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检索条件"任意字段=1st International Conference on Image Processing Machine Learning and Pattern Recognition"

共 1517 条记录，以下是11-20 订阅

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Enhanced Classification Performance through Joint Mutual Information for Feature Selection in Life Science Data 1

Enhanced Classification Performance through Joint Mutual Inf...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Saha, Soumitra Kumar, Rakesh Gupta, Meenu Zanke, Pankaj Chandigarh University Department of Cse Mohali India Spaiens International Corporation Atlanta United States

ISBN: (纸本)9798331529246

Multifarious approaches exist for data preprocessing in machine learning (ML), pattern recognition, and data mining, and feature selection (FS) is regarded as the most efficacious technique for dimensionality reduction in a structured way. The existence of irrelevant, redundant, and disproportionate features directly diminishes the model's performance and dramatically expands its complexity. Distinguishable FS algorithms can outperform others, and the most influential aspect of a well-performing FS algorithm is discovering momentous features that help optimize model performance by processing data more efficiently. Therefore, this study introduces a novel FS technique based on the joint mutual information (FSJMI) technique for life science data. This technique declines the number of features and enhances the model's performance, or at least maintains it in most cases, making it a unique and promising approach. It also enriches predictive accuracy while minimizing computational overhead by substituting the correlation between the features and the correlation substitution of the class values with the features. Comprehensive experiments utilizing ten diverse life science datasets have been conducted to validate the suggested model's performance. The experimental results demonstrate a substantial improvement in the proposed techniques compared to other well-known FS techniques and no selection (NoSel) in the primary dataset. The results conveyed using widely used ML algorithms such as Decision Tree (DT), K Nearest Neighbour (KNN), Naive Bayes (NB) Classifier, and Logistic Regression (LR) reveal that the presented algorithm demonstrated superior performance and outperformed NoSel and other FS algorithms in mean accuracy by at least 2.7%, 3.466%, 2.038%, and 1.792%, respectively. Additionally, regarding the LR model, the proposed algorithm outperforms one of the FS models by at most 11.631%. These findings of the proposed algorithm regarding outcomes highlight its capabili

关键词： Dimensionality reduction

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

1st International Conference on Advances in Artificial Intel...

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

ISBN: (纸本)9783031730672

The proceedings contain 51 papers. The special focus in this conference is on Advances in Artificial Intelligence and machine learning in Big Data processing. The topics include: Topological Navigation of Path Planning Using a Hybrid Architecture in Wheeled Mobile Robot;abnormal Behaviour Detection in Surveillance Videos;ISAApp – image Based Smart Attendance Application;a Self-learning Ai-Based Information Leak Protection System;enhancing Abnormal Object Detection in Camera-Based Systems Through Computer Vision and Deep learning Techniques;Detection and Classification of Brain Tumor in Magnetic Resonance images Using CNN;diagnosis of Parkinson’s Disease Using machine learning and Deep learning Techniques;a Survey on Deep learning Based Human Activity recognition System;a Deep learning Approach for Non - invasive Body Mass Index Calculation;Early-stage Detection of Alzheimer’s Disease Using MRI Scans with Deep learning;penguin Search Optimization with Deep learning Based Cybersecurity Malware Spectrogram image Classification;Detection and Classification of Skin Disease Using CNN;estimation of Above Ground Biomass Using machine learning and Deep learning Algorithms: A Review;URL Phishing Detection Using Deep learning and machine learning Techniques;enhanced Disease recognition and Classification in Black Gram Plant Leaves Using Deep learning;Ensemble Deep learning Approach for Identification of DDOS Attack;ROCLT: Enhanced Text Classifier for Sentiment on Imbalanced Multiclass Tweet Data Using Hybrid Deep learning Techniques;computer Vision to Animal Footprint Classification Based on Deep learning Model;Speech Emotion recognition Using CNN Classifier Based on Deep learning Model;face Detection and recognition for Criminal Identification System Using Deep learning.

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

1st International Conference on Advances in Artificial Intel...

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

ISBN: (纸本)9783031730641

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Cross-Modality image Quality Prediction for Time-Resolved CT from Breathing Signals

Cross-Modality Image Quality Prediction for Time-Resolved C...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Schwarz, Annette Dickmann, Jannis Hofmann, Christian Szkitsak, Juliane Bert, Christoph Maier, Andreas Arias-Vergara, Tomás Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Siemens Healthineers AG Forchheim Germany Department of Radiation Oncology Universitätsklinikum Erlangen Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Erlangen Germany

ISBN: (纸本)9783031845246

Four-dimensional computed tomography (4DCT) is a time-resolved, multi-modal imaging method that captures respiratory signals synchronised with the CT scan in order to track the movement of the lung. It is routinely used in radiation therapy treatment planning for lung or liver cancer patients. However, image artifacts during 4DCT scans caused by irregular patient breathing negatively impact treatment outcomes. This work proposes a method to automatically detect patients at high risk for severe image artifacts even before the scan is conducted based on an pre-scan analysis of their breathing. This can help to take proactive measures to improve image quality, such as changing the scan mode or providing in-depth patient coaching. A deep neural network is trained to predict the image quality score of 28 lung CT phantom scans, each rated by ten clinical experts. Different pretrained networks are investigated for feature generation and combined with two linear output heads to predict the average expert image quality score of unseen scans. We were able to predict the quality of a 4DCT with a mean absolute error of 8% using only the one-dimensional breathing signal as input. This accuracy is comparable to the rating consistency of our clinical experts, which were rating the images directly. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiotherapy

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Enhancing Foundation Model Robustness for Multi-center Real-World Medical image Analysis 1st

Enhancing Foundation Model Robustness for Multi-center Real...

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1st MICCAI Challenge Comprehensive Analysis and Computing of Real-World Medical images, CARE 2024 Held in Conjunction with 27th international conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Huang, Jinlong Sun, Xiao Wang, Lisheng Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031870088

Revolutionizing medical image analysis, the Segment Anything Model (SAM) stands out among emerging foundation models, yet its potential in accurately segmenting small, irregular ROIs from multi-center, diverse datasets remains untapped in real-world scenarios. Our study harnesses SAM’s capabilities to address the segmentation intricacies of clinically crucial, yet challenging, features in complex medical imaging. To bridge this gap, we embark on a comprehensive exploration anchored in Foundation models including SegVol and MedSAM, targeting three pivotal challenges: 1) enhancing the generalization of models across diverse medical image modalities and sequences, 2) reinforcing the stability of interactive segmentation, and 3) overcoming the segmentation hurdles posed by minuscule, atypical ROIs. We employ a coarse-to-fine segmentation strategy, using the CARE24 dataset alongside a meticulously publicly available dataset to fine-tune these models for enhanced real-world performance. To boost the stability and precision of interactive segmentation, we devise a retest mechanism, ensuring consistent outcomes in user-guided processes. Furthermore, recognizing the potential of multimodal data, we introduce a fusion strategy, capitalizing on complementary information to improve overall segmentation accuracy and adaptability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Complicated ROIs Foundation model Multi-center Segment Anything Model (SAM)

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1st international conference on pattern Analysis and machine Intelligence, ICPAMI 2024

1st International Conference on Pattern Analysis and Machine...

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1st international conference on pattern Analysis and machine Intelligence, ICPAMI 2024

ISBN: (纸本)9789819633487

The proceedings contain 28 papers. The special focus in this conference is on pattern Analysis and machine Intelligence. The topics include: Development of a Low Cost 3D LiDAR Using 2D LiDAR and Servo Motor;the Design of machine Vision-Based Waste Sorting System;ECLNet: Efficient Convolution with Lite Transformer for 3D Medical image Segmentation;exploring High-Performance 3D Object Detection with Partial Depth Completion;full-Scale Network for Remote Sensing Object Detection;Detection of Pedestrian Movement Poses in High-Speed Autonomous Driving Environments Using DVS;city-Scale Multi-Camera Vehicle Tracking System with Improved Self-Supervised Camera Link Model;an Efficient Transformer-Based Network for Remote Sensing image Change Detection;the Method for Three-Dimensional Visual Measurement of Circular Markers Based on Active Fusion Technology;intelligent image recognition and Classification Technology in Digital Media;Indoor Visible Light Positioning System Based on the image Sensor and CNN-GRU Fusion Neural Network;stock Investor Sentiment Analysis Based on NLP;Novel Audiobook System Based on BERT;student Enrollment Consultation Q&A Robot Based on Large Language Model;family Doctor Model Training Based on Large Language Model Tuning;composite Awareness-Based Knowledge Distillation for Medical Anomaly Detection;Improved CNN-GRU RF Fingerprint Feature recognition Method Based on Comb Filter;emotional state recognition of English Learners Based on Deep learning;Application of Classification Framework Based on CDR and CNN in Ophthalmic Prediagnosis;visual recognition and Recommendation System for Cultural Tourism Attractions Based on Deep learning;quadruped Robot System Based on Proprioceptive Vision and Complex Ground Mobility Capabilities;a Simulated Dataset to Evaluate the Visual-Inertial Odometry Algorithms.

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Intelligent image recognition and Classification Technology in Digital Media 1st

Intelligent Image Recognition and Classification Technology ...

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1st international conference on pattern Analysis and machine Intelligence, ICPAMI 2024

作者： Liang, Huan Guangzhou International Economics College Guangzhou510540 China

ISBN: (纸本)9789819633487

With the increasing popularity of digital media along with the fact that massive image data has already gained popularity, the automatic image recognition and classification have become a very critical and urgent problem, this study investigates and tests intelligent image recognition and classification techniques for digital media based on major methods such as CNN, SVM and RNN. Different algorithms and models are applied to training and test image data to evaluate the performance of various methods in terms of accuracy and processing time. The experimental results shows that the CNN method can achieve high accuracy in image classification task and the multi-layer convolution and pooling operations can effectively extract image features. The SVM method is better than the CNN in terms of processing time but have slightly lower accuracy. The RNN method is ranked between the CNN and SVM in terms of accuracy and processing time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image representation

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Vehicle Registration Plate recognition Using YoloV8 and IOT

Vehicle Registration Plate Recognition Using YoloV8 and IOT

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AIML-Applications for Engineering & Technology (ICAET), international conference on

作者： Juber Nadaf Amol Kadam Pramod Jadhav Prasad Kadam Vinod Patil Milind Gaikwad Department of Computer Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of CSBS Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of E & TC Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of Information Technology Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Number Plate recognition or the vehicle License Plate recognition is an interesting topic of research in smart cities which utilizes image processing techniques. Since the number of cars is rising at an exponential rate, automated systems are supposed to store vehicle data for a variety of applications. In this intended algorithm, an efficient technique has been identified for recognition of different vehicle number registration plates. Through this technique, we can easily deal with problems like low light, irregular font number plates, wrong angles. We use the method of Optical Character recognition to identify characters that are extracted from the registration plate. Even though this project works efficiently for common use cases, it has its own drawbacks and limitations even though we are not using deep learning or any other machine learning algorithms. One of the important steps in vehicle plate recognition is the character recognition which is a technique to obtain individual text characters. YOLOv8 (You Only Look Once), and Darknet- 53 are the methods that is used to extract the features of the data. In this model, the input data is the image of the vehicle that is captured from the camera sensors or cell phones and later cropped to display only the registration plate. We undergo data-pre-processing to improve the quality or the pixels of image on the number plate. Testing is done using two different models one is the model which is obtained with more pre-processing data and the other obtained without any pre-processing data. We evaluate two datasets of number plate images: one with no interference and one where the colour intensity of the image has been decreased to test, the accuracy of the model

关键词： Deep learning YOLO Training image recognition Accuracy Feature extraction Data models Internet of Things Character recognition License plate recognition

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Back to the Future: Challenges of Sparse and Irregular Medical image Time Series

Back to the Future: Challenges of Sparse and Irregular M...

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作者： Disch, Nico Albert Peretzke, Robin Roy, Saikat Ulrich, Constantin Zimmerer, David stiefelhagen, Rainer Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Karlsruhe Institute of Technology Karlsruhe Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany

ISBN: (纸本)9783031845246

In longitudinal medical image analysis, most work focuses on regularly sampled images, or on tasks like regression or classification. However, in the clinical context, images are frequently generated irregularly due to factors such as cost constraints. This work tackles the problem of irregularly sampled longitudinal medical imaging by evaluating methods in segmentation and reconstruction tasks. We examine the reconstruction for real-life MRI from patients with Alzheimer’s Disease (AD), where the model predicts future MRI scans from an arbitrary number of input images sampled at irregular intervals. However, experiments show that most models cannot surpass the performance of using an image from the same patient at a different time point as a baseline. Therefore, we conducted experiments on a synthetic dataset to better isolate effects in temporal learning. These experiments offer insights into model behavior and serve as a benchmark for validating models’ longitudinal learning capability, thus providing a straightforward method to assess temporal understanding in a controlled environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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1st international conference on Artificial Intelligence and its Applications, ICAIA 2023

1st International Conference on Artificial Intelligence and ...

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1st international conference on Artificial Intelligence and its Applications, ICAIA 2023

ISBN: (纸本)9783031843938

The proceedings contain 37 papers. The special focus in this conference is on Artificial Intelligence and its Applications. The topics include: The Hybrid Cardiac Risk Assessment and Prediction Model Using Convolutional Neural Networks;deep learning Applications for Malaria Detection and Diagnosis: A Review;Environmental Considerations in the Ethics of AI Adoption in Healthcare: striving for Sustainable and Responsible Practices;harnessing the Power of Cognitive Computing: Assessing Point-of-Care Decision Support Tools in Oncology Practice;Critical Analysis in Use of AI in Health Care Management;classification and Prediction of Spinal Tuberculosis Disease Using Optimization of Convolution Neural Network Using Spatial and Temporal Constraints;artificial Intelligence for Remote Healthcare in Underserved Areas: Enhancing Access and Quality of Healthcare Delivery;machine learning Based Skin Cancer Detection and recognitions Techniques in IoT Environment;validation of a Chronic Kidney Disease Prediction System Using machine learning Techniques;real-Time Feedback Detection Using Emotion Detection and Facial recognition;revolutionizing Vision Tasks: Unlocking Potential Through Patch-Based Approaches;automated Knee Implant Identification from 2D Templates Using image processing and Artificial Intelligence – An Experimental Approach;crop Analysis and Classification Based on Phenotype Using Ensemble learning;Classification and Identification with Health Benefit Assessment and Nutrient Profile of Brewed Tea Utilizing Computer Vision with ML and DL and Sensory Approaches;enhancing Industrial Automation Flexibility Through Neural Network-Empowered machine Vision Applications.

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