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检索条件"任意字段=19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020"

共 790 条记录，以下是1-10 订阅

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Proceedings - 19th ieee international conference on machine learning and applications, icmla 2020

Proceedings - 19th IEEE International Conference on Machine ...

引用

19th ieee international conference on machine learning and applications, icmla 2020

ISBN: (纸本)9781728184708

the proceedings contain 228 papers. the topics discussed include: what are they reporting? examining student cybersecurity course surveys through the lens of machine learning;enhancing piano transcription by dilated convolution;MuSeM: detecting incongruent news headlines using mutual attentive semantic matching;predicting purchase probability of retail items using an ensemble learning approach and historical data;PENet: object detection using points estimation in high definition aerial images;graph neural networks for model recommendation using time series data;machine learning fairness in justice systems: base rates, false positives, and false negatives;MaskedFusion: mask-based 6d object pose estimation;DeepGamble: towards unlocking real-time player intelligence using multi-layer instance segmentation and attribute detection;and a new clustering-based technique for the acceleration of deep convolutional networks.

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A theoretical Comparison of Federated learning with Differential Privacy and Blockchain for Security and Privacy in IoMT 19

A Theoretical Comparison of Federated Learning with Differen...

引用

19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Farooqi, Shaista Ashraf Rahman, Aedah Abd Saad, Amna Wisma Subang Jaya Jalan SS 15/4 Subang Jaya Malaysia Universiti Kuala Lumpur Malaysian Institute of Information Technology 1016 Jalan Sultan Ismail Kuala Lumpur50250 Malaysia

ISBN: (纸本)9798331507817

— the advancement of decentralized, real-time data collection through the Internet of Medical things is transforming the healthcare industry. However, this innovation brings forth significant privacy, security, and scalability challenges. Federated learning offers a reliable solution by enabling distributed machine learning while preserving data localization. this paper introduces two frameworks—Federated learning combined with Differential Privacy and Blockchain-enhanced Federated learning—to enhance robustness in IoMT systems. We compare these frameworks theoretically, evaluating their effectiveness in mitigating risks related to data confidentiality, adversarial resilience, scalability, and computational efficiency. FL-DP provides formal privacy guarantees through differential privacy techniques but is limited by the need to manage the privacy budget (Ε), especially in large-scale deployments. Alternatively, Blockchain-based FL maintains data integrity and decentralized trust using consensus mechanisms such as Proof of Work and Proof of Stake, but it encounters challenges related to scalability and computational efficiency. Our findings suggest that the choice between FL-DP and Blockchain-based FL depends on the specific security and privacy requirements of the IoMT application. FL-DP is better suited for privacy-critical applications where strict data confidentiality is paramount, while Blockchain-based FL is more appropriate when data integrity and trust are the primary concerns. © 2025 ieee.

关键词： Federated learning

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Comparative Analysis of Anxiety Disorder Classification Using Algorithm Naïve Bayes, Decision Tree and K-NN 19

Comparative Analysis of Anxiety Disorder Classification Usin...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Herman Darwis, Herdianti Nurfauziyah Puspitasari, Rahma Widyawati, Dewi Faradibah, Amaliah Faculty of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (纸本)9798331507817

this study aims to classify anxiety disorders in adolescents using three machine learning algorithms, namely naive bayes, C4.5, and K-Nearest Neighbor (K-NN). the data used was taken from the DASS-21 questionnaire, which contains 418 samples with 11 attributes, including age, gender, and seven anxiety-related questions. the algorithm was tested using the holdout technique with 80:20, 70:30, and 60:40 data splits, as well as the k-fold cross-validation technique. the results showed that the C4.5 algorithm performed best with 100% accuracy in the holdout technique, followed by naive bayes with 99% accuracy, and K-NN with 94% accuracy. In cross-validation testing, C4.5 also showed the highest accuracy of 98%, while naive bayes and K-NN achieved 89% and 92% respectively. this study concludes that the C4.5 algorithm is superior in classifying anxiety compared to naive bayes and K-NN, so it can be relied upon for machine learning-based diagnostic applications in supporting the detection of anxiety disorders efficiently. © 2025 ieee.

关键词： Adversarial machine learning

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Approaches, applications, and Challenges of Using Sentiment Analysis for False Information Detection: A Systematic Literature Review 19

Approaches, Applications, and Challenges of Using Sentiment ...

引用

19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Fanlau, Nathan Edmund Hiu, Michael Achmad, Said Sutoyo, Rhio Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331507817

In today’s digital age, the prevalence of false information on the internet, spanning from fake news to fake user accounts, poses a pressing concern that undermines the reliability of online information. Such misinformation can have harmful consequences, such as promoting biased ideologies, swaying public opinion, and inciting unrest. Implementing sentiment analysis technology in false information detection significantly impacts combating misinformation on digital platforms. It analyzes textual content in real time, which is crucial for effectively identifying and mitigating false information such as fake news, fake reviews, fake accounts, and fake websites on digital platforms. this paper explores sentiment analysis’s approaches, applications, and challenges for false information detection, providing insights for future research and application. A systematic literature review was conducted using the PRISMA methodologies to identify and evaluate relevant studies. the results of this paper contribute to understanding the current state of sentiment analysis in combating misinformation. this paper provided the common and available applications of sentiment analysis, such as fake news detection, fake review detection, fake social media account detection, and more. Approaches that can be taken to achieve these applications include the machine learning approach, neural network approach, hybrid approach, and more. Moreover, this work highlights the need for more advanced algorithms and ongoing research to address challenges such as sarcasm detection, bot account identification, and effectively interpreting informal language and slang in texts. © 2025 ieee.

关键词： false information machine learning natural language processing sentiment analysis

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End-to-end optimized ensemble model for Alzheimer’s disease detection 19

End-to-end optimized ensemble model for Alzheimer’s disease...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Hassan, Abdelrahman Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. this paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). the study proposes an ensemble model that integrates key modalities—demographics, cognitive scores, neuropsychological assessments, and MRI data—via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. this study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management. © 2025 ieee.

关键词： Adversarial machine learning

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Integrating Temporal Analysis with Hybrid machine learning and Deep learning Models for Enhanced Air Quality Prediction 19

Integrating Temporal Analysis with Hybrid Machine Learning a...

引用

19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Omer, Muhammad Ali, Sardar Jaffar Raza, Syed M. Le, Duc-Tai Choo, Hyunseung Dept. of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of James Watt School of Engineering University of Glasgow Glasgow United Kingdom Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798331507817

Air pollution remains a critical issue, adversely affecting public health and the environment. In this study, we utilize the Air Quality index dataset from Kaggle to analyze temporal and seasonal variations of key pollutants, specifically Carbon Monoxide (CO), Nitrogen Oxides (NOx), and Benzene (C6H6 ). Building upon this analysis, we predict Absolute Humidity (AH), a vital meteorological factor influencing pollutant dispersion, using machine learning (ML) and Deep learning (DL) techniques. three ML techniques, Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR), and three DL techniques, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are employed for predicting AH. the results indicate that while the RF model achieved the lowest Mean Absolute Error (MAE) among ML methods (0.02), the CNN model, despite having an MAE of 0.04, demonstrated statistical superiority through paired t-tests and Wilcoxon signed-rank tests (p © 2025 ieee.

关键词： Convolutional neural networks

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Fraudulent Transactions Identification Using a machine learning Approach 19th

Fraudulent Transactions Identification Using a Machine Lear...

引用

19th international conference on Soft Computing Models in Industrial and Environmental applications, SOCO 2024

作者： Vázquez-Noguera, Silvia García-Torres, Miguel Grillo, Sebastián Gómez-Vela, Francisco Arrua, Katherin Palma, Ricardo R. Bearzotti, Lorena Andrea Universidad Americana Asunción Paraguay Data Science and Big Data Lab Pablo de Olavide University ES-41013 Seville Spain Universidad Autónoma de Asunción Asunción Paraguay Universidad Nacional de Cuyo Mendoza Argentina Pontificia Universidad Católica de Valparaiso Valparaiso Chile

ISBN: (纸本)9783031750090

the rise of e-commerce, driven by digital business models, has made online shopping easier and more convenient. While this benefits both businesses and shoppers, the increase in online transactions also attracts fraudsters. In this work, we address the identification of fraudulent transactions using a machine learning approach. For this purpose we analyze the DataCo Supply Chain Dataset and apply a two-step preprocessing workflow to identify non-informative features. the predictive power of the resulting dataset is tested and compared with classification models induced after applying a feature selection approach. Results suggest that a small number of factors are helpful to build highly predictive models capable of identifying fraudulent transactions. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Deep learning Based Vehicle Detection from Aerial Images 19

Deep Learning Based Vehicle Detection from Aerial Images

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19th ieee international conference on machine learning and applications, icmla 2020

作者： Dikbayir, Huseyin Seckin Ibrahim Bulbul, Halil Gazi University Turkish Aerospace Ankara Turkey Gazi University Computer Education and Instructional Technologies Ankara Turkey

ISBN: (纸本)9781728184708

With the help of today's developing technology;many applications such as traffic monitoring, target detection, empty parking lot assignment etc. have become applicable with machine learning and deep learning methods. these applications are aimed to determine the desired object from the photographs. In this study, it is aimed to develop an approach that able to identify the vehicles through aerial images by using the YOLO (You Only Look Once) algorithm, feeding it with a trained convolutional neural network structure. As a result of the study, an application that can detect vehicles was developed, increased the performance rate of the YOLO algorithm by 3.2%. © 2020 ieee.

关键词： Vehicles

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machine learning Fairness in Justice Systems: Base Rates, False Positives, and False Negatives 19

Machine Learning Fairness in Justice Systems: Base Rates, Fa...

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19th ieee international conference on machine learning and applications, icmla 2020

作者： Russell, Jesse Reno NV United States

ISBN: (纸本)9781728184708

machine learning best practice statements have proliferated, but there is a lack of consensus on what the standards should be. For fairness standards in particular, there is little guidance on how fairness might be achieved in practice. Specifically, fairness in errors (both false negatives and false positives) can pose a problem of how to set weights, how to make unavoidable tradeoffs, and how to judge models that present different kinds of errors across racial groups. this paper considers the consequences of having higher rates of false positives for one racial group and higher rates of false negatives for another racial group. the paper examines how different errors in justice settings can present problems for machine learning applications, the limits of computation for resolving tradeoffs, and how solutions might have to be crafted through courageous conversations with leadership, line workers, stakeholders, and impacted communities. © 2020 ieee.

关键词： Errors

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Deduplication of Advertisement Assets Using Deep learning Ensembles 19

Deduplication of Advertisement Assets Using Deep Learning En...

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19th ieee international conference on machine learning and applications, icmla 2020

作者： Chakiat, Abhijith Oli, Nishant Modi, Varun Kumar An InMobi Group Company Glance Digital Experience Bangalore India In Mobi Bangalore India

ISBN: (纸本)9781728184708

Creative image deduplication is the process of detecting similar images among digital advertisement assets. We evaluated a novel ensemble of DenseNet based Siamese Network and Scale Invariant Feature Transformation (SIFT) which is able to outperform both individually. Our approach also outperforms a state-of-the-art model on the desired metrics in the adtech world. the evaluation was carried upon the advertisement assets of a major ad-network. Due to the difficulty in acquiring labelled images for the training dataset, we also present a novel strategy for semi-automatically generating labels. © 2020 ieee.

关键词： Deep learning

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