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A machine learning Approach for Detection of Heart Diseases Using Wearable Devices

A Machine Learning Approach for Detection of Heart Diseases ...

引用

Smart Information Systems and Technologies (SIST), IEEE international conference on

作者： Spandiyar Abutalip Meirzhan Baikuvekov Duisen Zanggar Department of Information Systems Al-Farabi Kazakh National University Almaty Kazakhstan Kazakh National Agrarian Research University Almaty Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

this research paper delves into the integration of wearable devices and deep learning techniques to enhance heart disease detection and monitoring. the study highlights the importance of continuous cardiac health assessment and the transformative potential of wearable technology in the healthcare landscape. Our investigation showcases their efficacy in processing intricate physiological data for early heart disease recognition. Key variables such as age, maximum heart rate, blood pressure, cholesterol levels, and various electrocardiography (ECG) metrics are explored as pivotal factors in predicting and comprehending heart disease risk factors. Furthermore, we emphasize the significance of data normalization methodologies and secure data storage solutions to ensure data reliability and accessibility. Correlation analyses elucidate the intricate interplay among these variables, providing valuable insights into their collective influence on cardiac health. As the realm of machine learning and healthcare technology continues to evolve, this research lays a solid foundation for advancing heart disease detection and management, ultimately aiming to enhance patient outcomes and elevate the quality of healthcare delivery.

关键词： Deep learning Measurement Memory Medical services Electrocardiography Reliability Biomedical monitoring

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Opinion mining and Sentiment Analysis in Legal AI Research

Opinion Mining and Sentiment Analysis in Legal AI Research

引用

international conference on ICT in Business Industry & Government (ICTBIG)

作者： Vaishali Gurav Sanjay Bang Christ University Bengaluru

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

the Protection of Women from Domestic Violence Act, 2005, is a critical legislative measure in India aimed at addressing and mitigating domestic abuse against women. this paper employs Artificial Intelligence (AI) methodologies, specifically opinion mining and sentiment analysis, to analyze the implementation of this Act with a particular focus on Women Protection Officers in Mumbai City. By extracting and interpreting data from research papers, news articles, and official reports, this study identifies key themes, sentiments, and challenges associated with the Act's execution. the analysis reveals significant implementation challenges, the critical role of Protection Officers, and mixed sentiments toward the effectiveness of the Act. the findings highlight the need for enhanced training programs, better resource allocation, and advanced analytical techniques to improve the Act's implementation. this AI-driven approach provides valuable insights and data-driven recommendations to policymakers and practitioners, aiming to enhance the support system for domestic violence victims and ensure the Act's objectives are met more effectively.

关键词： Training Sentiment analysis data analysis Law Urban areas Government machine learning Resource management data mining Protection

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Research on the Construction of Enterprise Financial Fraud Identification Model under data mining Technology

Research on the Construction of Enterprise Financial Fraud I...

引用

Artificial Intelligence, Internet and Digital Economy (ICAID), international conference on

作者： Pinge Yang Shandong Business Institute Yantai City Shandong Province China

ISBN: (数字)9798331510664

ISBN: (纸本)9798331510671

Under the influence of digital economy, the rapid development of capital markets at all levels has not only promoted technological innovation, product innovation and market structure changes, but also prompted new risks and challenges to show a trend of continuous increase, especially the frequent exposure of enterprise financial fraud incidents, which not only seriously damaged the interests of investors, but also undermined the fairness and transparency of the market. Faced with this situation, the traditional financial audit methods often rely on manual audit and sampling inspection, which has obvious limitations and can no longer meet the practical application requirements. In view of the current problems, this paper will discuss the application feasibility of data mining technology in this field, and put forward a set of hybrid enterprise financial fraud identification model construction scheme to make up for the shortcomings of traditional audit methods and improve the accuracy of identification results. Practice has proved that the whole model contains two parts: a primary learner and a secondary learner, in which the primary learning is composed of SVM model and the secondary learner is composed of Logistic regression model, in order to improve the accuracy and stability of the recognition results and provide strong technical support and guarantee for the healthy development of the capital market.

关键词： Support vector machines Technological innovation Logistic regression Accuracy Finance Market research data models Stability analysis Fraud data mining

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the 4th international Workshop on Epidemiology meets data mining and Knowledge Discovery (epiDAMIK 4.0 @ KDD2021) 21

The 4th International Workshop on Epidemiology meets Data Mi...

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27th ACM SIGKDD international conference on Knowledge Discovery and data mining (KDD)

作者： Adhikari, Bijaya Srivastava, Ajitesh Pei, Sen Kefayati, Sarah Yu, Rose Yadav, Amulya Rodriguez, Alexander Ramanathan, Arvind Vullikanti, Anil Prakash, B. Aditya Univ Iowa Iowa City IA 52242 USA Univ Southern Calif Los Angeles CA 90007 USA Columbia Univ New York NY 10027 USA IBM Corp Seattle WA USA Univ Calif San Diego La Jolla CA 92093 USA Penn State Univ University Pk PA 16802 USA Georgia Inst Technol Atlanta GA 30332 USA Argonne Natl Lab Argonne IL 60439 USA Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9781450383325

the 4th epiDAMIK@SIGKDD workshop is a forum to discuss new insights into how data mining can play a bigger role in epidemiology and public health research. While the integration of data science methods into epidemiology has significant potential, it remains under studied. We aim to raise the profile of this emerging research area of data-driven and computational epidemiology, and create a venue for presenting state-of-the-art and in-progress results-in particular, results that would otherwise be difficult to present at a major data mining conference, including lessons learnt in the 'trenches'. the current COVID-19 pandemic has only showcased the urgency and importance of this area. Our target audience consists of data mining and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work, and practitioners from the areas of mathematical epidemiology and public health.

关键词： epidemiology public health forecasting AI for good

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GCNs-Based Context-Aware Short Text Similarity Model 25

GCNs-Based Context-Aware Short Text Similarity Model

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25th international conference on pattern recognition (ICPR)

作者： Sun, Xiaoqi Wu, Shaochun Liu, Yue Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai Inst Adv Commun & Data Sci Shanghai Engn Res Ctr Intelligent Comp Syst Shanghai 200444 Peoples R China

ISBN: (纸本)9781728188089

Semantic textual similarity is a fundamental task in text mining and natural language processing (NLP), which has profound research value. the essential step for text similarity is text representation learning. Recently, researches have explored the graph convolutional network (GCN) techniques on text representation, since GCN does well in handling complex structures and preserving syntactic information. However, current GCN models are usually limited to very shallow layers due to the vanishing gradient problem, which can not capture non-local dependency information of sentences. In this paper, we propose a GCNs-based context-aware (GCSTS) model that applies iterated GCN blocks to train deeper GCNs. Recurrently employing the same GCN block prevents over-fitting and provides broad effective input width. Combined with dense connections, GCSTS can be trained more deeply. Besides, we use dynamic graph structures in the block, which further extends the receptive field of each vertex in graphs, learning better sentence representations. Experiments show that our model outperforms existing models on several text similarity datasets, while also verify that GCNs-based text representation models can be trained in a deeper manner, rather than being trained in two or three layers.

关键词： Semantics

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Research on the Differences in Enterprise Behavior in Mineral Resource Development and Utilization Statistics Based on machine learning 25

Research on the Differences in Enterprise Behavior in Minera...

引用

Proceedings of the 2025 4th international conference on Big data, Information and Computer Network

作者： Weiwei Qiu Yu Gao Zheng Li Qi Wu Shuhua Qi Xianyun Zhu Zhenwei Liu Ping Li Haibing Zhou Xueli Chen Information Center of Ministry of Natural Resources Beijing China Beijing Vastitude Technology Co. Ltd Beijing China Beijing Join-Cheer Software Co. Ltd. Beijing China

ISBN: (纸本)9798400712425

the Ministry of Natural Resources conducted a nationwide data survey on the development and utilization of mineral resources, which accumulated a large amount of data on the behavior of mining enterprises. In order to strengthen the mining administration and the healthy and orderly development of the industry, it is necessary to analyze the behavior of mining enterprises from multiple dimensions. Firstly, the idea of analyzing 123,743 enterprise behavior data in development and utilization statistics is put forward, and the index system of mining enterprise behavior in development and utilization statistics is constructed. Using machine learning models including distribution analysis, correlation analysis, cluster analysis and random forest, the behavior characteristics of mining enterprises are analyzed from multiple dimensions, and the behavior portraits of different regions are portrayed. Finally, the influencing factor model is constructed. It is concluded that the reporting behavior of mining enterprises has improved as a whole over time, indicating that mining administration has entered a more and more standardized stage. For mining enterprises in four areas in the third category, it is necessary to strengthen the management and supervision of mining administration. the seriousness of users' behavior in mining enterprises has a significant positive correlation with the total industrial output value of enterprises, and the positivity has a significant negative correlation with the total industrial output value of enterprises. We should further strengthen the intensive development and utilization of mineral resources, strengthen the publicity of mining administration systems in key areas, and optimize management processes to improve the efficiency of mining administration.

关键词： Corporate Behavior

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Real Time Fake news Detection Web App Enhanced by machine learning Algorithms

Real Time Fake news Detection Web App Enhanced by Machine Le...

引用

Intelligent Technologies (CONIT), international conference on

作者： V Gunasekaran D Karthikeyan School of Information and Technology Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

In this era, the pervasive proliferation of fabricated news has come to light as a formidable adversary, corroding the credibility of journalistic sources, sowing confusion, and distorting public discourse. To counteract this escalating predicament, this research introduces a pioneering and comprehensive solution-an interactive web application engineered to detect and mitigate the impact of fabricated news. through the strategic integration of sophisticated machine learning algorithms, this platform not only identifies spurious news content but also operates in real time, enabling swift responses to the proclamation of misinformation. the key aim of this study is to elevate the precision and efficiency of fabricated news identification. this is achieved through a meticulously devised amalgamation of advanced natural language processing (NLP) techniques, meticulous feature engineering strategies, and the robust framework of ensemble learning methods. the fusion of these techniques equips the system with an arsenal of tools to scrutinize news story, disentangling the intricate web of linguistic nuances that often obscure deceptive content. the real-time facet of the application is pivotal, ensuring that the detection process transpires in sync with the constant influx of news story across digital platforms. this immediacy minimizes the potential amplification of false narratives, thereby curbing the negative impact of fabricated news proclamation. the heart of the system lies in its fabricated news detection capability, fortified by the intricacies of modern machine learning. By training on vast repositories of verified news data and contrasting it with known instances of false information, the algorithms learn to recognize patterns that distinguish authentic news from falsified narratives. Accessed through standard web browsers, the web application interface offers an intuitive and accessible medium for users to engage with the detection process.

关键词： Training Heart machine learning algorithms Linguistics Real-time systems Natural language processing pattern recognition

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Root Cause Analysis Framework for Intelligent Computing Center

Root Cause Analysis Framework for Intelligent Computing Cent...

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Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE international conference on

作者： Junpeng Lin Yifei Gao Jiantao Ye Dandan Miao Yinzhu Zhang Ke Lin Yiyue Wang Chen Zhang Department of Industrial Engineering Tsinghua University Beijing China Songshan Lake Research Center Huawei Technologies Dongguan China Shanghai Research Center Huawei Technologies Shanghai China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

the rapid development of AI technology has greatly increased the demand for computational power, leading many companies to deploy large-scale intelligent computing centers. However, the larger cluster sizes result in more frequent failures, making root cause analysis more challenging. To address these issues, we propose ASeqRCA, a novel framework for root cause analysis in intelligent computing center. We simplify the complex network structure of intelligent computing clusters and model the relationships between alarms in sequences by frequent-pattern mining and causal learning techniques. With an alarm causal graph, we apply depth-first search to identify potential alarm propagation paths. We illustrate the effectiveness of the proposed method through a real-world AI model training failure case.

关键词： Training learning systems Root cause analysis Computational modeling Distributed databases data models Maintenance data mining Robots Monitoring

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Student Performance Prediction Using Stacking Ensemble learning

Student Performance Prediction Using Stacking Ensemble Learn...

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Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Yibo Tao Yong Yang Fuheng Qu Yuyao Liu School of Computer Science and Technology Changchun University of Science and Technology Changchun China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Student grade prediction is an important research topic in educational data mining. To address the issues of low prediction accuracy and poor generalization encountered by single models in predicting student grades, this paper proposes a Stacking ensemble learning model that integrates multiple individual models. the proposed model uses Support Vector machine (SVM), Decision Tree, and Random Forest as base learners, with Logistic Regression (LR) serving as the metalearner, forming a two-layer Stacking ensemble learning framework. the results demonstrate that the Stacking ensemble learning model achieves an accuracy of 91.6%, precision of 92.1%, recall of 93.8%, and an F1-score of 93.0%. these performance metrics are significantly better than those of individual learning models, indicating that the proposed Stacking model can effectively predict students' academic performance.

关键词： Support vector machines Measurement Logistic regression Accuracy Computational modeling Stacking Predictive models Ensemble learning Decision trees Random forests

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2021 4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

2021 4th International Conference on Pattern Recognition and...

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4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

ISBN: (纸本)9781665413220

the proceedings contain 88 papers. the topics discussed include: using YOLOv5 for garbage classification;ultrasound vector flow mapping in left ventricle: a do it yourself study;a combined data-driven method for electricity theft identification;an unsupervised discriminative random vector functional link network for efficient data clustering;one-dimensional convolutional neural network for detecting internal defects of arc magnets;IV-WA: an information visual tool with WebAR;real-time simulation of first porta treatment based on position based dynamic;image style transfer based on DPN-CycleGAN;and towards automatic Ethiopian endemic animals detection on android using deep learning.

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