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Human Activity recognition Using machine learning Algorithms Based on IMU data 5

Human Activity Recognition Using Machine Learning Algorithms...

引用

5th international conference on Bio-engineering for Smart Technologies, BioSMART 2023

作者： Nia, Nafiseh Ghaffar Kaplanoglu, Erkan Nasab, Ahad Qin, Hong University of Tennessee at Chattanooga College of Engineering and Computer Science Chattanooga United States

ISBN: (纸本)9798350338492

Enhancing Human Activity recognition (HAR) using Inertial Measurement Unit (IMU) sensors has garnered significant attention in healthcare, particularly wearable devices. HAR plays a crucial role in prosthetic and wearable robotics by accurately identifying and responding to user movements, enabling personalized, adaptive, and intuitive control of these devices. IMU sensors capture both acceleration and angular velocity, delivering valuable data for HAR. these sensors find widespread use in prosthetic and wearable robotics, allowing for the detection and monitoring of user limb or body activities. Various machine learning algorithms can be leveraged further to advance HAR systems. these techniques hold promise in enhancing the accuracy and robustness of HAR systems, especially when dealing with complex and diverse movements. In this study, we implement four machine learning models, including Artificial Neural Networks (ANN), Decision Tree Classifiers (DTC), Random Forest Classifiers (RFC), and K-Nearest Neighbors (KNN). Our objective is to accurately recognize human activities such as walking upstairs, walking downstairs, walking normally, walking on the toe, walking on the heel, and performing sit-up tasks. Among these models, the RFC model exhibits superior performance compared to the others, achieving an impressive accuracy score of 97.67% across all evaluation metrics for activity recognition. this work is a foundational step in augmenting wearable robotics' functionality, usability, and personalization, ultimately improving the quality of life for individuals with disabilities or injuries. © 2023 IEEE.

关键词： Decision trees

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Rice varieties classification via Two-Stage weighted ELM method 5

Rice varieties classification via Two-Stage weighted ELM met...

引用

5th international conference on Computer Vision and data mining, ICCVDM 2024

作者： Yu, Haiyang Shang, Fanhua Wang, Yuxing Wang, datao College of Intelligence and Computing Tianjin University Tianjin300000 China Technology Company Limited Zhejiang Ningbo315000 China

ISBN: (纸本)9781510682863

Rice production is the key factor to ensure food security. Rice varieties classification is an important means to improve rice quality. Considering the problem that traditional machine learning methods lack analytic solutions and can not adapt parameters. this paper implements the ELM method based on two-stage weights. In the initial stage, the weights of the output layer can be calculated according to the Moore-Penrose generalized inverse. then regular function balances the expectation error and model complexity. In the model optimized stage, we calculated the model weights of the new function according to the predictive performance. these strategies help to reduce the fitting error of sequence fragments. Numerical experiment results are proved at the University of California Irvine data set, the results show that the proposed method not only has better generalization performance, but also guarantees the prediction effect of contrast method for different scenes. © 2024 SPIE.

关键词： machine learning Matrices data modeling Feature extraction Mathematical optimization data processing Performance modeling Computer security Education and training Semantics

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Research on pedestrian tracking detection based on YOLOv5 and improved DeepSORT 5

Research on pedestrian tracking detection based on YOLOv5 an...

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5th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2023

作者： Wang, Yingyun Mariano, Vladimir Y. National University College of Computing and Information Technologies Manila Philippines Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei Anhui230088 China

ISBN: (纸本)9798350359930

With the development and integration of big data and artificial intelligence, intelligent transportation, video surveillance, and other fields are receiving increasing attention. In the field of video surveillance, tracking targets is a challenging issue, especially in crowded environments where occlusion and false detection are prone to occur, and multi target detection frames are also prone to *** article uses the YOLOv5 algorithm as the foundation for pedestrian tracking and detection, proposes an improved metric called DeepSORT, and extracts the skeleton network part in a lightweight manner. the network is further pruned, greatly reducing the number of parameters without reducing too much performance. the experimental results demonstrate that the proposed method improves the target recognition and tracking capabilities of pedestrian tracking and detection. © 2023 IEEE.

关键词： Object detection

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Deep learning for Sentiment Analysis with Adaptive Neural Networks for Emotion Classification 5

Deep Learning for Sentiment Analysis with Adaptive Neural Ne...

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Jayalakshmi, V. thilakavathy, P. Manikandan, G. Deepa, R. Surendran, R. SRM Institute of Science and Technology Department of Computer Science and Applications Tamilnadu Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM institute of Science and Technology Department of Computing Technologies Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

In understanding human emotions, sentiment analysis has been invaluable in social media monitoring, consumer comment analysis, and psychological evaluations. Customer service, human-computer interface, and public mood comprehension depend on appropriate emotion categorisation. Despite its potential, sentiment analysis struggles with sarcasm, multimodal data integration, and emotion processing. Implementing real-time applications requires improving deep learning model computational complexity. Multimodal Optimised Emotion Analysis using Deep learning (MOEA-DL) uses CNNs for image processing, RNNs for text., and LSTM networks for sequential audio data. this improves emotion recognition by combining modality strengths. Marketing sentiment, mental health monitoring, social media sentiment, and consumer feedback use MOEA-DL. Multimodal solutions are better than text-based ones for communicating emotion. Customer contacts, film reviews, and social media platforms are used to evaluate the proposed strategy. According to simulations, MOEA-DL categorises emotions more accurately and robustly than previous sentiment analysis methods. Its adaptive neural network lets the system learn and perform well in many cultural and language environments. © 2024 IEEE.

关键词： Emotion recognition

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Application of machine learning for Air Quality Analysis 5th

Application of Machine Learning for Air Quality Analysis

引用

5th international conference on Applied Technologies, ICAT 2023

作者： Ocaña, Jesús Miñan, Guillermo Chauca, Luis Espínola, Karina Leiva, Luis Universidad Tecnológica del Perú Km 424 Panamericana Norte - Calle 56 Chimbote Peru Universidad Nacional del Santa Av. Pacifico 508 Urb. Buenos Aires – Nvo. Chimbote Peru

ISBN: (纸本)9783031589553

the main objective of this research work is to apply machine learning to gas sensors, to analyze air quality and transmit the data to a server through the Internet of things (IoT). the Arduino MEGA 2560 was used, the Ethernet shield module and four gas sensors, MQ2, MQ5, MQ7 and MQ135, were placed;machine learning was designed with Artificial Neural Networks (ANN) and greater effectiveness was obtained using the backpropagation training algorithm. the CRISP-DM methodology was used, which contains seven stages, first the problem was identified, in the second stage the data understanding, the third data preparation, in the fourth the machine learning with artificial neural networks was designed, the fifth was the modeling, the sixth is the implementation of the model and the seventh is the validation by performing the most common gas tests, resulting in the recognition of the gases in its environment, obtaining a functional system. In conclusion, it was verified and confirmed that machine learning with Artificial Neural Networks and the use of the Backpropagation algorithm can automatically detect gases and show the Air Quality Analysis. this research is exclusive for those interested who are beginning their investigation into the world of machine learning. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Neural networks

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A Review on data Balancing Techniques and machine learning Methods 5

A Review on Data Balancing Techniques and Machine Learning M...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Parmar, Gaurav Gupta, Rimi Bhatt, Tejas Sahani, G.J. Panchal, Brijeshkumar Y. Patel, Hiren S.V.I.T Dept. of Computer Engineering Gujarat Vasad India Software Developer Tüv Süd South Asia Pvt. Ltd India

ISBN: (纸本)9781665474672

Researchers in the discipline of data mining (DM) occasionally disregard the need of ensuring a dataset is evenly distributed. As such, it may have a major impact on how things are ultimately sorted. Most classifiers function on the concept that the data is roughly normally distributed. therefore, the categorization approach is no longer as successful as it once was and fixing this is essential. this research aims to construct an evaluation of several methods for producing labels for minority classes to prevent the model from developing bias toward protected attributes. In this study, different researcher's work learned to determine the most effective method for disseminating the data. there will be a discussion of ways to enhance categorization abilities in the conclusion. the future of data-balancing research is determined by weighing the relative merits of the several possible methodological amalgamations. © 2023 IEEE.

关键词： data mining

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Digital Technology Empowering Brand Management Based on Big data Research 5

Digital Technology Empowering Brand Management Based on Big ...

引用

5th international conference on Applied machine learning, ICAML 2023

作者： Li, Bin Qu, Luchun China Automotive Technology and Research Center Co. Ltd. Tianjing China Co. Ltd. Tianjing China BYDAutoIndustry Company Limited

ISBN: (纸本)9798350341416

the purpose of brand positioning is inseparable from brand communication. If the brand image of enterprise design is not conveyed to consumers, or consumers do not agree with the image of enterprise design, it proves that this brand positioning is a failure. this paper attempts to further study the brand management of digital technology empowerment based on big data. Among the two variables at the government level, the maximum value of each variable measurement item is no more than 7, and the minimum value is 1, and the option values are widely distributed. Judging from the average value of the variable measurement items, it is mainly concentrated in the range of 4.44-5.62, indicating that the respondents have a high degree of recognition of each item. According to the characteristics of consumers' consumption psychology, the attributes of products are designed to strengthen consumers' understanding of brands. Successful brand positioning can attract a large number of consumers and improve their loyalty, which is the key to the survival and sustainable development of enterprises. therefore, how to adapt to the trend of the times, use big data and digital technology to improve the efficiency of corporate brand communication. © 2023 IEEE.

关键词： Big data

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Analysis of active decision model based on big data and pattern recognition 5

Analysis of active decision model based on big data and patt...

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5th international conference on Internet of things, Automation and Artificial Intelligence, IoTAAI 2023

作者： Meng, Xiandong Zhang, Tingting Wang, Qixuan Liao, Minghao Sun, Yongjian Liaoning Institute of Science and Technology Liaoning Benxi117004 China

ISBN: (纸本)9798400716485

In order to further improve the generalization ability of the active decision model, a new active decision model based on big data and pattern recognition was constructed by taking agricultural decision making as an example. the characteristics of land remote sensing big data are obtained from two aspects of spectrum and shape. the features of remote sensing big data were input into support vector machine to realize the classification of farmland. Based on this, combined with vector description method, pattern recognition of agricultural environment is completed. Taking grain production, land ecological compatibility minimization and social and economic development as objective functions, an active decision-making model was constructed. the experimental results show that when using the constructed model to make active decisions on farmland, the recall and precision rates can both reach over 95%, and the time consumption is always less than 20ms. © 2023 ACM.

关键词： Support vector machines

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Real-Time Market Sentiment Analysis Using Natural Language Processing and ML 5

Real-Time Market Sentiment Analysis Using Natural Language P...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Brijesh Abilasha, N. Swarna, C. Cmr Institute of Technology Department of Mba Bangalore India Department of Management Studies Mulund West Mumbai India Rns First Grade College Commerce Bangalore India

ISBN: (纸本)9798350383522

Since the world of financial markets is becoming much faster, having to do with 'present' data promptly seems impossible and forgetting about unnecessary materials can hardly be done. the paper is aimed to present an innovative real-time market sentiment analysis approach that utilizes NLP and ML with the capture of social, media news source, or financial reports sentiments through a computational algorithm. the methodology is an NLP-based search for sentiment anomalies and uses machine learning algorithms based on historical market data utilized to pattern recognition. By merging these models immediately, it ensures timely changes that stimulate traders and investors to respond with the market when necessary. Finally, the ethical concerns are also considered to ensure there is enough transparency in using sentiment analysis algorithms that rely on automation for financial markets. this non-standard approach aims to transform the sentiments' dynamics reading and reaction of market players. It offers a good navigational aid through the labyrinth of modern financial market. With the objective of providing accurate information on market feelings, this method tried to utilize both NLP and ML in an integrated approach that enables decision-makers to stay a step ahead of trends by developing reasonable decisions. © 2024 IEEE.

关键词： Decision Support Financial Markets machine learning Real-time Sentiment Analysis

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Exploring Bias Analysis on Judicial data using machine learning Techniques 12

Exploring Bias Analysis on Judicial Data using Machine Learn...

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12th international conference on pattern recognition Systems (ICPRS)

作者： Silva, Bruno Santos F. Costa-Abreu, Marjory Univ Fed Rio Grande do Norte PPgSC DIMAP Natal RN Brazil Sheffield Hallam Univ Comp Sci Sheffield S Yorkshire England

ISBN: (数字)9781665466943

ISBN: (纸本)9781665466943

the use of data driven automation is not new, but it has gain a lot of attention recently with the wide-spread understanding that it is the solution to all problems in terms of 'fair' and 'non-bias' classification. this is not different in the law area, where 'artificial intelligence' became a 'magic word'. However, using historic data is a very tricky job which can quite easily propagate discrimination in a very efficient way. thus, this work is aimed to analyse data from legal proceedings looking for evidence related to the occurrence of bias in the judges' decision-making process, considering mainly the gender or social condition of the convicts. Supervised and unsupervised machine learning techniques, preceded by data analysis and processing procedures, were used to explain and find explicit data behaviour. Our results pointed to the fragility of the techniques to identify biases but suggest the need to improve data pre-processing and the search for more robust classification techniques.

关键词： bias data mining machine learning unsupervised learning supervised learning

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