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CardioNet: An Integrative AI-machine learning Framework for Enhanced Prediction and Management of Cardiovascular Diseases Using Deep data Analytics and Clinical Insights 4

CardioNet: An Integrative AI-Machine Learning Framework for ...

引用

4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Senthil, G.A. Prabha, R. thamarai, I. Roopa, D. Rajesh Kanna, R. Srimathi, C. Agni College Of Technology Department of Information Technology Chennai India Sri Sairam Institute of Technology Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai City Campus India Sri Sairam Institute of Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Campus Chennai India

ISBN: (纸本)9798331508845

Cardio Vascular Disease (CVD) causes dysfunction in our heart and blood arteries, which frequently results in death or paralysis. therefore, many lives can be prevented by the early and automatic recognition of CVD. Although this goal has been the subject of numerous studies, performance and dependability can yet be enhanced. this work represents another advancement in that direction. Given that cardiovascular diseases (CVDs) remain the primary cause of death worldwide, effective strategies for early detection and management are necessary. To help with CVD prediction and management, we introduce CardioNet, an integrated machine learning framework with strong data analytics and clinical insights. To accurately predict the risk and progression of CVD, CardioNet integrates state-of-the-art machine learning algorithms with a variety of medical data sources, including genetic profiles, imaging data, and electronic health records. CardioNet integrates clinical information and feedback mechanisms to offer customized risk assessment and therapy suggestions. this, in turn, improves patient outcomes and lowers the cost of healthcare. the potential of CardioNet as a comprehensive strategy to counter the widespread threat of CVDs is highlighted. © 2024 IEEE.

关键词： Electronic health record

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GREEN HARVEST: Using machine learning and Optimization Techniques to Predict Market Prices and the Ideal Crop Yield 4

GREEN HARVEST: Using Machine Learning and Optimization Techn...

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4th international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Anuradha, C. Dharani, M. Hemalatha, S.B. Bhavadharni, M. Priya, N. Sri Sairam Institute of Technology Chennai India Jain University Cse - Blockchain Technology FET Campus Bangalore India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Over 50% of India's population is employed in agriculture, which is vital to the country's economy but confronts enormous hurdles from environmental variability. this study reviews machine learning approaches to improve crop yield prediction (CYP) and agricultural decision-making in general, with a focus on Random Forest algorithms and optimisation techniques. We tackle the drawbacks of conventional techniques, namely lower prediction accuracy and challenges in capturing intricate nonlinear interactions. A wide range of functions are integrated into the suggested system, such as real-time weather forecasting, crop disease detection by picture recognition, soil health analysis, and crop yield prediction based on weather and soil data. In addition, it provides tools for farm management, market price analysis, individualized irrigation management, and a decision support system for customized advice. Extra features consist of Community Forums for information exchange, data visualization for improved understanding, alerts for important occurrences, advice on crop selection, and tools for supply chain management. this project attempts to develop a more accurate, effective, and user-friendly model to assist farmers in controlling agricultural operations and maximizing crop yields by utilizing cutting-edge machine learning techniques. © 2024 IEEE.

关键词： Accuracy Supply chain management Reviews Weather forecasting Soil Prediction algorithms Real-time systems Crop yield Optimization Random forests

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ISM 2022 - 4th international conference on Industry 4.0 and Smart Manufacturing

ISM 2022 - 4th International Conference on Industry 4.0 and ...

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4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

the proceedings contain 199 papers. the topics discussed include: infrastructure network support and leapfrogging Africa to Industry 4.0: the case of Tanzania;comparison of energy-use efficiency for lettuce plantation under nutrient film technique and deep-water culture hydroponic systems;an edge-cloud based reference architecture to support cognitive solutions in process industry;logistics 4.0 in intermodal freight transport;analysis of sustainable concrete obtained from the by-products of an industrial process and recycled aggregates from construction and demolition waste;intelligent concrete surface cracks detection using computer vision, pattern recognition, and artificial neural networks;spatial change recognition model using artificial intelligence to remote sensing;smart trip prediction model for metro traffic control using data mining techniques;encryption and generation of images for privacy-preserving machine learning in smart manufacturing;and using analytical and data-driven methods to develop a soft-sensor for flow rate monitoring in tube extrusion.

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IEEE international conference on data Engineering and Communication Systems, ICDECS 2024

IEEE International Conference on Data Engineering and Commun...

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4th IEEE international conference on data Engineering and Communication Systems, ICDECS 2024

ISBN: (纸本)9798350393354

the proceedings contain 84 papers. the topics discussed include: FingerPrintInsight: unveiling crime pattern through deep fingerprint analysis;music to score conversion using machine learning;reduced carbon foot print based localized energy demand supply using micro grids;multi-source word-aligned attention average pooling based bidirectional encoder representation from transformers for product review sentiment analysis;automated clean and cooling system for solar photovoltaic panels using IoT;securing data platforms in B2B enterprises: a framework for the protection of customer data;and statistical analysis of student data and machine learning models for performance prediction.

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Detection of Abnormality in Cognitive Test Scores Using Metric learning 4

Detection of Abnormality in Cognitive Test Scores Using Metr...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Wang, Song Waqar, Dania Maryam Hu, Mengyao Gonzalez, Richard Murphey, Yi Lu University of Michigan-Dearborn Dearborn United States University of Michigan Ann Arbor United States

ISBN: (纸本)9798350324303

We investigate deep learning neural networks to detect anomalies in cognitive test scores that are generated by either a trained human coder or a classification program based on the input of clock drawing images. We have developed a system, SNAD (Siamese Networks for Abnormality Detection) in ordinal classification. SNAD consists of three Siamese networks, one models class k, and the other two model the two neighboring classes, class k-1 and k+1, and an anomaly detection algorithm, OCAD (Ordinal Class Anomaly Detection). the Siamese networks were trained using metric learning and used to generate class distance distributions. the OCAD algorithm calculates the distances of each test image x to the distributions of class k, k-1, and k+1 using the Siamese networks and detects class score anomalies based on statistical analysis of x being out-of or indistributions of class k and its two neighboring class k-1 and k+1. Experiments were conducted on two sets of images collected through Clock Drawing Test (CDT) in the National Health and Aging Trends Study (NHATS), which is commonly used to detect cognitive impairment. the experimental results show that the proposed system, SNAD is effective in metric learning of class-specific distributions, detecting coding inconsistencies, and correcting coding errors. © 2023 IEEE.

关键词： Clocks

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WiFi gesture recognition method based on data enhancement 4

WiFi gesture recognition method based on data enhancement

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4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Li, Qi Wang, Peng Du, Yibo Zhongyuan University of Technology Zhengzhou450000 China Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou450000 China Purple Mountain Laboratories Nanjing210000 China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

In recent years, human motion recognition based on wireless sensing has been widely used in smart home, virtual reality and other fields. Among them, human motion recognition based on WiFi channel state information (CSI) has received great attention. Although the current large-action recognition method based on CSI has good performance, the recognition accuracy is low when performing fine-grained human gesture recognition, which is due to the difficulty of collecting CSI human gesture perception dataset and the small number of high-quality datasets. therefore, we convert CSI data into images and augment CSI images using super-resolution image generation methods. At the same time, we propose a SCBAM attention mechanism to further enhance the attention of the model to important features. In the experiment, we performed experimental analysis using the publicly available Widar3 dataset. the results show that the recognition accuracy of our mixed attention model in the same scene, across location, direction and scene reaches 99.73%, 97.41%, 94.57% and 95.98%, which is 0.07%, 0.29%, 0.05% and 0.99% higher than before data enhancement. © 2024 SPIE.

关键词： Large datasets

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data-driven Power Time Series Forcasting with Spatio-temporal Neural Networks 4

Data-driven Power Time Series Forcasting with Spatio-tempora...

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4th international conference on machine learning and Intelligent Systems Engineering (MLISE)

作者： Kang, Li Zhu, Peng Guo, Yihua Liao, Siyu Xu, Yanjun State Grid Shanghai Elect Power Co Elect Power Res Inst Shanghai Peoples R China Shanghai Hengnengtai Enterprise Management CO Ltd Puneng Power Technol Engn Branch Shanghai Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9798350375084;9798350375077

With the development of society, electricity has become an important part of infrastructure, affecting the operation of daily life, business and social service systems. Accurate load forecasting is not only related to grid operating costs and user charges, but can also achieve energy conservation and emission reduction goals by optimizing power system design. In order to support the safe, high-quality and green transformation of power and energy, relevant national departments have clearly proposed to accelerate the construction of smart energy technology application demonstration projects. the purpose is to cope with China's rapid development and urbanization's increasing dependence on electricity. the application of artificial intelligence technology in power load forecasting also shows great potential. Although it faces challenges such as large data volume and computing resource requirements, and professional talent training, it plays an important role in solving key issues in power system operation. this paper introduces a data-driven spatiotemporal model of electric load, using Fourier transform to capture periodic information, and extracting features through residual blocks and multi-head attention spatiotemporal neural networks, in order to achieve more accurate electric load forecasting. We conduct experiments on multiple different types of datasets, and the experimental results demonstrated the effectiveness of the prediction method in this paper.

关键词： time series forecasting spatiotemporal pattern Fourier transform residual blocks multi-head attention

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ShoppingGPT: A GPT-based Product Recommendation Dialogue System 4

ShoppingGPT: A GPT-based Product Recommendation Dialogue Sys...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Yu, Renjie Guan, Yujing Zhan, Yuancheng Guangxi University of XingJian College of Science and Liberal Arts Dept. of Computer and Information Technology Nanning China Guangxi University of Finance and Economics Dept. of School of Big Data and Artificial Intelligence Nanning China Technology of NanNing University NanNing University Dept. Global Institute of Software Nanning China

ISBN: (纸本)9798350324303

this article introduces a shopping recommendation dialogue system called ShoppingGPT, which aims to help people get comprehensive product recommendations based on individual needs and preferences. the system can leverage existing large-scale generative pre-trained models with the ability to communicate and understand human language to provide more detailed and comprehensive product recommendations through multiple conversations with users. By constructing the shopping recommendation dataset and using the large-scale language model fine-tuning methods, we successfully fine-tuned on a large-scale language model and transformed the shopping recommendation task into a sequential labeling task to adapt to our shopping recommendation dialogue system. And we design a specific method for unlabeled prompt, which uses the basic language knowledge of large-scale language models to output product information for unsupervised annotation for product recommendation. In the experiment, we evaluate several existing language model fine-tuning techniques and explore the generalization ability of the ShoppingGPT system on unlabeled samples. We also compared models before and after fine-tuning to see what the models learned from the data. In the discussion section, we provide a detailed analysis of the advantages and limitations of ShoppingGPT and its potential applications in the shopping field. In addition, we explore the potential security challenges that ShoppingGPT can pose and discuss how to address them. Finally, we discussed how to improve ShoppingGPT to make it perform better and more secure. © 2023 IEEE.

关键词： Large dataset

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Ulcerative Colitis Image Classification Using Federated Deep learning 4th

Ulcerative Colitis Image Classification Using Federated Deep...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Al-Refai, Mohammed Alkhaza'leh, Shahed Alzu'bi, Ahmad Jordan Univ Sci & Technol Fac Comp & Informat Technol Dept Comp Sci Irbid 22110 Jordan

ISBN: (纸本)9783031821523;9783031821530

Ulcerative Colitis is a chronic inflammatory bowel disease characterized by varying degrees of disease severity, often measured using the Mayo endoscopic score. the handling of medical data, encompassing sensitive personal health records and clinical information, necessitates stringent privacy protections. this study introduces a federated deep learning framework that leverages fine-tuned deep learning models trained via transfer learning on a dataset of endoscopic images. Employing DenseNet-121 as the foundational architecture, this approach enables the extraction and encoding of generic descriptors from ulcerative colitis images across multiple clients. We evaluated the training efficiency of our federated learning model in comparison to traditional centralized model, where the federated learning model outperformed the centralized approach by achieving an F1-score of 78% and a Quadratic Weighted Kappa score of 71%, and enhancing convergence speeds in terms of the reduction in the number of training rounds required. the results affirm the model's capability in accurately diagnosing ulcerative colitis while ensuring the confidentiality of patient data, underscoring the viability of federated learning in sensitive healthcare applications.

关键词： Federated learning Ulcerative Colitis Image Classification Neural Networks

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Proceedings - 4th IEEE 2023 international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

Proceedings - 4th IEEE 2023 International Conference on Comp...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

ISBN: (纸本)9798350306118

the proceedings contain 194 papers. the topics discussed include: comparative study of DDoS detection and mitigation techniques;a machine learning-based blockchain model for the storage of maternal health records and safety prediction;enhancing digital investigation: leveraging ChatGPT for evidence identification and analysis in digital forensics;managing metadata in data warehouse for data quality and data stewardship in telecom industry – a compact survey;a review on detection and prevention of the DDoS attacks in the blockchain;analysis of face recognition technique: plastic surgery altered face;image classification using federated averaging algorithm;navigating the gray area: a three-label framework for uncovering uncertainty in fake news;and improvement in validation score with loss function for breast cancer detection using deep learning.

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