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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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Network intrusion detection model based on K-means algorithm and Multi-CNNs 4

Network intrusion detection model based on K-means algorithm...

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4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Zhou, Yang Zhang, Wei Zeng, Haoyang Zheng, Zhourong China Electronic Technology Cyber Security Co. Ltd. Chengdu610041 China The 30th Research Institute of CETC Chengdu610041 China Army Unit 95486 Chengdu610041 China

ISBN: (纸本)9781510686106

Network intrusion detection has always been a pivotal area in the field of network security. this paper proposes a novel network intrusion detection model that integrates the K-means algorithm with multiple convolutional neural networks (Multi-CNNs). this model comprises two main modules: network attack learning and network attack recognition. the network attack learning module focuses on learning known traffic features. Initially, the K-means algorithm is utilized to cluster the traffic features. Subsequently, Multi-CNNs are constructed, each tailored to extract the spatial features of the data within its respective cluster. through supervised learning, a corresponding relationship is established between the inputs and the attack patterns. the output of this module includes the K-means clustering centers and the trained CNN models, which serve as the foundation for network attack recognition. the network attack recognition module is responsible for detecting unknown traffic. Firstly, the unknown traffic features are assigned to their respective clusters according to the distance from them to the K-means clustering centers. then, Multi-CNNs are employed to identify the attack pattern for each clustered feature independently. Finally, the detection outcomes from the Multi-CNNs are fused to achieve intrusion detection. the experimental results demonstrate that the integration of K-means clustering and Multi-CNNs can significantly enhance the performance of network intrusion detection. Specifically, for the UNSWNB15 and CICIDS2017 datasets, the proposed model achieves an improvement in F1-score by 11.11% and 0.27%, respectively, compared to the baseline model. © 2024 SPIE.

关键词： Network intrusion

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Research on YOLOv4 Object Detection Based on K-means Algorithm and Fusion Attention Mechanism 4

Research on YOLOv4 Object Detection Based on K-means Algorit...

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4th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

作者： Peng, Chufei Shenzhen MSU-BIT University Shenzhen China

ISBN: (纸本)9798350308082

Object detection technology is a machine learning application based on deep learning and computer vision technology, which can replace error-prone manual inspections in various scenarios, such as mask recognition applications. In view of the problems of easy omission and difficult error detection in traditional object detection algorithms, this paper proposes a research on YOLOv4 object detection based on K-means and fusion attention mechanism, with mask recognition as the research object. Firstly, the preprocessing of the dataset is carried out. the K-means algorithm is used to cluster and analyze the face mask dataset, generate more suitable anchor box prior boxes, and use the Mosaic method for data augmentation. then, the SE module and SPP module of the channel attention mechanism are introduced to improve the basic YOLOv4 model, and the SE-SPP3-YOLOv4 detection model is constructed. the experiment shows that the proposed improved model has significantly improved the detection rate and accuracy of both face and mask targets, and can perform accurate recognition of the wearing of masks on faces in specific application scenarios. © 2023 IEEE.

关键词： Deep learning

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machine learning Method Evaluates Upper-Limb Motor Function by fNIRS in Stroke Patients 4

Machine Learning Method Evaluates Upper-Limb Motor Function ...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Xu, Weiwei Tao, Yi Zhu, Jialin Yuan, Ziwen Guo, Jing Wang, Gang Xi'An Jiaotong University Key Lab. of Biomed. Info. Engineering of Ministry of Education School of Life Science and Technology Xi'an China First Affiliated Hospital of xi'An Jiaotong University Department of Rehabilitation Xi'an China

ISBN: (纸本)9798350324303

the upper limb motor impairment after stroke seriously affects patients' quality of life, and the evaluation of upper limb motor function depends on professionals, which is subjective, complicated, and time-consuming. Neurophysiological information evaluation Fugl-Meyer assessment of the upper limb (FMA-UL) is expected to solve this problem. Our study aims to estimate FMA-UL using the functional connectivity measures and network properties of task state functional near-infrared spectroscopy (fNIRS). FNIRS was recorded when the subjects performed the hand-grasping task, representing the oxyhemoglobin concentration changes. Functional connectivity was gauged via Pearson correlation coefficients, selectively retained within a 10% to 50% threshold. We calculated two macroscale and two mesoscale network properties based on graph theory. then the support vector regression (SVR) was applied to estimate the FMA-UL. Cross-validation with leave-one-out, resulted in an R^{\mathbfth} of 0.49, with a root mean square error of 4.96, 5 of the 15 subjects had absolute errors less than 0.75, when the sparsity of functional connectivity is 40%. Functional connectivity and network properties by fNIRS is a promising method to predict motor impairment. Our study demonstrated that functional connectivity and network properties are correlated with motor impairment and brain reorganization in stroke patients. the method proposed reflects prospects for finding neural biomarkers to predict FMA-UL and is feasible for using fNIRS to predict the motor function of stroke. © 2023 IEEE.

关键词： Graph theory

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