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Comprehensive Analysis of Pose Estimation and machine Learning Classifiers for Precise Yoga Pose Detection and Classification 3

Comprehensive Analysis of Pose Estimation and Machine Learni...

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3rd international conference on machine Learning and Data Engineering, ICMLDE 2024

作者： Meghana, J.H. Chethan, H.K. Santhosh Kumar, K.S. Prakash, S.P. Shiva Department of Studies in Computer Science University of Mysore Mysuru Karnataka570006 India Department of Computer Science and Engineering Maharaja Institute of Technology Thandavapura Mysuru571302 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering University of Mysore Mysuru Karnataka570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru Karnataka570006 India

Yoga contributes to mental and physical well-being by improving flexibility, strength, balance, and emotional stability when integrated into daily routines. This ancient practice can become more accessible and adaptable to a wider audience when combined with modern artificial intelligence (AI). This study introduces a comprehensive system for detecting and classifying yoga poses using computer vision and machine learning techniques. Central to this work is the application of posture estimation algorithms, such as MediaPipe, PoseNet, and OpenPose, to identify key points on the human body within a single image or video frame. These key points are analyzed in both two-dimensional (2D) and three-dimensional (3D) spaces to construct a skeletal representation of the body, enabling accurate classification of yoga poses. The study focuses on five distinct yoga poses: Downdog, Goddess, Plank, Tree, and Warrior II. To categorize these poses, machine learning classifiers including Support Vector machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes utilize the key points extracted from the pose estimation models. This research is distinctive in its thorough evaluation of various conventional classifiers across multiple yoga positions. A comprehensive comparative analysis is essential for identifying the most effective classifiers for posture detection and classification. The dataset used in this study has been carefully curated to encompass a wide array of yoga poses and is divided into training and testing sets at various ratios (90:10, 80:20, 70:30, and 60:40) to ensure robust validation. Results indicate the system's effectiveness, with SVM and KNN consistently achieving high values for AccuracyPE, PrecisionPE, RecallPE, and F1-S corePE across all yoga poses. Notably, Random Forest attains up to 100% AccuracyPE in detecting and classifying certain poses, demonstrating its robustness and reliability. This research highlights the potential of integrating p

关键词： Deep learning

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recognition of Active Deception Jamming for LFM Radar Based on Hyperdimensional Computing 3

Recognition of Active Deception Jamming for LFM Radar Based ...

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2022 3rd international conference on Electrical, Electronic Information and Communication Engineering, EEICE 2022

作者： Dong, Hao Wang, Yan Hu, Wen Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

Active deception jamming is one of the common means to jam radar signals. How to effectively recognize active deception jamming is a challenge of modern radar technology. To address the accuracy and real-time of radar active jamming recognition in practical applications, we propose a fast recognition method of radar active deception jamming based on hyperdimensional computing. First, to address the high feature dimensions, we use the sparse representation of time-frequency diagrams as the input, which time-frequency diagrams are constructed by the jamming plus echo signals in multiple consecutive pulse periods. Second, to achieve a high accuracy and real time of recognition, we employ the hyperdimensional computing to map the input to hyperdimensional space for training and recognition. Last, in the recognition stage, the Hamming distance is adopted to measure the similarity of samples. We validate our method on nine types of radar active deception jamming. The experimental results show that compared with the existing mainstream machine learning-based methods, the proposed method greatly shortens the training time on the premise of maintaining a high recognition accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Hamming distance

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Automatic Measurement Method of Pig Body Size Based on Improved Point Transformer Segmentation Network 3

Automatic Measurement Method of Pig Body Size Based on Impro...

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3rd international conference on Image Processing, Computer Vision and machine Learning, ICICML 2024

作者： Shi, Kaixi Geng, Yanli Liu, Sitong Fu, Yanfang Hebei University of Technology School of Artificial Intelligence China Hebei Provincial General Animal Husbandry Station China

ISBN: (纸本)9798350355413

At present, most of the non-contact body size measurement methods based on the complete point cloud of pigs are complex and susceptible to interference from other parts of the point cloud when selecting key points. Therefore, this paper proposes an automatic measurement method of pig body size based on improved Point Transformer point cloud segmentation network. Firstly, a point cloud acquisition channel for pigs was built to collect point clouds on the left and right sides of 56 Yorkshire pigs aged 80-110 days. Secondly, the point cloud is preprocessed to remove the noise outside the pig point cloud, and the pig point cloud segmentation data set including head, chest, abdomen, buttocks and tail is constructed. Then, based on the Point Transformer, the adaptive convolution (AdaptConv) is introduced into the upsampling module, and the ACPT network is proposed for pig point cloud segmentation by combining the offset attention mechanism and the Focal loss function. Finally, the key points of the five-part segmentation part of the pig are found for body size measurement. The experimental results show that the accuracy of the improved segmentation network is 88.5 %, which is 5.5 % higher than that of the original network and better than other networks. The absolute error between the automatic measurement results and the measured values was within 4.8 cm, and the average relative error was within 5.1 %. The research shows that this method improves the measurement accuracy and has a better application prospect in non-contact body measurement. © 2024 IEEE.

关键词： body size measurement non-contact measurement point cloud segmentation Point Transformer

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Implemention of a machine intelligence Pronunciation System Based on Mul Tran to Improve the Basic English Speaking Skills

Implemention of a Machine Intelligence Pronunciation System ...

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Artificial intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Jingjing Zou School of Foreign Languages Shenyang Jianzhu University Shenyang Liaoning China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

In the process of learning English, English pronunciation has always been difficult. This article was based on the Mul Tran (Multilingual Translation) platform and used various methods such as animation, sound, images, and text to learn and train English phonetics. It can provide effective pronunciation feedback for learners, guide and correct their continuous training, and improve their oral pronunciation. The system includes pronunciation demonstration, pronunciation follow-up, pronunciation comparison, and pronunciation scoring functions. The system also provides the function of “continuous following of original sounds”, which can continuously follow and score the original sounds, and comprehensively compare the pronunciation of each speech of the learners. For each pronunciation, according to the resonance peak image, the mouth shape was improved. The effective rate of correcting vowel pronunciation was 0.899, and the effective rate of correcting word pronunciation was 0.928, which had a certain guiding effect on student pronunciation correction.

关键词： Training Accuracy Shape Mouth Speech recognition Phonetics Speech Resonance Noise measurement machine intelligence

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Overview of Autonomous Marine Surveillance Device 3

Overview of Autonomous Marine Surveillance Device

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3rd international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Balaje, Dhivya Vibha, V. Pichumani, Harish Sarkar, Swagata Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9781665462754

The use of Autonomous Underwater Vehicles (AUVs) for environment exploration, data collection and research developments has been growing day by day. Beach checks and surveys are documented studies about land erosion, beach erosion, underwater mapping, and various other aspects of the marine environment. These surveys are done manually or with quad bikes, commercially coded hovercrafts, etc, that are ultimately powered by humans. We propose an autonomous beach-check survey device, making use of image processing and machine learning algorithms to inspect the entire camera in the underwater body and collect the data required. This vehicle will focus on covering all the operations to be performed which would reduce human intervention and manpower. © 2022 IEEE.

关键词： Autonomous vehicles

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Application of Gradient Boosting Classifier-Based Computational intelligence to Detect Drug Addiction Threat in Society 3rd

Application of Gradient Boosting Classifier-Based Computatio...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Kumar, Ashutosh Sinha, Abhigyan Bakshi, Tamoghno Choudhury, Sibashish Mishra, Sushruta Abualigah, Laith Kalinga Institute of Industrial Technology Deemed to be University Bhubaneswar India Computer Science Department Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Jadara Research Center Jadara University Irbid21110 Jordan

ISBN: (纸本)9789819708918

Currently, drug and alcohol addiction has become a major menace to society's youth. As responsible citizens of this country, we must act now to keep these young brains from succumbing to this lethal addiction. In this research, a method based on intelligent analytics is designed for forecasting the threat of drug addiction using machine learning algorithms. Initially, we identify several reasons for drug addiction by speaking with health professionals, individuals addicted to drugs, and reading related studies and articles. Then we gather information from both addicts and abstainers. After preprocessing the data, we use Random Forest, Naive Bayes, Adaptive Boosting, and Gradient Boosting machine and assess the interpretation of each of these classifiers using certain well-known performance criteria. Outcome with Gradient Boosting showed promising with the highest accuracy rate of 95.4% and minimum error rate of 0.0854. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adaptive boosting

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IoT-based simulation of flocking algorithm 3

IoT-based simulation of flocking algorithm

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3rd international conference on Signal Processing and machine Learning, CONF-SPML 2023

作者： Wang, Xiaojian College of Artificial Intelligence Nanjing Agricultural University Jiangsu Province Nanjing210000 China

The study of animal groups' flocking behavior is of great significance to the study of multi-agent system. Flocking algorithm is a typical algorithm for group control based on animal group behavior. The research on flocking algorithm is mature, but there is little research on the implementation of Flocking algorithm using digital twin. Digital twins, as a key technology in the direction of IoT, is potentially valuable for improving the efficiency of cloud and devices. Therefore, the main research goal of this paper is to simulate the flocking phenomenon in nature by using digital twins and flocking algorithm to realize the cooperative control of multi-agent systems under the scenarios of obstacle-free and obstacle. In this paper, the author uses digital twins and flocking algorithm to achieve simulations of animal flocking Behaviors. The final simulation results show that the Multi-agent system can converge agents and reach consensus, and finally cross the obstacles to reach the target node, which verifies the rationality and effectiveness of the algorithm. Through the way of digital twins, the burden of the intelligence body's computation on obstacle identification can be reduced, and the synchronization efficiency of the cloud and the device can be effectively elevated, thus in a practical sense, favoring the deployment implementation. © Published under licence by IOP Publishing Ltd.

关键词： Multi agent systems

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Evaluating Large Language Model Understanding of Due Process

Evaluating Large Language Model Understanding of Due Process

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Computing and machine intelligence (ICMI), international conference on

作者： Joshua P. Johnson Adrian P. Lauf Dept. of Computer Science and Engineering University of Louisville Louisville US

In this paper the Llama-2 and GPT-2 large language models are evaluated for their fundamental understanding of basic due process concepts. The reference implementations and versions fine-tuned on judicial opinions wer... 详细信息

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

关键词： Law Computational modeling Large language models Decision making Reliability machine intelligence Context modeling

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Feature Fusion Dehazing Network Based on Parallel Attention 3

Feature Fusion Dehazing Network Based on Parallel Attention

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3rd international conference on Image Processing, Computer Vision and machine Learning, ICICML 2024

作者： Ma, Leyu Ge, Weili Fang, Ruowei Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China

ISBN: (纸本)9798350355413

Images captured in haze-affected weather frequently exhibit many issues, including the degradation of image features, distortion of colour, and the loss of local detail. Previous deep learning based image dehazing models have shown unsatisfactory performance on real-world non-uniform haze datasets. Most existing models are unable to combine features of different scales, resulting in poor performance on real datasets. In this paper, we proposed a parallel attention-based feature fusion dehazing network. We designed a multi-scale feature fusion module and a parallel attention module to improve the ability to handle haze of different concentrations and thicknesses. A comparison of the dehazing experiments conducted on different models with synthetic haze images and real haze images reveals that our model is more effective in handling haze of varying densities and thicknesses while retaining greater detail in the images. © 2024 IEEE.

关键词： Attention Deep learning Feature fusion Image dehazing

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Enhancing Urban Mobility: Deep Learning-Based Traffic Sign Assistant for the Visually Impaired 3

Enhancing Urban Mobility: Deep Learning-Based Traffic Sign A...

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3rd international conference on Trends in Electrical, Electronics and Computer Engineering, TEECCON 2024

作者： Prasad, Baddepaka Marry, Prabhakar Chepuri, Samuel Ashwini, P. Cvr College of Engineering Computer Science and Engineering Hyderabad India Vignan Institute of Technology and Science Computer Science and Engineering Hyderabad India Nalla Narsimha Reddy Education Society's Group of Institutions Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331505714

Urban mobility presents significant challenges for visually impaired individuals, particularly in interpreting and navigating through traffic signs. In response, this paper introduces a novel Traffic Sign Assistant (TSA) system empowered by deep learning technology to aid the visually impaired in comprehending traffic signs and enhancing their mobility in urban environments. The proposed TSA system leverages state-of-the-art deep learning models, notably convolutional neural networks (CNNs), for real-time detection and recognition of traffic signs. Through robust deep learning algorithms, the system can accurately identify various types of traffic signs, including regulatory, warning, and informational signs, overcoming challenges such as variations in lighting conditions and occlusions. Evaluation of the proposed TSA system involves comprehensive testing and user feedback sessions to assess its effectiveness in real-world scenarios. Results demonstrate the system's ability to significantly improve the mobility and safety of visually impaired individuals in urban environments by providing timely and accurate interpretation of traffic signs. This research contributes to the advancement of assistive technologies tailored to the needs of the visually impaired, offering a promising solution to enhance urban mobility and accessibility through the integration of deep learning-based traffic sign assistance systems. © 2024 IEEE.

关键词： Artificial intelligence CCS CONCEPTS Computational Photography Computer Vision Computing Methodologies Image and Acquisition

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