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Multi-level Deep learning Vehicle Re-identification using Ranked-based Loss Functions 25

Multi-level Deep Learning Vehicle Re-identification using Ra...

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25th international conference on pattern recognition (ICPR)

作者： Kamenou, Eleni del Rincon, Jesus Martinez Miller, Paul Devlin-Hill, Patricia Queens Univ Ctr Secure Informat Technol CSIT Belfast Antrim North Ireland Thales UK Belfast Antrim North Ireland

ISBN: (纸本)9781728188089

Identifying vehicles across a network of cameras with non-overlapping fields of view remains a challenging research problem due to scene occlusions, significant inter-class similarity and intra-class variability. In this paper, we propose an end-to-end multi-level re-identification network that is capable of successfully projecting same identity vehicles closer to one another in the embedding space, compared to vehicles of different identities. Robust feature representations are obtained by combining features at multiple levels of the network. As for the learning process, we employ a recent state-of-the-art structured metric learning loss function previously applied to other retrieval problems and adjust it to the vehicle re-identification task. Furthermore, we explore the cases of image-to-image, image-tovideo and video-to-video similarity metric. Finally, we evaluate our system and achieve great performance on two large-scale publicly available datasets, CityFlow-ReID and VeRi-776. Compared to most existing state-of-art approaches, our approach is simpler and more straightforward, utilizing only identity-level annotations, while avoiding post-processing the ranking results (re-ranking) at the testing phase.

关键词： Vehicles

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MRI-Based Radiomics Analysis for Identification of Features Correlated with the Expanded Disability status Scale of Multiple Sclerosis Patients 21st

MRI-Based Radiomics Analysis for Identification of Features ...

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21st international conference on image Analysis and processing (ICIAP)

作者： Nepi, Valentina Pasini, Giovanni Bini, Fabiano Marinozzi, Franco Russo, Giorgio stefano, Alessandro Inst Mol Bioimaging & Physiol Natl Res Council IBFM CNR I-90015 Cefalu Italy Sapienza Univ Rome Dept Mech & Aerosp Engn Eudossiana 18 I-00184 Rome Italy

ISBN: (纸本)9783031133213;9783031133206

Multiple Sclerosis (MS) is considered a neurodegenerative disease that can cause multiple injuries within the Central Nervous System (CNS). MS can be diagnosed and qualitatively investigated with nuclear Magnetic Resonance (MR). In this study, a radiomics analysis is carried out considering 30 patients who underwent through MR studies consisting of Tl-weighted (T1W), T-2-weighted (T2W), Fluid Attenuated Inversion Recovery (FLAIR), and post-contrast administration T1W (T1WKS) images. Since radiomics features can vary greatly in relation to experimental extraction conditions, a first analysis is conducted by calculating the average percentage variations among features as the intensity bin size varies. A second analysis relates to the implementation of the predictive model based on the clinical outcome, namely the Expanded Disability status Scale (EDSS) of MS. To this aim, the extracted features are reduced, selected, and then used to build the predictive model based on a machine learning algorithm, namely the Linear Discriminant Analysis (LDA). The k-fold strategy is used to split data into training and validation sets. Performance metrics of each model associated with the four MR images (T1W, T2W, FLAIR, and T1WKS) as the bin size varies resulted in optimal predictive values close to 80%.

关键词： Radiomics Multiple Sclerosis machine learning image Analysis MRI

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HHAI2022: Augmenting Human Intellect - Proceedings of the 1st international conference on Hybrid Human-Artificial Intelligence

HHAI2022: Augmenting Human Intellect - Proceedings of the 1s...

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1st international conference on Hybrid Human-Artificial Intelligence, HHAI 2022

ISBN: (纸本)9781643683089

The proceedings contain 40 papers. The topics discussed include: the games we play: critical complexity improves machine learning;POMDP-based adaptive interaction through physiological computing;learning to cooperate with human evaluative feedback and demonstrations;a song of (dis)agreement: evaluating the evaluation of explainable artificial intelligence in natural language processing;towards an ai assistant for power grid operators;estimating value preferences in a hybrid participatory system;Exosoul: ethical profiling in the digital world;a sequence-based dialog management framework for co-regulated dialog;does personalization help? predicting how social situations affect personal values;effective task allocation in ad hoc human-agent teams;can ai reduce motivated reasoning in news consumption? investigating the role of attitudes towards ai and prior-opinion in shaping trust perceptions of news;abstracting minds: computational theory of mind for human-agent collaboration;and an empirical investigation of reliance on AI-assistance in a noisy-image classification task.

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A Hybrid 1DCNN-SVM Model for Bearing Fault recognition and Classification 27

A Hybrid 1DCNN-SVM Model for Bearing Fault Recognition and C...

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27th international conference on Automation and Computing, ICAC 2022

作者： Wang, Guoliang Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Rolling bearings play a significant role in the operation of rotating machinery. Finding bearing faults in the early stage can not only keep machinery running safely but also avoid economic loss. Traditional machine learning (ML) and deep learning (DL) are two important classes of fault diagnosis methods. The process of feature extraction is significant for ML based methods and many signal processing techniques have been used in this process. However, when dealing with massive and complex signals, features extracted by these techniques may not fully describe the characteristics of faults. Convolutional neural network (CNN) models are applied in many areas, including image identification and classification, pattern recognition, and speech processing. In this paper, a novel hybrid model by combining one dimensional CNN (1DCNN) and support vector machine (SVM) is proposed for bearing fault diagnosis and classification, which is called 1DCNN-SVM model or method. The first part of the model, 1DCNN, is used to extract features of raw data, whereas the second part, SVM, is employed to carry out the fault recognition and classification tasks. The evaluation of the proposed method is operated based on a real dataset. The experiment results show that the fault diagnosis accuracy of proposed method based on the hybrid model is highly improved compared to the traditional SVM based methods. © 2022 IEEE.

关键词： Failure analysis

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A Novel Approach for image Captioning using Deep learning Techniques

A Novel Approach for Image Captioning using Deep Learning Te...

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Advanced Computing and Emerging Technologies (ACET), international conference on

作者： Sharma Ji Vishal Jayaswal Kumari Deeksha Sushma Kumari Abhinav Kumar Prakash Bhagat Department of CSE Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

image caption generation involves automatically describing images with syntactically and semantically accurate entences. This process necessitates recognizing important objects, their attributes, and their relationships with in an image. Our proposed model utilizes Convolutional Neural Networks (CNN) and Long Short-Term Memory(LstM) networks to achieve this. The CNN extracts intricate visual features from input images, while the LstM generates contextually coherent captions. Leveraging pre-trained models for object detection, our system seamlessly integrates object recognition with caption generation. Extensively trained on large scale image-caption flickr 8k datasets, our model learns to connect written descriptions with graphic components to generate accurate and meaningful captions. This research significantly advances the realms of computer vision and natural language processing.

关键词： Visualization Accuracy Computational modeling Object detection Feature extraction Natural language processing Convolutional neural networks Web sites Object recognition Multimedia communication

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Application of Convolutional Neural Networks and Vision Transformer Models for Age and Gender Detection 6th

Application of Convolutional Neural Networks and Vision Tran...

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6th international conference on Information Systems and Management Science, ISMS 2023

作者： Adeniyi, Abidemi Emmanuel Brahma, Biswajit Awotunde, Joseph Bamidele Aworinde, Halleluyah Oluwatobi Bhuyan, Hemanta Kumar Department of Computer Science Bowen University Osun State Iwo Nigeria McKesson Corporation USA 32559 Lake Bridgeport St FremontCA94555 United States Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Andhra Pradesh Guntur India

ISBN: (纸本)9783031707889

This research explore the integration and application of advanced deep learning models, specifically Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) models, in the field of age and gender detection. The study begins by outlining the significance and challenges of accurate age and gender detection in various domains such as targeted advertising, security, and humancomputer interaction. It then delves into technical aspects of CNNs and ViTs, elucidating their architecture, working principles, and suitability for image-based task. The proposed techniques used in this study was able to differentiate between the following age groups: 0–15, 15–20, 20–25, 25–30, 30–35, and 40. The purpose is to offer a technique for creating and implementing accurate categorization and age estimation systems capable of operating and achieving high accuracy by integrating and applying a variety of feature extractors and algorithms. Pre-processing is evaluating preliminary data, configuring it, and transforming it to a standard format. The feature extraction component of the age and gender prediction technique is crucial. Three different extraction methods (ResNet 50, ViT Small, and ViT Base) are used in this section. Convolutional Neural Network (CNN) and Vision Transformer (ViT) classifiers were used. This optional component of pattern recognition system design focuses on system accuracy. To assess pattern recognition system performance, several approaches are employed, including Mean Absolute Error (MAE), Cumulative Score (CS), Leave-One-Out Cross-validation, and Confusion Matrix. In this study, however, gender and age prediction were tested using the Confusion Matrix and Mean Absolute Error (MAE). Python was the programming language utilized in this study. Python is a high-level, general-purpose programming language that is interpreted. Precision, recall, f1score and accuracy were the performance matrices used. A precision of 99% was achieved for male classification whi

关键词： Convolutional neural networks

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Detection of Pneumothorax from Chest X-Ray Data Using machine learning: A Comprehensive Survey 1st

Detection of Pneumothorax from Chest X-Ray Data Using Machin...

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1st international conference on Advancements in Interdisciplinary Research, AIR 2022

作者： Garg, Arpan Kaur, Bobbinpreet Chandigarh University Mohali India

ISBN: (纸本)9783031237232

Chest radiography is one of the most common and well-known medical imaging procedures. In humans, chest radiography is a very effective and reliable method of detecting thoracic and pneumothorax disorders. Due to recent improvements in computer technology, computer vision, and publically available large-scale datasets, machine learning-based techniques to automated pneumothorax diagnosis in chest radiography have risen in favour among academics. Our study examines existing machine learning approaches, datasets, and strategies for identifying pneumothorax in chest X-rays. The study includes widely available and publicly available labelled Chest X-Ray datasets with their requirements, as well as a detailed description of the labellers and labelling procedures used by them. Then, for Chest X-Ray images, popular and successful image processing approaches are provided. The paper then goes on to discuss the existing machine learning architectures in use, as well as the efficacy of Deep Convolutional Neural Networks in this regard. Finally, they look at gaps in the current literature for pneumothorax diagnosis, as well as uncharted territory and possible future collaborations in machine learning-based automated pneumothorax detection on Chest X-Rays. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Deep learning Model for Multiclass Classification of Diabetic Retinal Fundus images Using Gradient Descent Optimization 1

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1st international conference on Advances in Signal processing and Communication Engineering (ICASPACE)

作者： Mishra, Ram Krishn Dubai Int Acad City Dept Comp Sci BITS Pilani Dubai Campus Dubai 345055 U Arab Emirates

ISBN: (数字)9789811955501

ISBN: (纸本)9789811955525;9789811955501;9789811955495

Multiclass classification of diabetic retinal fundus images is a challenging task for classical methods due to presence of non-linear patterns in the images. Non-linearity in the shape and structure of blood vessels causes loss of crucial information used in accurate diagnosis of the disease. Hence, machine learning models became more prominent with high accuracy and speed of disease diagnosis. However, the efficacy of these methods largely depends on the feature extraction models. Convolutional neural networks (CNNs) provide solution to these difficulties by incorporating both feature extraction and classification in a single architecture. This work presents a gradient-descent (GD)-based optimization in CNN classifier that improves both speed and accuracy of classification. Experiments conducted on Kaggle, DRIVE, and stARE datasets, and the results are compared to the existing methods. The proposed approach shows a false positive rate (FPR) of 0.00075, specificity of 100%, precision of 99.9%, recall rate of 99.75%, and F-1-score of 99.99%.

关键词： Diabetic retinopathy Medical image classification Gradient descent Convolutional neural networks Optimization

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Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks

Automated plant leaf disease detection and classification us...

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1st international conference on Computations in Materials and Applied Engineering (CMAE)

作者： Ashwinkumar, S. Rajagopal, S. Manimaran, V Jegajothi, B. Natl Engn Coll Dept Informat Technol Kovilpatti 628503 Tamil Nadu India Sri Venkateswara Coll Engn Dept Informat Technol Chennai Tamil Nadu India

Agriculture is the major occupation in India and it loses 35% of the crop productivity annually owing to plant diseases. Earlier plant disease detection is a tedious process because of improper laboratory facil-ities and expert knowledge. Automated plant disease detection techniques are advantageous for reducing the laborious task of monitoring large crop farms and for identifying disease symptoms early on, i.e., when they appear on plant leaves. Recent advances in computer vision and deep learning (DL) models have demonstrated the value of developing automatic plant disease detection models based on visible symptoms on leaves. With this in mind, this article proposes an automated model for detecting and clas-sifying plant leaf diseases using an optimal mobile network-based convolutional neural network (OMNCNN). The proposed OMNCNN model operates on different stages namely preprocessing, segmen-tation, feature extraction, and classification. It involves bilateral filtering (BF) based preprocessing and Kapur's thresholding based image segmentation to identify the affected portions of the leaf image. In addition, the MobileNet model is applied as a feature extraction technique in which the hyperparameters are optimized by the use of emperor penguin optimizer (EPO) algorithm to enhance the plant disease detection rate. Finally, extreme learning machine (ELM) based classifier is utilized to allocate proper class labels to the applied plant leaf images. An extensive set of simulations were performed to highlight the superior performance of the OMNCNN model. The experimental outcome has shown promising results of the OMNCNN model over the recent state-of-art methods with the maximum precision of 0.985, recall of 0.9892, accuracy of 0.987, F-score of 0.985, and kappa of 0. 985. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st international Con-ference on Computations in Materials and Applied E

关键词： Plant leaf disease Deep learning image processing Intelligent models Computer vision MobileNet Parameter tuning

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1st international Congress of Electrical and Computer Engineering, ICECENG 2022

1st International Congress of Electrical and Computer Engine...

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1st international Congress of Electrical and Computer Engineering, ICECENG 2022

ISBN: (纸本)9783031019838

The proceedings contain 17 papers. The special focus in this conference is on Electrical and Computer Engineering. The topics include: A Hybrid machine learning Approach to Fabric Defect Detection and Classification;A Method of CNN Deep learning for Indonesia Ornamental Plant Classification;quantum Data Classification by a Dissipative Protocol with a Superconducting Quantum Circuit Implementation;sensor Application Ontology for Internet of Things;Response Times Comparison of MongoDB and PostgreSQL Databases in Specific Test Scenarios;MIMD-Simulators Based on Parallel Simulation Language;Real-Time Monitoring and Scalable Messaging of SCADA Networks Data: A Case study on Cyber-Physical Attack Detection in Water Distribution System;green Energy-Based Efficient IoT Sensor Network for Small Farms;potential Benefits of Apps for Bike Sharing Systems in Cairo: The Bikeroons App Example;preliminary Development of Virtual Try-On Platform for Batik Apparel Based on Mobile Augmented Intelligence Technology;low Light image Enhancement on Mobile Devices by Using Dehazing;document image Classification with Vision Transformers;rice Plant Disease Detection Using image processing and Probabilistic Neural Network;sentiment Analysis Covid-19 Spread Tracing on Google Play store Application;parameter Selection of Contrast Limited Adaptive Histogram Equalization Using Multi-Objective Flower Pollination Algorithm.

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