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检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1778 条记录，以下是271-280 订阅

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4th international conference on machine learning for Cyber Security, ML4CS 2022

4th International Conference on Machine Learning for Cyber S...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

ISBN: (纸本)9783031200984

the proceedings contain 152 papers. the special focus in this conference is on machine learning for Cyber Security. the topics include: machine learning Based Security Situation Awareness Method for Network data Transmission Process;multi-objective Hydrologic Cycle Optimization for Integrated Container Terminal Scheduling Problem;High Voltage Power Communication Network Security Early Warning and Monitoring System Based on HMAC Algorithm;Large Scale Network Intrusion Detection Model Based on FS Feature Selection;Privacy Preserving CSI Fingerprint Device-Free Localization;Research on Intelligent Detection Method of Automotive Network data Security Based on FlexRay/CAN Gateway;adversarial Attack and Defense on Natural Language Processing in Deep learning: A Survey and Perspective;a Novel Security Scheme for Mobile Healthcare in Digital Twin;Construction of Security Risk Prediction Model for Wireless Transmission of Multi Axis NC Machining data;spiking Neural Networks Subject to Adversarial Attacks in Spiking Domain;Diverse Web APIs Recommendation with Privacy-preservation for Mashup Development;network Security Evaluation Method of College Freshmen Career Counseling Service Based on machine learning;FedTD: Efficiently Share Telemedicine data with Federated Distillation learning;increase Channel Attention Based on Unet++ Architecture for Medical Images;distributed Power Load Missing Value Forecasting with Privacy Protection;A Novel Blockchain-MEC-Based Near-Domain Medical Resource Sharing Model;differentially Private Generative Model with Ratio-Based Gradient Clipping;differential Privacy Protection Algorithm for data Clustering Center;improved Kmeans Algorithm Based on Privacy Protection;symmetry Structured Analysis Sparse Coding for Key Frame Extraction;data Reconstruction from Gradient Updates in Federated learning;natural Backdoor Attacks on Speech recognition Models;boarding Pass Positioning with Jointly Multi-channel Segmentation and Perspective Transformation Co

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Improving Image Compression Performance by Spatial-Channel Context Adaptive Model 4

Improving Image Compression Performance by Spatial-Channel C...

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4th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Wang, Hao Wang, Huifen Xue, Junda Lu, Enmin Wang, Hanming Wu, Zijun Song, Yunlong Institute of Big Data and Artificial Intelligence China Telecom Corporation Limited Beijing Research Institute Beijing China

ISBN: (纸本)9798350314496

the significance of enhancing image compression efficiency for machine vision, analysis, and comprehension tasks has gained increasing recognition. In response to this need, we propose and implement a novel method called ELIC (Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding) to achieve high compression efficiency. Our method is evaluated on the classic OpenImage V6 Common Test Condition (CTC) eval datasets, and its performance is compared to baseline methods for machine vision tasks. the results of our study demonstrate a substantial enhancement in compression efficiency, suggesting that the ELIC technique holds promise for pushing the boundaries of state-of-the-art visual compression for vision tasks. Furthermore, we believe that our approach can promote the application of learning-based image compression. © 2023 IEEE.

关键词： Image compression

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4th international conference on machine learning for Cyber Security, ML4CS 2022

4th International Conference on Machine Learning for Cyber S...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

ISBN: (纸本)9783031201011

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Classifying Sign Language Gestures using Decision Trees: A Comparison of sEMG and IMU Sensor data 4

Classifying Sign Language Gestures using Decision Trees: A C...

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4th international conference for Emerging Technology, INCET 2023

作者： Nadaf, Akhtar Pardeshi, Sanjay E&TC Dept. NKOCET Solapur India Shivaji University Kolhapur Maharashtra India

ISBN: (纸本)9798350335750

the use of machine learning technologies for the identification of sign language has gained popularity recently. this arises from the recognition of sign language as a valuable means of communication specifically designed for individuals who are mute or hearing-impaired. In order to build an optimised model based on sEMG, accelerometer, gyro, and magnetometer data, this research article compares decision tree classifiers, notably J48, Random tree, REPTree, and Random forest. this data is collected through the Myo arm band worn on both forearms of the user. the experiment is designed using the open-source Waikato Environment for Knowledge Analysis (WEKA) framework. To evaluate the effectiveness of the four algorithms, three attribute selection techniques information gain-based, correlation-based, and learner-based feature selection were used. the trial results showed that, among the investigated algorithms, the Random Forest method had the highest accuracy, measuring 97.9472%. © 2023 IEEE.

关键词： Decision trees

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Proceedings - 2022 4th international conference on pattern recognition and Intelligent Systems, PRIS 2022

Proceedings - 2022 4th International Conference on Pattern R...

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4th international conference on pattern recognition and Intelligent Systems, PRIS 2022

ISBN: (纸本)9781450396080

the proceedings contain 17 papers. the topics discussed include: image data augmentation method based on style transfer;FPGA hardware implementation of Q-learning algorithm with low resource consumption;a machine-learning pipeline for semantic-aware and contexts-rich video description method;multi-scale and kernel-predicting convolutional networks for Monte Carlo denoising;single image dehazing algorithm based on generative adversarial network;biparted hyperboloid and sphere intersection algorithm;optimal sensor deployment in complex 3D terrain surface;weakly supervised road garbage quantification system based on WCCA;online detection of character and 3D surface defect in steel rail production;a credit scoring ensemble framework using Adaboost and multi-layer ensemble classification;and an LSTM-based method for recognition and prediction of aircraft formation.

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Governance Considerations of Adversarial Attacks on AI Systems 4

Governance Considerations of Adversarial Attacks on AI Syste...

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4th international conference on AI Research, ICAIR 2024

作者： Lekota, Nombulelo Faith Rio de Janeiro Brazil

ISBN: (纸本)9798331309466

Artificial intelligence (AI) is increasingly integrated into various aspects of daily life, but its susceptibility to adversarial attacks poses significant governance challenges. this paper explores the nature of these attacks, where malicious actors manipulate input data to deceive AI algorithms and their profound implications for individuals and society. Adversarial attacks can undermine critical AI applications, such as facial recognition and natural language processing, leading to privacy violations, biased outcomes, and eroding public trust. the discussion emphasizes understanding the threat vectors associated with adversarial attacks and their potential repercussions. It advocates for robust governance frameworks encompassing risk management, oversight, and legislative measures to protect AI systems. Such frameworks should prioritize AI technologies' confidentiality, integrity, and availability (CIA) while ensuring compliance with ethical standards. Furthermore, the paper examines various strategies for mitigating risks associated with adversarial attacks, including training and continuous monitoring of AI systems. It highlights the importance of accountability among developers and researchers in implementing preventive measures that align with principles of transparency and fairness. Organizations can enhance security and foster public trust by integrating legislative frameworks into AI development standards. As AI technologies evolve, continuous review of governance practices is essential to address emerging threats effectively. this paper ultimately focuses on the critical role of comprehensive governance in safeguarding AI systems against adversarial attacks, ensuring that technological advancements benefit society while minimizing risks. © Proceedings of the 4th international conference on AI Research, ICAIR 2024.

关键词： Adversarial machine learning

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Exercise Pose recognition and Counting System using Robust Topological Landmarks 18

Exercise Pose Recognition and Counting System using Robust T...

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18th international Joint Symposium on Artificial Intelligence and Natural Language Processing (ISAI-NLP) / 4th international conference on Artificial Intelligence and Internet of things (AIoT)

作者： Mya, Nyan Lin Deepaisarn, Somrudee Chonnaparamutt, Winai Laitrakun, Seksan Nakayama, Minoru Thammasat Univ Sirindhorn Int Inst Technol Pathum Thani Thailand Natl Sci & Technol Dev Agcy Natl Elect & Comp Technol Ctr Pathum Thani Thailand Tokyo Inst Technol Tokyo Tokyo Japan

ISBN: (纸本)9798350371215

the paper introduces the exercise recognition and counting system based on vision systems. the current detectable exercises include barbell curls, push-ups, and lateral raises from the "Workout/Exercise Images" dataset, an open-source dataset from Kaggle. the system can predict and count live streams in real-time using a mobile phone or laptop camera. We improve the robustness of the system by using centering and scaling. Furthermore, this research reduces the complexity of the trained data by half without losing the accuracy of the machine learning model by implementing Principal Component Analysis (PCA). the implications of this research extend beyond the realm of fitness tracking, with potential applications for in-home exercise, sports analysis, physical therapy, and interactive fitness technologies.

关键词： Pose Estimation Exercise recognition machine learning Random Forest Principal Component Analysis (PCA)

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Oral Voice recognition System Based on Deep Neural Network Posteriori Probability Algorithm 4

Oral Voice Recognition System Based on Deep Neural Network P...

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4th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Zhang, Neng School of Foreign Languages Fuzhou University of International Studies and Trade Fujian Fuzhou350202 China

the unstoppable trend of globalization has made the integration of voice recognition with science and technology increasingly close, and has called for the emergence of more powerful oral voice recognition systems. At present, most speech recognition research uses Hidden Markov Model-Gaussian Mixture Modell (HMM-GMM) and onnectionist Temporal Classification (CTC) end-to-end method, which has obvious limitations. therefore, based on neural network, this paper introduces deep neural network posterior probability algorithm into speech recognition system, and constructs the spoken speech recognition system based on deep neural network posterior probability algorithm. the design principle of the constructed speech system is discussed and the performance analysis and effectiveness evaluation are carried out. the experimental results show that the performance of the neural network-based speech recognition system constructed in this paper has made a breakthrough compared with the traditional technology, and the Character Error Rate (CER) is only 4.6%, and the End-to-End Latency and Equal Error Rate (EER) have reached a low level. this study points out the future development direction of speech recognition and provides the impetus to promote its further development. © 2024 the Authors.

关键词： Hidden Markov models

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Chess Piece Image recognition Using Transfer learning, Simple Neural Network, and Convolutional Neural Network 4

Chess Piece Image Recognition Using Transfer Learning, Simpl...

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4th international conference on Artificial Intelligence and data Sciences, AiDAS 2023

作者： Yohanes, Gabriel Nursalim, Mario Nicholas Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

Online chess has opened up a way for players to sharpen their skills through move analytics. Based on this feature, a support system called chess advisor can be utilized to assist players in a real-time match. However such system doesn't exist within the website itself, rather an involvement of 3rd party software is required. these kinds of software usually need assistance for input. this paper aims to create a system that is able to recognize chessboard state in real-time. We have proposed a chess piece image recognition system using transfer learning using ResNet50, a simple neural network, and a convolutional neural network (CNN). We collected our own 693 2D chess piece images dataset and performed data preprocessing to standardize the image dimensions. the proposed system detects and converts individual positions of each piece into a computer-readable output. this output can be used to facilitate the chess advisor in determining the best move. the transfer learning approach utilizing the ResNet50 model exhibited superior performance with an accuracy of 95.65%. this outperformed alternative methods such as the simple neural network and the CNN. the ResNet50 model's advantage stemmed from its pre-training on the ImageNet dataset, enabling effective pattern generalization. In contrast, the simple neural network and CNN achieved lower accuracies of 73.4% and 85%. these models encountered difficulties due to limited dataset quantity and quality, as well as the absence of regularization techniques. Nevertheless, the CNN displayed better consistency in generalizing patterns compared to the simple neural network. © 2023 IEEE.

关键词： Image recognition

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A Review on Skin Pigment and Vein pattern Detection Techniques 4th

A Review on Skin Pigment and Vein Pattern Detection Techniqu...

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4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Kharakwal, Komal Raiwani, Y. P. Verma, Rohan HNB Garhwal Univ Dept Comp Sci & Engn Srinagar Uttarakhand India

ISBN: (纸本)9783031243660;9783031243677

Over the past few years, image processing has dominated work in suspect detection, medical science, and meteorology. Facial recognition, finger knuckles, Relatively Permanent Pigmented or Vascular Skin Marks (RPPVSM), vein patterns, and biometrics are all included in this review paper. Unlike birthmarks, RPPVSM is inherited, whereas birthmarks are acquired. the two primary elements of RPPVSM are matching and detection. Consequently, it is improbable that redundant evidence could produce a vein pattern. Several cases involving child sexual assault or disharmony have been resolved by using RPPVSMs as a biometric attribute for recognition. In this paper, we also give a review on findings and results of various authors.

关键词： RPPVSM Vein Criminal Identification Biometric

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