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SHAP-based Interpretable models for Credit Default Assessment Using Machine Learning 14

SHAP-based Interpretable Models for Credit Default Assessmen...

引用

14th international conference on software Technology and engineering, ICSTE 2024

作者： Xu, Qingyang Liao, Yunlong Li, Qiutong Zhang, Jiaqi Song, Zhilan Wang, Linjun Yuan, Xiaochen Macao Polytechnic University Faculty of Applied Sciences China

ISBN: (纸本)9798350378955

In recent years, the issue of credit fraud risk has garnered increased attention from the banking and financial sectors. However, prevailing credit assessment models predominantly focus on predictive outcomes, often overlooking the imperative of model interpretability. Understanding the contributions of model features and their interactions is paramount for elucidating model behavior and furnishing vital insights for model enhancement and optimization. To address this gap, this paper proposes a machine learning model leveraging SHAP for explicating credit assessment. Utilizing publicly available datasets from Lending Club, this study validates the proposed model against four industry-standard machine learning approaches: SVM, MLP, XGBoost, and LightGBM. Experimental findings unveil feature importance rankings and elucidate relationships between features and target variables. Notably, the study identifies the predominant roles of loan interest rates and credit policies in credit fraud assessment within the dataset and endeavors to uncover interactions within individual key features. the SHAP framework, as demonstrated, holds promise for informing the design and construction of future credit risk assessment models, thereby bolstering support for financial decision-making and risk management endeavors. © 2024 IEEE.

关键词： Adversarial machine learning

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Application of Computer three-Dimensional Simulation in the Process of Teaching Students the Subject of engineering Graphics 6

Application of Computer Three-Dimensional Simulation in the ...

引用

6th international conference on Information Technologies in engineering Education, Inforino 2022

作者： Bobrova, T.A. Kapitanova, E.A. Kapitanov, I.R. National Research University MPEI Power Engineering Design Department Moscow Russia

the article considers the use of three-dimensional graphics during lectures and practical classes on engineering graphics for the development of spatial imagination in first-year students and the use of the textbook &... 详细信息

ISBN: (纸本)9781665405775

关键词： Graphics Solid modeling three-dimensional displays predictive models Synchronization Task analysis Surface finishing

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Design and implementation of vehicle type recognition system based on language large model 6

Design and implementation of vehicle type recognition system...

引用

6th international conference on Internet of things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Liu, Xiang Zhang, Yikuan Software Engineering Institute of GuangZhou Computer Science Department Guangzhou China Software Engineering Institute of GuangZhou Department of Electronics Guangzhou China

ISBN: (纸本)9798350386974

Using large language model to generate vehicle type recognition algorithm can reduce the burden of developers and realize the rapid development of projects. In this paper, LangChain large model interface provided by Baidu Qianfan large-scale platform is used to realize the auxiliary development of vehicle type recognition. With the support of large model, the training and testing model of vehicle type recognition is constructed by using deep learning method, and the fine-grained vehicle image classification and recognition system is realized. At present, the ResNet50 Enhance MEAL2 model is used for classification recognition, supporting 1748 types of vehicle classification and recognition. In the Car-Dataset of vehicle data, the Accuracy of the training set is about 95.9%, and the Accuracy of the test set is about 84.3%. the backbone network can support Googlenet. ResNet [18, 34, 50], Inception-v3, Mobilenet-v2 and other common models. the use of language large model in this project improves the efficiency of development. © 2024 IEEE.

关键词： Convolutional neural networks

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BuggIn: Automatic Intrinsic Bugs Classification Model using NLP and ML 19

BuggIn: Automatic Intrinsic Bugs Classification Model using ...

引用

19th international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Bhandari, Pragya Rodriguez-Perez, Gema Univ British Columbia Kelowna BC Canada

ISBN: (纸本)9798400703751

Recent studies have shown that bugs can be categorized into intrinsic and extrinsic types. Intrinsic bugs can be backtracked to specific changes in the version control system (VCS), while extrinsic bugs originate from external changes to the VCS and lack a direct bug-inducing change. Using only intrinsic bugs to train bug prediction models has been reported as beneficial to improve the performance of such models. However, there is currently no automated approach to identify intrinsic bugs. To bridge this gap, our study employs Natural Language Processing (NLP) techniques to automatically identify intrinsic bugs. Specifically, we utilize two embedding techniques, seBERT and TF-IDF, applied to the title and description text of bug reports. the resulting embeddings are fed into well-established machine learning algorithms such as Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors. the primary objective of this paper is to assess the performance of various NLP and machine learning techniques in identifying intrinsic bugs using the textual information extracted from bug reports. the results demonstrate that both seBERT and TF-IDF can be effectively utilized for intrinsic bug identification. the highest performance scores were achieved by combining TF-IDF with the Decision Tree algorithm and utilizing the bug titles (yielding an F1 score of 78%). this was closely followed by seBERT, Support Vector Machine, and bug titles (with an F1 score of 77%). In summary, this paper introduces an innovative approach that automates the identification of intrinsic bugs using textual information derived from bug reports.

关键词： software bugs classification intrinsic bugs extrinsic bugs natural language processing

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Revolutionizing Intensive Care: A Machine Learning Based Approach for ICU Patients' In-Hospital Mortality Prediction 6

Revolutionizing Intensive Care: A Machine Learning Based App...

引用

6th international conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Ali, Golam Abdullah Al-Kafi, G.M. Faiza, Jannateen Tajree Hoque, Md Iztahadul Suha, Sayma Alam Bangladesh Army International University of Science and Technology Department of Cse Cumilla Bangladesh Department of Cse Dhaka Bangladesh

ISBN: (纸本)9798350385779

Early prediction of in-hospital mortality in Intensive Care Unit (ICU) patients is crucial for optimizing resource allocation, informing prognosis discussions, and guiding treat-ment decisions. While conventional systems exist, they often lack precision and struggle to handle complex, multidimensional data with limited resource availability. this study investigates the potential use of machine learning (ML) techniques to enhance in-hospital mortality prediction in ICU patients with heart failure (HF). An ML-based approach has been proposed in this study utilizing electronic health records of ICU patients containing demographic, physiological, laboratory, and medication data. the significance of the features in the dataset has also been evaluated through a voting ensemble technique aggregating the results from multiple feature selection techniques. Various ML classification algorithms are trained, tested and compared to identify the model with the best predictive performance both with reduced feature set and all the features from the dataset. the model's generalizability and efficacy is evaluated through multiple performance parameters as well as error analysis. the Random Forest Classification model significantly outperforms other models attaining 96.8% accuracy & 0.032 Mean Absolute Error in predicting in-hospital mortality, achieving a higher area under the receiver operating characteristic curve (AUROC). Feature importance analysis here also reveals the most dominant 27 crucial attributes influencing mortality risk for this dataset, providing valuable insights for clinical decision-making. thus, this study can lead to more efficient resource utilization, personalized treatment strategies, and ultimately, improved patient outcomes particularly for ICU patients. © 2024 IEEE.

关键词： Intensive care units

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A Hybrid Model with CNN-LSTM for Link Quality Prediction 6

A Hybrid Model with CNN-LSTM for Link Quality Prediction

引用

6th international conference on Electronics Technology, ICET 2023

作者： Fanjiebin Liulinlan Nanchang Hangkong University School of Information Engineering Jiangxi China

ISBN: (纸本)9798350337693

Link quality prediction significantly improves network performance. Effective link quality prediction can ensure data transmission, and high-quality link transmission data can improve throughput, thereby extending the lifespan of the network. In order to better extract the temporal features of link quality parameters, this paper proposes a deep learning model that combines one dimensional convolutional neural network (IDCNN) and long short-term memory (LSTM). Firstly, local features are extracted from the original sequence using one dimensional convolution network. Secondly, temporal features are extracted using the sequence modeling advantages of LSTM. Finally, attention mechanism is introduced to improve the prediction performance of the model further. Experiments on real and public datasets have shown that the proposed CNN-LSTM has better predictive performance compared to shallow machine learning models and traditional sequence models. © 2023 IEEE.

关键词： Long short-term memory

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Forecasting of photovoltaic power generation leveraging multi-view attention model 6

Forecasting of photovoltaic power generation leveraging mult...

引用

6th international conference on Power and Energy Technology, ICPET 2024

作者： Liu, Hanjing Su, Qiankun College of Computer Engineering Jimei University Xiamen China

ISBN: (纸本)9798350375855

Forecasting of photovoltaic (PV) power generation is a challenging task since it requires to consider many factors synthetically, such as historical power generation data, meteorological data. the unstable and nonlinear nature of PV power generation contributes to the difficulty. Most existing predictive models rarely focus on the hidden information in the data and fail to capture spatio-temporal dependencies in the data. To address these challenges, we propose a hybrid deep learning framework that combines multi-view time-series representation with an attention mechanism, named Multi-View Attention model for PV Power Generation Prediction (PV-MVAT). Our model improves the accuracy of its predictions by the following two techniques: i) capture key features and information through the multi-view time-series representation technique;ii) leverage the attention mechanism to dynamically adjust the attention weights for different time points and features. Real data simulations from the Elia Group in Belgium demonstrate the viability of our design choices. In particular, compared with the best performance among ten baseline models, our model achieves 16.06%, 8.11% and 7.73% improvement in MSE, MAE and CVRMSE respectively. © 2024 IEEE.

关键词： Photovoltaics

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Model Checking Based Test Adaptation in Changing Business software 24

Model Checking Based Test Adaptation in Changing Business So...

引用

6th international conference on software engineering and Development, ICSED 2024

作者： Hallal, Hicham Faizan, Alisha Department Of Computer Science & Engineering American University Of Sharjah United Arab Emirates Department Of Computer Science and Engineering American University Of Sharjah United Arab Emirates

ISBN: (纸本)9798400718052

Testing large software applications poses a major challenge, especially in the presence of changes that are usually introduced after the initial deployment of said applications. this makes it rather difficult to anticipate the impact of the introduced changes throughout the initial development lifecycle. Any existing test suite that was used to validate the application before the changes needs to be modified accordingly to cover the updated or new functionality. Test adaptation techniques can be used to alleviate this issue, and to offer tools allowing the automated adaptation of the test suite to the changes. In this paper, we explore the problem of test adaptation in the case of changing business applications, where we focus on changes introduced at the level of the business logic. We propose a model checking based approach to test adaptation that uses behavioral models inferred from execution traces of application under test (AUT). models of different versions of the changing application are compared to detect the introduced changes, to evaluate the validity of existing test cases, and to adapt the ones invalidated by the changes. We illustrate the applicability of the approach using the example of a travel management business process. © 2024 Owner/Author.

关键词： Model checking

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Predicting User Purchase Behavior on ***: A Sequential Interaction and Engagement Depth Model 6

Predicting User Purchase Behavior on ***: A Sequential Inter...

引用

6th international conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2024

作者： Wang, Jingze Li, Jingchen School of Software Shandong University Jinan250101 China

ISBN: (纸本)9798331541798

this study utilized transaction-level data from ***, as compiled for the 2020 MSOM Data Driven Research Challenge. By analyzing user click and purchase records along with associated product information, diversified classification models were introduced to predict user purchasing behavior. Following rigorous data preprocessing, the study implemented feature engineering based on user behavioral patterns, product attributes, and the interaction data between users and products. Specifically, through hierarchical modeling of user behavior, key features such as total engagement volume, maximum engagement depth, and click-through conversion rate were extracted and constructed to capture users' information acquisition efficiency and purchase intentions effectively. these features accurately reflect the sequential model of user behavior from browsing and clicking through to interactive requests, collectively contributing significantly to the optimization of both the accuracy and complexity of the classification model. the findings indicate that the Neural Network Classifier model outperforms others in predicting user ordering behavior, achieving an accuracy of 85.38%. this research offers a novel perspective for enhancing the performance and user experience of recommendation systems. © 2024 IEEE.

关键词： Prediction models

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AI Data Analytics for Air Quality Modeling 6

AI Data Analytics for Air Quality Modeling

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6th international conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Barua, Annandip Babu, Md Abu Ahammed Cox's Bazar International University Department of Computer Science & Engineering Bangladesh Volvo Car Corporation Sweden

ISBN: (纸本)9798350385779

Air quality is a critical concern globally, with implications for public health and environmental sustainability. While improvements have been made since the implementation of the Clean Air Act of 1970, the adverse effects of air pollution persist, leading to significant health burdens and mortality rates worldwide. In this context, this study focuses on analyzing air quality in Leeds, UK, during the year 2020, leveraging data from monitoring stations administered by the Department for Environment, Food, and Rural Affairs (DEFRA). Given the unique circumstances of 2020, which included lockdowns due to the COVID-19 pandemic, this period offers valuable insights into the interplay between human activity and air pollution. the research employs a multi-phase approach, encompassing descriptive, inferential, machine learning, and deep learning analyses to comprehensively understand air quality patterns and predict pollutant concentrations. While existing literature predominantly focuses on other regions, particularly China and India, this study addresses the scarcity of research specific to the UK context. Findings reveal variations in pollutant levels, with particulate matter (PM10 and PM2.5), nitrogen oxides (NO2 and NO) and carbon monoxide (CO) being the primary pollutants of concern. Inferential analyses highlight significant differences in pollutant levels and correlations, potentially influenced by lock-down measures. Machine learning algorithms, notably random forests, demonstrate efficacy in predicting PM2.5 concentrations, mirroring findings from deep learning approaches. However, the study underscores the need for larger and more diverse datasets to enhance predictive accuracy and generalize findings. Future research directions include exploring hybrid deep learning algorithms, refining machine learning models, and expanding data collection efforts across various locations and time periods to better understand the dynamics of air quality. Overall, this research

关键词： Air quality

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