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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2812 条记录，以下是81-90 订阅

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A Comparative Study of Computational Methods for Multimodal Single-Cell data Integration 6

A Comparative Study of Computational Methods for Multimodal ...

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6th international conference on Multimedia Analysis and pattern recognition, MAPR 2023

作者： Tran, Binh Pham, Tri Huynh, Tin Hoang, Kiem The Saigon International University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350327410

Integrating multimodal single-cell data is formalized into predicting co-variation among DNA, RNA, and protein measurements within individual cells. Accurate prediction of single-cell table data across multiple modalities will provide a more in-depth understanding of the mechanisms underlying tissue function or dysfunction in health and disease. While previous studies highlight the potential of machine learning in multimodal single-cell integration, there is a lack of comparison studies that focus on technical aspects rather than biology-related details. therefore, this study encompassed the entire pipeline and compared the four highest-performing methods in the Multimodal single-cell data integration NeurIPS 2022 Competition through performance and efficiency, which provides data scientists with appropriate entry points. Experiment results revealed the significance of preprocessing and feature engineering in addressing problems and consolidating the trend of using machine learning techniques such as the Variational Autoencoder (VAE) and ensemble model. © 2023 IEEE.

关键词： data integration

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machine learning-Based Detection of Autism Spectrum Disorder Using Attention Mechanisms in Eye-Tracking data 6

Machine Learning-Based Detection of Autism Spectrum Disorder...

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6th international conference on pattern Analysis and Intelligent Systems, PAIS 2024

作者： Benabderrahmane, Brahim Gharzouli, Mohamed Benlecheb, Amira Misc Laboratory University of Abdelhamid Mehri Constantine Algeria

ISBN: (纸本)9798350350265

Effective and accurate diagnosis of Autism Spectrum Disorder (ASD) has become crucial for early intervention and a better outcome for patients. this study attempts to improve ASD detection through machine learning (ML) techniques applied to eye-tracking data. We developed a machine learning model that utilizes eye movement sequences, representing the participant's attention mechanisms, to enhance the accuracy of ASD detection. We trained our model, consisting of multiple GRUs and an ANN architecture, on a dataset of eye-tracking data without augmentation. the model was trained on sequences of distances between eye fixations, transformed sequences, and durations of fixations and saccades. To evaluate the performance of our model and compare it with other works, we employed a validation dataset. Our ML model demonstrated superior performance in ASD detection compared to other works on the same dataset. Our model achieved higher accuracy, precision, and recall. By incorporating sequence-based features, our model effectively distinguished between individuals with ASD and typically developing individuals, achieving an impressive precision of 90.68% on the validation dataset. Our findings highlight the potential of a temporal analysis for improved ASD detection and provide a promising direction for further advancements in the field of eye-tracking-based diagnosis and intervention for neurodevelopmental disorders. © 2024 IEEE.

关键词： Eye tracking

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6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

6th International Conference on Recent Trends in Image Proce...

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6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

ISBN: (纸本)9783031530814

the proceedings contain 31 papers. the special focus in this conference is on Recent Trends in Image Processing and pattern recognition. the topics include: UI/UX for Aerospace Qualification Business Processes;Enhancing Android Malware Detection: CFS Based Texture Feature Selection and Ensembled Classifier for Malware App Analysis;a Comprehensive Analysis of Blockchain Network Security: Attacks and their Countermeasures;Enhancing CT Image Visualization and Analysis through Rescaling Raw Pixel Values to Hounsfield Units;VAIDS: A Hybrid Deep learning Model to Detect Intrusions in MQTT Protocol Enabled Networks;leveraging Handwriting Impairment as a Biomarker for Early Parkinson’s Disease Diagnosis;blockchain for Patient data Integrity: Decentralised Storage and Retrieval in Modern Healthcare Systems;a Residual learning Approach Towards the Diagnosis of Colorectal Disease Effectively;Large-Language-Models (LLM)-Based AI Chatbots: Architecture, In-Depth Analysis and their Performance Evaluation;cincoCrypto - A Cryptocurrency Price Forecasting Tool for Everyone;Realistic Skin Image data Generation Leveraging Conditional GAN and Classification Using Deep CNN;Diabetic Retinopathy Blood Vessel Detection Using Deep-CNN-Based Feature Extraction and Classification;analyzing Pulmonary Abnormality with Superpixel Based Graph Neural Network in Chest X-Ray;a Stack Ensemble Approach for Early Alzheimer Classification Using machine learning Algorithms;federated Unlearning and Server Right to Forget: Handling Unreliable Client Contributions;optimal Resource Allocation for Energy Harvested Cognitive Radio Networks Based on Learn Heuristic Algorithm;Deep Neural Networks Based Security Solution for ATM Transactions;crypto Bank: Cryptocurrency Wallet Based on Blockchain;radiography and thermography Based Image Processing Techniques for Assessment of Lower Back Pain;modified Snapshot Ensemble Algorithm for Skin Lesion Classification;COVID-19 Disease Prediction Using Generative Adversarial

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6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

6th International Conference on Recent Trends in Image Proce...

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6th international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2023

ISBN: (纸本)9783031530845

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6th international conference on Security and Privacy in New Computing Environments, SPNCE 2023

6th International Conference on Security and Privacy in New ...

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6th international conference on Security and Privacy in New Computing Environments, SPNCE 2023

ISBN: (纸本)9783031736988

the proceedings contain 23 papers. the special focus in this conference is on Security and Privacy in New Computing Environments. the topics include: Consortium Blockchain Storage Optimization Based on Fountain Codes;an Incentive Mechanism and An Offline Trajectory Publishing Algorithm Considering Sensing Area Coverage Maximization and Participant Privacy Level;Research on Face recognition System Based on RLWE Homomorphic Encryption;feedback Feed-Forward Iterative learning Control for Non-affine Nonlinear Discrete-Time Systems with Varying Trail Lengths;open-Closed-Loop Iterative learning Control Based on Differential Evolution Algorithm for Nonlinear System;overview of Vehicle Edge Computing and Its Security;conFlow: Contrast Network Flow Improving Class-Imbalanced learning in Network Intrusion Detection;anomaly Detection of Unstable Log data Based on Contrastive learning;An Integration-Enhanced ZNN Approach for Chaotic Combination Synchronization with External Disturbances∗;a Lightweight Anomaly Detection Method for Industrial Processes Based on Event Correlation Behavior;a Novel Polar Code-Based Key Encapsulation Mechanism with Non-permutation Equivalent Public Key;two-Stage Multi-lingual Speech Emotion recognition for Multi-lingual Emotional Speech Synthesis;EncoderMU: machine Unlearning in Contrastive learning;NoCrypto: A Web mining Behavior Detection Method Based on RGB Images;image Copy-Move Forgery Detection in the Social Media Based on a Prior Density Clustering and the Point Density;detection of Speech Spoofing Based on Dense Convolutional Network;speech Emotion recognition Based on Recurrent Neural Networks with Conformer for Emotional Speech Synthesis;Route Privacy-Preserving Authentication Scheme Based on PUF in VANETs;Stable NICE Model-Based Picture Generation for Generative Steganography;computer-Generated Image Forensics Based on Vision Transformer with Forensic Feature Pre-processing Module;VoIP Steganalysis Using Shallow Multiscale Convolution and

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Spiking Neural Network for Microseismic Events Detection Using Distributed Acoustic Sensing data 6th

Spiking Neural Network for Microseismic Events Detection Usi...

引用

6th international conference on Soft Computing and data mining (SCDM)

作者： Bin Shahabudin, Mohd Safuwan Krishnan, Nor Farisha Binti Muhamad Mausor, Farahida Hanim Binti Univ Teknol PETRONAS Seri Iskandar 32610 Perak Malaysia

ISBN: (纸本)9783031669644;9783031669651

Microseismic detection events are critical in monitoring subsurface activities, including hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage and reservoir characterization, to guarantee safe and efficient energy extraction. this presents significant challenges as it generates large amounts of data. Despite the recent machine learning techniques being increasingly integrated into fiber-optic distributed acoustic sensor (DAS) systems to enhance their intelligent recognition capabilities, there is still a need to solve this. In some research, it has been observed that computational speed is time-consuming and overfitting, thus necessitating a more extensive investigation of this analysis. this study proposes a novel approach using DAS data to enhance microseismic event detection's precision, overfitting issue, and interpretability. this approach utilizes a specifically designed neural network architecture. the deep learning approach is highly effective for the real-time management of the substantial amounts of data recorded by DAS equipment. three phases of research methodology are proposed. the contribution of this research is that spiking neural network architecture for microseismic detection will bring advancements in microseismic monitoring.

关键词： Deep learning Signal Processing Microseismic Event Detection Spiking Neural Network Distributed Acoustic Sensing

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6th IEEE international conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, IEEE CloudTech 2023

6th IEEE International Conference on Cloud Computing and Art...

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6th IEEE international conference on Cloud Computing and Artificial Intelligence: Technologies and Applications, IEEE CloudTech 2023

ISBN: (纸本)9783031786976

the proceedings contain 15 papers. the special focus in this conference is on Cloud Computing and Artificial Intelligence: Technologies and Applications. the topics include: CAReNet: A Promising AI Architecture for Low data Regime Mixing Convolutions and Attention;Designing Converged Middleware for HPC, AI, and Big data: Challenges and Opportunities;key Mechanisms and Emerging Issues in Cloud Identity Systems;Power Consumption in HPC-AI Systems;integrated Architecture for Cloud and IoT with Logical Sensors and Actuators - Logical IoT Cloud;the Need for HPC in AI Solutions;facts and Issues of Neural Networks for Numerical Simulation;scalable Deep learning for Industry 4.0: Speedup with Distributed Deep learning and Environmental Sustainability Considerations;multi-domain dataset for Moroccan Arabic Dialect Sentiment Analysis in Social Networks;on the Challenges of Migrating to Microservices Architectures for Better Cloud Solutions;missing data Imputation Approach for IoT Using machine learning;holistic Approach for Enhanced Object recognition in Complex Environments;analyzing Sentiment in Arabic Tweets: A Study Using machine learning and Deep learning Techniques.

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6th international conference on New Trends in Information and Communications Technology Applications, NTICT 2022

6th International Conference on New Trends in Information an...

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6th international conference on New Trends in Information and Communications Technology Applications, NTICT 2022

ISBN: (纸本)9783031354410

the proceedings contain 14 papers. the special focus in this conference is on New Trends in Information and Communications Technology Applications. the topics include: A Novel Ensemble Method for Network-Based Anomaly Intrusion Detection System;New Security Mechanism of Health data Based on Blockchain–NFV;Use Multichannel EEG-Based Biometrics Authentication Signal in Real Time Using Neural Network;machine learning-Based DDoS Attack Detection in Software-Defined Networking;enhancing Service Classification for Network Slicing in 5G Using machine learning Algorithms;mesoNet3: A Deepfakes Facial Video Detection Network Based on Object Behavior Analysis;computer Vision Techniques for Hand Gesture recognition: Survey;a Comprehensive Review in Using the Advances of Deep learning in the 3D Race Classification;multi-agents as data mining Classifiers and Crawlers to Get Intelligent E-Advertisements;automated Multi-style Iraqi Cars License recognition Based on Convolutional Neural Network Technology;food Recommendation System Based on data Clustering Techniques and User Nutrition Records;EEG data Compression Using Tap9/7 Wavelet Transform and Double Shift-Coding.

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Deep Neural Networks Architectures from the Perspective of Manifold learning 6

Deep Neural Networks Architectures from the Perspective of M...

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Magai, German Higher School of Economics Department of Big Data and Information Retrieval Moscow Russia

ISBN: (纸本)9798350325485

Despite significant advances in the field of deep learning in applications to various areas, an explanation of the learning process of neural network models remains an important open question. the purpose of this paper is a comprehensive comparison and description of neural network architectures in terms of geometry and topology. We focus on the internal representation of neural networks and on the dynamics of changes in the topology and geometry of a data manifold on different layers. In this paper, we use the concepts of topological data analysis (TDA) and persistent homological fractal dimension. We present a wide range of experiments with various datasets and configurations of convolutional neural network (CNNs) architectures and transformers in CV and NLP tasks. Our work is a contribution to the development of the important field of explainable and interpretable AI within the framework of geometrical deep learning. © 2023 IEEE.

关键词： Topology

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Magnetostrictive Sensor Based Bridge Health Assessment Using machine learning Classifiers 6

Magnetostrictive Sensor Based Bridge Health Assessment Using...

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6th IEEE Pune Section international conference, PuneCon 2023

作者： Dolui, Cherosree Devi, Sagolshem Farishta Dasadhikari, Shamik Roy, Debabrata Indian Institute of Engineering Science and Technology Department of Electrical Engineering Howrah India

ISBN: (纸本)9798350324204

Bridge Health Monitoring (BHM) is a crucial research domain in engineering, involving the analysis of infrastructure conditions like bridges using sensor data. Structural Health Monitoring (SHM) systems comprise distinct phases, with pivotal stages being feature extraction and pattern recognition. Signal processing techniques aid in feature extraction, while machine learning algorithms play a crucial role in pattern recognition, all geared towards accurately assessing bridge health. Applying machine learning techniques permits the automation of the SHM procedure and intelligent damage detection. this study introduces an innovative method utilizing machine learning technologies, leveraging data from Galfenol-based Magnetostrictive Sensors. these sensors convert vibrations into electrical data, offering insights into bridge health. A Decision Tree classifier categorizes data with an 80:20 split for training and testing, achieving a 98.72% accuracy. the code also demonstrates its predictive capability with new experimental data. Furthermore, various classifiers, including the Support Vector Method (SVM) and Neural Network, are tested, revealing their comparable accuracy. this research provides a user-friendly avenue for SHM and assists researchers in selecting optimal classification methods. © 2023 IEEE.

关键词： Structural health monitoring

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