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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

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Deep Learning Approaches for Predicting Atrial Fibrillation Risk in Pacemaker Patients 9

Deep Learning Approaches for Predicting Atrial Fibrillation ...

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9th ieee international conference on Progress in Informatics and computing, PIC 2024

作者： Ngom, Charles Abdoulaye Ba, Mandicou Sarr, Simon Antoine Diop, Idy Mbaye, Moustapha Bah, Alassane Diao, Maboury Cheikh-Anta-Diop University of Dakar Polytechnic Higher School Research Institute for Development Unit of Mathematical Modeling and Computer Science of Complex Systems Dakar Senegal Cheikh Anta Diop University Department of Cardiology University Hospital Aristide Le Dantec Medical Department Dakar Senegal

ISBN: (纸本)9798350363203

Pacemaker wearers represent a vulnerable population, often facing cardiac issues and a heightened risk of cardiovascular diseases, such as Atrial Fibrillation (AF). Timely identification of patients at risk of developing AF is of paramount importance in preventing potentially serious complications, including strokes, limb ischemia, and other cardioembolic consequences. Deep Learning (DL) methods have demonstrated significant efficacy in AF prediction, offering a promising avenue to enhance the management of this condition through electrocardiogram (ECG) analysis. To overcome data scarcity challenges, we have embraced MLOps, a cutting-edge methodology. It offers a systematic framework to manage DL model lifecycles, ensuring robustness and reproducibility from development to deployment and monitoring in our research. However, the scarcity of digitized and labeled data prompted us to explore data generation techniques, including conditional generation and standard augmentation, subsequently subjected to meticulous evaluation. Assessment of diverse data generation methods reveals that ECGs synthesized via conditional DeepFake generation exhibit an improved distribution, characterized by MMD scores = 8.10-3 and MMD = 4.10-4. This consequently demonstrates acceptable FD, RMSE, and PRD scores relative to a baseline. A series of transfer learning efforts culminated in relatively conclusive outcomes, illustrated by a 98% F1- score and an AUC of 0.97, leveraging a larger dataset. Despite inherent limitations related to data size, this study, fortified by its rigorous methodology, highlights the efficacy of transfer learning, introduces a pertinent comparative approach to data generation techniques, and facilitates the assembly of a dedicated ECG dataset sourced from patients equipped with pacemakers. © 2024 ieee.

关键词： Pacemakers

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Interpretable MOOC Course Recommendations Based on Reinforcement Knowledge Graph 2

Interpretable MOOC Course Recommendations Based on Reinforce...

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2nd international conference on Big data and Privacy computing, BDPC 2024

作者： Zhang, Yifan Zhang, Yiwen Cao, Xiaolan Yu, Yuchengqing Anhui Jianzhu University Anhui Province Hefei China Anhui XinHua University Faculty of Big Data and Artificial Intelligence Anhui Province Hefei China Hefei No. 1 Middle School American High School Anhui Province Hefei China

ISBN: (纸本)9798350307597

With the large amount of educational information on online education platforms causing information overload, it is particularly important to select personalized content suitable for users from a large number of courses. This paper proposes a reinforcement learning method using MOOC knowledge graph as the environment to generate explainable recommended course paths, recommend courses to users and give clear explanations, so that students can understand the reasons and logic of the recommendations. The MOOC knowledge graph covers a variety of complex relationships and learners' historical records, while reinforcement learning simulates the interaction between users and courses to make recommendation decisions. Experiments were conducted on the MOOC data set, and the experimental results proved that the method combining knowledge graph and reinforcement learning proposed in this article improved the NDCG and HR indicators. © 2024 ieee.

关键词： Knowledge graph

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data Science for Social Good: Case Studies and Applications 1

Data Science for Social Good: Case Studies and Applications

引用

1st ieee international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Sharma, Vinod Sharma, Pranay Barot, Kaushal Tripathi, Abhijeet Nagpal, Tapsi Keerthana, D. SLP College Dept of CSE Gwalior India Madhav University Dept of CSE Pindwara India J.S. University Dept. of Computer Science Shikohabad India Lingayas Vidyapeeth Dept. of Computer Sci. and Engineering Faridabad India A.H. Edu. and Research Dept. of Psychology Meenakshi Chennai India

ISBN: (纸本)9798350356816

This abstract explores the potential of data science to solve pressing social issues through a series of case studies & practical applications. By examining diverse initiatives, such as fostering social justice, advancing environmental sustainability, & improving public health & education, this study illustrates how data-driven approaches can deliver impactful solutions for societal problems. The case studies presented show how advanced analytics, machine learning, & data visualization are used to identify trends, forecast outcomes, & inform strategic decisions. data science has emerged as a powerful tool for addressing complex social challenges & driving positive change in communities. data science has become an effective tool for tackling difficult social issues & bringing about positive change in communities. This abstract examines how data science can be used to address urgent social issues through a number of case studies & real-world applications. By looking at a variety of initiatives - from promoting social justice & environmental sustainability to improving public health & education - this study shows how data-driven approaches can result in significant solutions for societal issues. The case studies showcase how advanced analytics, machine learning, & data visualization are used to identify trends, forecast outcomes, & guide strategic decisions. © 2024 ieee.

关键词： Case Studies data Science data-Driven Solutions Education Environmental Sustainability Machine Learning Public Health Social Good Social Justice

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Reinforcement Learning in cognitive Robots for Autonomous Path Planning 5

Reinforcement Learning in Cognitive Robots for Autonomous Pa...

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5th international conference on data Intelligence and cognitive Informatics, ICDICI 2024

作者： Das, Archana Poornima, R. Indu Deepa, G. Gokila Sheela, K. Selva Akshya, J. Choudhry, Mani Deepak New Horizon College of Engineering Department of Information Science and Engineering Bengaluru India KGiSL Institute of Technology Department of Information Technology Coimbatore India Ppg Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India KGiSL Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350389609

cognitive robots are intelligent systems that learn from their environment, adapt to dynamic changes, and make decisions without a human's direct intervention. This research studies the integration of Reinforcement Learning in cognitive robots with Q-Learning for autonomous path planning. The study addresses the most critical issues in traditional A∗ and Dynamic Programming algorithms by devising path-planning techniques, mainly the lack of adaptability and computational efficiency in real-time and unpredictable environments. Our approach is based on Q-Learning, which is a model-free RL algorithm allowing the robots to find paths autonomously by optimizing their paths and avoiding obstacles. The Q-Learning algorithm provides the possibility of learning optimal policies for the robot through iterative interaction with its environment balanced between exploration and exploitation of the decision process. A reward system is utilized by the proposed model, encouraging the robot to explore shorter, collision-free paths and adjust based on feedback in real time from its surroundings. The model was found to be significantly better than its counterparts. In this context, as indicated, the Q-Learning model runs at 11.5 seconds, faster than A∗ at 18.3 seconds and Dynamic Programming at 16.7 seconds, yet with an accuracy of 94% and was found to have the highest collision avoidance rate at 98%. Additionally, the adaptability of the model about the environment presents a marked difference in terms of path length optimization, having done so with a mean path length of 12.3, compared to approaches or models. The robustness and scalability of the Q-Learning model make it highly applicable in real-world applications. © 2024 ieee.

关键词： Adversarial machine learning

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data-Driven Disease Prediction and Lifestyle Monitoring System 1

Data-Driven Disease Prediction and Lifestyle Monitoring Syst...

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1st international conference on cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Ganesh, Chilukuri Sai Kishore, Royyala Sai Varshith Goud, K. Kethireddy, Bhaskerreddy Rout, Saroja Kumar Ranith Reddy, S. Department of Information Technology Hyderabad India

ISBN: (纸本)9798350361780

The usage of machine learning techniques has been instrumental in the healthcare industry to a great extent for the past few years. The rise in disease rates highlights the urgent need for quick medical attention. Hospitals often struggle with efficiently sorting and prioritizing patients due to the overwhelming influx of individuals seeking care. The time-consuming manual review of patient records to identify their specific conditions has become a bottleneck in healthcare delivery. To tackle this challenge, artificial intelligence (AI) has emerged as a crucial tool in the healthcare sector. AI utilizes data from diagnostic procedures not only to deepen our understanding of diseases but also to significantly enhance treatment success rates. The proposed healthcare system, we employ various machine learning algorithms like logistic regression, linear regression, Support Vector Machine, and K Nearest Neighbors to offer precise and personalized predictions and insights. The Support Vector Classifier (SVC) achieves 87.2% accuracy for diabetes prediction, while the random forest model excels in breast cancer prediction with 95.98% accuracy. For heart disease, Support Vector Machine (SVM) attains 86.38% accuracy, and linear regression achieves 96.46% accuracy for caloric expenditure prediction, illustrating machine learning's versatility in healthcare. The research work serves as a user-friendly platform for disease prediction and health monitoring, aiming to empower individuals to actively manage their health. Ultimately, the integration of AI-driven machine learning models into healthcare applications represents a significant advancement. It streamlines patient prioritization and equips individuals with the knowledge to make informed health decisions. data mining techniques coupled with machine learning techniques can further increase the accuracy of disease prediction. © 2024 ieee.

关键词： Diseases

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Adaptive Framework for Comprehensive Quality Assessment in Unstructured Big data 7

Adaptive Framework for Comprehensive Quality Assessment in U...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Kaliyaperumal, Gopal Gatla, Anitha Lakhanpal, Sorabh Reddy, K. Jyothsna Najm, Rusul Raju, K. Butchi New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Department of Information Technology Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq Griet Department of Cse Bachupally Telangana Hyderabad India

ISBN: (纸本)9798350350067

The current growth and evolution have led to the emergence of structures such as unstructured big data in different fields creating a need and demand for effective quality assessment models specifically designed for big data. This work presents a new Big data Quality Assessment Model (BDQAM) that is tailored for use with unstructured data since they are highly diverse, a have no prior planned structure, and semantics are complex. The BDQAM, therefore, uses cutting edge machine learning algorithms and natural language processing tools to perform a real time assessment and validation on big data with the view of determining the quality of the data accumulated. The quality assessment is based on key quality dimensions and the assessment of accuracy, completeness, consistency and timeliness;the model is designed to learn from experience and improve the results. The effectiveness of the proposed model is affirmed by testing on a variety of large-scale unstructured datasets across different domains together with substantial enhancements in precision and recall measurements in contrast with the conventional quality assessment methodologies. The study results enhance practice in big data analysis by offering a solution that can be applied and is effective in checking the validity of Big data inside decision-making processes. © 2024 ieee.

关键词： data integrity

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Random Programming Model Analysis of Infant Development Impact Based on Random Forest Algorithm 2

Random Programming Model Analysis of Infant Development Impa...

引用

2nd international conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Cao, Yu Shen, Zihe Shi, Fengjing College of International Education Henan University of Technology Shangqiu China College of Artificial Intelligence Dongguan City University Shangqiu China College of International Education Henan University of Technology Zhengzhou China

ISBN: (纸本)9798350382952

Mothers' mental health status has a significant impact on infants' cognitive, emotional and social behaviour. In this paper, we first preprocess the data of infants' behavioural characteristics, and then use Spearman's coefficient to correlate the mother's characteristics with the infant's characteristics. Next, the trained SVM and RF are compared, and finally RF is chosen to classify infants whose types are not yet clear and calculate the importance of the features, and it is found that EPDS has a stronger relationship with the behavioural characteristics of infants. Then a planning model is built to calculate at least how much it needs to be spent to make the infant behavioural characteristics transformed. Finally, TOPSIS based on combined weights is established to make a comprehensive judgement on the sleep quality of infants, which is classified into four categories using K-means. It is concluded that it takes at least ∃65,785.3 to change the infant's behavioural characteristics from ambivalent to moderate, and CBTS is the main treatment;it takes at least ∃65,610 to change the infant's behavioural characteristics from ambivalent to quiet, and EPDS is the main treatment. © 2024 ieee.

关键词： K-means clustering

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Enhancing Electric Power System Reliability through Advanced data Quality Assurance with PowerdataQ Framework 7

Enhancing Electric Power System Reliability through Advanced...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Chandrashekar, Rakesh Kumar, Kirana Lakhanpal, Sorabh Keerthi, V. Saeed, Hazim Y. Reddy, R. Akhilesh New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Department of Electronics and Communication Engineering Telangana Hyderabad500043 India Lovely Professional University Phagwara India Griet Department of Cse Bachupally Telangana Hyderabad India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

In the case of big data implementations in electric power industry, there is increasing need for establishing good frameworks for the quality and soundness of the big data that feed key processes and initiatives. As a new and unique system, PowerdataQ has been tailored exclusively for evaluation of the quality of the numeric values associated with the electric power. It uses advanced machine learning algorithms and the most complex techniques used in data mining to address issues to do with accuracy, consistency, completeness and timeliness of data. PowerdataQ has been structured to have a multilayered architecture with an embedded real-time data validation and anomaly detection and data cleaning modules to deal with issues that are characteristic of big and heterogeneous data coming from many sources. There is emphasis on domain-specific ontologies for improving semantic comprehension of quality metrics, which in turn are made more context specific. Heavily supported by real-world experiments, the good performance of PowerdataQ is shown in terms of catching data quality problems and enhancing the accuracy and robustness of following power system studies. © 2024 ieee.

关键词： data reliability

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Leveraging Blockchain for Secure data Sharing and Cloud computing: Implementing Distributed Timestamping with Machine Learning and Cryptographic Protocols 1

Leveraging Blockchain for Secure Data Sharing and Cloud Comp...

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Mallreddy, Sukender Reddy Vasa, Yeshwanth Bonala, Sapan Bharadwaj Macha, Sharath chandra Salesforce Consultant City of Dallas DallasTX United States Milwaukee United States Middletown United States Dallas TX United States

ISBN: (纸本)9798350353648

In the last ten years-blockchain technology has changed a lot. It started as basic records shared across many computers and has become advanced networks that can do many things. Therefore, this study looks at how well blockchain systems can handle complex math problems for security. We focus on creating a system that puts timestamps on events. This method combines Schnorr signatures and Pedersen commitments. We use the Ethereum network to store and check data. The system uses a smart contract to manage data and connect servers and clients. Servers put timestamps on events and clients send data to be timestamped and checked. This setup makes sure they are clear and reliable. However, the study tests how well the system works by looking at cost-how much memory it uses and how fast it runs. The study also talks about using Ethereum. Ethereum lets everyone see timestamps and what happen-which is good for trust. But it can't keep secrets-so it's not good for private data. This study shows that blockchain is useful for more than just money. It can make sure events are real and safe. By mixing math ideas in blockchain-the study shows how to make data safer and more reliable in many places. © 2024 ieee.

关键词： Ethereum

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Research on Intelligent Agent Technology and Applications Based on Large Models 4

Research on Intelligent Agent Technology and Applications Ba...

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4th ieee international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Liu, Wenfu Chang, Wentai Shi, Chuan Wang, Yajing Hu, Ran Zhang, Caikun Ouyang, Haibo State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System China

ISBN: (纸本)9798350363586

With the rapid growth of available data, the continuous improvement of computing power, and the increasing maturity of large model technology, AI Agents based on large models are gradually becoming the core of AI research and applications. AI Agents possess characteristics such as learning ability, decision-making capability, adaptability, and initiative, enabling them to automatically make choices and take actions to achieve specified goals. Moreover, by integrating large-scale pre-trained model technology, AI Agents can continuously improve their performance based on interactions. This paper will review the background, concepts, and characteristics of AI Agents, focusing on the analysis of the core capabilities and implementation paradigms of large model-based intelligent agents. In line with industry demands, it constructs an electromagnetic target analysis and recognition intelligent agent application framework based on multi-modal large models, providing a reference for readers interested in the research and development of large model intelligence agents. © 2024 ieee.

关键词： AI agent ETAR agent large model large model Agent

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