2023 Intelligent Methods, Systems, and Applications (IMSA)最新文献

A Comparative Study of Predictive Data Mining Techniques for Customer Churn in the Banking Industry 银行业客户流失预测数据挖掘技术的比较研究

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217514

D. O. Orina, R. Rimiru, W. Mwangi

{"title":"A Comparative Study of Predictive Data Mining Techniques for Customer Churn in the Banking Industry","authors":"D. O. Orina, R. Rimiru, W. Mwangi","doi":"10.1109/IMSA58542.2023.10217514","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217514","url":null,"abstract":"The increasing competition in the banking industry has made customer churn analysis and prediction a crucial concern. Banks must now adopt customer retention strategies while also working towards acquiring new customers to expand their market share. In today’s business environment, machine learning techniques and algorithms are crucial for banks because acquiring new customers is more expensive than retaining existing ones. This proposed research aims to compare many supervised machine learning algorithms, and based on the experimental results, suggest the best-suited model for predicting customer churn. The process involves cross-validation, balancing data using the SMOTE algorithm, and utilizing both simple machine algorithms and ensemble methods for modeling. The experiments conducted in this study revealed that the random forest model performed the best, achieving an accuracy of 88%, an area under the curve (AUC) of 0.85, and an f1-score of 0.85 when using balanced data. This result is consistent with related research considered in this paper, which has demonstrated random forest as one of the most effective algorithms for customer predictive classification issues. Feature importance analysis from the optimization models indicated that the difference between depositing and withdrawing was the most significant attribute, while the maximum deposit per product had the least significance. The data mining techniques proposed to be used in this research include Decision Tree, Neural Networks, Support Vector Machine, Logistic Regression, Random Forest, XG-Boost, Ada-Boost, and K-Nearest Neighbor.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114179686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Study of Weight Initialization Techniques for Convolutional Neural Networks in COVID-19 Classification from X-ray Images. 卷积神经网络权重初始化技术在x射线图像COVID-19分类中的比较研究。

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217655

Abdelrahman Ezzeldin Nagib, M. Saeed, Shereen Fathy El-Feky, Ali Khater Mohamed

引用次数: 0

Distance-Based Meta-Features for Arabic Text Classification 基于距离的阿拉伯语文本分类元特征

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217428

Maroua Louail, Chafia Kara-Mohamed alias Hamdi-Cherif

{"title":"Distance-Based Meta-Features for Arabic Text Classification","authors":"Maroua Louail, Chafia Kara-Mohamed alias Hamdi-Cherif","doi":"10.1109/IMSA58542.2023.10217428","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217428","url":null,"abstract":"Text classification (TC) is the process by which the computer has the ability to provide a label to a given text based on its content. Term Frequency–Inverse Document Frequency (TF-IDF) is one of the popular methods used for Arabic text representation. The high number of dimensions and sparseness are among the main issues faced by the TF-IDF method, requiring large space storage, high computational costs and the risk of overfitting. In this paper, we focus on four distance-based meta-features: CosKNN, L2KNN, CosCent and L2Cent derived from the TF-IDF representations, as a dimensionality reduction method for Arabic text classification. Four well-known classifiers are used in the present work: K-Nearest Neighbors, Logistic Regression, Support Vector Machines and Random Forest to evaluate the impact of these distance-based meta-features on the classification performance. The obtained results prove that the proposed dimensionality reduction method improves the classification accuracy in 50% of the cases and speed up the training phase (between 8x and 1764x faster) when compared to the original TF-IDF. As far as we know, distance-based meta-features are used for Arabic text classification for the first time.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117073884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning model for predicting pancreatic fistula after pancreatoduodenectomy 预测胰十二指肠切除术后胰瘘的机器学习模型

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217619

Hager Saleh, Nora El-Rashidy, Eman Mohamed, Ahmad M Sultan, Ayman El Nakeeb, Shaker El-Sappagh

{"title":"Machine learning model for predicting pancreatic fistula after pancreatoduodenectomy","authors":"Hager Saleh, Nora El-Rashidy, Eman Mohamed, Ahmad M Sultan, Ayman El Nakeeb, Shaker El-Sappagh","doi":"10.1109/IMSA58542.2023.10217619","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217619","url":null,"abstract":"Pancreaticoduodenectomy (PD) is a complex surgery used mainly to treat tumors and other pancreas disorders. PD is considered one of the most challenging surgeries because it may have several complications, including bleeding and infections in the surgical area, temporary or permanent diabetes, and pancreatic leakage (PL), which may lead to morbidity and mortality. In this study, we build an accurate and medically oriented machine learning model that predicts PL after PD based on patient markers collected only before the PD operation. The study is made using a real-world dataset for 397 Egyptian patients. The proposed machine learning pipeline starts with a data preprocessing step that handles the missing data values by the median values. In the next step, diverse interpretable classifiers, including logistic regression, random forest, decision tree, support vector machine, XGBoost, and AdaBoost, are utilized to predict the PL. Hyperparameter optimization is done using grid search with k-fold cross-validation. The results indicate that XGBoost achieves the highest marks, outperforming the state-of-the-art techniques in several evaluation metrics (i.e., accuracy= 91%, precision= 90.96%, recall= 91.0%, F1-score= 90.97%, and AUC=89.78%. The resulting model is accurate enough to be medically relevant for PL prediction in real healthcare settings.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129414101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Session (6) 会话(6)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217770

引用次数: 0

Papers Statistics 论文的统计数据

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217513

引用次数: 0

A Comparative Analysis of Deep Learning Models for Brain Tumor Segmentation 深度学习模型在脑肿瘤分割中的比较分析

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217767

Maha AbdElwareth, Mariem Abdou, Michael Adel, Alaa Hatem, Login Darwish, Remon Mamdouh, Sahar Selim

{"title":"A Comparative Analysis of Deep Learning Models for Brain Tumor Segmentation","authors":"Maha AbdElwareth, Mariem Abdou, Michael Adel, Alaa Hatem, Login Darwish, Remon Mamdouh, Sahar Selim","doi":"10.1109/IMSA58542.2023.10217767","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217767","url":null,"abstract":"A brain tumor is an extremely hazardous illness that can affect people of any age. Less than 50% of individuals with brain cancer have a chance of surviving. As a result, precise segmentation of brain tumors is crucial for the diagnosis, planning of the course of treatment, and tracking of the tumor growth. Deep Learning (DL) models can increase the precision and speed of brain tumor diagnosis by precisely segmenting and identifying tumor locations in medical pictures. In this study, we compare four DL models for segmenting brain tumors, the 3D U-Net, the Attention Res U-Net, the U-Net++, and the U-Net Transformer (UNETR). We used 485 MRI (Magnetic Resonance Imaging) scans from the BraTS 2018 dataset, which include annotated ground truth tumor segmentations. We carried out preprocessing operations such as label merging, cropping, and z-score normalization. We evaluated the performance of two models using the dice coefficient metric. Our findings demonstrated that the Attention Res U-Net has a higher segmentation accuracy than the other three U-Net models, with a testing dice coefficient of 0.79 against 0.78, 0.77, 0.72 for the 3D U-net, UNETR, and U-net++ respectively. The results point to the Attention Res U-Net as a potentially useful method for brain tumor segmentation tasks.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127640555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Named Entity Recognition for Arabic Medical Texts Using Deep Learning Models 使用深度学习模型的阿拉伯医学文本命名实体识别

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217658

Hamada Nayel, Nourhan Marzouk, Ahmed N. Elsawy

{"title":"Named Entity Recognition for Arabic Medical Texts Using Deep Learning Models","authors":"Hamada Nayel, Nourhan Marzouk, Ahmed N. Elsawy","doi":"10.1109/IMSA58542.2023.10217658","DOIUrl":"https://doi.org/10.1109/IMSA58542.2023.10217658","url":null,"abstract":"Named Entity Recognition (NER) plays a vital role in extracting meaningful information from textual data in the medical domain. This paper focuses on NER for Arabic medical texts, specifically targeting the recognition of disease entities. The study presents a comparative analysis of deep learning techniques, including Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), LSTM-CRF, and Bidirectional LSTM (BiLSTM), applied to a dataset comprising Arabic medical texts related to diseases. The dataset is meticulously annotated, ensuring accurate labelling of disease entities for training and evaluation purposes. The models are trained and evaluated using appropriate loss functions and evaluation metrics, such as precision, recall, and F1-score. Comparative experiments are conducted to assess the performance of each model on the disease dataset. The results demonstrate the effectiveness of deep learning techniques for NER in Arabic medical texts, with the LSTM-CRF and BiLSTM-CRF models outperforming the standalone CRF and LSTM models. LSTM-CRF and BiLSTM-CRF models reported F1-score of 0.97 and 0.94. These hybrid models achieve higher precision, recall, and F1-score, showcasing their ability to accurately identify disease entities in Arabic medical texts. The findings of this study contribute to the advancement of NER techniques for Arabic medical texts, focusing on disease entities. The comparative analysis of CRF, LSTM, LSTM-CRF, and BiLSTM models provides valuable insights into their respective strengths and limitations of NER for Arabic medical texts. These insights can guide the selection and implementation of appropriate models for disease entity recognition in Arabic medical texts, facilitating accurate information extraction and analysis in the medical domain.","PeriodicalId":110239,"journal":{"name":"2023 Intelligent Methods, Systems, and Applications (IMSA)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127691492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Biometric Authentication Using Face Thermal Images Based on Neural Fuzzy Extractor 基于神经模糊提取的人脸热图像生物特征认证

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/IMSA58542.2023.10217752

A. Sulavko, I. Panfilova, A. Samotuga, Samal Zhumazanova

引用次数: 0

End of Session (14) 会议结束(14)

2023 Intelligent Methods, Systems, and Applications (IMSA) Pub Date : 2023-07-15 DOI: 10.1109/imsa58542.2023.10217657

引用次数: 0