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A stacked ensemble approach for symptom-based monkeypox diagnosis

Computers in Biology and Medicine 2025年 191卷 110140-110140页

作者： Nagro, Shimaa College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The recent monkeypox outbreak has raised global health concerns. Caused by a virus, it is characterized by symptoms such as skin lesions. Early detection is critical for treatment and controlling its spread. This study uses advanced machine learning and deep learning techniques, including Tab Transformer, Long Short-Term Memory, XGBoost, LightGBM, and a Stacking Classifier, to predict the presence of the virus based on patient symptoms. The performance of these models is evaluated using accuracy, precision, recall, and F1-score metrics. The experiments reveal that the Stacking Classifier significantly outperforms the other models, achieving an accuracy of 87.29 %, precision of 86.12 %, recall of 87.47 %, and an F1 score of 87.89 %. Additionally, applying Conditional Tabular GAN to generate synthetic data helps address data imbalance issues, further improving model robustness. These results highlight the proposed approach's potential for timely, accurate monkeypox detection, aiding in effective disease management and control. © 2025 The Author

关键词： Deep learning

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Understanding the Student Experience Better: Analyzing Remote Learning Tools Through Engagement, Participation, and Liveness Concepts 17

Understanding the Student Experience Better: Analyzing Remot...

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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Gomes, Genildo Silva, Paulo Magalhães, Sâmila Galvão, Leandro Lauschner, Tanara Steinmacher, Igor Conte, Tayana Gadelha, Bruno Institute of Computing Federal University of Amazonas AM Manaus Brazil Department of Design and Graphic Expression Federal University of Amazonas AM Manaus Brazil School of Informatics Computing and Cyber Systems Nothern Arizona University FlagstaffAZ United States

ISBN: (纸本)9789897587467

Technologies have changed how students engage and participate during remote classes, whether by encouraging interaction via chat on platforms such as Google Meet or by using voting features such as Poll. Our research aims to better understand the role of these tools in the educational context and how their interactive features influence student experience through the lens of engagement, participation, and liveness. In this sense, we address the following question: How can educational technologies be classified by the Engagement, Participation, and Liveness lens? To explore the relationship between the lens and the interactive features of Mentimeter, Google Meet, and Google Presentations, we conducted a study with three independent evaluations, measuring agreement using Cohen's Kappa coefficient. Our results suggest that these interactive features can be used as a lens to understand different types of experiences and how they can be applied to promote various dynamics, such as competitive and collaborative activities. Our results expand the professor's perspective on the student experience, providing ways for professors to understand the emotional and student behavioral reactions from using interactive tools in remote learning environments. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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Designing Lookahead Relocation Rules for the Container Relocation Problem with Genetic Programming 28th

Designing Lookahead Relocation Rules for the Container Rel...

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28th European Conference on Genetic Programming, EuroGP 2025, Held as Part of EvoStar 2025

作者： Đurasević, Marko Đumić, Mateja Gil-Gala, Francisco Javier Jakobović, Domagoj Faculty of Electrical Engineering and Computing University of Zagreb Zagreb10000 Croatia School of Applied Mathematics and Informatics J. J. Strossmayer University of Osijek Osijek31000 Croatia Department of Computing University of Oviedo Gijón Spain

ISBN: (纸本)9783031899904

The container relocation problem is an important combinatorial optimisation problem commonly found in warehouses and container ports. The goal of this problem is to retrieve all of the containers from the yard with the fewest container relocations between the stacks. Since the problem is NP-hard, various heuristics have been proposed to solve it, among which are relocation rules (RRs), simple constructive heuristics that incrementally construct the solution. However, it is quite difficult to design such RRs manually, so genetic programming has been applied to design new RRs automatically. A significant problem with RRs, whether manually or automatically designed, is that they usually have a limited view of the problem. This means that they will often make decisions that can negatively influence the future, meaning that the current decision would cause additional relocations. Therefore, this study investigates different relocation schemes that can be used within RRs to obtain rules with lookahead ability. These rules will enable containers to be relocated based on future information and, consequently, arranged better in the yard. For that purpose, three novel relocation schemes for automatically designed RRs are defined and evaluated on an existing problem set. The results demonstrate that integrating additional elements to evolve lookahead RRs can significantly improve the results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic programming

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Stunting Classification Among Toddlers Using Decision Tree C4.5 in Berastagi City

Stunting Classification Among Toddlers Using Decision Tree C...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Hygea Saveria Putu Harry Gunawan Informatics School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Stunting is a condition where a child's height significantly falls below the average for their age, primarily due to prolonged malnutrition and inadequate nutrient intake. This condition poses long-term challenges, affecting both physical growth and cognitive development. This study focuses on developing a predictive model using the C4.5 Decision Tree algorithm to classify the stunting status of children under five. The dataset comprises nutritional status data of toddlers collected from the Berastagi Health Center. Incorporating attributes such as gender, birth weight and height, age in months, weight, height, and the height-to-age ratio. The model aims to enhance the accuracy of stunting classification to support targeted prevention and treatment strategies. When compared to Naive Bayes, the Decision Tree C4.5 demonstrated superior performance, achieving an F1 score and average accuracy of 86%. This reflects a robust balance between precision and recall, underscoring its reliability in identifying children at risk of stunting. The research highlights the critical role of data-driven methodologies in public health. With its high accuracy, the model serves as a promising tool for healthcare providers, enabling more effective interventions to reduce stunting prevalence and improve nutritional outcomes among children under five years of age.

关键词： Pediatrics Accuracy Prevention and mitigation Urban areas Vegetation Predictive models Prediction algorithms Decision trees Reliability Public healthcare

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Mobile Learning Technologies as drivers of School Enrolment Levels in South Africa: A Policy Simulation Experiment using Machine Learning-Driven Dynamic Autoregressive Distributed Lag Model 24

Mobile Learning Technologies as drivers of School Enrolment ...

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Proceedings of the 37th International BCS Human-Computer Interaction Conference

作者： Fatima Ahmad Muazu Festus Adedoyin Huseyin Dogan Paul Whittington Nicholas Mavengere Dept. of Computing & Informatics Bournemouth University UK

This study addresses the imperative challenge of enhancing school enrolment in South Africa by investigating the dynamics of mobile learning technologies, internet access, and key socio-economic variables. Spanning 35 years (1998–2022), the research draws from the National Digital and Future Skills Strategy, aiming to provide insights into the factors influencing educational access. Employing an analytical framework that integrates Autoregressive Distributed Lag (ARDL), dynamic ARDL (dynARDL) simulations, and Kernel-based Regularized Least Squares (KRLS) machine learning, the study finds that economic prosperity, as represented by real GDP per capita, positively influences secondary school enrolment. Mobile phone subscribers emerge as a significant driver, emphasizing the transformative potential of digital technologies. Surprisingly, an inverse relationship between internet users and enrolment prompts a reassessment of the role of internet access in education. The dynARDL simulations introduce counterfactual shocks, highlighting the positive impact of a 10% increase in mobile subscribers and the nuanced consequences of changes in internet users. KRLS analysis reinforces the significance of economic indicators, digital technologies, and human development in shaping enrolment. Drawing policy implications, the study advocates targeted investments in digital infrastructure, strategic approaches to internet access optimization, and policies fostering sustainable economic growth.

关键词： Mobile Learning

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Empirical Study on Data Attributes Insufficiency of Evaluation Benchmarks for LLMs 31

Empirical Study on Data Attributes Insufficiency of Evaluati...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Liu, Chuang Jin, Renren Yao, Zheng Li, Tianyi Cheng, Liang Steedman, Mark Xiong, Deyi College of Intelligence and Computing Tianjin University China School of Electrical Engineering and Computer Science University of Queensland Australia School of Informatics University of Edinburgh United Kingdom

ISBN: (纸本)9798891761964

Previous benchmarks for evaluating large language models (LLMs) have primarily emphasized quantitative metrics, such as data volume. However, this focus may neglect key qualitative data attributes that can significantly impact the final rankings of LLMs, resulting in unreliable leaderboards. In this paper, we investigate whether current LLM benchmarks adequately consider these data attributes. We specifically examine three attributes: diversity, redundancy, and difficulty. To explore these attributes, we propose a framework with three separate modules, each designed to assess one of the attributes. Using a method that progressively incorporates these attributes, we analyze their influence on the benchmark. Our experimental results reveal a meaningful correlation between LLM rankings on the revised benchmark and the original benchmark when these attributes are accounted for. These findings indicate that existing benchmarks often fail to meet all three criteria, highlighting a lack of consideration for multifaceted data attributes in current evaluation datasets. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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A Novel Polynomial Activation for Audio Classification Using Homomorphic Encryption

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IEEE Access 2025年 13卷 87834-87847页

作者： Linh Nguyen Quoc Bao Phan Lan Zhang Tuy Tan Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA

With the increasing popularity of audio recording devices and recognition technology, audio data is increasingly used in speech recognition, event detection, and biometric authentication. Audio data often contains sensitive information, raising privacy concerns when using artificial intelligence on cloud platforms. Homomorphic encryption (HE) addresses this problem by allowing direct computation on encrypted data without decryption. However, HE has high computational costs, especially for deep learning models, which require many nonlinear operations, such as activation functions. Traditional HE methods have difficulty handling these nonlinear operations, affecting performance and accuracy. To overcome this limitation, we propose optimized polynomial-degree activation functions that enhance the compatibility of HE with deep learning models while maintaining high performance. Experimental results on musical instrument and audioMNIST datasets confirm the effectiveness of our method, highlighting its promising potential for secure audio data processing in various applications.

关键词： Cryptography Polynomials Computational modeling Accuracy Homomorphic encryption Training Convolutional neural networks Chebyshev approximation Time-frequency analysis Seals

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Machine Learning in Small-Molecule Mass Spectrometry

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Annual review of analytical chemistry (Palo Alto, Calif.) 2025年第1期18卷 193-215页

作者： Hong, Yuhui Ye, Yuzhen Tang, Haixu Luddy School of Informatics Computing and Engineering Indiana University Bloomington Bloomington Indiana USA

Tandem mass spectrometry (MS/MS) is crucial for small-molecule analysis;however, traditional computational methods are limited by incomplete reference libraries and complex data processing. Machine learning (ML) is transforming small-molecule mass spectrometry in three key directions: (a) predicting MS/MS spectra and related physicochemical properties to expand reference libraries, (b) improving spectral matching through automated pattern extraction, and (c) predicting molecular structures of compounds directly from their MS/MS spectra. We review ML approaches for molecular representations [descriptors, simplified molecular-input line-entry (SMILE) strings, and graphs] and MS/MS spectra representations (using binned vectors and peak lists) along with recent advances in spectra prediction, retention time, collision cross sections, and spectral matching. Finally, we discuss ML-integrated workflows for chemical formula identification. By addressing the limitations of current methods for compound identification, these ML approaches can greatly enhance the understanding of biological processes and the development of diagnostic and therapeutic tools.

关键词： molecular representation learning retention time prediction small-molecule identification tandem mass spectra prediction

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DeCAP: Context-Adaptive Prompt Generation for Debiasing Zero-shot Question Answering in Large Language Models

arXiv

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arXiv 2025年

作者： Bae, Suyoung Choi, YunSeok Lee, Jee-Hyong College of Computing and Informatics Sungkyunkwan University Korea Republic of

While Large Language Models (LLMs) excel in zero-shot Question Answering (QA), they tend to expose biases in their internal knowledge when faced with socially sensitive questions, leading to a degradation in performance. Existing zero-shot methods are efficient but fail to consider context and prevent bias propagation in the answers. To address this, we propose DeCAP, a method for debiasing LLMs using Context-Adaptive Prompt Generation. DeCAP leverages a Question Ambiguity Detection to take appropriate debiasing actions based on the context and a Neutral Answer Guidance Generation to suppress the LLMs make objective judgments about the context, minimizing the propagation of bias from their internal knowledge. Our various experiments across eight LLMs show that DeCAP achieves state-of-the-art zero-shot debiased QA performance. This demonstrates DeCAP’s efficacy in enhancing the fairness and accuracy of LLMs in diverse QA settings. Our code is publicly available at https://***/BaeSuyoung/DeCAP. © 2025, CC BY.

关键词： Zero-shot learning

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Can Quality Survive Scale? Toward an Equal-Quality Instance-Dependent Label Noise Model

Can Quality Survive Scale? Toward an Equal-Quality Instance-...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Song, Haohao Xiang, Qiao Shu, Jiwu Institute of Artificial Intelligence Xiamen University China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University China Minjiang University China

ISBN: (纸本)9798350368741

Label noise model is a technique to construct controlled noisy datasets for evaluating noise-robust algorithms. However, the quality of the generated noise has not been evaluated thoroughly. In this paper, we propose a novel research question: Do the constructed datasets with the same noise rate have equal effects? We answer this question through a carefully designed experiment: We sequentially generate a series of noisy datasets with equal noise rate by excluding the previous noisy samples while controlling the clean samples. Models trained on these datasets show discrepancy generalization performance, indicating the inequality of noise. Our in-depth analysis reveals that the reasons come from (1) the introduction of non-hard samples and (2) the inequality between hard samples. We propose a primary equal-quality instance-dependent label noise model termed EQ-IDN, which alleviates both issues based on the identification of hard samples and stratified sampling. We compare the noise quality generated by EQ-IDN and other models. Experimental results demonstrate that the noise generated by EQ-IDN has lower difference and more stable quality. © 2025 IEEE.

关键词： label noise quality

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