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8th EAI International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, FABULOUS 2024

作者： Šimić, Goran Jevremović, Aleksandar Strugarević, Danilo School of Electrical and Computer Engineering at Academy of Technical and Art Applied Studies Belgrade11000 Serbia Faculty of Informatics and Computing Singidunum University Belgrade11000 Serbia Academy of Applied Preschool Teaching and Health Studies Kruševac37000 Serbia

ISBN: (纸本)9783031723926

Teaching is a process that requires permanent observation and improvement. With the rapid development of e-learning, there was a need to review, improve and optimize the process of evaluating students’ performance. The main objective of this study is to develop and implement the procedure for automatized generation of assessment tests. For this purpose, the proposed solution includes the application of a genetic algorithm. The procedure is developed on a sample of 29 existing test cases of "Internet programming" course. The optimization resulted in better selection and reduced number of questions used for evaluation of students` performance. The results of the study and the topicality of the issue point to the need for further research, both in the aspect of students` performance assessment, as well as in other aspects of higher education. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Conceptual framework e-learning Genetic algorithm Higher education Students` performance

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Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations 19th

Improving Adversarial Robustness in Android Malware Detecti...

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19th International Workshop on Data Privacy Management, DPM 2024, 8th International Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control Systems and of Cyber-Physical Systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in Computer Security, ESORICS 2024

作者： Bostani, Hamid Zhao, Zhengyu Moonsamy, Veelasha Digital Security Group Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Horst Görtz Institute for IT Security Ruhr University Bochum Bochum Germany

ISBN: (纸本)9783031823619

Machine learning (ML) has demonstrated significant advancements in Android malware detection (AMD);however, the resilience of ML against realistic evasion attacks remains a major obstacle for AMD. One of the primary factors contributing to this challenge is the scarcity of reliable generalizations. Malware classifiers with limited generalizability tend to overfit spurious correlations derived from biased features. Consequently, adversarial examples (AEs), generated by evasion attacks, can modify these features to evade detection. In this study, we propose a domain adaptation technique to improve the generalizability of AMD by aligning the distribution of malware samples and AEs. Specifically, we utilize meaningful feature dependencies, reflecting domain constraints in the feature space, to establish a robust feature space. Training on the proposed robust feature space enables malware classifiers to learn from predefined patterns associated with app functionality rather than from individual features. This approach helps mitigate spurious correlations inherent in the initial feature space. Our experiments conducted on DREBIN, a renowned Android malware detector, demonstrate that our approach surpasses the state-of-the-art defense, Sec-SVM, when facing realistic evasion attacks. In particular, our defense can improve adversarial robustness by up to 55% against realistic evasion attacks compared to Sec-SVM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial robustness Android malware detection Spurious correlation

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End-to-End Optimized Ensemble Model for Alzheimer's Disease Detection

End-to-End Optimized Ensemble Model for Alzheimer's Disease ...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

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

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

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.

关键词： Support vector machines Accuracy Biological system modeling Neural networks Predictive models Boosting Feature extraction Alzheimer's disease Tuning Optimization

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A Deep Learning Framework with Attention Mechanism for the Classification of Mild Cognitive Impairment using Brain MRI

A Deep Learning Framework with Attention Mechanism for the C...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： T. Illakiya B. Vidhya S. Prince Chelladurai S. Hariprasad Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Mild Cognitive Impairment (MCI) refers to a neurological disorder marked by observable changes in cognitive decline. MCI signifies an intermediate phase linking normal aging and Alzheimer's Disease (AD). It is typically categorized into two types: Early MCI (EMCI) and Late MCI (LMCI), representing varying levels of cognitive impairment. An effective classification between EMCI and LMCI is vital, as it allows timely interventions that can potentially slow the progression of AD, improving patient outcomes and quality of life. Identifying the subtle features and patterns in EMCI and LMCI is essential for early diagnosis and effective intervention. This work proposes a novel Deep Learning (DL) network integrating EfficientNet-B0, a Global Context Network (GCN), and Neighborhood Attention blocks. The EfficientNet-B0 acts as the foundation for feature extraction in the proposed model. It uses its efficient architecture and pre-trained capabilities to capture meaningful patterns. To further enhance feature representation, a Global Context Network (GCN) is employed to capture long-range dependencies and global semantic information. Additionally, a Neighborhood Attention Module is used to refine local features by focusing on important regions in the MRI scans. The proposed framework balances global semantic understanding and localized feature extraction, resulting in improved classification performance. With systematic training and evaluation, the model achieves an accuracy of 95.3%, outperforming existing approaches and providing a better solution for neuroimaging-based MCI subtype classification.

关键词： Deep learning Training Accuracy Systematics Magnetic resonance imaging Visual analytics Semantics Feature extraction Brain modeling Convolutional neural networks

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A Novel Hybrid Approach-Based on Heartbeat and Accelerometer Signals for Enhanced Security in WBSNs

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IEEE Sensors Journal 2025年第6期25卷 10295-10308页

作者： Yan, Guixiang Zhang, Guanghe Liu, Fanghua Williams Samuel, Oluwarotimi Aborokbah, Majed Guo, Jiquan Zhu, Liqing Wei, Sikang Jiangxi Normal University School of Computer and Information Engineering Nanchang330022 China University of Derby School of Computing and Data Science Research Centre DerbyDE22 3AW United Kingdom INTI International University Faculty of Data Science and Information Technology Nilai71800 Malaysia University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Jiangxi Normal University School of Digital Industry Shangrao334000 China

In wireless body sensor networks (WBSNs), ensuring secure and efficient key distribution is critical, particularly given the limited computational and energy resources of the sensors. Existing methods often struggle to balance security with these resource constraints, especially in environments involving physiological data such as acceleration (ACC) and electrocardiogram (ECG) signals. For the first time, this study proposes a novel hybrid approach that integrates fuzzy commitment with ACC signal noise and ECG features for efficient key generation and distribution in WBSNs. By employing a low-pass filter to process ACC signal noise, we generated highly random binary sequences (BSs), leveraging the inherent randomness of the signal for secure key generation. Concurrently, an optimized coding scheme was built for ECG feature construction to ensure secure key distribution between devices. Extensive experiments, including entropy analysis and National Institute of Standards and Technology (NIST) statistical tests, confirm the robustness and security of our method. The proposed scheme achieves a false acceptance rate (FAR) of 5.23%, demonstrating superior performance across multiple databases in comparison to benchmark approaches. This novel dual-key generation strategy that combines the unpredictability of ACC noise with the individual-specific traits of ECG signals can significantly enhance the security, applicability, and versatility of WBSNs. © 2001-2012 IEEE.

关键词： Acceptance tests

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Impact of Artificial Intelligence on Decision-Making in Information Systems

Impact of Artificial Intelligence on Decision-Making in Info...

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Advanced Research in computing (ICARC), International Conference on

作者： RWMR Dharani Rajasinghe Dept. of Software Engineering & Information Systems Faculty of Computing NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues.

关键词： Industries Surveys Ethics Adaptation models Technological innovation Explainable AI Law Decision making Data models Information systems

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Leveraging Large Language Models for Smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency

Leveraging Large Language Models for Smart Pharmacy Systems:...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Abdelrahman Osheba Ahmed Abou-El-Ela Osama Adel Youssef Maaod Tamer Abuhmed Shaker El-Sappagh Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University South Korea

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications.

关键词： Drugs Electric potential Hospitals Large language models Mortality Medical services Chatbots Real-time systems Safety Electronic medical records

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A Deep Learning Framework with Attention Mechanism for the Classification of Mild Cognitive Impairment using Brain MRI

A Deep Learning Framework with Attention Mechanism for the C...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Illakiya, T. Vidhya, B. Chelladurai, S. Prince Hariprasad, S. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798331521394

关键词： Convolutional neural networks

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Sound Judgment: Properties of Consequential Sounds Affecting Human-Perception of Robots 25

Sound Judgment: Properties of Consequential Sounds Affecting...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Aimee Allen Tom Drummond Dana Kulić Faculty of Engineering Monash University Melbourne Australia School of Computing and Information Systems University of Melbourne Melbourne Australia

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Positive human-perception of robots is critical to achieving sustained use of robots in shared environments. One key factor affecting human-perception of robots are their sounds, especially the consequential sounds which robots (as machines) must produce as they operate. This paper explores qualitative responses from 182 participants to gain insight into human-perception of robot consequential sounds. Participants viewed videos of different robots performing their typical movements, and responded to an online survey regarding their perceptions of robots and the sounds they produce. Topic analysis was used to identify common properties of robot consequential sounds that participants expressed liking, disliking, wanting or wanting to avoid being produced by robots. Alongside expected reports of disliking high pitched and loud sounds, many participants preferred informative and audible sounds (over no sound) to provide predictability of purpose and trajectory of the robot. Rhythmic sounds were preferred over acute or continuous sounds, and many participants wanted more natural sounds (such as wind or cat purrs) in-place of machine-like noise. The results presented in this paper support future research on methods to improve consequential sounds produced by robots by highlighting features of sounds that cause negative perceptions, and providing insights into sound profile changes for improvement of human-perception of robots, thus enhancing human robot interaction.

关键词： Surveys Psychoacoustics Noise Human-robot interaction Trajectory Robots Videos

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Experimental Validation of Intelligent Ad Hoc System for Sea Surface Velocity Approximation

Experimental Validation of Intelligent Ad Hoc System for Sea...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Karlo Jakac Luka Lanča Ante Sikirica Stefan Ivić Faculty of Engineering University of Rijeka Rijeka Croatia Center for Advanced Computing and Modelling University of Rijeka Rijeka Croatia

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

This paper presents the validation of a smart ad hoc system designed to approximate the sea surface velocity field using measurements obtained from multiple floating sensors. Recognizing that point data alone cannot fully capture the domain's circulation, we propose a method that uses a simplified two-dimensional flow model as a surrogate for submesoscale flow, reconstructing the entire velocity field from the scattered data. To ensure reliable flow dynamics, the approach addresses the problem as a model-fitting process, adjusting the boundary conditions of numerical simulations to align the flow with measured data. Experimental validation was performed with the collected measurements, which involved the deployment of Global Positioning System (GPS) drifters in the Kvarner Bay area. The results highlight the system's adaptability to various domains, measurement types, and conditions, demonstrating its effectiveness for real-world submesoscale applications where an approximate velocity field is acceptable.

关键词： Sea surface Surface reconstruction Sea measurements Data models Numerical models Velocity measurement Wind forecasting Optimization Intelligent sensors Global Positioning System

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