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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

共 14631 条记录，以下是191-200 订阅

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A Vision-Based System for Real-Time Monitoring of Water Quality in Natural and Artificial Reservoirs

A Vision-Based System for Real-Time Monitoring of Water Qual...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Shripad Bhatlawande Shrihari Mohole Yash Pahade Swati Shilaskar Aditya Munjale Vishwakarma Institute of technology Pune India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Manual sampling and laboratory testing are the mainstays of traditional water quality monitoring methods, which causes delays in data processing and decision-making. This study proposes an enhanced monitoring of water quality system that uses machine learning algorithms to classify and segment water samples in real-time, thereby overcoming these restrictions. Water samples are classified as “Clean” or “Polluted” having an accuracy of 83.99% using a CNN model. The CNN model, trained over 20 epochs, effectively learns patterns from the dataset to deliver precise results. Additionally, a U-Net model is implemented for image segmentation, generating masks that accurately highlight areas of interest within the water samples, with a segmentation accuracy of 68%. Streamlit's intuitive interface makes it simple for users to upload images and get immediate feedback on water quality. This system helps to improve the health of the environment monitoring with automating the method of water quality evaluation. It does this by providing a simpler, quicker, and scalable solution that enables prompt, data-driven actions.

关键词： image segmentation Accuracy Water quality Manuals Streaming media User interfaces Reservoirs Water pollution Real-time systems Testing

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Prediction of Cervical Cancer Staging via Diffusion-Weighted MR Radiomic Features Analysis

Prediction of Cervical Cancer Staging via Diffusion-Weighted...

引用

Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International conference for

作者： Zarina Ramlia Sri Herwiningsih Mohd Saiful Asmal Mohd Amiruddin Zaharudin Haron Muhammad Khalis Abdul Karim Department of Physics Universiti Putra Malaysia Selangor Malaysia Department of Physics Universitas Brawijaya Malang Indonesia Department of Radiology Institut Kanser Negara Putrajaya Malaysia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Contemporary technology has made it possible to diagnose diseases with greater precision, enabling doctors to determine tumor stages by measuring and analyzing features in medical images. This study aimed evaluate radiomic features of DWI-MRI cervical and to classify tumors based on their stages using several machine learning models. The study involved 60 patients from the National Cancer Institute in Putrajaya using which 852 radiomic were derived from each patient. The patients were divided into two groups based on the stage of cervical cancer: Group one is defined by stages I and II, while group two is labelled by stages iii and IV. A semi-automatic process was used to segment the image data and extract various features such as shape, wavelength, and both first and second-order statistics (GLSZM) from the segmented images. In our study, we used three different model algorithms: Logistic Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT). We conducted a detailed analysis to compare their performance. The SVM algorithm stood out as the top performer, achieving the highest accuracy among the models tested. We evaluated its effectiveness using multiple performance metrics, which confirmed its superiority in processing features extracted from enhanced images. Notably, the SVM classifier demonstrated excellent results, with an accuracy of 0.77, and precision of 0.63. It also showed a robust area under the ROC curve of 96%, highlighting its effectiveness in classification tasks. Furthermore, this research emphasizes the significance of image enhancement as the useful preprocessing step which increases the classification accuracy.

关键词： Support vector machines image segmentation Technological innovation Accuracy Feature extraction Decision trees image enhancement Cervical cancer Radiomics Tumors

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Dcha: Distributed-Centralized Heterogeneous Architecture Enables Efficient Multi-Task processing for Smart Sensing

Dcha: Distributed-Centralized Heterogeneous Architecture Ena...

引用

Design, Automation and Test in Europe conference and Exhibition

作者： Erxiang Ren Cheng Qu Li Luo Yonghua Li Zheyu Liu Xinghua Yang Qi Wei Fei Qiao School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China MakeSens AI Beijing China College of Science Beijing Forestry University Beijing China Department of Precision Instrument Tsinghua University Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The rapid development of artificial intelligence (AI) has accelerated the progression of IoT technology into the smart era. Integrating AI processing capabilities into IoT devices to create smart sensing systems holds significant promise. In this work, we propose a distributed-centralized heterogeneous architecture that enables efficient multitask processing for smart sensing. This architecture improves the operational efficiency of sensing systems and enhances the deployment scalability through collaborative computing across end, edge, and center nodes. Specifically, we partition the network in traditional centralized sensing systems into several parts and perform algorithm-hardware co-design for each part on its respective deployment platform. We developed a sample design to validate the proposed architecture. By implementing a lightweight image encoder, we achieved an 88x reduction in encoder parameters and up to 9873x energy gain, facilitating deployment on resource-constrained devices. Experimental results demonstrate that the proposed architecture effectively reduces overall energy consumption by 0.0573x to 0.0889x, while maintaining robust multitask inference capabilities. Moreover, energy consumption reductions of 2.88x to 3.22x on edge nodes and 6311.56x to 10037.23x on end nodes were observed.

关键词： Performance evaluation Energy consumption Soft sensors Scalability Collaboration Computer architecture Sensors Partitioning algorithms Internet of Things Artificial intelligence

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Bone Fracture Detection using CNN and VGG

Bone Fracture Detection using CNN and VGG

引用

Ambient Intelligence in Health Care (ICAIHC), International conference on

作者： Kasvi Soni Aanya Gupta Karishma Dhyani Sunil Kumar Department of IoT and IS Manipal University Jaipur Jaipur India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Technology has played a huge role in human evolution since time immemorial. Now it is not only limited to engineers and data scientists, but far beyond, to even medicine. With increasing load on medical professionals and healthcare systems, digitalization can come handy. The traditional X-ray imaging gives unclear images of bone components in tissue making it difficult for surgeons to diagnose. Using technology, accurate diagnosis can be achieved. This includes a series of steps comprising pre-processing, edge detection, feature extraction and machine learning classifications. Machine learning algorithms such as Convolutional Neural Networks(CNN) and Visual Geometry Group(VGG) were used on a dataset consisting of 9246 training, 506 test and 829 validation images with 0.95 and 0.85 accuracies respectively. The data loss for CNN was found to be 0.24 in comparison to 0.4 for VGG. So from statistical inference, CNN is better.

关键词： Visualization Accuracy image edge detection Medical services Machine learning Feature extraction Bones Convolutional neural networks Medical diagnostic imaging X-ray imaging

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LoG Feature Extraction and Supervised SVM Classification for Accurate Lung Malignancy Detection and Classification in CT Scans

LoG Feature Extraction and Supervised SVM Classification for...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： Dudekula. Subhan Boya. Kowshik D. Lokanath Chowdary K. Uma Maheswari Dept. of Electronics & Communication G. Pullaiah College of Engineering & Technology Kurnool A.P India Dept. of Electronics & Communication G. Pullaiah College of Engineering & Technology A.P India Dept. of Electronics & Communication G. Pullaiah College of Engineering and Technology Kurnool A.P India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Pulmonary cancer is still one of the most prevalent forms of carcinoma along with breast cancer is the realms of growing focus for fundamentals healthcare. This project focuses on the identification of lung neoplasm based on the study of CT scan images using these methods. In this case investigation begins with image preparation, which Median filtering is used to decrease noise. while maintaining important image data. mathematical morphological transformations are then used for picture segmentation. The goal is to separate tumor tissues from the remainder of the image for additional investigation. Area, perimeter, eccentricity and texture features from the segmented images are extracted for GLCM, which are then supplied to the SVM classifier. This task is best suited for the SVM because it is known that the SVM is good at working within high dimensional spaces and is proficient at separating cancerous from non cancerous tumors. First estimates prove that the proposed system is a lot better than traditional systems at pose estimation detection because there are less mistakes made by the operator. The incorporation of these advanced image processing techniques together with machine learning algorithms have great prospects for boosting the detection rate of lung cancer.

关键词： Support vector machines image segmentation Accuracy Filtering Lungs Computed tomography Lung cancer Feature extraction Telecommunication computing Tumors

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An Intelligent Lane, Object Detection and Speed Estimation for Ego-Vehicles using Perception algorithms

An Intelligent Lane, Object Detection and Speed Estimation f...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Kukkamudi Sunidhi J. N.V.R. Swarup Kumar Gorla Srinivas Cetty Yamini Dept. of CSE GST GITAM Deemed to be University India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Autonomous vehicles promise to revolutionize transportation by expanding mobility and improving traffic safety. All of this future auto tech hinges on autonomous vehicle perception - the processing of visual input to vital Advanced Driving Assistance functions such as lane detection, vehicle and pedestrian recognition, and traffic sign detection. The goal of this work is to increase the level of autonomy in ego vehicles, which can drive through challenging traffic situations (completely autonomously). This research will then take on one of the portions with a low latency and high accuracy target, which is safety + efficiency (using ADAS – Advanced Driver Assistance systems). The system proposes an ego vehicle-based computer vision solution for pedestrian, vehicle, and traffic sign detection along with lane detection. The entire system consists of many small systems like the YOLO-NAS Module, Gaussian Blur Module, Hough line transform, and Canny Edge Detection. A detailed flow diagram is given at the end and is curated during the experiment analysis meeting.

关键词： YOLO Pedestrians Accuracy Lane detection image edge detection Estimation Transforms Real-time systems Safety Autonomous vehicles

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2nd International conference on Information Technologies and Their Applications, ITTA 2024

2nd International Conference on Information Technologies and...

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2nd International conference on Information Technologies and Their Applications, ITTA 2024

ISBN: (纸本)9783031734168

The proceedings contain 60 papers. The special focus in this conference is on Information Technologies and Their Applications. The topics include: Turkish Legal Single-Document Summarizing;technologies and systems for Monitoring the Onset of Accidents at Strategic Construction;optimizing Feature Distributions for Unsupervised Deep Learning-Based Fabric Defect Detection and Localization;conservation of Wildlife in Hunting Tourism Using Artificial Intelligence and image processing in Smart Firearms;optimizing Microarray Gene Selection in Colon Cancer: An Enhanced Metaheuristic Algorithm for Feature Selection;securing Vehicular Ad-Hoc Networks: A Blockchain and Software Defined Network Approach for Enhanced Efficiency, Safety, and Security;influence of Process Noise Biases to Satellite Attitude Filters’ Estimates;information-Retrieval System of Rock Pictures of Different Countries and Continents;convergence of Sharpness-Aware Minimization with Momentum;vulnerability Assessment and Penetration Testing of University Network;preliminary Orbit Estimation for Turksat 5B via Extended and Unscented Kalman Filtering;Three-Axis Attitude Determination of a Nanosatellite Using Optimized TRIAD algorithms;computer System of Evaluation of the Mass Exam Results Based on Recognition of Handprinted Azerbaijani Characters;Empirical Analysis of Conformer Impact on the CTC-CRF Model in Kazakh Speech Recognition;ontological Approach to Describing the Interaction of Information Processes in Intelligent systems;LANet: Lightweight Attention Network for Medical image Segmentation;Lightweight Hybrid CNN Model for Face Presentation Attack Detection;disaggregation Analyses of the External/Internal Parameters for Chloride Diffusion in Concrete Structures: Artificial Intelligence by k-means Algorithm;k-means for Small Earthquakes. Alternative Disaggregation Analyses by Considering Wave Components and Soil Types;algorithm for Distributing Incoming Messages Among Handlers.

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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

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)9783031730672

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