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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

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Modulation pattern recognition based on ShuffleNet neural network 23

Modulation pattern recognition based on ShuffleNet neural ne...

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6th international conference on Signal Processing and Machine Learning, SPML 2023

作者： Guohao, Shi Z., Rui C., Jiuxiao B., Mengmeng Xijing University China

ISBN: (纸本)9798400707575

Modulation pattern recognition is widely used in civil and military fields. In this paper, a modulation pattern recognition technology based on convolutional neural networks is proposed and the accuracy of modulation signal recognition based on three kinds of convolutional neural networks is compared to analyze the performance of the neural network model. the experimental results show that the convolutional neural network can achieve feature extraction and classification recognition at the same time. the simulation results show that ShuffleNet neural network has higher recognition accuracy and classification consistency for modulation pattern recognition, and its feature extraction performance is better than that of VGG and ResNet neural network. During the experiment, modulation pattern recognition based on VGG neural network model has high computational complexity and model size, which requires high hardware level. In this paper, modulation pattern recognition technology based on ShuffleNet neural network is proposed. Combined with the characteristics of neural network: point-to-point deep convolution and channel random rearrangement, the network model is constructed. the theoretical analysis and simulation results show that the modulation pattern recognition method based on ShuffleNet network model has lower computational complexity, fewer model parameters and smaller model size while ensuring the recognition accuracy. © 2023 ACM.

关键词： Feature extraction

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6th international conference on Machine Learning for Networking, MLN 2023

6th International Conference on Machine Learning for Network...

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6th international conference on Machine Learning for Networking, MLN 2023

ISBN: (纸本)9783031599323

the proceedings contain 18 papers. the special focus in this conference is on Machine Learning for Networking. the topics include: Attentive, Permutation Invariant, One-Shot Node-Conditioned Graph Generation for Wireless Networks Topology Optimization;enhancing Social Media Profile Authenticity Detection: A Bio-Inspired Algorithm Approach;a Deep Learning Based Automatic Outdoor Home Video Surveillance Approach;detecting Abnormal Authentication Delays In Identity And Access Management Using Machine Learning;SIP-DDoS: SIP Framework for DDoS Intrusion Detection Based on Recurrent Neural Networks;deep Reinforcement Learning for Multiobjective Scheduling in Industry 5.0 Reconfigurable Manufacturing Systems;Toward A Digital Twin IoT for the Validation of AI Algorithms in Smart-City Applications;data Summarization for Federated Learning;ML Comparison: Countermeasure Prediction Using Radio Internal Metrics for BLE Radio;towards Road Profiling with Cooperative Intelligent Transport Systems;Study of Masquerade Attack in VANETs with Machine Learning;detecting Virtual Harassment in Social Media Using Machine Learning;Leverage Data Security Policies Complexity for Users: An End-to-End Storage Service Management in the Cloud Based on ABAC Attributes;machine Learning to Model the Risk of Alteration of Historical Buildings;a Novel Image Encryption Technique Using Modified Grain-128;transformation Network Model for Ear recognition.

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6th international conference on Dynamics of Information Systems, DIS 2023

6th International Conference on Dynamics of Information Syst...

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6th international conference on Dynamics of Information Systems, DIS 2023

ISBN: (纸本)9783031503191

the proceedings contain 18 papers. the special focus in this conference is on Dynamics of Information Systems. the topics include: Dispersion of Personal Spaces;an Evolutionary Approach to Automated Class-Specific Data Augmentation for Image Classification;augmented Lagrangian Method for Linear Programming Using Smooth Approximation;the Coherent Multi-representation Problem for Protein Structure Determination;the Effects of Shift Generation on Staff Rostering;consumers Financial Distress: Prediction and Prescription Using Machine Learning;optimizing Classroom Assignments to Reduce Non-essential Interactions Among University-Level Students During Pandemics;introducing a New Metric for Improving Trustworthiness in Real Time Object Detection;a Numerical Scheme for a Generalized Fractional Derivative with Variable Order;a Risk-Cost Analysis for an Increasingly Widespread Monitoring of Railway Lines;a Collaborative Multi-objective Approach for Clustering Task Based on Distance Measures and Clustering Validity Indices;iranian Architectural Styles recognition Using Image Processing and Deep Learning;a Quasi-extreme Reduction for Interval Transportation Problems;assignment of Unexpected Tasks in Embedded System Design Process Using Genetic Programming;Improving Handwritten Cyrillic OCR by Font-Based Synthetic Text Generator;a Critical Node-Centric Approach to Enhancing Network Security.

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3D point clouds simplification based on geometric primitives and graph-structured optimization 26

3D point clouds simplification based on geometric primitives...

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

作者： Guinard, Stephane A. Daniel, Sylvie Badard, thierry Univ Laval Dept Sci Geomat Quebec City PQ G1V 0A6 Canada

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

We present a new method for the simplification of 3D point clouds for digital twin city models. Such data usually contains a large amount of redundant information, noise and outliers. this implies that most of subsequent processing tasks are costly both in terms of processing times and hardware infrastructure. the core idea of this paper is that most of the objects present in such scenes can be approximated as a combination of simple geometric primitives. this approximation can, in turn, be simplified by keeping only points and edges that effectively describe the shape of the input scene. Our main contribution is a formulation of the approximation of 3D point clouds with simple geometric primitives as a global optimization problem. We then introduce a new algorithm to efficiently solve this problem with a graph-cut-based approach. We measure the performances of our approach against state-of-the-art methods by comparing the geometric quality of the approximations with the amount of information needed to represent the simplified models. the evaluation of our approach is done on Mobile Laser Scanning (MLS) acquisitions in urban areas. Test areas include single objects and street portions to show the adaptability of our method to various geometries. We show improvements both in terms of geometrical error and final model size.

关键词： Point cloud compression Geometry Solid modeling Adaptation models three-dimensional displays Urban areas Vegetation

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Reinforcement-based Display Selection for Frugal Learning 26

Reinforcement-based Display Selection for Frugal Learning

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

作者： Deschamps, Sebastien Sahbi, Hichem Sorbonne Univ UPMC CNRS LIP6 Paris France Theresis Thales Paris France

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Most of the existing learning models, particularly deep neural networks, are reliant on large datasets whose hand-labeling is expensive and time demanding. A current trend is to make the learning of these models frugal and less dependent on large collections of labeled data. Among the existing solutions, deep active learning is currently witnessing a major interest and its purpose is to train deep networks using as few labeled samples as possible. However, the success of active learning is highly dependent on how critical are these samples when training models. In this paper, we devise a novel active learning approach for label-efficient training. the proposed method is iterative and aims at minimizing a constrained objective function that mixes diversity, representativity and uncertainty criteria. the proposed approach is probabilistic and unifies all these criteria in a single objective function whose solution models the probability of relevance of samples (i.e., how critical) when learning a decision function. We also introduce a novel weighting mechanism based on reinforcement learning, which adaptively balances these criteria at each training iteration, using a particular stateless Q-learning model. Extensive experiments conducted on staple image classification data, including Object-DOTA, show the effectiveness of our proposed model w.r.t. several baselines including random, uncertainty and flat as well as other work.

关键词： Training Adaptation models Uncertainty Self-supervised learning Linear programming Probabilistic logic Data models

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ICECC 2023 - Proceedings of the 2023 6th international conference on Electronics, Communications and Control Engineering

ICECC 2023 - Proceedings of the 2023 6th International Confe...

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6th international conference on Electronics, Communications and Control Engineering, ICECC 2023

ISBN: (纸本)9798400700002

the proceedings contain 47 papers. the topics discussed include: image recognition accuracy, number of parameters and computational complexity using channel reduction by dimensional compression and attention function;face recognition (FR) integration on MABIS: a mobile automated biometric identification system for law enforcement in the Philippines;identifying users based on their activity pattern using machine learning;no reference stereoscopic video quality assessment considering self-attention and different resolution level;graph structure exploration for reinforcement learning state embedding – train Tetris agent with graph neural network;biomechanical signal measurement of the hand and condition assessment system using fuzzy logic in Parkinson’s patients;design and implementation of a portable audio signal spectrum analyzer for noise management;and mitigation of greenhouse emissions from transportation systems through modelling and data.

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Biometric Muzzle pattern recognition for Cattle Identification using Deep Learning 6

Biometric Muzzle Pattern Recognition for Cattle Identificati...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Jayakumar, Nithya Nithish Kumar, R. Ruthrakumar, A. Saikavya, K. Department of Information Technology K. S. Rangasamy College of Technology Tiruchengode India

ISBN: (纸本)9798350398496

the ability to identify specific cows is a critical aspect of keeping cattle. Registration is essential for the production, distribution, and breeding of cows. It is dangerous to brand cows in the ear in the traditional manner. To solve this issue, we recommend using a biometric scanner for identification. Numerous studies have shown that the muzzle pattern can be used to distinguish animals and eliminate inconsistent patterns. this strategy might be effective in reducing spurious matching muzzle patterns. On the tip of their nose or muzzle, the majority of animals have a distinctive pattern. An animal's characteristic pattern is obvious to the unaided eye once it is born. Animals can be recognized by human muzzle patterns, just like with fingerprints. Systems for identifying animals are needed, as they would be useful for submitting loan and insurance applications. Based on a biometric muzzle scanner, the approach of machine learning, image processing, and computer vision has been assessed for identification purposes. © 2023 IEEE.

关键词： Biometrics

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the Conglomerate Compression Approach for Visual pattern recognition Networks in Edge AI Applications 6

The Conglomerate Compression Approach for Visual Pattern Rec...

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6th IEEE Pune Section international conference, PuneCon 2023

作者： Bhalgaonkar, Seema Munot, Mousami Anuse, Alwin SCTR's Pune Institute of Computer Technology & PES's Mcoe Sppu Dept of E&tc Pune India SCTR's Pune Institute of Computer Technology Sppu Dept of E&tc Pune India School of Ece Dr. Vishwanath Karad MIT-WPU Pune411038 India

ISBN: (纸本)9798350324204

Deep Neural Networks have shown remarkable performance in various applications of Visual pattern recognition (VPR). this field continues to grow due to emergence of new architectures, availability of huge data, and powerful computing resources. However, because of their high computational complexity that subsequently demands higher memory requirements, and power consumption, the deployment of such DNN-based Visual pattern recognition Networks (VPRN) models in edgeAI applications is hindered. Such overheads necessitate the compression of VPRN models while retaining their performance. this paper explores multiple compression techniques which combines Knowledge Distillation (KD), pruning and quantization that are applied to DNNs to achieve higher compression ratio. © 2023 IEEE.

关键词： Distillation

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Image Grid recognition and Regression for Fast and Accurate Face Detection 26

Image Grid Recognition and Regression for Fast and Accurate ...

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

作者： Zhou, Liguo Chen, Guang Zhang, Chao Knoll, Alois Tech Univ Munich Inst Informat 6 Garching Germany Tongji Univ Sch Automot Studies Shanghai Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

CNN-based face detection methods have achieved significant progress in recent years. However, for high performance face detection, there are still many challenging problems, e.g., the speed-accuracy balance and the performance degradation in adverse conditions. In this paper, by taking advantage of the characteristic of CNN, we propose an effective anchor generation and bounding-box regression method that can make a good balance between speed and accuracy, and also work well in bad conditions. the classic structure of CNN produces pyramid-like feature maps due to the pooling or other downscale operations. According to the size of a feature map, we divide the image into grids. Each grid corresponds to a point in the feature map. We make the corresponding feature point responsible for identifying the content of the grid. If this grid area belongs to the face area, it is a natural anchor for face bounding-box regression. Since this anchor is square, it is reasonable to use it to predict the face bounding-box which is square-like. the points in the lower-level feature map correspond to smaller grids, which are dedicated to predicting the bounding-boxes of smaller faces. the points in the higher-level feature maps correspond to larger grids, which are responsible for predicting the bounding-boxes of larger faces. Hence our method can effectively detect multi-scale faces. With this effectiveness, our method can achieve a high detection accuracy using fewer parameters which leads to a fast detection speed. the experiments demonstrate the effectiveness of our method.

关键词： Degradation Image recognition Face recognition Graphics processing units Feature extraction Face detection

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Adversarial Attack on Semantic Segmentation Preprocessed with Super Resolution 26

Adversarial Attack on Semantic Segmentation Preprocessed wit...

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

作者： Lim, Gyeongsup Kim, Minjae Hur, Junbeom Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Computer vision tasks, such as image classification, semantic segmentation, and super resolution, are broadly utilized in many applications. Recent studies revealed that machine learning-based models for the computer vision tasks are vulnerable to adversarial attacks. Since the adversarial attack can disturb the computer vision models in real-world systems, many countermeasures have been proposed against the adversarial attacks, such as denoising, resizing, and machine learning-based super resolution model as a preprocessing. Recently, a prior work demonstrated that the super resolution model as a preprocessing can be vulnerable to the adversarial attack targeted to the preprocessing itself, only when the perturbation is inactive before the preprocessing. However, we also found that the perturbation before the preprocessing can be another serious threat if the super resolution model is used for a mitigation of adversarial attacks. In this paper, we propose Layered Adversary Generation (LAG) that generates the adversarial example by recursively injecting noises to clean image in white-box environment. We then show that LAG is effective to attack a semantic segmentation model even if the super resolution models with/without two countermeasures as auxiliary methods such as resizing and denoising are adopted to mitigate the adversarial attacks. Furthermore, we demonstrate that LAG is transferable across other super resolution models. Lastly, we discuss our attack method in gray-box and black-box environments, and suggests a mitigation for robust preprocessing.

关键词： Computer vision Computational modeling Semantic segmentation Perturbation methods Superresolution Noise reduction pattern recognition

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