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共 729 条记录，以下是121-130 订阅

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AD Classification and Detection Using Neuroimaging Data 3

AD Classification and Detection Using Neuroimaging Data

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3rd international Mobile, Intelligent, and Ubiquitous Computing conference, MIUCC 2023

作者： Abed, Salma Mourad, Mohamed Abdelkreem, Mohamed Mohamed Ashraf, Mostafa Talaat, Youssef Elashmawi, Walaa H. Misr International University Computer Science Cairo Egypt

ISBN: (纸本)9798350306231

Alzheimer's disease (AD) is the most prevalent type of dementia, a progressive brain illness that typically manifests in old age. Alzheimer's disease will affect 1.2 percent of the world's population by 2046, according to the World Health Organisation. If nothing is done to reduce that figure, the number of fatalities from Alzheimer's and dementia in Egypt will reach 6,975 in 2020. Egypt is rated 148th in the world. Early Alzheimer's diagnosis is critical for informing patients about the severity of their condition and empowering them to take medication and other preventative measures. MRI and PET scans, have been used to aid in diagnosing along with machine learning approaches. Early AD diagnosis was required to accurately distinguish AD cases from standard control (NC) subjects. Naive Bayes (NB), Support Vector machine (SVM), and k-Nearest Neighbors (KNN) are machine learning algorithms that have been utilized to predict the possibility of Mild Cognitive Impairment (MCI) participants getting Alzheimer's, allowing the disease's progression rate to be determined. These strategies, however, were unreliable due to the difficulties of defining the condition as AD or ND, the likelihood of late or seriously demented patients, and a lack of access to large or diverse data sets. A thorough analysis discovered that the proposed workflows might be substantially enhanced by focusing on the constraints of previous studies. The suggested Alzheimer's disease technique categorizes AD patients into one of four stages, using a data set of 1288 MRI images for training and pre-processing procedures such as skull stripping, noise reduction, and motion correction. This method utilizes a convolutional neural network model (CNN) and deep learning architecture in order to enhance accuracy while minimizing the computational resources required to access ROIs (regions of interest). The technology can effectively diagnose patients with minimal work and time while providing much potential. © 2

关键词： Neuroimaging

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Research on Visual Communication of Graphic Design Based on machine Vision 3rd

Research on Visual Communication of Graphic Design Based on ...

引用

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

作者： Zhou, Qihao Liu, Teng Wonkwang Univ Iksan South Korea

ISBN: (纸本)9789811924484;9789811924477

Visual communication graphic design as a difficult point in modern technology research, its original intention is to use visual graphics to transmit some ideas or ideological content to people, belongs to the visual image with creative thinking, people can according to their own visual sense to obtain the special language form contained in it. Whether it is commercial circulation or social culture, graphic design of visual communication has shown a profound influence. Therefore, in this paper, the machine vision graphics processing system is applied to the visual communication graphic design, and the system structure, functional modules, design process and other contents are simply understood, and according to the injection mold trademark design and processing practice cases are verified and analyzed. The final results prove that the visual communication graphic design system with machine vision as the core has played a positive role in the current technological development.

关键词： machine vision Visual communication Graphic design CAD The image processing

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Intelligent information processing of Fiber Bragg Grating biosensor for stress monitoring in microfluidics system 3

Intelligent information processing of Fiber Bragg Grating bi...

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3rd international Computing Imaging conference, CITA 2023

作者： Zhang, Zijun Lu, Zhenghao Wang, Jing Pan, Han Li, Peiyun Li, Xiaoyao Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China Departmen of Physics and Astronomy University College London London United Kingdom Key Laboratory of More Electric Aircraft Technology of Zhejiang Province University of Nottingham Ningbo China Ningbo China Departmen of Applied Computational Science and Engineering Imperial College London London United Kingdom

ISBN: (纸本)9781510671409

A Fiber Bragg Grating (FBG) biosensor with good biocompatibility promises label-free, anti-electromagnetic interference, and non-destructive detection of stress monitoring, it holds the potential to rapidly identify the stress changes in microfluidics systems. Based on signal processing, the correlation between wavelength shift in FBG and flowrate is discussed, an alternative to manual processing of wavelength offset calculation is analyzed, and the automatic selection of FBG wavelength change under high noise conditions is realized. In addition, we propose some technologies of intelligent processing algorithms such as machine learning (BP—back propagation neural network) and moving average. Finally, the feasibility and stability of the signal processing method are verified by experimental validation and multiple data sets testing. This study provides a promising intelligent information processing strategy for rapid and accurate stress sensing of microfluidics systems. © 2023 SPIE.

关键词： Fiber Bragg gratings

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A Technique Based on Ensemble machine learning for The Analysis of Electronic Nose signals 3

A Technique Based on Ensemble Machine Learning for The Analy...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Taneja, Amit Rajamani, Santhosh Kumar Shekhawat, Deepika Chanti, Yerrolla Joshi, Kapil Garg, Ashish Chandigarh University AIT-CSE India MAEER MIT Pune's MIMER Medical College and Dr BSTR Hospital TalegaonDabhade Department of E.N.T Maharashtra Pune India Manipal University Department of Computer Science & Engineering Rajasthan Jaipur India SR University School of Computer Science and Artificial Intelligent Telangana Warangal India Uttaranchal University Uttaranchal Institute of Technology India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350399264

Numerous applications have been documented for electronic nose (e-nose) devices, which are valued for their speed, affordability, and lack of invasiveness. The e-nose scheme is a useful instrument for analyzing volatile chemicals as a distinct 'fingerprint,' especially in the meat industry. The pattern identification algorithm's capacity to decode e-nose signals is crucial to the e-nose system's effectiveness in a wide variety of uses. However, positive results using ensemble approaches have been reported in a number of data types. In this study, an ensemble learning strategy is proposed for e-nose signal analysis, particularly for evaluating beef quality. Not only are learning algorithms and sensor array optimization using ensemble approaches. Ensemble FSA is created by combining three filter-based feature selection algorithms (FSAs) - reliefF, chi-square, and Gini index - for sensor array optimization. When using a single FSA on a collection of homogenous e-nose data for beef quality monitoring, it is common for the results to be inconsistent or unexpected. In addition, multi-class regression and categorization problems are handled by using ensemble learning techniques. We use bagging and boosting algorithms, shown here by Random Forest (RF) and Adaboost. Both a support vector machine (SVM) and a decision tree are used as independent learners, and their results are compared to those of the ensemble. Experiments show that our ensemble method is effective and generalizable for e-nose signal processing. Optimized sensor combination using filter-based FSA consistently outperforms previous methods in both classification and regression. Additionally, despite using a reduced sensor complement, Adaboost as a boosting algorithm yields the highest quality prediction. © 2023 IEEE.

关键词： Support vector machines

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Multiple Feature Extraction Based on PPG signal to Realize Blood Pressure Measurement of Composite Multi-channel Convolutional Neural Network Model 3

Multiple Feature Extraction Based on PPG Signal to Realize B...

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3rd IEEE international conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Lu, Cheng-Gang Wang, Lei Department of Electrical Engineering Feng Chia University Taichung Taiwan

ISBN: (纸本)9798350333862

Photoplethysmography (PPG) changes according to arterial blood pressure (ABP). The change in the physiological signal reflects the change in the functional state of the heart and vascular system. To prevent cardiovascular disease due to hypertension, the PPG signal has been widely used for non-invasive continuous estimation of blood pressure. In recent years, artificial neural network-based algorithms have found the relationship between ABP and related physiological signals. Compared with traditional cuff measurement methods, it is more intuitive to understand the overall blood pressure trend and avoid invasive pressure Measured bleeding risk. Most artificial neural networks use an electrocardiogram (ECG) and PPG at the same time, and the ECG acquisition device needs electrodes and wires to the chest cavity, which indirectly affects the convenience of physiological signal acquisition. Using three feature extraction techniques with different detailed attributes for a single PPG measurement and substituting short-time Fourier transform (STFT), continuous wavelet transform (CWT), and Hilbert-Huang transform (HHT) into the compound multi-channel convolutional neural network(CMCNN) model, we evaluate the overall blood pressure value. Considering that the distorted data caused by motion artifacts causes the training results to fail to converge, we select Physionet's MIMIC-III database and use data purification-based processing steps. We screen out the artifact labels for PPG and ABP at first and then use the artifact labels to perform seven-level data segmentation. Finally, using STFT, CWT, and HHT, we obtain spectrogram, scalogram, and Hilber spectrum. The original waveform of PPG and the spectrum, the scalogram, and the Hilbert spectrum are used as input data for learning and analysis. Compare with the traditional machine learning method, the blood pressure estimation results is closer to the actual situation. © 2023 IEEE.

关键词： Blood pressure

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3rd international Workshop on New Approaches for Multidimensional signal processing, NAMSP 2022

3rd International Workshop on New Approaches for Multidimens...

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3rd international Workshop on New Approaches for Multidimensional signal processing, NAMSP 2022

ISBN: (纸本)9789811978418

The proceedings contain 23 papers. The special focus in this conference is on New Approaches for Multidimensional signal processing. The topics include: Development of IoT Indoor Monitoring System for Independent Elderly;improving the Process of Evaluating User Stories Using the Paraconsistent Annotated Evidential Logic Eτ;neural Network Algorithm Applied in Electrical Engineering Automation;Truss Structure Optimization Design Based on FE-PSO-SQP Algorithm;practice System of Ant Colony Optimization Algorithm in Business Administration;literature Review of Smart Contracts Using Blockchain Technology;a Comprehensive Study of 5th Generation Scheduling Algorithms;a Comparative Analysis of Homogeneous and Heterogeneous Protocols to Maximize the Lifetime of Wireless Sensor Network for Precision Agriculture;the Integration Development and Upgrading Path of Industry 4.0 Architecture Industrial Engineering Network Driven by Big Data;video Tracing of Moving Objects by Fusing Three-Term Decompositions;SAAS Application Prospect Analysis in Hrm and Methods to Upgrade the Contemporary System;All Digital Phase Locked Loop (ADPLL) and Its Blocks—A Comprehensive Knowledge;on Realization of Smart Logistic Warehouse Management with Internet of Things;development and Design of Intelligent Traction System for Bulk Cargo Terminal;deep learning Approaches for Classroom Audio Classification Using Mel Spectrograms;Tensor Spectral Pyramid for Color Video Sequences Representation, Based on 3D FO-AHKLT;electromyography signal Acquisition, processing, Optimization and Its Applications;research on the Radar signal Classification Method Based on the Deep Faith Network Model;measuring machine Intelligence Using Black-Box-Based Universal Intelligence Metrics;COVID Detection Using ECG Image Reports: A Survey;disease Detection Techniques in Plants: Transition from Manual to Automation.

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Real-Time Traffic Management System Using machine learning and Image processing 3rd

Real-Time Traffic Management System Using Machine Learning a...

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3rd international conference on Data Science, machine learning and Applications, ICDSMLA 2021

作者： Sheela, S. Nataraj, K.R. Rekha, K.R. Don Bosco Institute of Technology Bengaluru India SJB Institute of Technology Bengaluru India

ISBN: (纸本)9789811959356

In major and rapidly developing urban regions across the world, traffic congestion is unavoidable. Congestion during rush hour is an unavoidable consequence, especially in large metropolitan. In the current circumstance, the traditional technique works well only if the count is low, as the density of vehicles on one side of the lane road grows if the traffic is heavier on one side of the road than on the other;otherwise, the approach fails. As a result, our goal is to create a traffic system that can switch signals as well as track and handle signals in real time. signal switching will be done in this project based on real-time image detection and accuracy of vehicular traffic. Another goal is to put in place a traffic management system that gives priority to the lane where the ambulance arrives that offers ambulance detection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Traffic congestion

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A Review of signal processing Techniques for Detection and Classification of Faults in Transmission lines

A Review of Signal Processing Techniques for Detection and C...

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Power Electronics & IoT Applications in Renewable Energy and its Control (PARC), 2020 international conference on

作者： Hiya Agarwal Ravi Shankar Tiwari Jai Prakash Sharma Department of Electrical Engineering GLA University Mathura

In a contemporary power system, the prompt and precise removal of faults occurring on high-voltage transmission lines is of utmost importance. This research explores the categorization and pinpointing of faults in the protection of power transmission lines based on signal processing techniques. The paper reviews the effective, promising, and modern approaches for fault detection such as machine learning (ML) and artificial intelligence (AI) methodologies. It also highlights the application of Wide Area Measurement (WAM), and Phasor Measurement Unit (PMU) in the protection of transmission lines.

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Data Urbanity: Smart City Evolution Through IoT and Data Science 3

Data Urbanity: Smart City Evolution Through IoT and Data Sci...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Singh, Ajay Kumar, Manni Chandigarh University Department.of Computer Science and Engineering Punjab Mohali India

ISBN: (纸本)9798350343632

The rapid increase in population density in urban cities has created a demand for suitable infrastructure and services to meet the needs of urban residents. To address this demand, urban planners and developers are turning to data science for assistance in creating smart city solutions. The Internet of Things (IoT) has played a significant role in the application of data science in urban planning, as IoT devices such as sensors and smart devices provide vast amounts of data that can be used to gain insights into the operations of the city and make data-driven decisions to enhance its productivity, efficiency, and overall quality of life. This paper provides a general overview of data science in urban planning for smart city development. It highlights the importance of data science in urban planning and the role it plays in creating smarter, more effective cities. By applying data analytics, machine learning, and other data science approaches, urban planners can gain insights into various aspects of urban life, including traffic patterns, energy usage, and public safety. These insights can then be used to develop strategies that improve traffic flow, reduce energy waste, and enhance overall quality of life in cities. The study aims to describe the function of data science in urban planning, examine case studies and best practices, discuss the challenges and limitations associated with applying data science in urban planning, and provide suggestions for further research and advancements in the field. By investigating how data science can be effectively used in urban planning for smart city development, this study contributes to the growing body of knowledge on creating more sustainable and livable cities. © 2023 IEEE.

关键词： Artificial intelligence Biomarkers cardiovascular disease Computed Tomography (CT) Data analysis Deep learning Diagnostic methods Early detection Electrocardiogram (ECG/EKG) Health informatics heart disease machine learning Magnetic Resonance Imaging (MRI) Medical imaging Predictive modeling Remote monitoring Risk assessment signal processing Ultrasound Wearable devices

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3rd EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

3rd EAI International Conference on Cognitive Computing and ...

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3rd EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

ISBN: (纸本)9783031289743

The proceedings contain 22 papers. The special focus in this conference is on Cognitive Computing and Cyber Physical Systems. The topics include: Performance Evaluation of Fast DCP Algorithm for Single Image Dehazing;Non Destructive Analysis of Crack Using Image processing, Ultrasonic and IRT: A Critical Review and Analysis;ioT Enabled Driver Compatible Cost-Effective System for Drowsiness Detection with Optimized Response Time;Voice Based Objects Detection for Visually Challenged Using Active RFID Technology;depth Estimation and Navigation Route Planning for Mobile Robots Based on Stereo Camera;Water Level Forecasting in Reservoirs Using Time Series Analysis – Auto ARIMA Model;water Quality Monitoring Using Remote Control Boat;study of Smart City Compatible Monolithic Quantum Well Photodetector;frequency Reconfigurable Antenna for 5G Applications at n77 and n78 Bands;solar Energy Prediction using machine learning with Support Vector Regression Algorithm;analysis of Acoustic Channel Model Characteristics in Deep-Sea Water;A Comprehensive Review on Channel Estimation Methods for Millimeter Wave MIMO Systems;comparison of Acoustic Channel Characteristics in Shallow and Deep-Sea Water;price Estimation of Used Cars Using machine learning Algorithms;machine learning Framework for Identification of Abnormal EEG signal;alzheimer’s Disease Detection Using Ensemble of Classifiers;publishing Data Objects in Data Aware Networking;youTube Comment Analysis Using Lexicon Based Techniques;Information Theoretic Heuristics to Find the Minimal SOP Expression Considering Don’t Care Using Binary Decision Diagrams;single Image Dehazing Through Feed Forward Artificial Neural Network.

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