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Physical Exercise Classification from Body Keypoints Using machine learning Techniques 3

Physical Exercise Classification from Body Keypoints Using M...

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Rahman, Aadhila Saji, Aldrin Teresa, Avelin Nair, Divya R Saritha, S. Rajagiri School of Engineering and Technology Department of Computer Science and Engineering Kerala Rajagiri Valley Kochi India

ISBN: (纸本)9798350375190

In today's world, the importance of physical workout and exercise is being realized by the human community. However, there are a lot of challenges faced by people, in terms of convenience and cost, as they need a trainer to monitor their workouts. This research work aims to alleviate this challenge and proposes a methodology that automates the physical workout monitoring process. The proposed framework can take live video streams and identify the type of exercises performed by a person using machine learning techniques. This task is achieved by identifying keypoints from the human pose caught in the live video and classifies it to a physical exercise. The methodology also offers utilities like counter and feedback for physical exercises classified. A comparative evaluation of different machine learning techniques is experimented for physical exercise classification. It is observed from the evaluation that the Random Forest technique gives significant values in terms of precision 89% and recall 89%, as well as in F1-score with 89%. © 2024 IEEE.

关键词： machine learning

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Real-time Tweets Analysis using machine learning and Bigdata 3

Real-time Tweets Analysis using Machine Learning and Bigdata

引用

3rd IEEE North Karnataka Subsection Flagship international conference, NKCon 2024

作者： Reddy, P Nandieswar Sai Aswath, S. Alapati, Rithvika Radha, D. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350364569

Twitter's exponential increase in data has transformed it into a rich source for machine learning research, unveiling patterns of opinions and behaviors. This research introduces an automated pipeline built on Kafka, adept at handling large volumes of real-time Twitter data. Utilizing the Twitter API, tweets bearing the hashtag #justice are retrieved and efficiently processed via Kafka. PySpark facilitates data analysis by enabling aggregation, Jaccard similarity computation, and k-means clustering. Preprocessing with Python libraries and TF-IDF enhances analysis accuracy. Jaccard similarity aids in comparing tweets across socio-economic categories. Aggregated data is stored in MongoDB for further examination. Additionally, Streamlit offers visualization tools for effortless exploration of Twitter discourse trends. Thus, this project exemplifies the potential of big data and its impact on investigating social justice discourse in the digital age. © 2024 IEEE.

关键词： Tweets

来源：评论

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Optimization of Power System Balance and Short Term Generation Prediction Model Based on machine learning 3

Optimization of Power System Balance and Short Term Generati...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Gao, Chen Zhan, Shian Lin, Yinrong Zheng, Xiaohui Hong, Shouli State Grid Info-Telecom Great Power Science and Technology Co. Ltd. Fujian Fuzhou350003 China

ISBN: (纸本)9798350380989

To achieve balance in the power system, it is necessary to accurately predict the demand for electricity and the ability to generate electricity. In the construction of the entire short-term power generation prediction model, traditional methods are based on statistical models and rules, but these methods often cannot fully consider the complexity and uncertainty of the power system. Therefore, introducing machine learning has become an important way to improve the efficiency and reliability of the power system. This article conducted relevant research on the importance analysis of power system balance and short-term power generation prediction, and used BPNN (back propagation neural network) to optimize and improve the short-term power generation prediction model. Combining variable input module, data processing module, multi task learning module, BPNN module, and power generation prediction output module, a short-term power generation prediction optimization model was designed. Finally, based on the experiment, the following conclusion can be drawn: the power generation prediction optimization model based on BPNN has improved efficiency and accuracy by approximately 14.4% and 9.5% compared to traditional methods. Finally, the power balance system proposed in this article can ensure that the power supply meets the needs of users while maximizing the utilization of power generation resources, which has certain reference value. © 2024 IEEE.

关键词： BP Neural Network machine learning Power System Balance Short-term Power Generation Prediction

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A Novel Approach for Credit Scoring System for Loan approval using Ensemble learning Algorithm 3

A Novel Approach for Credit Scoring System for Loan approval...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Kumar, P. Maneesh, U.L. Mano Sanjay, G. Rajalakshmi Engineering College Department of CSE Chennai India Rajalakshmi Engineering College Department of Csd Chennai India

ISBN: (纸本)9798350381931

In order to manage credit risk for financial institutions and help with loan acceptance decisions, credit scoring is essential in determining an applicant's creditworthiness. This research provides an extensive analysis of credit rating for loan approval utilizing ensemble learning algorithms. The project develops and assesses ensemble credit scoring models using a variety of datasets containing financial, demographic, and previous credit data about loan applicants. In order to improve credit scoring, this article looks into feature selection strategies, ensemble parameter adjustment, and model interpretability. The outcomes show how ensemble learning algorithms outperform individual classifiers in credit scoring and produce accurate judgments of credit risk. © 2024 IEEE.

关键词： Risk assessment

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Advanced machine learning Frameworks for Multimodal Image and Speech Signal Processing 3

Advanced Machine Learning Frameworks for Multimodal Image an...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Ramesh Naidu, P. Shandilya, Saurabh Srividya, C.N. Gujar, Shantanu Sudhir Devi, Supriya Dankan Gowda, V. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560064 India Poornima College of Engineering Department of Advance Computing Rajasthan Jaipur India BGSIT Adichunchanagiri University Faculty of Engineering Management & Technology Dept. of ECE B G Nagara571448 India Rochester Institute of Technology Golisano College of Computing and Information Sciences Department of CyberSecurity RochesterNY14623 United States Alliance University Department of Mathematics Karnataka Bangalore562106 India BMS Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350354171

Multimodal data processing, especially the fusion of image and speech modality, is important for future human computer interface, medical applications and security surveillance. This research proposes the new machine learning approach for the efficient handling of multimodal data in which feature extraction from images and from the speech signal is obtained by employing CNN and LSTM networks, respectively. These features are combined by a hybrid fusion approach so that the resulting algorithm would be both accurate and fast. CIFAR-10 and LibriSpeech were used to assess the framework;the highest accuracy was 92% whilst the conventional methods like MDNN and EFMS could only reach 86%. Moreover, the analysis showed that the proposed method can take a processing time of 0.15 seconds to taxonomy and 0.45 seconds for speech data, thus making it preferable for real time data. These results illustrate the framework's efficiency in predicting the multimodal input and demonstrate superiority over the prior methods. © 2024 IEEE.

关键词： Deep learning

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Novel Contract-based Runtime Explainability Framework for End-to-End Ensemble machine learning Serving 24

Novel Contract-based Runtime Explainability Framework for En...

引用

IEEE/ACM 3rd international conference on AI engineering - Software engineering for AI (CAIN)

作者： Minh-Tri Nguyen Hong-Linh Truong Tram Truong-Huu Aalto Univ Aalto Finland Singapore Inst Technol Singapore Singapore

ISBN: (数字)9798400705915

ISBN: (纸本)9798400705915

The growing complexity of end-to-end machine learning (ML) serving across the edge-cloud continuum has raised the necessity for runtime explainability to support service optimizations, transparency, and trustworthiness. That involves many challenges in managing ML service quality and engineering runtime explainability based on ML service contracts. Currently, consumers use ML services almost as a black box with insufficient explainability for not only inference decisions but also other contractual aspects, such as data/service quality and costs. The generic explainability for ML models is inadequate to explain the runtime ML usage for individual consumers. Moreover, ML-specific metrics have not been addressed in existing service contracts. In this work, we introduce a novel contract-based runtime explainability framework for end-to-end ensemble ML serving. The framework provides a comprehensive engineering toolset, including explainability constraints in ML contracts, report schemas, and interactions between ML consumers and the components of the ML serving for evaluating service quality with contract-based explanations. We develop new monitoring probes to measure ML-specific metrics on data quality, inference confidence, inference accuracy, and capture runtime ML usage. Finally, we present essential quality analyses via an observation agent. That interprets ML inferences and evaluates contributions of ML inference microservices, assisting ML serving optimization. The agent also integrates ML algorithms for detecting relations among metrics, supporting constraint developments. We demonstrate our work with two real-world applications for malware and object detection.

关键词： ML Explainability engineering ML Contract SLA Management Ensemble ML ML Serving

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An intelligent approach for Speed Bump Detection 3

An intelligent approach for Speed Bump Detection

引用

3rd international conference on Computing and machine Intelligence (ICMI)

作者： Hussein, Abrar Ahmed Omar, Mariam Mohamed Altamimi, Jawaher Suliman Ead, Waleed M. Beni Suef Univ Comp & Artificial Intelligence Bani Suwayf Egypt Najran Univ Appl Coll Dept Comp Najran Saudi Arabia

ISBN: (纸本)9798350372977;9798350372984

In this paper, we propose an intelligent approach that uses YOLO algorithms to detect marked and unmarked bumps. The proposed approach for speed bumps based on real data collected from the Upper Egypt region in Egypt. Several data augmentations were performed on it. The proposed approach reached a mAP of 81% in the model while also calculating the distance from the car to the bump. We have used the Jetson Nano and the Xbox Kinect camera hardware to achieve this objective. Based on road preview using a camera, this work can be used to inform and alert the driver via a mobile application to make the ride more safe, prevent accidents, and reduce vehicle damage.

关键词： Speed bumps intelligence systems safe roads detection Deep learning Kinect camera

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Comparison Analysis of Forecasting Accuracy for Electricity Consumption Using Extreme learning machine and Backpropagation Algorithms 3

Comparison Analysis of Forecasting Accuracy for Electricity ...

引用

3rd FORTEI-international conference on Electrical engineering, FORTEI-ICEE 2024

作者： Miefthawati, Nanda Putri Akbar, Sukma Aini, Zulfatri Sutoyo Department of Electrical Engineering State Islamic University of Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (纸本)9798331542207

Accurate calculations are essential in forecasting. Therefore, it is important to select the appropriate forecasting method, which can be determined by testing the accuracy level of the forecasting results using the MAPE value. The purpose of this research is to analyze the comparison of the accuracy of electricity consumption forecasts using the Extreme learning machine and Backpropagation algorithms. The data used are monthly electricity consumption data as input pattern designs. The input data will be divided into two parts: training data and testing data, followed by simulations using the Extreme learning machine and Backpropagation algorithms to obtain the 2022 forecast results. The accuracy of each algorithm's forecast results will then be calculated using the MAPE value. The research results indicate that the Extreme learning machine algorithm has a MAPE value of 4.67%, where the error rate is higher than the Backpropagation algorithm, which has only 2.27%. From the algorithm testing stages, the most accurate forecasting result for electricity consumption was obtained using Backpropagation, as it has the lowest MAPE value, indicating the highest accuracy and lowest error rate. © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Approach for Bitcoin Price Prediction 3

A Novel Approach for Bitcoin Price Prediction

引用

3rd IEEE North Karnataka Subsection Flagship international conference, NKCon 2024

作者： Karna, Rajan Lal Rajbanshi, Bishal Babu Mishra, Priyanshu Karnataka Bangalore India Karnataka Bangalore India

ISBN: (纸本)9798350364569

This research explores the dynamic space of cryptocurrency like Bitcoin using advanced machine learning (ML) techniques to predict Bitcoin prices. The approach uses historical data of the closing price of bitcoin and incorporates types of machine learning model, including real-time analysis using ARIMA and deep learning models i.e. LSTM and GRU. The research aims not only to increase the performance & accuracy of Bitcoin price predictions but also to reveal the underlying structures and patterns that govern its fluctuations. This research provides investors, traders, and policymakers with valuable resources as they navigate the complex cryptocurrency market and bridges the gap between financial analysis and traditional and new learning technologies. © 2024 IEEE.

关键词： Adversarial machine learning

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Physics-informed Neural Networks for Modeling Incompressible Laminar Flows with Mixed-Variable Formulation 24

Physics-informed Neural Networks for Modeling Incompressible...

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3rd international conference on Frontiers of Artificial Intelligence and machine learning (FAIML)

作者： Li, Tiantian Pan, Yao Chen, Liang Xiong, Biao Li, Mei China Three Gorges Univ Yichang Peoples R China Power China Hubei Elect Power Engn Co LTD Handan Hubei Peoples R China

ISBN: (纸本)9798400709777

Physics-Informed Neural Networks (PINN) have emerged as a formidable tool for addressing sophisticated computational physics challenges, offering an innovative approach to integrating physical laws directly into deep learning models. By incorporating the residuals of physical equations into the loss function, PINN are trained to minimize discrepancies while adhering to physical constraints, leading to enhanced predictive accuracy even with limited data. This paper introduces a specialized PINN tailored for fluid dynamics, proficient in modeling low Reynolds number flows, both steady and transient. The adoption of a mixed-variable formulation has proven to refine the model's training process and solution precision, as validated against established numerical solutions. In contrast to traditional deep learning models that may falter in the presence of domain shifts, PINN exhibit robust generalization by virtue of their inherent physical constraints. This robustness is pivotal for practical applications involving multifaceted physical scenarios. Looking ahead, PINN are poised to expand their role in computational physics, offering advanced simulation capabilities that will benefit scientific and engineering endeavors. Their integration with deep learning is expected to foster further innovation and pave the way for new research directions and applications.

关键词： Deep learning Fluid dynamics Incompressible laminar flow Physics-informed neural networks

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