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Enhancing Hospital Mortality Prediction for Heart Failure Patients through Hyperparameter Optimization and Interpretability Analysis with LIME and SHAP 1

Enhancing Hospital Mortality Prediction for Heart Failure Pa...

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1st international conference for Women in computing, InCoWoCo 2024

作者： Meena, S. Murthy, Monisha Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Heart failure is one of the primary causes for deaths caused in the hospital. Predicting mortality rate of such patients is extremely important for the efficient use of health care resources. This research aims to estimate the hospital mortality rate of Heart Failure(HF) patients using machine learning(ML). Using computational technique, the obtained model has given the most efficient accuracy. This paper uses the classification model to detect two outcomes (0 and 1, i.e., if the patient will survive or not). The classification models used for the hospital mortality prediction are, single classifiers such as Logistic regression, SVM, KNN, Decision tree (Ginni and Entropy);with Logistic regression obtaining highest accuracy of 88.9%, Bagging and boosting models such as Randomforest, Adaptive boosting, Gradient boosting, XG boosting, Categorical boosting;with CatBoost obtaining highest accuracy of 90.2%. To achieve the best accuracy, ensemble models were used out of which ensemble classifier ensemble gave 91.1% accuracy. To understand the features playing important role in decision making, explainable AI(XAI) is used. LIME and SHAP are considered. LIME generated local explanations, it explained the decision of a complex model for a specific instance or observation. In contrast, SHAP provided global explanations, it explained the overall behavior of the model across all instances. © 2024 ieee.

关键词： Adaptive boosting

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A Detailed Structured Monitoting System for ICU Patient Mortality rate by using AI Algorithm 5

A Detailed Structured Monitoting System for ICU Patient Mort...

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5th ieee international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Joshi, Shivani Kumar, Rajiv Rai, Vipin Dwivedi, Avinash Tripathi, Vikash Chandra G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Nvidia Ai and Learning & Research Center Department of Applied Computational Science and Engineering Greater Noida India Galgotias University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

The complexity and seriousness of diseases in ICU patients, though, make it difficult for medical practitioners to identify high-risk patients who may face irreversible harm. With ANN (Artificial Neural Network), this study will provide an ICU admission prediction model to help identify high-risk patients at an earlier stage. After collecting and analyzing extensive data from various perspectives including demographics, medical history, vital signs, test results and treatment modalities, exploratory data analysis has been conducted. These are used as markers on how statistical techniques and machine learning algorithms can be used to predict mortality among critically ill patients. Consequently, the parameters are designed within the ANN model to create a predictive model that will be able to effectively classify those at higher risk. Advanced machine learning algorithms together with modern tools such as deep learning techniques will aid in the prompt analysis of complex information for accurate forecasts by this predictive model. The ANN model's training set will include a large dataset of ICU patients so that it can become reliable and valid. This makes the predictive model developed capable of accurately identifying high risk cases expeditiously © 2024 ieee.

关键词： accuracy Artificial Neural Networks (ANN) exploratory data analysis high-risk patients ICU patient admission medical technology mortality outcomes periodic updates prediction model

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Query Processing And Optimization Method of Ethnic Music data Based on Deep Belief Network 3

Query Processing And Optimization Method of Ethnic Music Dat...

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

作者： He, Sixian He, Siyuan Gao, Xin College of Music and Dance North Minzu University School Ningxia Yinchuan750001 China College of Music Ningxia University Ningxia Yinchuan750001 China Faculty of Music and Performing Arts Universiti Pendidikan Sultan Idris Tanjung Malim Perak35900 Malaysia School of Materials Science & Engineering North Minzu University Ningxia Yinchuan750001 China

ISBN: (纸本)9798331519032

With the development of the Internet and the large-scale digital music industry, the acquisition and listening of music are presented to users in a more convenient way. How to find the music that users like to hear from the massive digital music data on the Internet has become a key issue in the field of ethnic music information retrieval. Key issues and main goals addressed. There is a complex relationship between ethnic music data, which is suitable for modeling and query processing with data structure. However, the existing query processing algorithms only focus on structural features, and are not optimized for folk music metadata and music content data, and the query efficiency is not high. Currently, the amount of data in various media is exploding, and digital music is everywhere and increasing day by day. At the same time, various music applications have also greatly contributed to the advancement of information technology, such as music sharing, which made the ancient peer-to-peer technology mainstream at the end of the 20th century. The method used in this paper is a deep belief network. Compared with other classification algorithms, its error convergence speed is faster, and the final recognition error for the data set is relatively smaller. © 2024 ieee.

关键词： Structured Query Language

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A Provenance-Aware Approach to Big data Workflow Management in Heterogeneous Cloud Environments 9

A Provenance-Aware Approach to Big Data Workflow Management ...

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9th international conference on Cloud computing and Big data Analytics, ICCCBDA 2024

作者： Kashliev, Andre Tracy, Kaitlyn Eastern Michigan University Department of Computer Science Ypsilanti United States

ISBN: (纸本)9798350373554

Big data workflows have emerged as a powerful paradigm that enables researchers and practitioners to run complex multi-step computational processes in the cloud to gain insight into their large datasets. To create a workflow, a user logs on to a specialized software, called Big data Workflow Management System, or simply BDW system, to select and connect together various components, or tasks, into a workflow. The workflow is then mapped onto a set of distributed compute resources, such as Virtual Machines (VMs), and storage resources, such as S3 buckets and EBS volumes. It is then executed, with different branches and tasks of the workflow running in parallel on different nodes. During execution, the BDW system captures provenance, which is the history of data derivation that describes data processing steps that yielded each output result. Workflow management, including workflow composition and schedule refinement, is a challenging problem. This problem is further exacerbated by the growing number and heterogeneity of workflow tasks and cloud resources, as well as by the growing size and complexity of workflow structures. Few efforts were made to leverage provenance for facilitating workflow composition and schedule refinement. To address these issues, we 1) produce a comprehensive conceptual model for big data workflow provenance that captures the complexity and heterogeneity of cloud-based workflow execution, 2) propose a scalable Cassandra database schema for provenance-aware workflow composition and schedule refinement, 3) outline a four-step provenance-based schedule refinement process for balancing workflow execution time and cost, and 4) present a scalable and highly available microservices-based reference architecture for big data workflow management in the cloud. Our proposed loosely coupled architecture ensures superior scalability, as well as operational and technological independence of each module within the BDW system. © 2024 ieee.

关键词： Workflow management

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Trace-based Multi-Dimensional Root Cause Localization of Performance Issues in Microservice Systems 24

Trace-based Multi-Dimensional Root Cause Localization of Per...

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44th ACM/ieee international conference on Software Engineering, ICSE 2024

作者： Zhang, Chenxi Dong, Zhen Peng, Xin Zhang, Bicheng Chen, Miao Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University China

ISBN: (纸本)9798400702174

Modern microservice systems have become increasingly complicated due to the dynamic and complex interactions and runtime environment. It leads to the system vulnerable to performance issues caused by a variety of reasons, such as the runtime environments, communications, coordinations, or implementations of services. Traces record the detailed execution process of a request through the system and have been widely used in performance issues diagnosis in microservice systems. By identifying the execution processes and attribute value combinations that are common in anomalous traces but rare in normal traces, engineers may localize the root cause of a performance issue into a smaller scope. However, due to the complex structure of traces and the large number of attribute combinations, it is challenging to find the root cause from the huge search space. In this paper, we propose TraceContrast, a trace-based multidimensional root cause localization approach. TraceContrast uses a sequence representation to describe the complex structure of a trace with attributes of each span. Based on the representation, it combines contrast sequential pattern mining and spectrum analysis to localize multidimensional root causes efficiently. Experimental studies on a widely used microservice benchmark show that TraceContrast outperforms existing approaches in both multidimensional and instance-dimensional root cause localization with significant accuracy advantages. Moreover, Trace-Contrast is efficient and its efficiency can be further improved by parallel execution. © 2024 ACM.

关键词： Spectrum analysis

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Global-local Fourier Neural Operator for Accelerating Coronal Magnetic Field Model

Global-local Fourier Neural Operator for Accelerating Corona...

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2024 ieee international conference on Big data, Bigdata 2024

作者： Du, Yutao Li, Qin Gnanasambandam, Raghav Du, Mengnan Wang, Haimin Shen, Bo Njit Newark United States Njit Department of Physics Newark United States FAMU-FSU TallahasseeFL United States Njit Department of Data Science Newark United States

ISBN: (纸本)9798350362480

Exploring the outer atmosphere of the sun has remained a significant bottleneck in astrophysics, given the intricate magnetic formations that significantly influence diverse solar events. Magnetohydrodynamics (MHD) simulations allow us to model the complex interactions between the sun's plasma, magnetic fields, and the surrounding environment. However, MHD simulation is extremely time-consuming, taking days or weeks for simulation. The goal of this study is to accelerate coronal magnetic field simulation using deep learning, specifically, the Fourier Neural Operator (FNO). FNO has been proven to be an ideal tool for scientific computing and discovery in the literature. In this paper, we proposed a global-local Fourier Neural Operator (GL-FNO) that contains two branches of FNOs: the global FNO branch takes downsampled input to reconstruct global features while the local FNO branch takes original resolution input to capture fine details. The performance of the GL-FNO is compared with state-of-the-art deep learning methods, including FNO, U-NO, U-FNO, Vision Transformer, CNN-RNN, and CNN-LSTM, to demonstrate its accuracy, computational efficiency, and scalability. Furthermore, physics-based analysis from domain experts is also performed to demonstrate the reliability of GL-FNO. The results show that GL-FNO not only accelerates the MHD simulation (a few seconds for prediction, more than ×20,000 speed up) but also provides reliable prediction capabilities, thus greatly contributing to the understanding of space weather dynamics. Our code implementation is available at https://***/Yutao-0718/GL-FNO. © 2024 ieee.

关键词： Magnetoplasma

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Efficient Canine Vision: Accurate Dog Breed Classification with EfficientNet 2

Efficient Canine Vision: Accurate Dog Breed Classification w...

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

作者： Godwin James, T. Prabahar Poonguzhali, C. Selvakumaran, S. Malathi, G. Kaliappan, S. Venu, Nookala Department of Computer Science and Engineering Sri Sairam Institute of Technology India Department of Electronics and Communication Engineering S.A. Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Saravanampatti Coimbatore India Madhya Pradesh Gwalior India

ISBN: (纸本)9798350384277

Understanding and classifying dog breeds is a fascinating yet challenging task due to the vast diversity within the canine species. In this project, we present a novel approach to dog breed classification utilizing the state-of-the-art Efficient Net architecture. Leveraging a comprehensive dataset comprising 133 distinct breeds, our method aims to accurately identify and categorize each breed with high precision. Efficient Net's innovative scaling method allows us to strike a balance between model complexity and computational efficiency, enabling robust classification performance even with limited computational resources. Through meticulous preprocessing techniques and data augmentation strategies, we enhance the model's ability to generalize across diverse breed characteristics, minimizing the risk of overfitting. Our experimental results demonstrate the efficacy of the proposed approach, achieving impressive classification accuracy on the challenging dog breed dataset. We conduct extensive evaluations and comparisons with existing methodologies, showcasing the superiority of Efficient Net in handling complex classification tasks with large-scale image datasets. Furthermore, we provide insights into the interpretability of the model's predictions, shedding light on the learned representations and key features contributing to breed classification. This project not only advances the state-of-the-art in dog breed classification but also contributes to our understanding of deep learning techniques applied to animal recognition tasks. In sum up, our work highlights the potential of leveraging advanced neural network architectures like Efficient Net for comprehensive and accurate dog breed classification, paving the way for applications in veterinary medicine, pet care, and animal welfare. © 2024 ieee.

关键词： Veterinary medicine

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Design of Wind Power Generation System Based on Interval Optimization 2

Design of Wind Power Generation System Based on Interval Opt...

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2nd ieee international conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Zhu, Jijin Luo, Chan Wu, Qinqin Guangdong University of Science and Technology College of Mechanical and Electrical Engineering Dongguan China Dongguan Guangming Primary School Mathematics Teaching and Research Office Dongguan China

ISBN: (纸本)9798350317558

According to the analysis of the mechanism model of the doubly fed variable speed constant frequency wind power generation system, it can be concluded that the system is a complex system with nonlinearity, uncertainty, and constraints. Therefore, based on the above characteristics, this paper uses predictive control method to control it, and uses interval optimization theory to solve the optimal control quantity in rolling optimization. Based on the mathematical transformation model, develop a nonlinear interval number optimization algorithm with certain engineering practicality. The main difficulty to solve is the low efficiency caused by two-layer nested optimization, and further improve the real-time performance of the algorithm by adopting parallel computing strategy on the basis of designing the solution algorithm. The results show that compared with classical big data mining technology and traditional data analysis methods, interval optimization technology have better accuracy and convergence in feature recognition, and can judge feature recognition. © 2024 ieee.

关键词： Big data Feature recognition Interval Optimization traditional data analysis method Wind Power Generation System

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YOLO-MSP: Effective Cigarette Detection YOLO Network for Surveillance Scenario 10

YOLO-MSP: Effective Cigarette Detection YOLO Network for Sur...

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10th international conference on Systems and Informatics, ICSAI 2024

作者： Zhang, Zhiwei Lu, Xiaobo School of Automation Southeast University Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China

ISBN: (纸本)9798331510138

This paper presents an new approach to enhance the detection of smoking behavior using object detection neural networks, focusing on the challenge of small object detection, namely cigarettes in video frames. We introduce a Multi-Spatial Pooling (MSP) bottleneck mechanism integrated into a YOLOv8 backbone, which significantly improves the network's ability to discern smaller objects without compromising real-time inference capabilities. This enhancement facilitates the extraction of relevant features across various scales and efficiently handles the computational overhead, thereby boosting the detection accuracy and operational efficiency. Our results demonstrate a substantial improvement in performance metrics over traditional and existing neural network approaches, making this method highly suitable for real-time applications in diverse scenarios. © 2024 ieee.

关键词： Deep learning

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Big data-Driven Long Term Accurate CO2 Emission Forecasting Using Advanced Recurrent Neural Networks

Big Data-Driven Long Term Accurate CO2 Emission Forecasting ...

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2024 ieee international conference on Big data, Bigdata 2024

作者： Verma, Anil Routhu, Lalit Chandra Chigrupaatii, Rishikant Choubey, Anurag Misra, Rajiv Singh, T.N. Indian Institute of Technology Patna Dayalpur Department of Computer Science and Engineering Daulatpur801106 India Bennett University Scset Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350362480

Global climate change driven by carbon dioxide (CO2) emissions necessitates accurate forecasting for effective mitigation strategies. Traditional forecasting models, such as ARIMA and statistical methods, often struggle to handle the non-linear and long-term temporal dependencies in CO2 emission data. This paper evaluates the performance of advanced Recurrent Neural Network (RNN) architectures, specifically Long Short-Term Memory (LSTM), Stacked LSTM, Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) with attention mechanisms, in forecasting long-term CO2 emissions. These models are compared against traditional machine learning algorithms like Random Forest and Gradient Boosted Trees (GBT). The Random Forest and Gradient Boosted Trees, implemented using PySpark framework for distributed computing, are leveraged for efficient big data handling, enabling scalable solutions for CO2 emission forecasting. Our experimental results, using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), show that the Stacked LSTM model achieved the lowest MAE of 25.795, RMSE of 80.678, and MAPE of 1.072, outperforming Random Forest (MAE: 72.4, RMSE: 310.5, MAPE: 1.32) and GBT (MAE: 75.6, RMSE: 356.8, MAPE: 1.57). The findings demonstrate that advanced RNN models, particularly the Stacked LSTM and Bi-LSTM with attention, provide superior performance over traditional models, effectively capturing complex temporal patterns in the data. This study offers valuable insights into CO2 emission forecasting approaches, contributing to the development of accurate tools for climate policy and strategy formulation. © 2024 ieee.

关键词： Low emission

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