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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Rabiha, Suciana Ghadati Wibowo, Antoni Lukas Heryadi, Yaya Computer Science Department BINUS Graduate Program-Doctor of Computer Science. Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University 11480 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University 11480 Indonesia

ISBN: (纸本)9781665433945

One of the health problems that require special attention is diabetes, besides the growth of this disease infection is increasing in various circles ranging from children, adults, men, women and the elderly. So to detect diabetes at an early stage, machine learning algorithms are the right choice to help detect indications of diabetes. This paper presents experimental results related to the exploration of the use of a Super Vector Machine (SVM) for data classification of Diabetes. The model used in this experiment is the Binary Classification Model. Based on the test results, the highest accuracy is obtained by using a total of 700 lines of data, which is 75,7143%. The Best estimated feasible point in Box Constraint is 272.29 with a KernelScale area of 72.627. © 2021 IEEE

关键词： Face recognition

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Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics

arXiv

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arXiv 2023年

作者： Murali, Nihal Puli, Aahlad Yu, Ke Ranganath, Rajesh Batmanghelich, Kayhan Intelligent Systems Program University of Pittsburgh United States Department of Computer Science New York University United States Department of Electrical and Computer Engineering Boston University United States

Deep Neural Networks (DNNs) are prone to learning spurious features that correlate with the label during training but are irrelevant to the learning problem. This hurts model generalization and poses problems when deploying them in safety-critical applications. This paper aims to better understand the effects of spurious features through the lens of the learning dynamics of the internal neurons during the training process. We make the following observations: (1) While previous works highlight the harmful effects of spurious features on the generalization ability of DNNs, we emphasize that not all spurious features are harmful. Spurious features can be "benign" or "harmful" depending on whether they are "harder" or "easier" to learn than the core features for a given model. This definition is model and dataset dependent. (2) We build upon this premise and use instance difficulty methods (like Prediction Depth (Baldock et al., 2021)) to quantify "easiness" for a given model and to identify this behavior during the training phase. (3) We empirically show that the harmful spurious features can be detected by observing the learning dynamics of the DNN’s early layers. In other words, easy features learned by the initial layers of a DNN early during the training can (potentially) hurt model generalization. We verify our claims on medical and vision datasets, both simulated and real, and justify the empirical success of our hypothesis by showing the theoretical connections between Prediction Depth and information-theoretic concepts like V-usable information (Ethayarajh et al., 2021). Lastly, our experiments show that monitoring only accuracy during training (as is common in machine learning pipelines) is insufficient to detect spurious features. We, therefore, highlight the need for monitoring early training dynamics using suitable instance difficulty metrics. © 2023, CC BY.

关键词： Deep neural networks

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Empowering Urban Accessibility: A Prototype System for People with Disability

Empowering Urban Accessibility: A Prototype System for Peopl...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Arlley Dos Santos Costa Luis Carlos Costa Fonseca Sofiane Labidi Graduate Program in Computer and Systems Engineering State University of Maranhão – UEMA São Luís Brazil Bachelor of Science and Technology Program -BICT Federal University of Maranhão – UFMA São Luis Brazil

ISBN: (数字)9798350362053

ISBN: (纸本)9798350362060

This work demonstrates the development of a prototype system that could improve urban and digital accessibility for People with Disability - PwD. The research involves using an academic questionnaire to enable a wider perspective of the central subject and assessing the effectiveness of the proposed solution. The main goal is to help social integration and make the future solution implementable for smart cities through an experiment in an academic campus. The dissertation is related to technological and social issues that might be used as a resource by researchers, public or private bodies interested in more profound studies. The document recognizes the lack of studies on this subject, especially in Brazil because there are few available articles. The prototype system is designed to tackle the issues faced by persons with disability in urban settings through platforms, applications and mobile phones as it proposes a comprehensive set of solutions when addressing accessibility for PwDs. In turn, this research fosters knowledge advancements in the field of urban accessibility and provides vital recommendations for developing inclusive technologies and policies.

关键词： Smart cities Prototypes People with disabilities Mobile handsets

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Exploring Augmented Reality's Influence on Cognitive Load and Emotional Dynamics within UAV Training Environments

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IEEE Transactions on Learning Technologies 2025年 18卷 581-592页

作者： Rahimi, Fatema Sadeghi-Niaraki, Abolghasem Song, Houbing Wang, Huihui Choi, Soo-Mi Sejong University Department of Computer Science and Engineering Convergence Engineering for Intelligent Drone XR Research Center Seoul Korea Republic of Department of Information Systems BaltimoreMD21250 United States St. Bonaventure University Cybersecurity Program St. BonaventureNY14778 United States

This study investigates the cognitive and emotional processes involved in augmented reality (AR)-based learning. The study looks at learning outcomes, emotional responses, meditation, and attention using a comprehensive approach that includes self-assessment, EEG data gathering, and post-experiment questionnaires. Twelve participants, selected based on their English proficiency and lack of prior knowledge of the course material, engaged in AR-based learning, while a baseline reading condition was included to contextualize cognitive and emotional engagement. The study findings indicate that the AR group's participants demonstrated notably elevated attention and meditation levels, indicating heightened engagement and focus that is advantageous for efficient assimilation and retention of knowledge. Furthermore, AR learners reported feeling less tired and exhausted, which may have mitigated the negative emotional states that are frequently connected to learning activities. However, no significant differences in negative emotions were observed between the reading and AR groups. These results emphasize the value of customized AR environments for education goals and the need for more study to maximize learning outcomes and affective experiences in AR learning contexts. © 2008-2011 IEEE.

关键词： engineering education

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS 15

CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BAS...

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15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on computer science and Information systems, MCCSIS 2022

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. dos Coelho, Leandro S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

ISBN: (纸本)9789898704405

Early recognition of clinical deterioration (CD) has vital importance in patients’ survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network’s performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models’ performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals’ data. © 2022 15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on computer science and Information systems, MCCSIS 2022. All rights reserved.

关键词： Neural networks

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A Comparison Diagnosis Algorithm for Conditional Fault Local Diagnosis of Multiprocessor systems 25th

A Comparison Diagnosis Algorithm for Conditional Fault Loca...

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25th International computer Symposium on New Trends in computer Technologies and Applications, ICS 2022

作者： Lv, Yali Lin, Cheng-Kuan Hsu, D. Frank Fan, Jianxi School of Information Technology Henan University of Chinese Medicine Zhengzhou China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taoyuan City Taiwan Department of Computer and Information Science Fordham University New York United States School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9789811995811

An efficient diagnosis is very important for a multiprocessor system. In this paper, we present a (α, β) -trees combination S(u, X, α, β) and give some conclusions about the local diagnosis. Based on the (α, β) -trees combination, we give a conditional fault local diagnosis algorithm to identify the fault or fault-free status of each processor correctly under the MM ∗ model when the number of faulty nodes does not exceed α+ 2 β- 3 and every node has at least one fault-free neighboring node. According to our results, a connected network with a (α, β) -trees combination S(u, X, α, β) for a node u is conditionally locally (α+ 2 β- 3 ) -diagnosable at node u and the time complexity of our algorithm to diagnose u is O(α2β+ αβ2). As an application, we show that our algorithm can identify all the faulty nodes of n-dimensional star graph Sn if the faulty node number does not exceed 3 n- 8. Compared with existing algorithms, our algorithm allows more faulty node in a multiprocessor system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational complexity

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Model for Anomaly Detection in Reducing Water Losses in the Distribution Network of Peru using Machine Learning Algorithms

Model for Anomaly Detection in Reducing Water Losses in the ...

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Software engineering and computer science (CSECS), International Conference on

作者： Mayra Rivera Pricila Lucero José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Group: Health Computing and Intelligent System Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Water leakage in distribution networks is a significant challenge, especially in regions with limited infrastructure like Huancayo, Peru, where losses account for 32.82% of the distributed volume. This study introduces a machine learning-based approach to detect leaks using four algorithms: Autoencoder LSTM, Isolation Forest, One-Class SVM, and K-Nearest Neighbors (KNN). The methodology involved preprocessing historical consumption data (2018–2024) into 12-month temporal sequences per client and evaluating the models based on F1 Score, Precision, and Mean Absolute Error (MAE). Among the algorithms, the Autoencoder LSTM demonstrated superior performance with the highest precision (0.89) and the lowest MAE (0.00402). Its robustness in high-variability contexts enables early and reliable leak detection, providing a cost-effective solution for optimizing water management in resource-constrained environments.

关键词： Support vector machines Machine learning algorithms Autoencoders Distribution networks Forestry Nearest neighbor methods Robustness Leak detection Long short term memory Water resources

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Experimental Evaluation of a Checklist-Based Inspection Technique to Verify the Compliance of Software systems with the Brazilian General Data Protection Law

arXiv

引用

arXiv 2023年

作者： Cerqueira, Diego André de Mello, Rafael Maiani Travassos, Guilherme Horta Systems Engineering and Computer Science Graduate Program Federal University of Rio de Janeiro Brazil Computing Institute Federal University of Rio de Janeiro Brazil

Background/Context: Recent laws to ensure the security and protection of personal data establish new software requirements. Consequently, new technologies are needed to guarantee software quality under the perception of privacy and protection of personal data. Therefore, we created a checklist-based inspection technique (LGPDCheck) to support the identification of defects in software artifacts based on the principles established by the Brazilian General Data Protection Law (LGPD). Objective/Aim: To evaluate the effectiveness and efficiency of LGPDCheck for verifying privacy and data protection (PDP) in software artifacts compared to ad-hoc techniques. Method: To assess LGPDCheck and ad-hoc techniques experimentally through a quasi-experiment (two factors, five treatments). The data will be collected from IoT-based health software systems built by software engineering students from the Federal University of Rio de Janeiro. The data analyses will compare results from ad-hoc and LGPDCheck inspections, the participant's effectiveness and efficiency in each trial, defects' variance and standard deviation, and time spent with the reviews. The data will be screened for outliers, and normality and homoscedasticity will be verified using the Shapiro-Wilk and Levene tests. Nonparametric or parametric tests, such as the Wilcoxon or Student's t-tests, will be applied as appropriate. © 2023, CC BY-NC-ND.

关键词： Data privacy

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UAV-Based Optimization for Fruit Counting in Greenhouses

UAV-Based Optimization for Fruit Counting in Greenhouses

引用

International Conference on Unmanned Aircraft systems (ICUAS)

作者： João Vítor R. Vasconcelos Thayron M. Hudson André G. dos Santos Martin Saska Alexandre S. Brandão Department of Electrical Engineering Núcleo de Especialização em Robótica Graduate Program on Computer Science Federal Univer-sity of Vicosa Vicosa MG Brazil Graduate Program on Computer Science Federal University of Vicosa Vicosa MG Brazil Department of Cybernetics Multi-robot Systems Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a solution for counting fruit in agricultural greenhouses using Unmanned Aerial Vehicles (UAV s). Initially, a heuristic based on Simulated Annealing was used to optimize the UAV's trajectory, ensuring efficient coverage of the beds. Next, digital image processing (DIP) techniques were implemented to count the fruit, including depth segmentation, application of bounding boxes, color filtering, and element counting. The DIP accuracy was evaluated in multiple scenarios and the results indicate high reliability in fruit counting, with the potential to optimize agricultural operations and provide valuable information to producers. Possible future improvements could include further refinements in image processing to increase the accuracy of counting other fruits. Ultimately, this work contributes to the advancement of automation in agriculture by offering a viable and efficient solution for counting fruit in greenhouses using UAV s.

关键词： Image segmentation Accuracy Image color analysis Greenhouses Simulated annealing Autonomous aerial vehicles Trajectory

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Implementation of AI-Based Support Vector Machine Algorithm for Fraud Detection in Digital Payment systems

Implementation of AI-Based Support Vector Machine Algorithm ...

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International Seminar on Research of Information Technology and Intelligent systems (ISRITI)

作者： Edy Prayitno Rikie Kartadie Uning Lestari Munengsih Sari Bunga Accounting Information Systems Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Computer Engineering Study Program Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Informatics Study Program Faculty of Science and Information Technology Universitas Akprind Indonesia Yogyakarta Indonesia Department of Computer Engineering Smart City Information Systems Politeknik Negeri Indramayu Indramayu Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to detect fraud in digital payment systems. One of the main challenges addressed in this study is the severe class imbalance in the dataset, where fraudulent transactions account for only 0.17% of total transactions. To overcome this, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to balance the dataset, allowing the model to better recognize fraudulent patterns. The results indicate that the SVM model achieved an accuracy of 99.93%, with a precision of 86.23% and a recall of 75.51%. These results demonstrate that SVM, combined with SMOTE and RBF kernel, is highly effective in detecting fraudulent transactions while minimizing false positives. This research provides a strong foundation for improving fraud detection models in the context of digital payment systems, offering enhanced security and trust for users. Further research could explore hybrid models and real-time data analysis to improve performance.

关键词： Support vector machines Seminars Adaptation models Accuracy Data models Real-time systems Fraud Security Kernel Intelligent systems

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