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2025 International Conference on Advancement in Data Science, E-learning and Information System, ICADEIS 2025

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

ISBN: (纸本)9798331513320

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. © 2025 IEEE.

关键词： Health risks

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Reinforcement Learning-Based Voting for Feature Drift-Aware Intrusion Detection: An Incremental Learning Framework

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IEEE Access 2025年 13卷 37872-37903页

作者： Shyaa, Methaq A. Ibrahim, Noor Farizah Zainol, Zurinahni Binti Abdullah, Rosni Anbar, Mohammed School of Computer Sciences Penang11800 Malaysia Albukhary International University School of Computing and Informatics Alor Setar05200 Malaysia Penang11800 Malaysia

In Intrusion Detection Systems (IDS), stream data classification faces significant challenges due to concept drifts and feature evolution, where traditional methods struggle to maintain accuracy over time. One critical challenge is feature drift, which refers to changes in the relevance of features over time, directly impacting the model’s classification accuracy. This paper introduces the Incremental Feature Drift-Aware Genetic Programming Combiner (IFDA-GPC), which integrates a Voting Enhanced Deep Q-Network Multi-Agent Feature Selection (VE-DQN-MAFS) mechanism to address these challenges. The framework extends the existing IGPC architecture by incorporating dynamic feature selection and employing a multi-agent system with voting-based aggregation. This approach enhances feature selection decisions, especially in cases where agents provide conflicting assessments of feature relevance. By reconciling these variations, the framework ensures consistency and reliability in real-time classification tasks. The framework was evaluated using benchmark datasets, including KDD Cup ’99, CICIDS-2017, HIKARI-2021, and ISCX2012, under both evolving and non-evolving scenarios. Results demonstrate that GPC-KOS-DFS, derived from IFDA-GPC, significantly outperformed existing methods in accuracy, F1-score, recall, and AUC metrics. Notably, it achieved an accuracy of 93% on the CICIDS-2017 dataset, showcasing its effectiveness in handling feature drifts while maintaining high classification performance. These findings establish IFDA-GPC as a robust solution for managing evolving data streams in intrusion detection systems. © 2025 The Authors.

关键词： Network intrusion

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Efficient Medical File Protection via Adaptive CRP-based Ephemeral Key Encapsulation and Robust Error Correction 15

Efficient Medical File Protection via Adaptive CRP-based Eph...

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15th IEEE Annual computing and Communication Workshop and Conference, CCWC 2025

作者： Alshammari, Adil Miandoab, Dina Ghanai Assiri, Sareh Cambou, Bertrand Riggs, Brit School of Informatics and Computing Northern Arizona University FlagstaffAZ United States

ISBN: (纸本)9798331507695

Ahstract- The healthcare sector faces significant challenges in balancing adaptability, security, and resource efficiency in medi-cal file management. This paper introduces a lightweight protocol that optimizes cryptographic components for using ephemeral key encapsulation, combining adaptive Challenge Response Pair with robust error correction. Designed for limited resource environments, the proposed approach minimizes computational overhead, ensures reliable key recovery, and provides privacy protection. The execution results validate its performance, making it a practical solution for a decentralized healthcare system. © 2025 IEEE.

关键词： Electronic health record

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Public Participation in Municipal Solid Waste Management: A Mobile Persuasive App

Public Participation in Municipal Solid Waste Management: A ...

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7th EAI International Conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2024

作者： Arinaitwe, Irene Jonathan, Ssemakula Nakakawa, Agnes Maiga, Gilbert School of Computing and Informatics Technology Makerere University Kampala Uganda

ISBN: (纸本)9783031864926

Effective waste management relies on active public participation, yet engaging citizens in sustainable waste management remains a challenge. This study introduces a novel approach by developing a mobile application to support public involvement in waste management processes. Drawing upon the Persuasive Systems Design (PSD) framework, the application incorporates persuasive strategies to motivate users to adopt responsible waste handling behaviors. The developed mobile application features a set of functionalities aimed at facilitating user engagement and behavior change. These include a leaderboard to foster competition and motivation among users, a chat room for community interaction, a sorting guide for proper waste categorization, and a module for locating nearby waste collectors, thereby promoting accessibility. To assess the effectiveness and user satisfaction of the application, a structured walkthrough evaluation method was employed, complemented by questionnaire items based on the Attention, Relevance, Confidence, and Satisfaction (ARCS) model. Through this evaluation process, the study measured users’ attention to the application’s features, the relevance of its content to their needs, their confidence in using the application, and their overall satisfaction with the experience. The findings of the evaluation demonstrate the potential of the mobile application in effectively engaging users and promoting positive changes in waste management behaviors. © ICST Institute for Computer Sciences, Social informatics and Telecommunications Engineering 2025.

关键词： Municipal solid waste

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A Robust Resource-Aware Framework for YOLO-Based Object Detection in Dynamic and Multi-Platform Resource-Constrained Environments

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IEEE Access 2025年 13卷 79977-79995页

作者： Shiddiqi, Ary Mazharuddin Navastara, Dini Adni Studiawan, Hudan Romadhona, Yasinta Azhar, Muhammad Fath Mushaffa Rahma, Fayruz Bramantoro, Arif Alzahrani, Ahmad A. Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Informatics Surabaya60111 Indonesia Telkom University Informatics Study Program Surabaya Campus Surabaya60231 Indonesia Universitas Islam Indonesia Department of Informatics Yogyakarta55584 Indonesia Universiti Teknologi Brunei School of Computing and Informatics Bandar Seri BegawanBE1410 Brunei King Abdulaziz University Faculty of Computing and Information Technology 21589 Saudi Arabia

Efficient management of resources such as CPU, RAM, and disk storage is a critical challenge for object detection systems that rely on multi-camera video streaming, especially for resource-constrained devices. These constraints frequently result in the risk of overheating and potential system failure during high workloads. This paper presents a novel Resource-Aware Framework (RA-Framework), designed to optimize resource allocation using dynamic adaptation mechanisms. The RA-Framework dynamically manages CPU, RAM, and disk storage resources for YOLO-based object detection, supporting multiple camera streams and scheduling mechanisms. The framework was implemented in resource-constrained environments using various YOLO models with single and dual-camera setups. Experimental results showed that the RA-Framework successfully reduced average CPU usage by up to 68.62%, decreased RAM consumption by approximately 13.47%, and extended the time for disk usage to increase by 1% by more than 500% in specific scenarios. In contrast, systems without the RA-Framework experienced higher resource utilization and greater susceptibility to resource scarcity or even overheating. © 2013 IEEE.

关键词： Resource allocation

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Designing Everyday Family Technologies: Supporting Health and Wellbeing in Family-Food Interactions with Children 25

Designing Everyday Family Technologies: Supporting Health an...

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2025 ACM International Conference on Supporting Group Work, GROUP Companion 2025

作者： Wu, Yuxing Luddy School of Informatics Computing and Engineering Indiana University Bloomington BloomingtonIN United States

ISBN: (纸本)9798400711879

Technology is pervasive in everyday family life. However, creating meaningful sociotechnical systems that are useful for (and desired by) families remains a complex and evolving challenge. To design for families in the digital age, HCI research requires holistic understanding and in-depth investigation to unpack social dynamics and situated use beyond the individual. My dissertation applies a family-centered approach to examine how technologies might be better designed to support everyday social, health, and developmental goals and challenges. Focusing on family-food interactions, my work unpacks complexities in navigating various food-related experiences to inform design implications. © 2025 Copyright held by the owner/author(s).

关键词： Food ingredients

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LSTM-QGAN: Scalable NISQ Generative Adversarial Network

LSTM-QGAN: Scalable NISQ Generative Adversarial Network

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

作者： Chu, Cheng Hastak, Aishwarya Chen, Fan Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

ISBN: (纸本)9798350368741

Current quantum generative adversarial networks (QGANs) still struggle with practical-sized data. First, many QGANs use principal component analysis (PCA) for dimension reduction, which, as our studies reveal, can diminish the QGAN's effectiveness. Second, methods that segment inputs into smaller patches processed by multiple generators face scalability issues. In this work, we propose LSTM-QGAN, a QGAN architecture that eliminates PCA preprocessing and integrates quantum long short-term memory (QLSTM) to ensure scalable performance. Our experiments show that LSTM-QGAN significantly enhances both performance and scalability over state-of-the-art QGAN models, with visual data improvements, reduced Fréchet Inception Distance scores, and reductions of 5× in qubit counts, 5× in single-qubit gates, and 12× in two-qubit gates. © 2025 IEEE.

关键词： Long Short-Term Memory NISQ Quantum Generative Adversarial Network

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Gemina: A Coordinated and High-Performance Memory Deduplication Engine 31

Gemina: A Coordinated and High-Performance Memory Deduplicat...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Zhang, Zhehua Wu, Suzhen You, Wenyan Du, Chunfeng Mao, Bo Xiamen University Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Fujian Xiamen China

ISBN: (纸本)9798331506476

Memory deduplication is widely used to effectively reduce the memory footprint in operating systems, while huge pages are employed to enhance access performance by increasing TLB hit rates. Unfortunately, duplicate huge pages are very rare in memory, leading to most huge pages being split into base pages during deduplication, which degrades access performance by up to {4 3%}. Although asynchronous huge page promotion attempts to elevate multiple base pages to huge pages to maximize performance, deduplication disrupts the uniformity of page attributes, limiting the effectiveness of such promotions. To address this problem, we designed Gemina, a new memory deduplication scheme that coordinates huge page management. Gemina employs a fine-grained detector to distinguish pages, redundancy and hotness, a distributor to process pages conservatively and avoid frequent switches, and an adapter to organize memory for fast conversion. This approach balances the trade-off between performance and space efficiency in memory management. Experimental evaluations show that, compared to the naive KSM-based deduplication scheme, Gemina can achieve 96.1% of the memory savings of KSM while also improving memory access performance by {4 3. 1%} in random access. © 2025 IEEE.

关键词： Memory management

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Bias analysis of AI models for undergraduate student admissions

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Neural computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the school of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

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Towards Enhancing Linked Data Retrieval in Conversational UIs Using Large Language Models 25th

Towards Enhancing Linked Data Retrieval in Conversational U...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Mussa, Omar Rana, Omer Goossens, Benoît Orozco-terWengel, Pablo Perera, Charith School of Computer Science and Informatics Cardiff University Cardiff United Kingdom School of Biosciences Cardiff University Cardiff United Kingdom College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9789819605729

Despite the recent broad adoption of Large Language Models (LLMs) across various domains, their potential for enriching information systems in extracting and exploring Linked Data (LD) and Resource Description Framework (RDF) triplestores has not been extensively explored. This paper examines the integration of LLMs within existing systems, emphasising the enhancement of conversational user interfaces (UIs) and their capabilities for data extraction by producing more accurate SPARQL queries without the requirement for model retraining. Typically, conversational UI models necessitate retraining with the introduction of new datasets or updates, limiting their functionality as general-purpose extraction tools. Our approach addresses this limitation by incorporating LLMs into the conversational UI workflow, significantly enhancing their ability to comprehend and process user queries effectively. By leveraging the advanced natural language understanding capabilities of LLMs, our method improves RDF entity extraction within web systems employing conventional chatbots. This integration facilitates a more nuanced and context-aware interaction model, critical for handling the complex query patterns often encountered in RDF datasets and Linked Open Data (LOD) endpoints. The evaluation of this methodology shows a marked enhancement in system expressivity and the accuracy of responses to user queries, indicating a promising direction for future research in this area. This investigation not only underscores the versatility of LLMs in enhancing existing information systems but also sets the stage for further explorations into their potential applications within more specialised domains of web information systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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