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检索条件"任意字段=8th International Conference on Data Science and Machine Learning Applications, CDMA 2025"

共 693 条记录，以下是11-20 订阅

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data-Driven machine learning Models for Enhanced Fetal Health Classification and Monitoring 8

Data-Driven Machine Learning Models for Enhanced Fetal Healt...

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8th international conference on data science and machine learning applications, cdma 2025

作者： Almadi, Meteb Alotaibi, Faisal Almudawah, Rayan Ali, Asmaa Nasser, Yousef Nasser, Nidal College of Engineering Alfaisal University Riyadh Saudi Arabia Western University London Canada St. George's University Northumbria University Newcastle United Kingdom

ISBN: (纸本)9798331539696

Fetal health classification plays a critical role in prenatal care by identifying potential risks and ensuring timely intervention. this study explores the application of multi-class classification techniques to categorize cardiotocogram (CTG) features into three distinct fetal health states: Normal, Suspect, and Pathological. CTG data, which includes fetal heart rate patterns, uterine contractions, and accelerations/decelerations, serves as a vital tool for assessing fetal well-being. Using advanced machine learning algorithms such as Logistic Regression, K Nearest Neighbors, Support Vector Classifier, Decision Trees Classifier, Random Forests Classifier, and MLP Classifier-Neural Network, we analyze and classify CTG features to predict the fetal health state with high accuracy. the results demonstrate the effectiveness of these models in distinguishing subtle variations in CTG data, contributing to improved diagnostic precision. With high accuracy for Random Forest Classifier 92%. this research highlights the potential of leveraging data-driven approaches for enhanced fetal monitoring, paving the way for better clinical outcomes and more informed decision-making in obstetric care. © 2025 IEEE.

关键词： Fetal monitoring

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the Bagged Tree Ensemble Model for Predicting Steel Industry Energy Consumption Profile 8

The Bagged Tree Ensemble Model for Predicting Steel Industry...

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8th international conference on data science and machine learning applications, cdma 2025

作者： Etem, Taha Cankiri Karatekin University Computer Engineering Cankiri Turkey

ISBN: (纸本)9798331539696

this study presents a novel approach to predicting energy consumption profiles in the steel industry using a Bagged Tree Ensemble Model. the steel sector, being energy-intensive and a significant contributor to global carbon emissions, requires advanced predictive models to optimize energy usage and enhance operational efficiency. the research focuses on developing a classification model to categorize energy consumption into three classes: Light Load, Medium Load, and Maximum Load. We compared the performance of a standard Decision Tree algorithm with an Ensemble Bagged Tree approach. the Bagged Tree Ensemble model, optimized through hyperparameter tuning and evaluated using 5-fold cross-validation, achieved an accuracy of 91.11% on the validation set, outperforming the standard Decision Tree model. the model's effectiveness was further validated through confusion matrix and ROC curve evaluation, demonstrating excellent class distinction capabilities. this research contributes to the field of industrial energy management by providing a robust predictive tool that can aid in production planning, cost reduction, and environmental impact mitigation in the steel industry. the findings underscore the value of machine learning techniques in addressing the complex challenges of energy efficiency and sustainability in energy-intensive industries. © 2025 IEEE.

关键词： classification energy consumption ensemble learner machine learning

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Hybrid Feature Selection Technique for Bigdata Intrusion Detection Systems 8

Hybrid Feature Selection Technique for BigData Intrusion Det...

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8th international conference on data science and machine learning applications, cdma 2025

作者： Abdulhamid, Shafii M Alsulaiti, Yousef Albishri, Faisal Al-Hadidy, Sanad Rana, Nadim Al-Ghushami, Abdullah H. Community College of Qatar Department of Information Technology Lusail Qatar Federal University of Technology Department of Cybersecurity Science Minna Nigeria Jazan University Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798331539696

the advancement of Big data has posed significant challenges for intrusion detection systems (IDS) due to the high dimensionality and volume of data, which impairs traditional IDS methods' effectiveness. Addressing this, our study presents a novel hybrid feature selection technique that integrates hybrid feature selection, correlation-based feature selection (CFS), and dominance-based Initial set algorithms. the method aims to enhance classifier performance by reducing non-correlation and inconsistencies within Big data, streamlining the decision analysis process. the method prioritizes computational efficiency and classification accuracy by applying three algorithms in sequence, beginning with data reduction and ending with feature extraction using Wicca software. the experiments and evaluations conducted on Korean datasets with different properties and sizes confirm the reliability of the proposed approach. the methodology significantly improves classification accuracy, outperforming traditional models with a notable 97% accuracy for both the Neural Network (NB) and Support Vector machine (SVM) classifiers and a 65% accuracy for Logistic Regression (LR). © 2025 IEEE.

关键词： Network intrusion

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Improving Cloud Efficiency: A machine learning-Based Stacking Model for CPU Utilization Prediction 8

Improving Cloud Efficiency: A Machine Learning-Based Stackin...

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8th international conference on data science and machine learning applications, cdma 2025

作者： Javeed, Ashir Borg, Anton Grahn, Hakan Lundberg, Lars Patel, Dhyey Shirinbab, Sogand Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden Ericsson AB Karlskrona Sweden

ISBN: (纸本)9798331539696

With the rapid growth of internet technologies, IT businesses are transferring to cloud-based systems, and cloud-based services are in high demand among internet users. therefore, appropriate allocation of resources in cloud computing environments is essential. the companies can reduce costs by saving energy by dynamically scaling up or down the number of active servers. In this context, this study presents a machine learning-based model for accurate prediction of CPU utilization. Previous studies employed timestamp-based data to predict CPU utilization in cloud computing, while the proposed work uses incoming user requests to predict CPU workload so that a timely decision can be made to scale up or scale down the servers in a cloud computing environment. the proposed model is based on several machine learning algorithms that are stacked into a single model called the stacking model for CPU workload prediction. the effectiveness of the proposed stacking model was tested on several evaluation metrics to validate its performance. Furthermore, the performance of the proposed stacking model is also compared with other state-of-the-art machine learning models such as support vector machines (SVM), decision trees (DT), random forests (RF), gradient boosting, and extreme gradient boosting (XGBoost). © 2025 IEEE.

关键词： Prediction models

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Federated Semi-Supervised Medical Image Classification Using Improved Inter-Client Relation Matching 8

Federated Semi-Supervised Medical Image Classification Using...

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8th international conference on data science and machine learning applications, cdma 2025

作者： Ozdemir, Muhammed Said Sabbagh, Mais Erdem, Cigdem Eroglu Korcak, Omer Uludag, Kubra Marmara University Faculty of Eng. Department of Computer Engineering İstanbul Turkey Ozyegin University İstanbul Turkey Dept. of Electrical and Electronics Eng. Istanbul Turkey Marmara University Faculty of Engineering Department of Computer Engineering İstanbul Turkey

ISBN: (纸本)9798331539696

Traditional centralized machine learning faces chal-lenges with 'data label scarcity' and 'data privacy', especially in medical data classification. Semi-supervised learning leverages unlabeled data, while federated learning protects data privacy by decentralizing model training. In this study, we introduce an improved inter-client relation matching algorithm for semi-supervised federated medical image classification (iFedIRM). the FedIRM algorithm defines a loss function to transfer the knowledge of disease relationships from labeled clients to unlabeled clients to extract discriminative information from unlabeled data using per-category mean feature vectors. Our approach enhances the FedIRM algorithm by incorporating per-category feature covariance matrices for better feature representation and using group-based averaging for model aggregation. We utilize the ConvNeXt architecture as the backbone, and apply a confidence threshold to filter unreliable pseudo-labels. Experiments on three medical datasets show improved accuracy, F1, and AUC metrics. Code will be available at https://***/msoz7/iFedIRM. © 2025 IEEE.

关键词： Federated learning

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Predicting the Length of Hospital Stay Using machine learning: An Explainable Artificial Intelligence Approach 8

Predicting the Length of Hospital Stay Using Machine Learnin...

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8th international conference on data science and machine learning applications, cdma 2025

作者： Basubeit, Omar Sabri, Aznul Qalid Md Kuala Lumpur Malaysia

ISBN: (纸本)9798331539696

this study delves into predicting the length of hospital stays leveraging machine learning and Explainable Artificial Intelligence (XAI) techniques. the journey commenced with a strategic approach to data selection, focusing on meticulously acquiring reliable MIMIC III data. A meticulous selection of four tables, coupled with merging and a thorough cleaning process, culminated in the creation of a robust dataset. the refined dataset served as the cornerstone for the modeling phase, initially exploring eleven regression models that, unfortunately, yielded sub-optimal results. this setback acted as a catalyst, prompting a pivotal shift towards classification approaches. An in-depth exploration of nine multi-classification models, categorizing the Length of Stay (LOS) variable into four classes, exhibited modest success, achieving a 45% accuracy using Support Vector Classification (SVC). Subsequent exploration of binary classification with the same models witnessed a remarkable performance boost, reaching a 70% accuracy with the SVC. Despite the inherent complexities in real-world medical data, this level of accuracy was deemed acceptable. Four XAI distinct methods, starting with permutation importance and advancing to more intricate approaches such as SHAP, LIME, and ICE, converged on identifying the top 5 features-respiratory, circulatory, infectious, injury, and digestive-as paramount in influencing patient stay. XAI techniques provided clear insights, revealing that patients with respiratory conditions were more likely to experience prolonged stays compared to their counterparts. the nuanced insights provided by XAI techniques not only navigated challenges but also shed light on the complexities inherent in predicting patient outcomes in real-world medical scenarios. © 2025 IEEE.

关键词： Contrastive learning

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Utilizing Pretrained Vision Transformers and Large Language Models for Epileptic Seizure Prediction 8

Utilizing Pretrained Vision Transformers and Large Language ...

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8th international conference on data science and machine learning applications, cdma 2025

作者： Parani, Paras Mohammad, Umair Saeed, Fahad Knight Foundation School of Computing and Information Sciences Florida International University MiamiFL United States

ISBN: (纸本)9798331539696

Repeated unprovoked seizures are a major source of concern for people with epilepsy. Predicting seizures before they occur is of interest to both machine-learning scientists as well as clinicians, and is an active area of research. the variability of EEG sensors, type of seizures, and specialized knowledge required for annotating the data complicates the large-scale annotation process essential for supervised predictive models. To address these challenges, we propose the use of Vision Transformers (ViTs) and Large Language Models (LLMs) that were originally trained on publicly available image or text data. Our work leverages these pre-trained models by refining the input, embedding, and classification layers in a minimalistic fashion to predict seizures. Our results demonstrate that LLMs outperforms the ViTs in patient-independent seizure prediction achieving a sensitivity of 79.02% which is 8% higher compared to ViTs and about 12% higher compared to a custom-designed ResNet-based model. Our work demonstrates the successful feasibility of pre-trained models for seizure prediction with its potential for improving the quality of life of people with epilepsy. Our code and related materials are available open-source at: https://***/pcdslab/UtilLLM_EPS/. © 2025 IEEE.

关键词： Electroencephalography

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Automating Accessibility Compliance: Leveraging machine learning to Analyze Developer Challenges with WCAG Guidelines 8

Automating Accessibility Compliance: Leveraging Machine Lear...

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8th international conference on data science and machine learning applications, cdma 2025

作者： Alghamdi, Asmaa Mansour Aljedaani, Wajdi Ludi, Stephanie Javed, Yasir University of North Texas United States Jazan University Saudi Arabia Saudi Authority for Data and Artificial Intelligence Saudi Arabia Prince Sultan University Saudi Arabia

ISBN: (纸本)9798331539696

the prevailing strategy for ensuring web application accessibility relies heavily on adherence to established guidelines, particularly the Web Content Accessibility Guidelines (WCAG). However, the process of interpreting and implementing these guidelines presents significant challenges for developers, which are often compounded by the time-consuming nature of manual analysis required to ensure compliance. Despite the critical importance of accessibility, there remains limited exploration of these challenges within the development process. In this paper, we aim to address these challenges by leveraging machine learning techniques to automatically classify and analyze accessibility-related issues discussed by developers on Stack Overflow, with a focus on understanding their connection to WCAG 2.2 guidelines. To achieve this, we analyzed 5,644 developers' posts related to web accessibility collected from Stack Overflow between 2008 and 2022. these posts were categorized according to the principles of WCAG 2.2 using advanced machine learning models, including Random Forest, Support Vector machine (SVM), Gradient Boosting, AdaBoost, and Multi-Layer Perceptron (MLP). the results highlight a strong emphasis among developers on addressing complex, non-linear accessibility issues. Our analysis demonstrated that Random Forest and Gradient Boosting models were most effective, achieving accuracy rates of 0.987 and 0.986, respectively. these findings underscore the challenges developers face in aligning with accessibility standards, particularly in areas such as time-based media customization and screen reader configuration, and the potential for machine learning to support more efficient and accurate compliance with accessibility guidelines. © 2025 IEEE.

关键词： Adversarial machine learning

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Comparative Analysis of Federated learning Techniques in Big data-Driven Intelligent Transportation Systems 8

Comparative Analysis of Federated Learning Techniques in Big...

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8th international conference on data science and machine learning applications, cdma 2025

作者： Kaleem, Sarah Babar, Muhammad Qureshi, Basit Ei-Affendi, Mohammed Koubaa, Anis Prince Sultan University Riyadh Saudi Arabia lqra University EIAS Lab Islamabad Pakistan CClS Prince Sultan University Robotics and Internet of Things Lab Riyadh Saudi Arabia CCIS Prince Sultan University KSA RIOTU Lab Saudi Arabia CCIS Prince Sultan University KSA EIAS Data Science Lab Saudi Arabia

ISBN: (纸本)9798331539696

the advancement of Intelligent Transportation Systems (ITS) represents a pivotal development in IoT technology, improving traffic management, reducing accidents, and optimizing transport networks. ITS generates vast data streams from interconnected devices, offering valuable insights into travel and road conditions. However, leveraging this extensive data poses significant performance, privacy, and scalability challenges. In this context, Federated learning (FL) emerges as a crucial solution. this paper contributes a comprehensive comparative analysis of various FL algorithms in processing ITS Big data, focusing on their effectiveness in managing and analyzing data while preserving user privacy and ensuring scalability. Using a dataset from Udacity, provided by Roboflow, consisting of 15,000 high-quality images across 11 categories formatted for YOLO, we conduct a comparative evaluation of FedSGD, FedAvg, and FedProx. through this comparative evaluation, we demonstrate the potential of FL to manage and analyze ITS data while preserving user privacy and ensuring scalability. Our results highlight that FedAvg achieves the highest accuracy at 79.01%, outperforming FedSGD at 78.10% and FedProx at 78.61%. © 2025 IEEE.

关键词： Federated learning

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Modelling of Tsunami Wave Generated by Volcanic EruptionUsing Impulsive Disturbance for 2022, Tonga Island Tsunami 8

Modelling of Tsunami Wave Generated by Volcanic EruptionUsin...

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8th international conference on data science and machine learning applications, cdma 2025

作者： Syed Mohamed, E. Yasmin Regina, M. B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600048 India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Civil Engineering Tamil Nadu Chennai600048 India

ISBN: (纸本)9798331539696

Tsunamis caused by volcanic eruptions have received less research, because of the intricacy of the generation and the rarity of their occurrence in the past. Frequency of occurrence of a volcanic event in the Pacific Rim and the ensuing harm to mankind, it is now vital to forecast the genesis and behavior of tsunamis caused by volcanic eruptions to avoid catastrophic destruction. Tsunami generation is modelled in this study using the eruption's impulse force and a flow rate comparison between the erupted materials and the seawater. the initiated tsunami wave propagation height is computed using the energy of displaced seawater, and the tsunami propagation phase is modelled using the Boussinesq approximation and solitary wave theory. the tsunami's phases are modelled for the Hunga Tonga-Hunga Ha'apai volcanic eruption event in 2022, and the tsunami wave characteristics are projected to reach Nuku'alofa, Tonga's capital city. the wave height and travel time of the tsunami reaching the coast of Nuku'alofa are calculated as 0.8338 m and 19 minutes 34 seconds, therefore the tsunami hit Nuku'alofa at 04:46:34 UTC. © 2025 IEEE.

关键词： Tsunamis

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