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Adv-Cut Paste: Semantic adversarial class specific data augmentation technique for object detection 26

Adv-Cut Paste: Semantic adversarial class specific data augm...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Arun, Kumar S. Pal, Abhijit Mopuri, Konda Reddy Gorthi, Rama Krishna Indian Inst Technol Tirupati Tirupati Andhra Pradesh India Indian Inst Technol Guwahati Gauhati India

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

data augmentation has been a prevalent approach in improving the performance of deep learning models against slight variations in data. Adversarial learning is one such form of data augmentation. In this work, we aim to introduce a framework to generate harder examples for a specific object class and an adversarial attack for the object detection task. We have also presented our study on the effect of training against such generated harder examples and adversarial samples in object detection. We have applied this adversarial learning technique to a YOLOv3 model and due to the nature of the attack, we demonstrated a substantial improvement in average precision (AP) for a single class of the COCO dataset. As per the literature, we are the first to introduce this kind of class-specific data augmentation strategy in object detection. With our approach, we have shown an improvement of 23.34% in AP for Cat class and 3.1% on overall mAP of YOLOv3 model on clean validation data, while 43.5% improvement in AP for the Cat class on the composite images with class-specific adversarial samples.

关键词： Training Deep learning Semantics Object detection data models Adversarial machine learning pattern recognition

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5th international conference on machine learning, Image Processing, Network Security, and data Sciences, MIND 2023

5th International Conference on Machine Learning, Image Proc...

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5th international conference on machine learning, Image Processing, Network Security, and data Sciences, MIND 2023

ISBN: (纸本)9783031622168

the proceedings contain 29 papers. the special focus in this conference is on machine learning, Image Processing, Network Security and data Sciences. the topics include: the Potential of 1D-CNN for EEG Mental Attention State Detection;marker-Based Augmented Reality Application in Education Domain;detection and Classification of Waste Materials Using Deep learning Techniques;Deep learning Based EV’s Charging Network Management;internet of Medical things: Empowering Mobility and Health Monitoring with a Smart Walking Stick;SynText - data Augmentation Algorithm in NLP to Improve Performance of Emotion Classifiers;preface;Phishing Detection Using 1D-CNN and FF-CNN Models Based on URL of the Website;A Comparative Analysis of ML Based Approaches for Identifying AQI Level;a Review of Authentication Schemes in Internet of things;advancements in Facial Expression recognition: A Comprehensive Analysis of Techniques;a Deep learning Method for Obfuscated Android Malware Detection;COVID-19 Detection from Chest X-Ray Images Using GBM with Comparative Analysis;hate Speech Detection Using machine learning and Deep learning Techniques;effectiveness of Influencer Marketing on Gen Z Consumers;compressors Using Modified Sorting and Parallel Counting;violence Detection in Indoor Domestic Environment Using Multimodal Information;Code-Mixed Language Understanding Using BiLSTM-BERT Multi-attention Fusion Mechanism;diabetes Prediction Using machine learning Classifiers;crop Yield Prediction Using machine learning Approaches;Comparative Analysis of Economy-Based Multivariate Oil Price Prediction Using LSTM;MRI Based Spatio-Temporal Model for Alzheimer’s Disease Prediction;bridging the Gap: Condensing Knowledge Graphs for Metaphor Processing by Visualizing Relationships in Figurative and Literal Expressions;a Novel Unsupervised learning Approach for False data Injection Attack Detection in Smart Grid;a Multi-stage Encryption Technique Using Asymmetric and Various Symmetric Ciphers;speed-Invar

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Multi-Class Hypersphere Anomaly Detection 26

Multi-Class Hypersphere Anomaly Detection

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Kirchheim, Konstantin Filax, Marco Ortmeier, Frank Otto von Guericke Univ Dept Comp Sci Magdeburg Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

machine learning-based classification algorithms typically operate under assumptions that assert that the underlying data generating distribution is stationary and draws from a finite set of categories. In some scenarios, these assumptions might not hold, but identifying violating inputs - here referred to as anomalies - is a challenging task. Recent publications propose deep learning-based approaches that perform anomaly detection and classification jointly by (implicitly) learning a mapping that projects data points to a lower-dimensional space, such that the images of points of one class reside inside of a hypersphere, while others are mapped outside of it. In this work, we propose Multi-Class Hypersphere Anomaly Detection (MCHAD), a new hypersphere learning algorithm for anomaly detection in classification settings, as well as a generalization of existing hypersphere learning methods that allows incorporating example anomalies into the training. Extensive experiments on competitive benchmark tasks, as well as theoretical arguments, provide evidence for the effectiveness of our method. Our code is publicly available(1).

关键词： learning systems Training Codes Performance gain Benchmark testing Linear programming Classification algorithms

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A Traffic Sign recognition qnd Speech-BasedFeedback Generation System to Assist Drivers 5

A Traffic Sign Recognition qnd Speech-BasedFeedback Generati...

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5th IEEE international conference on Cybernetics, Cognition and machine learning Applications, ICCCMLA 2023

作者： Sarma, Himangshu Saharia, Navanath IIIT Department of Computer Science & Engineering Andhra PradeshSri City India IIIT Data Engineering Lab CSE Senapati Manipur India

ISBN: (纸本)9798350338287

According to the World Health Organization (WHO), 1.3 million people loss their life annually as a result of road accidents. By alerting or assisting the driver while they are driving, information technology such as computer vision can play a significant part in reducing the accident rate. Keeping that in mind, in this article we presents one traffic sign recognition tool using deep neural network (DNN) models with speech-based feedback for vehicle drivers. the proposed system can give voice-based feedback to driver to direct or inform them to take the appropriate action while driving, such as 'Drive slow, speed breaker ahead', 'Maximum Speed Limit is 80km/hr', 'School Ahead, drive slow', 'Don't take overtake' etc. © 2023 IEEE.

关键词： Deep neural networks

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machine learning Model to Selection of Job Applicant Documents to Increase the Human Resources Department Performance 5

Machine Learning Model to Selection of Job Applicant Documen...

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5th international conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Inayatulloh Mangruwa, Rajiv Dharma Cahya S, Prasetya Putri, Nuril Kusumawardani Soeprapto Bina Nusantara University School of Information System Information Systems Department Jakarta11480 Indonesia Communication and Business-Telkom University Bandung Indonesia University of Technology Malaysia Azman Hashim International Business School Malaysia

ISBN: (纸本)9798350369489

the planning of the company's existing and future human resource needs is included in the process of human resource recruitment. the availability of human resources, in terms of both quality and quantity, will be significantly impacted by the recruitment process, if it is both efficient and successful. the initial stages of the employee recruitment process involve selecting applicant documents according to company needs. Currently, the document selection process is done manually by studying the applicant documents one by one and then grouping them into several categories. this mechanism is inefficient and risks producing the wrong choices. On the other hand, machine learning has text recognition and image processing capabilities to increase the effectiveness and efficiency of the process. So, the purpose of this research is to help companies, especially the human resources division, improve the effectiveness and efficiency of new employee selection, especially the selection and classification of job applicant documents. the research method uses a qualitative approach with a literature review to identify new employee recruitment problems was continued by designing data sets, training data, and testing with machine learning using the LVQ method. the result of this research is that a machine learning model builds a model for selecting job applicant documents using machine learning with the learning Vector Quantization (LVQ) method. © 2023 IEEE.

关键词： Character recognition

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Text Classification Experiments on Contextual Graphs Built by N-Gram Series 4th

Text Classification Experiments on Contextual Graphs Built b...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Sen, Tarik Uveys Yakit, Mehmet Can Gumus, Mehmet Semih Abar, Orhan Bakal, Gokhan Abdullah Gul Univ TR-38080 Kayseri Turkiye Osmaniye Korkut Ata Univ TR-80000 Osmaniye Turkiye

ISBN: (纸本)9783031821493;9783031821509

Traditional n-gram textual features, commonly employed in conventional machine learning models, offer lower performance rates on high-volume datasets compared to modern deep learning algorithms, which have been intensively studied for the past decade. the main reason for this performance disparity is that deep learning approaches handle textual data through the word vector space representation by catching the contextually hidden information in a better way. Nonetheless, the potential of the n-gram feature set to reflect the context is open to further investigation. In this sense, creating graphs using discriminative ngram series with high classification power has never been fully exploited by researchers. Hence, the main goal of this study is to contribute to the classification power by including the long-range neighborhood relationships for each word in the word embedding representations. To achieve this goal, we transformed the textual data by employing n-gram series into a graph structure and then trained a graph convolution network model. Consequently, we obtained contextually enriched word embeddings and observed F1-score performance improvements from 0.78 to 0.80 when we integrated those convolution-based word embeddings into an LSTM model. this research contributes to improving classification capabilities by leveraging graph structures derived from discriminative n-gram series.

关键词： text-graph transformation graph convolution network deep learning text mining

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A Deep learning Method for Home Health Monitoring 5th

A Deep Learning Method for Home Health Monitoring

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5th international conference on data Science, machine learning and Applications, ICDSMLA 2023

作者： Reddy, I. Ravi Prakash Khan, Munazza Autonomous Hyderabad India

ISBN: (纸本)9789819780426

In today’s world, health condition recognition is still a challenge. Many neglect their early symptoms of various health diseases and do not maintain any personal digital health records. Moreover, in the recent pandemic we have seen that the shortage and Due to the lack of medical care, early COVID-19 identification at home was necessary to stop the virus's spread and provide the patient with prompt care. the continuous development of machine learning models, deep learning has also been widely used and tt has aided in the development of image processing and computer vision. Keeping this in mind, this paper proposes a web application for image processing technology and applies deep learning-based image processing to the detection of medical problems using the CNN VGG-16 model. For monitoring health conditions from home. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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EEITE 2024 - Proceedings of 2024 5th international conference in Electronic Engineering, Information Technology and Education

EEITE 2024 - Proceedings of 2024 5th International Conferenc...

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5th international conference in Electronic Engineering, Information Technology and Education, EEITE 2024

ISBN: (纸本)9798350372878

the proceedings contain 52 papers. the topics discussed include: evaluating the performance of a MIMO channel under tree shadowing for low-altitude UAVs;eco-design applied to FDM 3D printing technology;a tethered unmanned aircraft system for crowd dynamics and unusual pattern extraction;the future of privacy: a review on AI’s role in shaping data security;enhancing industry 4.0 cybersecurity training through cyber range platform;the selectively integrated returns intelligent optimization system – SΕIRIOS;segment anything model for scan-to-structural analysis in cultural heritage;a naive bayes classifier for fault detection and classification using dimension reduction technique;bias detection and correction methods for machine learning algorithms;and perceptions of international students in a higher education institute in Sweden.

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Autoencoder for Synthetic to Real Generalization: From Simple to More Complex Scenes 26

Autoencoder for Synthetic to Real Generalization: From Simpl...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Da Cruz, Steve Dias Taetz, Bertram Stifter, thomas Stricker, Didier IEE SA Bissen Luxembourg Univ Kaiserslautern Kaiserslautern Germany German Res Ctr Artificial Intelligence Berlin Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

learning on synthetic data and transferring the resulting properties to their real counterparts is an important challenge for reducing costs and increasing safety in machine learning. In this work, we focus on autoencoder architectures and aim at learning latent space representations that are invariant to inductive biases caused by the domain shift between simulated and real images showing the same scenario. We train on synthetic images only, present approaches to increase generalizability and improve the preservation of the semantics to real datasets of increasing visual complexity. We show that pre-trained feature extractors (e.g. VGG) can be sufficient for generalization on images of lower complexity, but additional improvements are required for visually more complex scenes. To this end, we demonstrate a new sampling technique, which matches semantically important parts of the image, while randomizing the other parts, leads to salient feature extraction and a neglection of unimportant parts. this helps the generalization to real data and we further show that our approach outperforms fine-tuned classification models.

关键词： Visualization Semantics machine learning Feature extraction Complexity theory Safety pattern recognition

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machine learning Algorithms for Disease Prediction Analysis 5

Machine Learning Algorithms for Disease Prediction Analysis

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Govindaraj, M. Asha, V. Saju, Binju Sagar, M. Rahul Master of Computer Application New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9781665474672

People suffer from a wide variety of diseases due to their environment and lifestyle factors. As a result, early disease prediction becomes critical. On the other hand, making a diagnosis solely on symptoms is too difficult for a doctor. the most difficult challenge is predicting disease accurately. In order to solve this problem, data mining is considered as an important and effective method for disease prediction. data mining reveals hidden pattern information from massive amounts of medical data derived from disease data. To aid in early diagnosis and treatment, this research work presents a disease prediction prototype to identify potential illnesses based on their symptoms. It reduces the time required to complete the patient's full diagnosis;additionally, the system's recommendations can be used to determine the disease. attempts to accurately predict a disease by using machine learning techniques. the proposed system produces results that are up to 87% accurate. this method has enormous potential for more accurately predicting potential diseases. the primary goal of this study is to help nontechnical people and young doctors to make accurate opinions about the disease. © 2023 IEEE.

关键词： Forecasting

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