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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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Heart Disease Prediction Using data Analysis and Effective Machine Learning Techniques 1

Heart Disease Prediction Using Data Analysis and Effective M...

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

作者： Tripathy, Tushar Das, Mamatarani Mohapatra, Sourav Behera, Sarthak Kumar Maihi, Guru Charan Computer Science and Engineering C.V. Raman Global University Bhubaneswar India

ISBN: (纸本)9798350361780

Cardiovascular disease stands as a prominent global health concern, claiming a significant number of lives. The accurate prediction of such diseases poses a substantial challenge in the realm of data analysis. The heart, being the second most vital organ in the human body, necessitates dedicated efforts in medical research. Timely identification and diagnosis of heart diseases are pivotal in saving lives, particularly in cases of conditions like coronary heart disease. The utilization of machine learning algorithms, including Gaussian Naive Bayes, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (K-NN), and Decision Tree classifiers, along with diverse grouping algorithms such as voting, facilitates the creation of a comprehensive model by amalgamating these approaches. The main aims of this research paper strike on using two different approaches of ensemble algorithms the stacking and voting approaches with different unique datasets. The testing phase demonstrates an impressive accuracy rate of approximately 80.4% in the stacking approach and 82.6% for the voting ensemble learning. © 2024 ieee.

关键词： Support vector machines

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Coordinating Search with Foundation Models and Multi-Agent Reinforcement Learning in complex Environments 8

Coordinating Search with Foundation Models and Multi-Agent R...

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8th international conference on Robotic computing

作者： Allred, Christopher Haight, Jacob Justice, Chandler Petersen, Isaac Scalise, Rosario Hromadka, Ted Pusoy, Jason Harper, Mario Utah State Univ Dept Comp Sci Logan UT 84322 USA DEVCOM Army Res Lab ARL Autonomous Syst Branch AEOP Contract W9115R 15 2 Adelphi MD USA Univ Washington Dept Comp Sci Seattle WA 98195 USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9798331521561;9798331521554

We present a multi-agent system simulation designed for efficient coordination and collaboration among multiple robots, particularly suited for search operations. This simulation reflects unstructured and complex outdoor scenarios where significant obstruction and occluded terrain surfaces cause difficulties in search. The software integrates well with reinforcement learning (RL) and a centralized Multi-Agent Transformer (MAT) to enable autonomous robots to collect, process, and integrate data. Search, coverage, and complex mobility planning can be tested in this simulation with dynamic and unstructured environments. The project code and videos can be found at https://***/ DIRECTLab/Coordinating- MAT-Env

关键词： Isaac Sim Multi-Agent Simulation Reinforcement Learning Unstructured Environment Simulation Software

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AI Strategies for complex Math Problems: Optimization 2

AI Strategies for Complex Math Problems: Optimization

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2nd ieee international conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Rani, Anju Bakshi, Shweta Bansal, Rajan GD Goenka University India Sushant University India

ISBN: (纸本)9798350368109

Finding efficient solutions to hard combinatorial optimization problems is one of the prominent challenges of modern computing. The quest to improve those has been innovated by the advent of artificial intelligence (AI) that has reshaped the quest and enhanced the same with new heuristics improving those algorithms by the format. The first section of the paper explains why algorithm optimization is required in modern computing With the ongoing explosion of data generation and the need for larger and more complex compute tasks, there is an ever-growing demand for algorithms that can produce solutions to mathematical problems in seconds and with high fidelity. In addition, metaheuristic algorithms form general-purpose optimization frameworks capable of enhancing various algorithms, without relying on the problem domain. Here, metaheuristic algorithms (simulated annealing, ant farm optimization, particle swarm optimization, etc.) are robust and flexible algorithms capable of exploring large search spaces to find optimal or near-optimal solutions to different remedial tasks. © 2024 ieee.

关键词： Optimization algorithms

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Online monitoring method of electric power enterprise operation state based on data driving

Online monitoring method of electric power enterprise operat...

引用

2024 international conference on Intelligent computing and data Mining, ICDM 2024

作者： Jiang, Jiang Feng, Xiaoqiang Chen, Zhen Guangdong Power Grid Co. Ltd. CSG Guangzhou510050 China China Southern Power Grid Guangdong Zhanjiang Supply Bureau Zhanjiang524000 China

ISBN: (纸本)9798331506261

Traditional methods of monitoring and controlling power enterprises are difficult to deal effectively with complex dynamic systems due to limitations such as inaccuracy, lack of real-time performance, and inability to adapt to system changes. To overcome these shortcomings, we propose a data-driven online monitoring method, based on the long-term and short-term memory network (LSTM) and self attention mechanism to build an intelligent real-time monitoring system. The model uses multiple regression analysis to screen indicators, double-layer LSTM to learn the relationship between indicators, and the attention layer to enhance the processing ability of time series data. The data set of a power plant in Guangdong is used in the experiment, and the short-term power load is taken as the target. The experimental results show that the precision rate of the model in the prediction task is 93.14%, the recall rate is 91.44%, and the F1 score is 92.64%. This method improves the prediction accuracy and shows value in actual operation. Future research directions include introducing more complex deep learning models, integrating external factor data and developing adaptive algorithms. This study provides innovative ideas and practical tools for the intelligent transformation of electric power enterprise. © 2024 ieee.

关键词： data driven key operational indicators long-term and short-term memory network self attention mechanism

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Enhancing the Security of Virtualized Infrastructure through Big data Analytics in Cloud computing 1

Enhancing the Security of Virtualized Infrastructure through...

引用

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

作者： Bisen, Anand Singh Kumar, Ravi Hema, B. Jenasamanta, Abhinash Shakya, Lalit Mohan Shanthi, R. Vikrant University Dept. of CSE and IT Gwalior India ITM Skill University Dept of CSE Mumbai India Dhanalakshmi Srinivasan College of Engineering Dept. of Electronics and Communication Engineering Coimbatore India Usha Martin University Dept. of CSE Jharkhand India Science J.S. University Dept. of Computer Shikohabad India Meenakshi A.H.E.R Dept. of Mathematics Chennai India

ISBN: (纸本)9798350356816

This research paper presents a novel approach to enhancing the security of virtualized environments through big data analytics in cloud computing. While virtualization and cloud computing provide flexibility and cost-efficiency, they also pose unique security challenges. Traditional security mechanisms often struggle to detect and mitigate advanced persistent threats (APTs) and sophisticated cyberattacks. To overcome these challenges, the proposed method leverages big data analytics to analyze extensive and complex datasets, identifying patterns, anomalies, and potential threats. The paper includes a comprehensive literature review on big data analytics for cloud and virtualized security, alongside a framework for threat detection and prevention utilizing machine learning algorithms, data mining, and predictive analytics. The study evaluates the benefits and limitations of this method and highlights directions for future research. This approach is designed to strengthen the security of virtualized environments and provide robust protection against emerging and advanced cyber threats. © 2024 ieee.

关键词： Cloud security

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Enhancing Accuracy in Obesity Prediction and Nutrition Guidance through KNN and Decision Tree Models 5

Enhancing Accuracy in Obesity Prediction and Nutrition Guida...

引用

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

作者： Vineetha, B. Surendran, R. Madhusundar, N. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Obesity remains defined as having too much body mass. This research aims to advance the precision of obesity prediction and refine nutrition guidance by using advanced machine learning techniques, specifically K-Nearest Neighbors (KNN) and Decision Tree models. In an era where lifestyle-related health concerns are on the rise, the significance of accurate obesity prediction and tailored nutritional advice cannot be overstated. This study explores the complex interactions between multiple factors that contribute to obesity, utilizing KNN and Decision Tree algorithms to improve prediction accuracy. © 2024 ieee.

关键词： Obesity Accuracy Prevention and mitigation Machine learning Nearest neighbor methods Predictive models Prediction algorithms Decision trees cognitive informatics

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Research on the Design of Digital Museum Information Presentation Based on cognitive Load Theory 24

Research on the Design of Digital Museum Information Present...

引用

2024 international conference on Cloud computing and Big data, ICCBD 2024

作者： Zhang, Jinfeng Gong, Shiqing Liu, Yihao College of Artificial Intelligence and E-Commerce Zhejiang Gongshang University Hangzhou College of Commerce China

ISBN: (纸本)9798400710223

With the rapid development of digital technology, digital museums are increasingly attracting attention as important cultural and educational platforms. However, how to effectively design the information presentation of digital museums to optimise visitors' learning experience is still an urgent problem. Based on the cognitive load theory, this study explored the mechanism of the impact of the information presentation design of digital museums on visitors' learning effects. The study collected 308 valid samples through questionnaires, and used structural equation modelling to analyse the complex relationship between intrinsic cognitive load, extrinsic cognitive load, cognitive processing and learning effects. This study not only enriches the application of cognitive load theory in digital learning environments, but also provides an important theoretical basis and practical guidance for the design and optimisation of digital museums. The research results can help to enhance the effectiveness of digital museums as educational platforms and promote cultural learning and lifelong education for the public. Future research can further explore the cognitive load management strategies for different types of users, as well as the impact of emerging technologies such as AR/VR in digital museums on cognitive load and learning outcomes. © 2024 ACM.

关键词： cognitive load theory Digital museums information presentation design learning outcomes

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Adaptive Machine Learning Model for complex data Processing 3

Adaptive Machine Learning Model for Complex Data Processing

引用

3rd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Ma, Rui Wei, Xiuzhuo Yang, Yuzhu Wan, Ruoyan Zhao, Huinan Changchun Humanities and Sciences College Jilin Changchun130000 China

ISBN: (纸本)9798331519032

With the rapid development of information technology today, data is emerging at an unprecedented speed and scale, with diverse forms of existence and extremely wide sources. Especially in the medical field, the complexity of data is particularly prominent, including multi-source, incremental, high-dimensional, and significant noise interference. To address these challenges, this article delves into adaptive machine learning (ML) models for complex data processing and focuses on their application in the field of medical diagnosis. ML technology, with its excellent data processing and pattern recognition capabilities, has opened up new paths for the precision and intelligence of medical diagnosis. This paper proposes a medical diagnosis model based on multimodal data fusion and ML to address the complexity of medical data. This model integrates multiple types of data from different sources and utilizes advanced fusion techniques to achieve comprehensive information extraction and deep fusion. The experimental results show that the medical diagnosis model proposed in this paper exhibits excellent performance in various disease diagnosis tasks, significantly improving the accuracy and efficiency of diagnosis. © 2024 ieee.

关键词： data fusion

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Health data Tracking for Effective Health Care Monitoring System Using Fog computing 2

Health Data Tracking for Effective Health Care Monitoring Sy...

引用

2nd international conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Jayakumar, Nithya Soundappan, G. Sumanthan, S. Tamilarasan, R. K.S.Rangasamy College of TechnologyTiruchengode Department of Information Technology Tamil Nadu Namakkal India

ISBN: (纸本)9798350349221

The fog computing provides more flexibility and lower costs, holders of data under pressure to move a complex systems for handling data from on-site sites to private cloud providers. Since data privacy requires encryption of sensitive data before outsourcing, the traditional method of using the data which relies on keyword searches without encryption is too antiquated. To meet the effective demand for data retrieval, search services must enable multi-keyword searches and result similarity rating, especially considering the volume of users and documents stored in cloud storage. Comparable attempts with searchable encryption typically concentrate on a single word or are unable to distinguish among the search. The extent of fog computing has been discussed in the current context. Numerous businesses have started utilizing cloud computing. Over the past few years, fog computing has become extremely popular. Fog computing refers to edge computing. The need and application of such infrastructure in cloud computing are highlighted here Fog computing is recognized for its ability to inherit cloud features. © 2024 ieee.

关键词： Fog computing

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Framework for Movie Recommendation System using GNN and Textual data 2

Framework for Movie Recommendation System using GNN and Text...

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2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Akhter, Moaz Bhagat, Vandana Ganie, Ashaq Hussain Bangalore India

ISBN: (纸本)9798350384277

The surge in on-demand movie platforms and the constant influx of new film releases presents a significant challenge in effectively recommending content to users. With a vast array of movies uploaded daily, online platforms struggle to host diverse content and ensure tailored discovery for users based on their preferences. The platform's effectiveness relies heavily on recommending content aligned with user interests, making recommendation engines crucial for user retention and overall success. These systems typically involve creating algorithms to learn user and movie features and provide recommendations. Despite the wealth of user-related data, obtaining accurate and timely movie information, especially for newly introduced films, remains complex. The absence of historical data for recent movies complicates accurate recommendations. Movie subtitles, containing comprehensive dialogues, can serve as a valuable information source for recommendation engines. This paper leverages the growing popularity of Graph Neural Networks (GNNs) in recommendation systems to capitalize on their ability to discern intricate relationships between entities such as movies, users, and actors. Graph structures naturally represent relationships between entities, allowing the explicit modeling of complex relationships, dependencies, and interactions. By combining subtitle analysis with graph-based learning, the proposed system addresses the challenge of recommending movies without relying on historical user interaction data. © 2024 ieee.

关键词： Graph neural networks

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