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检索条件"任意字段=5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022"

共 261 条记录，以下是81-90 订阅

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applied Health Informatics for Diabetes Risk Prediction from Food Consumption Behavior of Rural Communities in Northern thailand with data analytics 5th

Applied Health Informatics for Diabetes Risk Prediction from...

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5th international conference on Computing and Network Communications, CoCoNet 2023

作者： Nuankaew, Pratya Kadkasame, Apatcharaporn Sawasit, Kunthida Nasa-Ngium, Patchara Sararat, thapanapong Nuankaew, Wongpanya S. School of Information and Communication Technology University of Phayao Phayao56000 Thailand Faculty of Science and Technology Rajabhat Maha Sarakham University Maha Sarakham44000 Thailand

ISBN: (纸本)9789819747108

this research has three critical research objectives. the first objective is to study the context and risk of diabetes from the food consumption behavior of thai people in the northern region. the second objective is to develop a model for predicting diabetes risk from the food consumption behavior of thai people in the northern region. the last objective is to evaluate the efficiency of the model for predicting diabetes risk from the dietary behavior of thai people in the northern region. the population and sample in this research were 370 people in Chiang Muan District, Phayao Province, aged between 20 and 99 years. the research tools include an IOC-approved questionnaire and data mining analysis using machine learning and artificial intelligence tools known as CRISP-DM. Model performance was evaluated using data partitioning and testing known as split-validation methods and confusion matrix performance. the research results found that the context of rural people in northern thailand toward food consumption behavior was normal. Overall, the risk of people developing diabetes was at an acceptable level. the developed model has the highest level of accuracy with an accuracy value of 81.80%. It is given in Tables 11 and 12 that people have good food consumption habits. Moreover, the model for predicting the risk of diabetes in rural communities was found to have a high level of accuracy by comparing various techniques, as given in Table 4. this research, therefore, concludes that all objectives were achieved. the results and expectations from the research are to know the food consumption context of rural people in the northern region of thailand, which can be used to design and plan strategies to prevent the increase in people at risk of diabetes in thailand. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： applied informatics Diabetes risk prediction Food consumption behavior Health forecasting model Health informatics

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Traffic Flow Prediction Using Deep learning Models 5

Traffic Flow Prediction Using Deep Learning Models

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5th international conference on Computing and Big data, ICCBD 2022

作者： Wu, China-Seh Manthena, Satya Pranavi Kale, Saurabh Kulkarni, Maithili Vinayak San Jose State University Department of Computer Science San JoseCA United States

ISBN: (纸本)9781665457163

For the effective implementation of Intelligent Transportation Systems (ITS), accurate and timely traffic flow information is critical. Traffic data has exploded in recent years, ushering in the era of big data. the main issue of a traffic flow prediction system is determining how to build an adaptive model based on past data. Existing traffic flow forecast approaches rely on shallow learning models, which are unsatisfactory for many real-world applications. this situation prompts us to reconsider traffic flow prediction using deep learning models and large amounts of traffic data. In this paper, we present the Long Short-Term Memory (LSTM) model, Gated Recurrent Unit (GRU) model, Stacked Auto Encoder (SAE), and an Auto-Regressive Integrated Moving Average (ARIMA) model, all of which are deep learning methods. these techniques are applied to real-world traffic big data collected by performance measurement systems. Compared to other deep learning and shallow machine learning prediction networks, experimental results demonstrate that the gated recurrent unit model is more applicable and performs better. © 2022 IEEE.

关键词： Forecasting

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Performance Comparison of Credit Card Fraud Detection System using machine learning 5

Performance Comparison of Credit Card Fraud Detection System...

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5th international conference on Signal Processing and Information Security, ICSPIS 2022

作者： Panthakkan, Alavikunhu Valappil, Najiya Appathil, Majida Verma, Seema Mansoor, Wathiq Al-Ahmad, Hussain University of Dubai Dubai United Arab Emirates Banasthali Vidyapith Rajasthan India

ISBN: (纸本)9781665492652

Cyber security is becoming an integral part of modern life, tackling abnormal activities being the main challenge in the domain. To find and abort a fishy transaction is the procedure for maintain the security in credit cards. With the availability of past datasets, machine learning algorithms have made it possible to detect abnormal activities among the transactions. the aim of the paper is to create a balanced data from a pre-existing dataset and to apply six different machine learning algorithms like Decision tree, K-Nearest Neighbor, Logistic regression, SVM, Random Forest and XGBoost to detect fraud activities. Later, the results are compared and discussed on the basis of evaluation matrices which include precision, recall, f1-score and accuracy. All the algorithms are applied on three sets of data, where the ratio of fraud to valid transactions is varied. three cases are considered with ratios as 1:100, 1:10 and 1:1. A balanced dataset showed greater performance in the terms of all evaluation matrices. © 2022 IEEE.

关键词： Decision trees

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Bidding Behavior Analysis in Joint Electricity and Carbon Market by Hybrid Experimental learning 5

Bidding Behavior Analysis in Joint Electricity and Carbon Ma...

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5th IEEE international Electrical and Energy conference, CIEEC 2022

作者： Ye, Jianmin Hu, Yarong Liu, Jinjie Liu, Wenxuan Liang, Gaoqi Shenzhen Power Supply Co.Ltd Shenzhen China The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

It is important to model the causality behind social behaviors in the electricity market. Existing methods, including theoretical models and economic experiments, are difficult to be applied in practice. In this paper, a data-driven approach, named Hybrid Experimental learning (HEL) which combined machine learning and experimental economics, is proposed to model social behavior. With the historical and experiment data, HEL applies a machine learning generative model for understanding social behaviors. the output of the generative model is fed into a causal estimator to explain the causality. According to the pilot spot market rule and potential carbon market rule in Guangdong, bidding strategies of generators are generated by Wasserstein generative adversarial networks (WGAN). In addition, an instrumental variable (IV) method based on the local surrogate model is applied for the causal inference between the inputs and outputs of WGANs for coal generators, which can be described as their bidding mechanisms. the effectiveness is verified on bidding strategies simulation in a simplified Guangdong power system. © 2022 IEEE.

关键词： Generative adversarial networks

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Uncovering Multivariate Structural Dependency for Analyzing Irregularly Sampled Time Series

Uncovering Multivariate Structural Dependency for Analyzing ...

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5th international Workshop on learning with Imbalanced Domains - theory and Applications / European conference on machine learning and Principles and Practice of Knowledge Discovery in databases (ECML PKDD)

作者： Wang, Zhen Jiang, Ting Xu, Zenghui Gao, Jianliang Wu, Ou Yan, Ke Zhang, Ji Zhejiang Lab Hangzhou Peoples R China Cent South Univ Changsha Peoples R China Tianjin Univ Tianjin Peoples R China Natl Univ Singapore Singapore Singapore Univ Southern Queensland Toowoomba Qld Australia

ISBN: (纸本)9783031434235;9783031434242

Predictive analytics on Irregularly Sampled Multivariate Time Series (IS-MTS) presents a challenging problem in many real-world applications. Previous methods have primarily focused on incorporating temporal information into prediction while little effort is made to exploit the intrinsic structural information interchange among different IS-MTS at the same or across different timestamps. Recent developments in graph-based learning have shown promise in modeling spatial and structural dependencies of graph data. However, when applied to IS-MTS, they face significant challenges due to the complex data characteristics: 1) variable time intervals between observations;2) asynchronous time points across dimensions resulting in missing values;3) a lack of prior knowledge of connectivity structure for information propagation. To address these challenges, we propose a multivariate temporal graph network that coherently captures structural interactions, learns time-aware dependencies, and handles challenging characteristics of IS-MTS data. Specifically, we first develop a multivariate interaction module that handles the frequent missing values and adaptively extracts graph structural relations using a novel reinforcement learning module. Second, we design a correlation-aware neighborhood aggregation mechanism to capture within and across time dependencies and structural interactions. third, we construct a novel masked time-aware self-attention to explicitly consider timestamp information and interval irregularity for determining optimal attention weights and distinguishing the influence of observation embeddings. Based on an extensive experimental evaluation, we demonstrate that our method outperforms a variety of competitors for the IS-MTS classification task.

关键词： Irregularly sampled multivariate time series graph structure learning temporal attention multivariate feature interaction

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How Big data Analytical Framework has Redefined Academic Sciences 5th

How Big Data Analytical Framework has Redefined Academic Sci...

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5th international conference on Recent Innovations in Computing , ICRIC 2022

作者： Banday, Imran Rashid Zaman, Majid Quadri, S.M.K. Butt, Muheet Ahmed Fayaz, Sheikh Amir Department of Computer Sciences University of Kashmir Srinagar India Directorate of IT and SS University of Kashmir Srinagar India Department of Computer Science Jamia Millia Islamia New Delhi India

ISBN: (纸本)9789819906000

Academic institutions generate a large amount of heterogeneous data and academic leaders want to make the most of that data by evaluating it for improved decision-making. the volume is not the only difficulty;the organization's data format (structured, semi-structured, and unstructured) creates complication in academic functioning and decision-making on a daily basis. Academic data sets have expanded in amount and proportion to the point that standard data processing and analytics cannot provide satisfying results;moreover, it is not only about volume but also about what is to be done with data. In this paper, Big data is used in an academic institution (University of Kashmir) to analyse data (structured, semi-structured, and unstructured) from multiple sources for smart decision-making, and machine learning models on Big data are used to predict academic behaviour such as student performance, academic bias, and job market viability. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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A machine-learning Ensemble Method for Temporal-aware Sales Forecasting 5

A Machine-Learning Ensemble Method for Temporal-aware Sales ...

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5th international conference on data Science and Information Technology, DSIT 2022

作者： Aun, Yichiet Khaw, Yen-Min Jasmina Gan, Ming Lee Chern, Debra Lee Hooi Universiti Tunku Abdul Rahman Faculty of Information Communication and Technology Malaysia

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

machine-learning-driven sales forecasting is gaining momentum in inventory management based on customer shopping behaviors. However, it is difficult to build a one-size-fits-all sales forecasting model for general purpose use due to the volatility of product turnaround and complex consumer behaviors. Traditionally, single forecasting models such as ARIMA modeling are used to predict sales. However, existing models have little real- world applicability given the apparent forecast disparity between experimental and real-world predictions due to model overfitting. this project proposes an ensemble machine-learning method using weighted XGBoost, ARIMA, and Holt-Winters for sales forecasting of a grocery chain to address model overfitting issues. the intuition of the work is to combine several ML models to reduce variance (bagging), bias (boosting), and improve predictions (stacking). the contribution of this work includes: (1) implementation of exploratory modeling for in-depth exploratory data analysis (EDA) in grocery datasets and (2) feature engineering for the weighted integration of designed forecasting models using a lag and sliding window techniques. the preliminary result of the research places the model developed at the 70th percentile of the Corporacion Favorita Sales Forecasting Competition in Kaggle. © 2022 IEEE.

关键词： Big data

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Cluster analysis for cancer omics data using Neural Network with data augmentation 22

Cluster analysis for cancer omics data using Neural Network ...

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5th international conference on Signal Processing and machine learning, SPML 2022

作者： Ma, Yingning Department of School of Engineering and Applied Science George Washington University WashingtonDC United States

ISBN: (纸本)9781450396912

In recent years, with the popularity of machine learning, more and more cancer analysis based on machine learning has been rapidly researched and developed. Previous studies have shown that neural networks can not only detect cancer, but also classify gene expression subtypes. In addition to these supervised learning studies, unsupervised learning can also be exploited to study cancer subtypes based on patients’ molecular profiling data. In this work, we propose a method using ResNet with virtual adversarial training and mutual information maximization for cluster analysis. the results show that our method achieves higher accuracy than other clustering methods. Next, we compared datasets with different numbers of features of same cancer, and showed that higher-dimensional data can produce better clustering performance. © 2022 Association for Computing machinery.

关键词： Cluster analysis

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Diagnosis and Prognosis of Diabetes Mellitus with Deep learning 5

Diagnosis and Prognosis of Diabetes Mellitus with Deep Learn...

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5th IEEE international conference of Women in data Science (WiDS-PSU)

作者： Afzal, Erum Saba, Tanzila Javed, Kashif Ali, Haris Karim, Adnan Natl Univ Sci & Technol Islamabad Pakistan Prince Sultan Univ AIDA Lab CCIS Riyadh Saudi Arabia

ISBN: (纸本)9781665408127

Diabetes being a metabolic disease is affecting people worldwide. It brings them under the life risk often. Early diagnosis and prognosis are required to control the disease's associated health problems and complications to prevent the damage of internal organs that may become fatal to life. In this research efficient deep learning models naming: XGBOOST and LGBM have been implemented on the dataset provided during WiDS 2021 datathone. Before applying deep learning algorithms, an extensive feature engineering process was administered to get better insight into correlated features. It highlighted the concerns including filling missing values, class imbalanced, and age groups as important participatory factors in predicting anomalous results. data segregation on groups including gender, age, ethnicity and max glucose didn't show visible differences among the classes so the machine learning procedures were implemented on the complete dataset. model evaluation was carried out using ROC evaluation method with 0.87 accuracy.

关键词： KNN Catboost Diabetes Mellitus Deep learning xgboost LGBM Healthcare data sciences

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Artificial Intelligence Tools for University Fundraising 5.0: A Comprehensive Analysis 5

Artificial Intelligence Tools for University Fundraising 5.0...

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5th IEEE international conference on Modern Electrical and Energy System, MEES 2023

作者： Cherniavska, Olena Belov, Alexandr Shmygol, Nadiia Jarvis, Marina Cherniavska, Oleksandra Tsalko, Tetiana Swiss Federal Institute of Technology Eth Zürich Chair of Technology and Innovation Management Zurich Switzerland Kyiv National University of Technology and Design Faculty of Management and Business Design Kyiv Ukraine Institute of Social and Economic Initiatives Department of Innovation Research and Development Poltava Ukraine Warsaw University of Technology Division of Management and Quality Warsaw Poland Zaporizhzhya Politechnik National University Departament of Management Zaporizhzhya Ukraine Department of Business Administration Tallinn Estonia Kyiv Education Training and Youth Centre Departament of Management Kyiv Ukraine Kyiv National University of Technology and Design Department of Management and Smart Innovation Kyiv Ukraine

ISBN: (纸本)9798350359787

University Fundraising 5.0 represents the latest paradigm shift in this domain, one that resonates with the European Union's transformative vision of Industry 5.0. this visionary approach to fundraising not only incorporates cutting-edge technologies but also emphasizes collaboration, sustainability, and social impact, aligning seamlessly with the broader European framework of Industry 5.0. We applied horizon scanning methods, such as AI-Human Collaboration through information scanning and technological analysis, to classify and functionally organize artificial intelligence (AI) tools that can be applied in universities. through the analysis of 61 platforms and services related to AI and fundraising, along with a systematic classification of AI tools based on functionality, implementation, and other relevant criteria, this research offers valuable insights for universities in selecting the most suitable AI tools to meet their specific fundraising requirements and objectives. the analyzed platforms provide various solutions, including AI tools, cloud computing, machine learning, data analytics, data visualization, natural language processing, fraud protection, contact data management, social media community management, content optimization and personalization, as well as fundraising and donor database management tools for non-profit organizations. Study highlights the potential future prospects and implications of integrating AI technologies in university fundraising beyond donor interactions. It opens up avenues for further research on AI applications in areas such as grant management, corporate partnerships, and foundation collaborations. Exploring these perspectives can provide universities with a comprehensive understanding of the broader impact of AI on their overall fundraising strategies and financial sustainability. the research findings contribute to advancing the understanding of AI's potential in university funding and fostering innovation in higher educat

关键词： data analytics

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