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11th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2025

作者： Pongsathornwiwat, Akkaranan Kulmanochwong, Pattaraporn Department of Logistics Management Graduate School of Applied Statistics National Institute of Development Administration Bangkok Thailand Department of Business Analytics and Data Science Graduate School of Applied Statistics National Institute of Development Administration Bangkok Thailand

ISBN: (纸本)9789819646029

This study explores the application of the Length, Frequency, Monetary, Claim, and Profit (LFMCP) customer value model to predict customer churn in a Thai general insurance company with a primary focus on B2B clients. The research provides valuable insights into the insurance industry, particularly for organizations aiming to enhance customer retention strategies. The churn prediction model is developed in two phases. In the first phase, LFMCP variables are used to distinguish between valued and non-valued customers. In the second phase, the classification performance of five predictive techniques—k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree, Random Forest (RF), and XGBoost (XGB)—is compared. Results show that LFMCP significantly improves churn classification, with XGBoost emerging as the best-performing method. The study also investigates the importance of LFMCP variables using the Analytical Hierarchy Process (AHP). While the analysis explores varying weight configurations, findings suggest that equal weighting of the LFMCP variables results in superior predictive performance for churn classification in the insurance context. By incorporating additional predictors beyond the traditional RFM (Recency-Frequency-Monetary) model, the study introduces a novel and effective framework for churn prediction in the insurance industry. This expanded model, combined with insights from the AHP evaluation, forms the key contribution of the research, offering practical value for enhancing decision-making and customer retention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Support vector machines

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Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass 1

Improving Heart Disease Prediction with Stacked Ensemble Lea...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Amirthaa, M. Mary, Gerardine Immaculate Julian, Anitha Selvarathinam, Anto Lourdu Xavier Raj School of Electronics Engineering Vellore Institute of Technology Vellore India Saveetha Engineering College Computer Science and Engineering Tamilnadu Chennai India Data Science and Analytics Grand Valley State University MI United States

ISBN: (纸本)9798331518943

Heart disease prediction remains a critical area of research due to its significant impact on public health. This paper, titled 'Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass,' presents a comprehensive study comparing the performance of binary and multiclass classification models for heart disease prediction. Utilizing two distinct datasets, one for binary classification and one for multiclass classification. The study evaluates various machine learning and deep learning models, with a focus on stacked ensemble methods. The Binary Classification Results leveraging the Preprocessing-and-PCA-on-Heart-Disease-dataset, the study evaluated several models including Random Forest, Support Vector Machine (SVM), XGBoost, Gradient Boosting, Deep Learning model, and Logistic Regression. The results indicate that the Stacking Ensemble model achieved the highest accuracy of 90.2%, outperforming individual models where Random Forest (88.5%), SVM (89.1%), XGBoost (89.7%), Gradient Boosting (88.0%) and Deep Learning (89.1%). The Multiclass Classification study using the UCI Heart Disease dataset, when applied the same models, the Stacking Ensemble model achieved an accuracy of 64.1%, which, although superior to other individual models such as Random Forest (58.1%), SVM (54.3%), Gradient Boosting (59.2%) and Deep Learning model (57.6%) fell short compared to XGBoost, which attained an accuracy of 65.8%. This outcome highlights the challenges associated with multiclass classification in heart disease prediction. © 2024 IEEE.

关键词： Contrastive Learning

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Monitoring Recombinant Protein Titer in Escherichia Coli Fermentations Using Filtering Techniques

Monitoring Recombinant Protein Titer in Escherichia Coli Fer...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Okafor, Paul Siddique, Zahed Razzaghi, Talayeh University of Oklahoma Data Science and Analytics Institute Norman United States University of Oklahoma School of Aerospace and Mechanical Engineering Norman United States University of Oklahoma School of Industrial and Systems Engineering Norman United States

ISBN: (纸本)9798350362480

Recombinant protein production in Escherichia coli (E. coli) is crucial for biotechnological applications, but optimizing protein titer is challenging and often requires iterative experimentation and expert guidance. This study utilizes advanced filtering techniques - Kalman, particle, bilateral, and extended Kalman filters - to enhance titer estimation accuracy using fermentation data provided by Cytovance Biologics. The proposed techniques effectively impute missing titer values and are validated on a secondary dataset from Chinese Hamster Ovary (CHO) cell lines, demonstrating robustness and accuracy. By reducing the need for extensive sampling and iterative experimentation, these filtering techniques enable predictive control over fermentation conditions. One significant cost-saving advantage is identifying the protein titer plateau, allowing lab scientists to conclude the fermentation process at the optimal time. This approach ensures maximum protein production while preventing unnecessary resource consumption. As a result, this method significantly lowers the overall cost of recombinant protein production in E. coli while enhancing process efficiency. © 2024 IEEE.

关键词： Wiener filtering

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Managing Large dataset Gaps in Urban Air Quality Prediction: DCU-Insight-AQ at MediaEval 2022

Managing Large Dataset Gaps in Urban Air Quality Prediction:...

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2022 MediaEval Workshop, MediaEval 2022

作者： Cuong, Dinh Viet Le-Khac, Phuc H. Stapleton, Adam Eichlemann, Elke Roantree, Mark Smeaton, Alan F. Insight Centre for Data Analytics Dublin City University Ireland School of Computing Dublin City University Ireland School of Biology and Environmental Science University College Dublin Ireland

Calculating an Air Quality Index (AQI) typically uses data streams from air quality sensors deployed at fixed locations and the calculation is a real time process. If one or a number of sensors are broken or offline, then the real time AQI value cannot be computed. Estimating AQI values for some point in the future is a predictive process and uses historical AQI values to train and build models. In this work we focus on gap filling in air quality data where the task is to predict the AQI at 1, 5 and 7 days into the future. The scenario is where one or a number of air, weather and traffic sensors are offline and explores prediction accuracy under such situations. The work is part of the MediaEval'2022 Urban Air: Urban Life and Air Pollution task submitted by the DCU-Insight-AQ team and uses multimodal and crossmodal data consisting of AQI, weather and CCTV traffic images for air pollution prediction. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Air quality

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Real-Time Pothole Detection at night to prevent road accidents using YOLOV5 6

Real-Time Pothole Detection at night to prevent road acciden...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Mandal, Gouranga De, Anurag Dey, Arindam Gangarapu, Sudeepthi Chekuru, Sharmila Rani Nandini, Chekuri Vellore Institute of Technology School of Computer Science and Engineering Andhra Pradesh 522237 India Vellore Institute of Technology School of Computer Science and Engineering with Specialization in Data Analytics Andhra Pradesh 522237 India

ISBN: (纸本)9798350394702

This article proposes a technique to create a pothole detection system that can operate both during the day and at night. This model was developed using the real-time object identification YOLO (You Only Look Once) technique. The potholes are located using a pre-trained model with YOLO v5. In its creation, the PyTorch framework is utilized. The first YOLO model created using the PyTorch framework is YOLOv5, which is a lot more user-friendly and lightweight. In a manner similar to real-time object detection systems, the proposed method displays potholes in real time and emphasizes them with boundary boxes for accurate identification. The accuracy achieved with this method is 97% during the day and 92% at night which is found to be satisfactory. © 2023 IEEE.

关键词： Image acquisition

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KaustubhSharedTask@LT-EDI 2023: Homophobia-Transphobia Detection in Social Media Comments with NLPAUG-driven data Augmentation 3

KaustubhSharedTask@LT-EDI 2023: Homophobia-Transphobia Detec...

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3rd Workshop on Language Technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Lande, Kaustubh Ponnusamy, Rahul Kumaresan, Prasanna Kumar Chakravarthi, Bharathi Raja Indian Institute of Technology Kharagpur India Insight SFI Research Centre for Data Analytics University of Galway Ireland School of Computer Science University of Galway Ireland

ISBN: (纸本)9789544520847

Our research in Natural Language Processing (NLP) aims to detect hate speech comments specifically targeted at the LGBTQ+ community within the YouTube platform shared task conducted by the LT-EDI workshop1. The dataset provided by the organizers exhibited a high degree of class imbalance, and to mitigate this, we employed NLPAUG, a data augmentation library. We employed several classification methods and reported the results using recall, precision, and F1-score metrics. The classification models discussed in this paper include a Bidirectional Long Short-Term Memory (BiLSTM) model trained with Word2Vec embeddings, a BiLSTM model trained with Twitter GloVe embeddings, transformer models such as BERT, DistilBERT, RoBERTa, and XLM-RoBERTa, all of which were trained and fine-tuned. We achieved a weighted F1-score of 0.699 on the test data and secured fifth place in task B with 7 classes for the English language. © 2023 LTEDI 2023 - 3rd Workshop on Language Technology for Equality, Diversity and Inclusion, associated with the 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Embeddings

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Locally varying geostatistical machine learning for spatial prediction

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Artificial Intelligence in Geosciences 2024年第1期5卷 28-45页

作者： Francky Fouedjio Emet Arya Rio Tinto Data&Analytics152-158 St Georges TerracePerthWA 6000Australia Kaplan Business School Pty Ltd Perth Campus1325 Hay StWest PerthWA6005Australia Edith Cowan University School of Science270 Joondalup DriveJoondalupWA 6027Australia

Machine learning methods dealing with the spatial auto-correlation of the response variable have garnered significant attention in the context of spatial ***,under these methods,the relationship between the response variable and explanatory variables is assumed to be homogeneous throughout the entire study *** assumption,known as spatial stationarity,is very questionable in real-world situations due to the influence of contextual ***,allowing the relationship between the target variable and predictor variables to vary spatially within the study region is more ***,existing machine learning techniques accounting for the spatially varying relationship between the dependent variable and the predictor variables do not capture the spatial auto-correlation of the dependent variable ***,under these techniques,local machine learning models are effectively built using only fewer observations,which can lead to well-known issues such as over-fitting and the curse of *** paper introduces a novel geostatistical machine learning approach where both the spatial auto-correlation of the response variable and the spatial non-stationarity of the regression relationship between the response and predictor variables are explicitly *** basic idea consists of relying on the local stationarity assumption to build a collection of local machine learning models while leveraging on the local spatial auto-correlation of the response variable to locally augment the training *** proposed method’s effectiveness is showcased via experiments conducted on synthetic spatial data with known characteristics as well as real-world spatial *** the synthetic(***)case study,the proposed method’s predictive accuracy,as indicated by the Root Mean Square Error(RMSE)on the test set,is 17%(resp.7%)better than that of popular machine learning methods dealing with the response variable’s spatial auto-correla

关键词： data augmentation Geostatistics Local stationarity Machine learning Conditional simulation Spatial auto-correlation Spatial non-stationarity Spatial uncertainty

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Harnessing Medical Big data: Integrating Computational Insights for Enhanced Patient Outcomes 24

Harnessing Medical Big Data: Integrating Computational Insig...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Syed Masood, Jafar Ali Ibrahim Vinayagam, Jai Kumar Onteru, Ranjit Kumar Kopparapu, Umamaheswararao Parabathini, Bulah Pushpa Rani Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Centre for Big Data Analytics QIS College of Engineering and Technology Andhra Pradesh Ongole India

ISBN: (纸本)9798400709586

The rapid proliferation of medical big data has opened unprecedented opportunities for enhancing patient outcomes through advanced computational analysis. This paper explores the integration of big data analytics with clinical practices to deliver personalized medicine, optimize treatment protocols, and improve patient care. Our findings demonstrate that a synergistic approach between computational insights and clinical decision-making can significantly advance healthcare outcomes. This study provides a comprehensive framework for harnessing medical big data, addressing key challenges, and proposing solutions to integrate these insights into everyday clinical practice. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Personalized medicine

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A Hybrid Approach to Classify the Multiclass Imbalanced datasets 5

A Hybrid Approach to Classify the Multiclass Imbalanced Data...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Sridhar, S. Anusuya Srm Institute of Science and Technology Department of ComputerScience and Engineering Tamilnadu Chennai India Saveetha School of Engineering Simats Department of Data Analytics TN Chennai India

ISBN: (纸本)9798350321425

The imbalanced nature of a dataset often leads classifiers to show more favor towards recognizing the class with a higher number of data instances. Handling this imbalance in a multiclass environment presents an additional challenge compared to binary class environments. Previous research primarily focused on addressing the imbalance in binary class scenarios, but there is a growing shift towards addressing the imbalance in multiclass environments. In imbalanced environments, data balancing methods are used to address the imbalance issue by equalizing the number of data instances in both the major and minor class groups. This approach aims to avoid bias in learning by modifying the class distribution. Algorithmic-level methods enable the learning model to adjust its learning process towards the class with a lower number of data instances, aiming to improve the prediction rate. The existing multiclass imbalance handling methods either attempted to solve the imbalance problem either by transforming the problem into binary class problems or by approaching the problem as a whole and finds the solution. In both the cases the classification model suffers from the issues like overlapping problem, least recognition of minority class samples, ignoring the minority class samples as outlier and over fitting. This research study proposes a hybrid approach that combines both data level balancing and algorithm level tuning methods. The proposed hybrid approach combines the effort of algorithm level tuning to create a cost associated dataset which avoids the models' biasness towards majority class samples and the data level balancing method to improve the count of the least class samples count and thereby avoiding the ignorance problem and makes the learning model to have pay more attention towards the minority class samples. © 2023 IEEE.

关键词： Decision trees

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Synthesizing CTA Image data for Type-B Aortic Dissection using Stable Diffusion Models 46

Synthesizing CTA Image Data for Type-B Aortic Dissection usi...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Abaid, Ayman Ali Farooq, Muhammad Hynes, Niamh Corcoran, Peter Ullah, Ihsan University Hospital Galway University of Galway Newcastle Road Ireland University of Galway School of Computer Science Ireland University of Galway School of Engineering College of Science and Engineering Ireland University of Galway Insight Sfi Research Center for Data Analytics Ireland

ISBN: (纸本)9798350371499

Stable Diffusion (SD) has gained a lot of attention in recent years in the field of Generative AI thus helping in synthesizing medical imaging data with distinct features. The aim is to contribute to the ongoing effort focused on overcoming the limitations of data scarcity and improving the capabilities of ML algorithms for cardiovascular image processing. Therefore, in this study, the possibility of generating synthetic cardiac CTA images was explored by fine-tuning stable diffusion models based on user defined text prompts, using only limited number of CTA images as input. A comprehensive evaluation of the synthetic data was conducted by incorporating both quantitative analysis and qualitative assessment, where a clinician assessed the quality of the generated data. It has been shown that Cardiac CTA images can be successfully generated using using Text to Image (T2I) stable diffusion model. The results demonstrate that the optimized T2I CTA diffusion model successfully render images with features that are typically unique to acute type B aortic dissection (TBAD) medical conditions. © 2024 IEEE.

关键词： Computerized tomography

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