The main goal of this work is to forecast Transient Energy Conversion (TEC) indicators used to evaluate the security and stability of wind-integrated power systems. Accurate stability assessments are crucial because w...
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Reducing road accidents remains a paramount concern worldwide, given their profound impact on public safety and well-being. In this paper, we propose a novel methodology for creating a labelled graph data model to pre...
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ISBN:
(纸本)9783031741852;9783031741869
Reducing road accidents remains a paramount concern worldwide, given their profound impact on public safety and well-being. In this paper, we propose a novel methodology for creating a labelled graph data model to predict accident hotspots in urban areas, leveraging accident and road network infrastructure data. The proposed methodology focuses on the creation of a labelled graph where intersections serve as nodes and road segments as arcs by associating accident data with intersections. In order to demonstrate the usability of the proposed methodology, experiments are conducted using simple machinelearning models to predict accident hotspots, which serve to identify high-risk areas.
作者：
Guo, YanhuaSchool of Culture
Tourism and International Education Henan Polytechnic Institute Nanyang473000 China
To manage rural leisure tourism reasonably and achieve intelligent guidance, this paper adopts intelligent scheduling of passenger flow, combining Wi-Fi detection technology and time-window-based analysis method for p...
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Tau leptons are important to several testable predictions of the standard model, including lepton spin polarization, and the Higgs Yukawa coupling to leptons. Tau leptons are also vital in the search for beyond the st...
This research investigates the effectiveness of machinelearning and deep learning models in forecasting voltage swell peak amplitudes within grid-connected photovoltaic (PV) systems, aiming to enhance power quality m...
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Understanding the significance of features in data mining is crucial for accurately analyzing customer behavior, constructing reliable credit scoring models, and detecting fraud within the credit card approval process...
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ISBN:
(纸本)9783031777370;9783031777387
Understanding the significance of features in data mining is crucial for accurately analyzing customer behavior, constructing reliable credit scoring models, and detecting fraud within the credit card approval process. This paper explores the application of data mining techniques in the credit industry, with a specific focus on credit card approval classification. We investigate seven feature importance testing techniques and three classification methods, assessing their performance through various metrics. The research demonstrates that FLOFO with linear regression and ShapFlex with agnostic causal relations substantially improve the performance of all classifiers, with SVM emerging as the most effective classifier across all feature selection techniques. Feature importance testing is pivotal as it not only enhances model accuracy but also provides deeper insights into the factors driving credit card approval decisions. The findings underscore the essential role of data mining in financial risk analysis and credit approval processes, offering valuable perspectives for advancing research and practices in financial technology. The results emphasize the potential of specific feature importance testing techniques and classificationmethods in refining credit card approval classification tasks.
In the current economic environment, the investment decisions of firms are of crucial importance to ensure continuous and stable growth. The present study explores the potential of explanatory problem machinelearning...
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We used deep learning methods to create an innovative system for early detection of depression based on user comments on social networks. This revolutionary approach exploits large amounts of textual data available on...
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The proceedings contain 152 papers. The topics discussed include: swarm intelligence based data selection mechanism for reputation generation in social cloud;trust aware multi-objective metaheuristics for workflow sch...
ISBN:
(纸本)9781665496025
The proceedings contain 152 papers. The topics discussed include: swarm intelligence based data selection mechanism for reputation generation in social cloud;trust aware multi-objective metaheuristics for workflow scheduling in cloud computing;deploying mobile application on cloud;threshold based dynamic resource balancing (TDRB) algorithm in cloud computing;user knowledge modelling through azure machinelearning studio;challenges and issues in energy efficient load balancing in the cloud computing environment;leaf disease detection using convolutional neural network;wind power deviation charge reduction using time series models;proposed methodology for early detection of lung cancer with low-dose CT scan using machinelearning;comparison of different machinelearning algorithms based on intrusion detection system;potato plant disease classification through deep learning;and face-mask recognition and detection using deep learning.
Accurate classification of respiratory abnormality levels is crucial for early detection and diagnosis of respiratory diseases, making it a pivotal area in the field of medical diagnostics. This study proposes a novel...
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Accurate classification of respiratory abnormality levels is crucial for early detection and diagnosis of respiratory diseases, making it a pivotal area in the field of medical diagnostics. This study proposes a novel artificial intelligence approach for accurate classification of respiratory abnormality levels. By transforming respiratory sound time-series data into image representations using recurrent plot, Markov transition field, and Gramian angular field, we capture intricate temporal patterns and spatial relationships. A deep neural network autonomously extracts discriminative features from these representations, subsequently integrated into machinelearning classifiers. Leveraging the internationalconference on Biomedical and Health Informatics (ICBHI) database, our methodology achieves remarkable classification accuracy of 100% for both binary and four- class scenarios, accurately distinguishing normal from abnormal sounds, and discriminating between crackles, wheezes, and their combinations. The SHapley Additive exPlanations (SHAP) method enhances interpretability, providing insights into feature importance and decision-making processes. This interpretable and high- performing approach offers significant promise for enhancing the accuracy and reliability of respiratory disorder diagnosis and treatment planning in clinical settings, potentially improving patient outcomes and healthcare efficiency.
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