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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

共 777 条记录，以下是51-60 订阅

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Dual-view Label and Feature Supervision Network for Semi-supervised Medical Image Segmentation 24

Dual-view Label and Feature Supervision Network for Semi-sup...

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7th international conference on signal processing and machine learning, spml 2024

作者： Zhou, Zhen Li, Zuoyong Wang, Zhengyuan Teng, Shenghua Wang, Tao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798400717192

Semi-supervised learning has garnered significant attention, particularly in medical image segmentation, owing to its capacity to leverage a large number of unlabeled data and a limited amount of labeled data to improve performance. However, most existing semi-supervised segmentation methods exhibit shortcomings in supervising unlabeled data, both in the label space due to potential noise in pseudo-labels, and the feature space due to indistinct class boundaries. To address the issues above, we propose Dual-view Label and Feature Supervision Network, termed DLFS-Net, to enhance label and feature supervision for unlabeled data. Our approach is based on a dual-view learning strategy that incorporates two modules: the Confidence Fusion Supervision Module (CFS) and the Dual-view Prototype learning Module (DPL). Specifically, the CFS module generates weight matrices to integrate complementary information from two model outputs, producing more accurate pseudo-labels in the label space. the DPL module reduces intra-class variations in the feature space from two different views through prototype learning to generate clear class boundaries. Experiments on the LA and Pancreas-CT datasets demonstrate that our framework show solid gains (e.g.,1.76% Dice and 2.37% Jaccard improvement on Pancreas-CT dataset with 20% labeled data) compared with the state-of-the-art methods. © 2024 ACM.

关键词： Self-supervised learning

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Degradation Trajectory Tracking of MOSFETs Based on machine learning and CEEMDAN 5

Degradation Trajectory Tracking of MOSFETs Based on Machine ...

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5th international conference on Intelligent Control, Measurement and signal processing, ICMSP 2023

作者： Li, Zhaorui Shi, Dongqiang Zhang, Ying Li, Kai Gui, Huazhan Yuan, Feng School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin150001 China Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (纸本)9798350336030

MOSFET is one of the most popular silicon carbide power electronic devices, is widely popular in base station, energy storage, aerospace and railway scenarios. As one of the score electronic components in power electronic system, power MOSFETs degradation performance is closely related to the overall operation of power of power electronic system, which directly affects the stability and security of the system. In this work, taking the MOSFETs accelerated thermal aging datasets provided by NASA as the research project, we proposed a deep extreme learning based on CEEMDAN(complete ensemble empirical mode decomposition) with adaptive noise to simulate the degradation trend of MOSFETs. the experimental results show that the method can fit the degradation trend well on the high noise dataset. © 2023 IEEE.

关键词： Deep learning

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Driver Drowsiness Monitoring System Using Visual Behavior And machine learning 5

Driver Drowsiness Monitoring System Using Visual Behavior An...

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5th international conference on Multimedia, signal processing and Communication Technologies (IMPACT)

作者： Swathi, A. Kumar, Ashwani Swathi, V. Sirisha, Y. Bhavana, D. Latheef, Shaik Abdul Abhilash, A. Mounika, G. Sreyas Inst Engn & Technol Dept CSE Hyderabad Telangana India SRM Inst Sci & Technol Fac Engn & Technol NCR Campus Ghaziabad Uttar Pradesh India

ISBN: (纸本)9781665476478

One of the leading causes of car accidents is drowsy driving. the driver drowsiness detection system detects fatigue and helps to avoid accidents. Classic methods include vehicle-based, behavior-based, and physiological-based techniques. Some of these solutions, however, are inconvenient for the driver, while others necessitate the purchase of costly sensors and devices. As a result, this project demonstrates how to build low-cost, real-time driver drowsiness with long-term accuracy. In this system, drowsiness is detected using a webcam. this webcam captures the driver's frames using image processing techniques. For each frame, the eye aspect ratio (EAR) and mouth opening ratio (MOR) is calculated. the computed values and the observed threshold values are used to detect drowsiness.

关键词： machine learning algorithms EAR MOR Image processing techniques

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Deep learning and IoT-Enabled Framework for Accurate Classification and Monitoring of Alzheimer's Disease Based on EEG signal Analysis 5

Deep Learning and IoT-Enabled Framework for Accurate Classif...

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5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Sathish, R. Muthukumar, R. Dhivya, K. Karthikkumar, S. Builders Engineering College Department of Electrical and Electronics Engineering Tamilnadu Tirupur India Erode Sengunthar Engineering College Department of Electrical and Electronics Engineering Tamilnadu Perundurai India Jai Shriram Engineering College Department of Electrical and Electronics Engineering Tamilnadu Tirupur India

ISBN: (纸本)9798350356236

Alzheimer's disease is a brain ailment that impairs thinking, memory, and behaviour. the efficacy of Brain- Computer Interface (BCI) systems must be enhanced to increase their prevalence in the biomedical sector. the biomedical sector is crucial for precise and efficient patient diagnostics using digital signal processing and wireless sensor network technologies. Alzheimer's disease (AD) is an irreversible brain disorder. Inadequate Alzheimer's disease diagnosis in the early stages, together with exact identification, may aid in halting the progression of the illness. Precise and effective detection and categorization of Alzheimer's disease enhance quality of life and increase life expectancy for patients. the advanced deep learning technology surpasses traditional machine learning methods in classifying complex configurations in high-dimensional composite data, particularly in digital signal processing. the deep learning application for the early detection and accurate classification of Alzheimer's disease might be suggested for a neuron-signal methodology in extensive data analysis. this research paper proposes an intelligent decision-making system (IDMS) for the diagnosis and categorization of Alzheimer's disease (AD) and the continuous monitoring of patients utilizing connected wearable devices. Methods for measuring neuronal synchrony are proposed to diagnose and characterize Alzheimer's disease. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Comparison of Food Calorie Measurement Using Image processing and machine learning Techniques 5

Comparison of Food Calorie Measurement Using Image Processin...

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5th international conference on signal processing and Information Security, ICSPIS 2022

作者： Alshujaa, Boshra Alnuaimi, Fatima Mansoor, Wathiq Atalla, Shadi University of Dubai College of Engineering and IT Dubai United Arab Emirates

ISBN: (纸本)9781665492652

this paper investigates the accuracy of the different machine learning algorithms used for calorie estimation through food image processing. Moreover, the most popular food image datasets and calorie calculation techniques will be compared. the conducted comparative study considers the accuracy of ML algorithms and the features of training and testing food image datasets. It utilizes the research efforts in the last five years to propose the most reliable combination of techniques and data to develop an efficient food image processing system. In terms of machine learning algorithms, the comparative study results showed that convolutional neural network (CNN) and support vector machine (SVM) algorithms are the most reliable machine learning algorithms for food image processing. Moreover, the most suitable food image datasets for training and testing food image processing ML algorithms are Food-101, UEC-Food100, and ECUSTFD. And in terms of the calorie estimation techniques, the mathematical model technique was the most efficient approach to calculating the food calorie content. the implication of this study is to guide developers to improve the current food image processing applications by using reliable algorithms, datasets, and computation strategies. © 2022 IEEE.

关键词： Image enhancement

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Weather Prediction with machine learning Techniques: A Systematic Mapping 5

Weather Prediction with Machine Learning Techniques: A Syste...

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5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Sonia Nandal, Rainu Kharb, Seema Joshi, Kamaldeep University Institute Of Engineering And Technology Maharshi Dayanand University Computer Science And Engineering Rohtak India Srm University Department Of Computer Science And Engineering Sonepat India

ISBN: (纸本)9798350330861

Weather Prediction techniques are widely used to predict the almost accurate weather at a particular place on a particular time. After getting success in various fields like image processing, sound prediction etc. machine learning is doing well in weather prediction as well. In this paper, a survey in the form of systematic mapping was conducted for Weather Prediction with machine learning techniques covering the time period of 2011-2022. After compiling a pool of 2394 papers and applying a set of inclusion and exclusion criteria, a final pool of 2227 relevant papers were obtained. these existing publications were downloaded on goggle drive and a systematic analysis was done with the help of Python pyBibx library. © 2023 IEEE.

关键词： machine learning

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Proceedings of the 2023 5th international conference on Image, Video and signal processing, IVSP 2023

Proceedings of the 2023 5th International Conference on Imag...

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5th international conference on Image, Video and signal processing, IVSP 2023

ISBN: (纸本)9781450398381

the proceedings contain 28 papers. the topics discussed include: human activity role identification using feature vector and encoding techniques on natural language sentences;access control, biometrics, and the future;real-time privacy preserving human activity recognition on mobile using 1DCNN-BiLSTM deep learning;empirical study of emotional state recognition using multimodal fusion framework;monitoring application-driven continuous affect recognition from video frames;a human body part semantic segmentation enabled parsing for human pose estimation;automatic segmentation of pneumothorax in chest radiographs based on dual-task interactive learning method;transformer for hyperspectral image classification based on multi-feature learning;optimal parameter values for estimating rotational eye movement by using vascular images in the white part of the eyeball;and a computational investigation on precision autism and metabolic disorders: predictive machine learning for hepatic ailment classification.

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machine learning-Based FSOC Link Performance Estimation 1

引用

5th international conference on VLSI, Communication and signal processing, VCAS 2022

作者： Mankala, Rohith Prajapati, Yogendra Kumar ECED Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj India

ISBN: (数字)9789819909735

ISBN: (纸本)9789819909728

Free space optical communication (FSOC) is the optical wireless technology that provides enormous bandwidth, unlicensed spectrum, and excellent performance in comparison with radio frequency (RF) technology, which has its own impairments that degrade signal quality in the environment. Numerous innovative techniques, including as channel coding, diversity approaches, adaptive optics, higher-order spatial modes, and machine learning, have been included into the FSO. In the proposed study, a performance analysis of the FSOC system is conducted, and five machine learning (ML) techniques are used to determine the entire system's quality factor. Coefficient of determination and mean absolute error (MAE) are used to evaluate the performance of these models. the collected findings demonstrate that the random forest ML model predicts the quality factor with the lowest MAE and the highest coefficient of determination compared to the other considered ML models. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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machine learning Based Obfuscated Malware Detection in the Cloud Environment with Nature-Inspired Feature Selection 5

Machine Learning Based Obfuscated Malware Detection in the C...

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5th international conference on Multimedia, signal processing and Communication Technologies (IMPACT)

作者： Ghazi, Mohd. Rehan Raghava, N. S. Delhi Technol Univ Dept Elect & Comm Engn Delhi India

ISBN: (纸本)9781665476478

One of the most significant and widely used IT breakthroughs nowadays is cloud computing. Today, the majority of enterprises use private or public cloud computing services for their computing infrastructure. Cyber-attackers regularly target Cloud resources by inserting malicious code or obfuscated malware onto the server. these malware programmes that are obfuscated are so clever that they often manage to evade the detection technology that is in place. Unfortunately, they are discovered long after they have done significant harm to the server. machine learning (ML) techniques have shown to be effective at finding malware in a wide range of fields. To address feature selection (FS) challenges, this study uses the wrapperbased Binary Bat Algorithm (BBA), Cuckoo Search Algorithm (CSA), Mayfly Algorithm (MA), and Particle Swarm Optimization (PSO), and then k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector machine (SVM) are used to classify the benign and malicious records to measure the performance in terms of various metrics. CIC-MalMem-2022, the most recent malware memory dataset, is used to evaluate and test the proposed approach and it is found that the proposed system is an acceptable solution to detect malware.

关键词： Cloud Security Malware detection machine learning Random Forest SVM kNN Cloud Computing

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2022 5th international conference on Multimedia, signal processing and Communication Technologies, IMPACT 2022

2022 5th International Conference on Multimedia, Signal Proc...

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5th international conference on Multimedia, signal processing and Communication Technologies, IMPACT 2022

ISBN: (纸本)9781665476478

the proceedings contain 97 papers. the topics discussed include: circular synthetic aperture radar for near-field terahertz imaging;an integrated vulnerability assessment tool for web applications;a grounded flux controlled incremental/decremental memristor emulator;a novel high performance d-latch using gate engineered Schottky device;electrical and optical characterization of photo anode and metal free counter electrode;advancements in intrusion detection systems for internet of things using machine learning;cold start problem in recommendation system: a solution model based on clustering and association rule techniques;machine learning based obfuscated malware detection in the cloud environment with nature-inspired feature selection;mitigation of higher order mode in thz patch antenna using graphene ring;Fourier analysis based respiration rate estimation using corrupted photoplethysmogram signal;and molecular nano communication networks: architectures, protocols and technologies.

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