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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...

引用

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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Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images Using SegFormer 4th

Semantic Segmentation of Unmanned Aerial Vehicle Remote Sens...

引用

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

作者： Spasev, Vlatko Dimitrovski, Ivica Chorbev, Ivan Kitanovski, Ivan Univ Ss Cyril & Methodius Fac Comp Sci & Engn Skopje North Macedonia

ISBN: (纸本)9783031821554;9783031821561

the escalating use of Unmanned Aerial Vehicles (UAVs) as remote sensing platforms has garnered considerable attention, proving invaluable for ground object recognition. While satellite remote sensing images face limitations in resolution and weather susceptibility, UAV remote sensing, employing low-speed unmanned aircraft, offers enhanced object resolution and agility. the advent of advanced machine learning techniques has propelled significant strides in image analysis, particularly in semantic segmentation for UAV remote sensing images. this paper evaluates the effectiveness and efficiency of SegFormer, a semantic segmentation framework, for the semantic segmentation of UAV images. SegFormer variants, ranging from real-time (B0) to high-performance (B5) models, are assessed using the UAVid dataset tailored for semantic segmentation tasks. the research details the architecture and training procedures specific to SegFormer in the context of UAV semantic segmentation. Experimental results showcase the model's performance on benchmark dataset, highlighting its ability to accurately delineate objects and land cover features in diverse UAV scenarios, leading to both high efficiency and performance.

关键词： Semantic segmentation Deep learning SegFormer UAV images

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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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Healthcare Management System to Improve data Analytics Using Support Vector machine Algorithm 4

Healthcare Management System to Improve Data Analytics Using...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Raj, A. Edwin Marin, V. Bini Balakrishnan, C. Elangovan, K. Meenakshi, R. Muthulekshmi, M. Hindustan Institute of Technology and Science Department of Mathematics Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Management Studies Tamil Nadu Chennai India S.A.Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Engineering Dhanalakshmi Srinivasan Engineering College Department of Mechanical Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

Knowledge discovery in databases (KDD) is concerned with progress in data analysis tools. data mining is a crucial parts of the KDD process. data mining is a method for discovering and extracting patterns from huge medical datasets. there are a number of benefits to using the Support Vector machine (SVM) algorithm in healthcare systems. SVM is a strong and flexible machine learning algorithm that may excel at certain healthcare analytics applications. SVM is preferred in healthcare systems because of their adaptability, ability to manage imbalanced datasets, feature interpretability, handling of high-dimensional data, handling of classification, predictive modeling tasks, handling of non-linear relationships, resistance to overfitting and provision of a distinct margin of separation. It is essential to carefully consider the unique needs and features of the healthcare application when choosing an algorithm. Healthcare systems that use SVM algorithms do so with the intention of capitalizing on SVM's capabilities to tackle domain-specific problems and tasks. In light of this development, the relevant literature on this topic with regard to approach, SVM algorithm, and outcomes are surveyed. the publications evaluated in this study have been categorized according to their relevance to the disciplines, models, tasks, and approaches discussed. data mining is the practice of sifting through massive amounts of unstructured data for meaningful patterns and insights. Results show 90% accuracy and 98% precision and F measures of the given medical dataset. © 2024 IEEE.

关键词： Support vector machines

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A Review of Advancements in Driver Emotion Detection: Deep learning Approaches and dataset Analysis 4

A Review of Advancements in Driver Emotion Detection: Deep L...

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4th international conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2024

作者： Alfaras, Mohammed Shukur Karan, Oǧuz Altinbas University Electrical And Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350309508

this review paper delves into the rapidly evolving field of driver emotion detection, with a specific focus on the contributions of deep learning methodologies and the diverse datasets that facilitate this research. Facial emotion detection and recognition is a highly dynamic and challenging field in machine learning (ML) and Artificial Intelligence (AI). It has gained attraction for several decades, but it is extremely challenging due to the intrinsic complexities associated with understanding and interpreting human emotions. Understanding and responding to a driver's emotions is increasingly important when autonomous or assisted driving becomes common. To ensure safety, comfort, and optimal interaction between the driver and the car's systems, predicting the emotional state of the driver is essential, also of great significance in practical applications. We give a comprehensive survey of the state-of-the-art driver emotion recognition works that can effectively make use of the recent deep-learning approaches to identify complex emotional cues. Moreover, we explain a variety of datasets that play a vital role in flourishing this field along with the analysis of their effect, like AffectNet, CK+, and EMOTIC. Via this survey we try to investigate the challenges authors are faced with involving this field, e.g., concerns about data privacy, real-time processing demands, and the need for interdisciplinary collaboration. More importantly, the potential of these technologies to improve the driving experience and road safety has been highlighted. We hope this survey can benefit researchers and practitioners to have more insights and provide directions for advancing drivers' emotions. © 2024 IEEE.

关键词： Emotion recognition

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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

4th EAI International Conference on Cognitive Computing and ...

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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

ISBN: (纸本)9783031488900

the proceedings contain 39 papers. the special focus in this conference is on Cognitive Computing and Cyber Physical Systems. the topics include: Drug Recommendations Using a Reviews and Sentiment Analysis by RNN;optimizing Real Estate Prediction - A Comparative Analysis of Ensemble and Regression Models;estimation of Power Consumption Prediction of Electricity Using machine learning;medical Plants Identification Using Leaves Based on Convolutional Neural Networks;An Efficient Real-Time NIDS Using machine learning Methods;Maixdock Based Driver Drowsiness Detection System Using CNN;A Novel Approach to Visualize Arrhythmia Classification Using 1D CNN;exploring machine learning Models for Solar Energy Output Forecasting;the Survival Analysis of Mental Fatigue Utilizing the Estimator of Kaplan-Meier and Nelson-Aalen;an Optimized Ensemble machine learning Framework for Multi-class Classification of Date Fruits by Integrating Feature Selection Techniques;EEMS - Examining the Environment of the Job Metaverse Scheduling for data Security;text Analysis Based Human Resource Productivity Profiling;a Novel Technique for Analyzing the Sentiment of Social Media Posts Using Deep learning Techniques;comparative Analysis of Pretrained Models for Speech Enhancement in Noisy Environments;Use of Improved Generative Adversarial Network (GAN) Under Insufficient data;unraveling the Techniques for Speaker Diarization;LUT-Based Area-Optimized Accurate Multiplier Design for Signal Processing Applications;speaker recognition Using Convolutional Autoencoder in Mismatch Condition with Small dataset in Noisy Background;face Emotion recognition Based on Images Using the Haar-Cascade Front End Approach;textRank – Based Keyword Extraction for Constructing a Domain-Specific Dictionary;harmonizing Insights: Python-Based data Analysis of Spotify's Musical Tapestry;Enlighten GAN for Super-Resolution Images from Surveillance Car.

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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

4th EAI International Conference on Cognitive Computing and ...

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4th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

ISBN: (纸本)9783031488870

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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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Lightweight Conditional Model Extrapolation for Streaming data under Class-Prior Shift 26

Lightweight Conditional Model Extrapolation for Streaming Da...

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

作者： Tomaszewska, Paulina Lampert, Christoph H. Warsaw Univ Technol Fac Math & Informat Sci Warsaw Poland Inst Sci & Technol Austria ISTA Machine Learning & Comp Vis Grp Klosterneuburg Austria

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

We introduce LIMES, a new method for learning with non-stationary streaming data, inspired by the recent success of meta-learning. the main idea is not to attempt to learn a single classifier that would have to work well across all occurring data distributions, nor many separate classifiers, but to exploit a hybrid strategy: we learn a single set of model parameters from which a specific classifier for any specific data distribution is derived via classifier adaptation. Assuming a multi-class classification setting with class-prior shift, the adaptation step can be performed analytically with only the classifier's bias terms being affected. Another contribution of our work is an extrapolation step that predicts suitable adaptation parameters for future time steps based on the previous data. In combination, we obtain a lightweight procedure for learning from streaming data with varying class distribution that adds no trainable parameters and almost no memory or computational overhead compared to training a single model. Experiments on a set of exemplary tasks using Twitter data show that LIMES achieves higher accuracy than alternative approaches, especially with respect to the relevant real-world metric of lowest within-day accuracy.

关键词： continual learning non-stationary data incremental training meta-learning class-prior shift

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Leveraging DQN-Based Recommender Systems for E-Commerce in Smart Cities 4th

Leveraging DQN-Based Recommender Systems for E-Commerce in S...

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

作者： Abakarim, Sana Qassimi, Sara Rakrak, Said Cadi Ayyad Univ Fac Sci & Tech L2IS Lab Marrakech Morocco

ISBN: (纸本)9783031821523;9783031821530

In the age of digital urban transformation, smart cities are emerging as ecosystems where technology, infrastructure and data converge to improve quality of life, sustainability and economic prosperity. In this context, e-commerce plays a central role, providing platforms for businesses to thrive by enabling seamless transactions, personalised shopping experiences and greater market reach. However, the dynamic and evolving nature of user preferences presents a significant challenge, requiring more adaptive and smart recommendation systems. this paper presents an approach by integrating Deep Q-Network (DQN), a reinforcement learning technique, into recommendation systems for e-commerce in smart cities. By comparing the proposed DQN model based recommender system with traditional models such as MLP, DeepFM, LSTM and CNN using metrics such as MSE, RMSE and NDCG@5, we demonstrate its superior performance in predicting user preferences and dynamically adapting to changes in user behaviour. the results highlight the potential of DQN models to revolutionise e-commerce recommender systems, delivering more personalised and adaptive user experiences in the interconnected environments of smart cities.

关键词： Deep Q-Network Recommender Systems Reinforcement learning Smart Cities E-Commerce machine learning Deep Reinforcement learning

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