race can lead to unethical outcomes. Since fairness metrics vary in definition and strength, and some may even contradict others, making fairness evaluation more complex. In this paper, ten fairness metrics were chosen, explored, and implemented on four AutoML tools, Vertex AI, AutoSklearn, AutoKeras, and PyCaret. We identified two open educational datasets and built both prediction and classification models on those AutoML frameworks. We report our work in evaluating different machine learning models created by AutoML and provide discussions about the challenges in evaluating fairness in those models and our effort to mitigate and resolve the problems of algorithmic bias in educational data mining.

关键词： AutoML Automated machine learning algorithmic bias educational data mining fairness

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Keynote Speaker: Advances in Edge-Based and In-Network Media processing for Adaptive Video Streaming

Keynote Speaker: Advances in Edge-Based and In-Network Media...

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IEEE conference on Multimedia Information processing and Retrieval (MIPR)

作者： Hermann Hellwagner Alpen-Adria Universität Klagenfurt Klagenfurt Austria

Media traffic (mainly, video) on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research was the HTTP Adaptive Streaming (HAS) technique. While this technique is widely used and works well in industrial networked multimedia services today, challenges exist for future multimedia systems, dealing with the trade-offs between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, low latency), and (iii) quality of experience (QoE). this situation sets the stage for our research work in the AthENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://***/), jointly funded by public sources and industry. In this talk, I'll explore one facet of the AthENA research, namely how and with which benefits edge-based and in-network media processing can cope with adverse network conditions and/or improve media quality/perception. Content Delivery Networks (CDNs) are the classical example of supporting content distribution on today's Internet. In recent years, though, techniques like Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Function Virtualization (NFV), Peer Assistance (PA) for CDNs, and machine learning (ML) have emerged that can additionally be leveraged to support adaptive video streaming services. In the talk, I'll present several approaches of edge-based and in-network media processing in support of adaptive streaming, in four groups: 1)Edge Computing (EC) support, for instance transcoding, content prefetching, and adaptive bitrate algorithms at the edge. 2)Virtualized Network Function (VNF) support for live video streaming. 3)Hybrid P2P, Edge and CDN support including content caching, transcoding, and super-resolution at various layers of the system. 4)Machin

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