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19th International Conference on Web Information Systems and Technologies, WEBIST 2023

作者： Nikolaou, Michalis Drakopoulos, Georgios Mylonas, Phivos Sioutas, Spyros Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian Univerity Kerkyra Greece Informatics and Computer Engineering Department University of West Attica Athens Greece

ISBN: (纸本)9789897586729

Intelligent agents (IAs) are highly autonomous software applications designed for performing tasks in a broad spectrum of virtual environments by circulating freely around them, possibly in numerous copies, and taking actions as needed, therefore increasing human digital awareness. Consequently, IAs are indispensable for large scale digital infrastructure across fields so diverse as logistics and long supply chains, smart cities, enterprise and Industry 4.0 settings, and Web services. In order to achieve their objectives, frequently IAs rely on machine learning algorithms. One such prime example, which lies in the general direction of evaluating the network structure integrity, is link prediction, which depending on the context may denote growth potential or a malfunction. IAs employing machine learning algorithms and local structural graph attributes pertaining to connectivity patterns are presented. Their performance is evaluated with metrics including the F1 score and the ROC curve on a benchmark dataset of scientific citations provided by Neo4j containing ground truth. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Intelligent agents

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Addressing Imbalance in Health Datasets: A New Method NR-Clustering SMOTE and Distance Metric Modification

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computers, Materials & Continua 2025年第2期82卷 2931-2949页

作者： Hairani Hairani Triyanna Widiyaningtyas Didik Dwi Prasetya Afrig Aminuddin Department of Electrical Engineering and Informatics Faculty of EngineeringUniversitas Negeri MalangMalang65145Indonesia Department of Computer Science Universitas BumigoraMataram83127Indonesia Department of Computer Graphic and Multimedia Faculty of ComputingCollege of Computing and Applied SciencesUniversiti Malaysia Pahang Al-Sultan AbdullahPekan26600Malaysia

An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, several weaknesses of the SMOTE method have been identified in generating synthetic minority class data, such as overlapping, noise, and small disjuncts. However, these studies generally focus on only one of SMOTE’s weaknesses: noise or overlapping. Therefore, this study addresses both issues simultaneously by tackling noise and overlapping in SMOTE-generated data. This study proposes a combined approach of filtering, clustering, and distance modification to reduce noise and overlapping produced by SMOTE. Filtering removes minority class data (noise) located in majority class regions, with the k-nn method applied for filtering. The use of Noise Reduction (NR), which removes data that is considered noise before applying SMOTE, has a positive impact in overcoming data imbalance. Clustering establishes decision boundaries by partitioning data into clusters, allowing SMOTE with modified distance metrics to generate minority class data within each cluster. This SMOTE clustering and distance modification approach aims to minimize overlap in synthetic minority data that could introduce noise. The proposed method is called “NR-Clustering SMOTE,” which has several stages in balancing data: (1) filtering by removing minority classes close to majority classes (data noise) using the k-nn method;(2) clustering data using K-means aims to establish decision boundaries by partitioning data into several clusters;(3) applying SMOTE oversampling with Manhattan distance within each cluster. Test results indicate that the proposed NR-Clustering SMOTE method achieves the best performance across all evaluation metrics for classification methods such as Random Forest, SVM, and Naїve Bayes, compared t

关键词： SMOTE modification Clustering-SMOTE manhattan distance

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Secure & Traceable Companies File Management System Using Blockchain and IPFS 1

Secure & Traceable Companies File Management System Using Bl...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Arissabarno, Cahyo Sukaridhoto, Sritrusta Winarno, Idris Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350365191

With the advancement of cloud-based file management platforms, sharing and storing files in the company's area can be easily achieved through a variety of file-sharing platforms. However, these platforms often lack of security and adequate access logs or activity records for the shared files, resulting in a lack of control and traceability for users. This research proposes an traceable and secure system for managing files in companies using blockchain and IPFS. The content ID of uploaded files to IPFS will be stored on the blockchain along with user access rights and access logs. Integration with the existing company file management system is also facilitated by the use of the change data capture method. Experimental findings demonstrate that the system offers the desired traceability of access and activity while also meeting data security requirements such as confidentiality, integrity, and availability. Furthermore, performance tests indicate satisfactory outcomes with 0.8 seconds per MB for file storage on IPFS, and 4.1 seconds per transaction for data storage on Blockchain. The experimental results revealed that the proposed system, integrating blockchain and IPFS, successfully addressed the need for traceability of access and activity for the company's data, providing a significant improvement in data security and management over existing solutions. © 2024 IEEE.

关键词： Cloud platforms

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Satu Sehat Interoperability Agent

Satu Sehat Interoperability Agent

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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Martono, Hero Yudo Yunanto, Andhik Ampuh Santoso, Dian Septiani Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798331511470

Data integration from all health care facilities in Indonesia to the Satu Sehat platform is an obligation that must be carried out, but it turns out that until early October 2024, around 57% of health care facilities have not succeeded in carrying out this integration. With this condition, it can be concluded that there are difficulties for health care facilities to carry out integration and this is estimated to be due to differences in data formats between health care facilities and Satu Sehat. For this reason, an interoperability agent is needed to bridge health care facilities with Satu Sehat. We built the first version of the interoperability agent that bridges this integration. This agent works by entering the client database, searching for the appropriate data in each health care facility, creating a data format that matches the Satu Sehat format, and then sending the format to Satu Sehat. This agent application will evolve over time. For the first version, this interoperability agent has been able to produce Satu Sehat data formats for eight types of resource types including encounter, procedure, condition, composition, medication, medication_request and medication_dispense. Trials have been carried out on each resource type and have run smoothly. In the future, this interoperability agent will be developed so that the number of resource types is in accordance with that determined by Satu Sehat. © 2024 IEEE.

关键词： Electronic health record

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Handwritten Signature Recognition Using Siamese Neural Network

Handwritten Signature Recognition Using Siamese Neural Netwo...

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2024 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Setiowati, Yuliana Fariza, Arna Luthfi Ramadhan, Muhamad Rifqi Politeknik Elektronika Negeri Surabaya Informatics and Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350368802

This research proposes a novel approach to addressing the issue of signature authenticity identification by implementing Deep Metric Learning (DML). The aim of this study is to develop a DML model with high accuracy in training and testing processes that can be integrated with a mobile application for signature authenticity verification. The data used in this research are signature images from 90 individuals, divided into two categories: genuine signatures and forged signatures. Testing results show that the developed model has an accuracy of 85.7%, with high precision for the forged signature class at 95.9% and recall for the genuine signature class at 94.9%. The conclusion of this research is that the developed DML model is reliable for signature verification, although further development is needed to improve the model's performance, especially in detecting forged signatures. Suggestions for future research include increasing the amount and variety of training data, integrating the model with a mobile application, and testing the model under various conditions and on different devices. © 2024 IEEE.

关键词： Authentication Protocol

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An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

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computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

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Architecting Immersive Education: Designing an Intelligent Online Virtual University 4th

Architecting Immersive Education: Designing an Intelligent O...

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4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Strousopoulos, Panagiotis Troussas, Christos Krouska, Akrivi Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo12243 Greece

ISBN: (纸本)9783031733437

This paper presents a roadmap for the architecture of an intelligent online virtual university that promises to redefine distance learning through its 3D immersive environment. This Unity-based solution offers a rich, interactive, and highly dynamic educational experience. The system is designed to overcome the challenges faced within modern education: accessibility, interactivity, and tailored learning paths. The level of Artificial Intelligence and Natural Language Processing provides a personalized and adaptive learning experience. The robust backend ensures optimum and secure data management, while the cloud solution guarantees scalability and performance. The content management system has user-friendly interfaces, providing a seamless experience for all users. Integration with the GPT-4o API expands the capabilities of virtual professors to a new level. It is designed based on the principles of being comprehensive, flexible, and safe for the interest of modern learners. This combination of leading technologies is undoubtedly a leap toward the future of online education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Virtual environments

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Screen Reading Regions in Social Media Comments: An Eye-Tracking Analysis of Visual Attention on Smartphones 27

Screen Reading Regions in Social Media Comments: An Eye-Trac...

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27th Pan-Hellenic Conference on Progress in Computing and informatics with International Participation, PCI 2023

作者： Dritsa, Stavroula Mallas, Andreas Xenos, Michalis University of Patras Department of Computer Engineering and Informatics Greece

ISBN: (纸本)9798400716263

Our study examines how formatted text influences reading behavior concerning the smartphone screen regions users utilize on social media comments. Aiming to research visual attention in scrollable content, we investigate the differences in the distribution of visual attention in screen regions between social media comment sections and the users' preferred reading regions across all cases examined. An experiment was conducted with participants (n = 47) engaging in reading activities on the comment section of four social media -Twitter, YouTube (in two versions), Facebook, and Instagram- chosen due to their popularity and the users' familiarity with them. Results showed that users' visual attention is distributed differently between social media. Different reading styles were observed, with users not utilizing the entirety of the screen but instead focusing on specific screen regions that varied between users. © 2023 Owner/Author.

关键词： Smartphones

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Comparison of Q&A Classification Using BiLSTM Based on Closed Domain Knowledge 12

Comparison of Q&A Classification Using BiLSTM Based on Close...

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12th Electrical Power, Electronics, Communications, Controls and informatics Seminar, EECCIS 2024

作者： Gunawan, Alifi Lazuardi Bachtiar, Fitra Abdurrachman Setiawan, Budi Darma University Malang Faculty of Computer Science Brawijaya Informatics Engineering Department Indonesia

ISBN: (纸本)9798350379914

The Faculty of computer Science, Universitas Brawijaya (Filkom UB) is committed to providing quality services for the users especially internal and external stakeholders, one of which is through the HaloFilkom service. HaloFilkom services have limitations in terms of time. HaloFilkom services are not available 24 hours due to limited working hours. Questions asked by users are not answered directly. This weakness in the HaloFilkom system can be overcome by using a chatbot system. Chatbot is an interactive system that works with natural human language and can work 24 hours. Thus, the current study explores the basic chatbot model by classifying the Q&A in the closed domain knowledge. The dataset in this research is in the form of pairs of questions and answers regarding various topics at the Filkom UB. The knowledge is preprocessed using text preprocessing which includes case folding, tokenization, padding, and tensorization. One of the chatbot models is a generative model. Creating a generative chatbot model can be done using the Seq2Seq model mechanism which consists of an encoder and decoder. The model created consists of four different architectures, namely a model with an LSTM encoder without attention and with attention and a BiLSTM model encoder without attention and with attention. Hyperparameter tuning was conducted to obtain the best hyperparameter combination. The experiment results show the best hyperparameter combination obtained is hidden size 448, drop out rate 0.5, learning rate 0.001, batch size 64, and teacher force 0. The model with the best loss is obtained with a BiLSTM encoder architecture without an attention mechanism with a train loss of 0.120. The model with the highest BLEU Score was obtained by a model with a BiLSTM encoder architecture without an attention mechanism with a BLEU Score of 0.8587 on the training data. Testing using prompt testing obtained an average BLEU Score of 0.3745 on the BiLSTM encoder without an attention mechanism mo

关键词： Teaching

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Electricity demand forecasting at distribution and household levels using explainable causal graph neural network

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Energy and AI 2024年第2期16卷 385-395页

作者： Amir Miraki Pekka Parviainen Reza Arghandeh Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergenNorway Department of Informatics University of BergenBergenNorway

Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With the increasing integration of renewable energy resources into the grid, forecasting the demand for electricity is critical at all levels, from the distribution to the household. Most existing forecasting methods, however, can be considered black-box models as a result of deep digitalization enablers, such as deep neural networks, which remain difficult to interpret by humans. Moreover, capture of the inter-dependencies among variables presents a significant challenge for multivariate time series forecasting. In this paper we propose eXplainable Causal Graph Neural Network (X-CGNN) for multivariate electricity demand forecasting that overcomes these limitations. As part of this method, we have intrinsic and global explanations based on causal inferences as well as local explanations based on post-hoc analyses. We have performed extensive validation on two real-world electricity demand datasets from both the household and distribution levels to demonstrate that our proposed method achieves state-of-the-art performance.

关键词： Causal inference Electricity demand forecasting Explainable artificial intelligence(XAI) Graph neural network

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