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

作者： Bousdekis, Alexandros Kaftantzis, Savvas Theodoropoulou, Georgia Miaoulis, Georgios Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031733437

Process mining allows the extraction of useful information from the event logs and historical data of business processes. Predictive business process monitoring is a sub-field of process mining and deals with runtime methods aimed at generating predictive models about various objectives related to a process instance by taking advantage of Machine Learning (ML) and deep learning algorithms. However, despite the large amount of research works dealing with predictions about next activity prediction and remaining time of running process instances, the objective of cycle time prediction has been overseen although it can provide higher level insights targeted the strategic planning. At the same time, Automated Machine Learning (AutoML) has not been investigated in the predictive business process monitoring domain in order to automate the time-consuming process of designing and optimizing ML pipelines. Therefore, motivated by its promising results in other domains, we propose an approach for remaining cycle time prediction in business processes based on AutoML, and specifically on the Tree-Based Pipeline Optimization Tool (TPOT) method. The results demonstrate that the proposed approach achieves high accuracy, while it minimizes human intervention in the configuration of ML pipelines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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Customer Segmentation in Steel Sales : Optimizing Profitability with RFM Model and K-Means Algorithm

Customer Segmentation in Steel Sales : Optimizing Profitabil...

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2024 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics, ICoABCD 2024

作者： Delimayanti, Mera Kartika Hidayati, Anita Kurniawati, Dewi Rahmadani, Nadya Zahra Faydhurrahman, Mas Brillianesa Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia

ISBN: (纸本)9798350368451

This paper is on customer segmentation in steel sales, whose objective is to improve profitability and customer relationship management. The current research categorizes customers based on purchasing behaviors by using the RFM model and the K-Means clustering algorithm. Due to this approach, the company will be able to formulate stratified marketing plans for the requirements and Preferences of various customer segments identified and hence ensure high retention and satisfaction. The RFM model assesses the three main customer behaviors of purchase recency, purchase frequency, and monetary value. This gives a set of metrics that provide the overall depth of customer engagement, which is important in segmentation. Then, K-Means is applied to cluster these similar patterns of customer purchasing, to precisely target market customers. The goodness of the segmentation strategy is measured through metrics such as the Elbow Method, Davies-Bouldin Index, and Silhouette Coefficient, which provide proper cluster selection and meaningful segmentation. This eventual outcome has proved that this combined approach will maximize the revenue potential and enhance customer satisfaction tremendously through personalized marketing efforts. Eventually, the embedding of the RFM model within K-Means clustering offers a robust framework for strategic decision-making and realizing competitive advantage within the steel industry. © 2024 IEEE.

关键词： Profitability

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FHRP Performance Measurement Using Etherchannel and Routing protocol OSPFv2, IGRP and BGPv4 on Three-Tier Enterprise Network Architecture Topology 12

FHRP Performance Measurement Using Etherchannel and Routing ...

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12th International Conference on Cyber and IT Service Management, CITSM 2024

作者： Arini Fiade, Andrew Matin, Iik Muhamad Malik Rifandi, Ahmad Solahudin Informatics Engineering Uin Syarif Hidayatullah Jakarta Jakarta Indonesia Jakarta State Polytechnic Computer and Informatics Engineering Jakarta Indonesia Uin Syarif Hidayatullah Jakarta Informatics Engineering Jakarta Indonesia

ISBN: (纸本)9798331528799

Networks in enterprise scale require connectivity that runs continuously so that a backup network is needed that functions as a substitute for the main network in case of problems. Network problems can affect data traffic and Quality. This can be overcome by using a gateway redundancy protocol. To determine the performance of a computer network when switching from the main network to the backup network, performance measurements are needed. Performance measurements are carried out with several scenarios using TCP and UDP packet transmissions to determine the value of Quality of Service, namely Throughput, Jitter, Packet loss and Delay. The results of this study show a comparison of protocol redundancy (FHRP) using EIGRP and OSPF routing by producing the HSRP protocol has the best QoS performance using EIGRP and OSPF routing but has a delay value that is slightly not better than other combinations in several scenarios. © 2024 IEEE.

关键词： Redundancy

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Enhancing Collaborative Learning through Virtual Escape Rooms: Impact on Participants' Perceptions 19

Enhancing Collaborative Learning through Virtual Escape Room...

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19th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2024

作者： Krouska, Akrivi Troussas, Christos Karkazis, Panagiotis Mylonas, Phivos Sgouropoulou, Cleo University of West Attica Department of Informatics and Computer Engineering Egaleo Greece

ISBN: (纸本)9798331504502

Digital revolution has transformed education, allowing students to interact, communicate, and collaborate beyond traditional classroom settings. computer-Supported Collaborative Learning (CSCL) provides opportunities for teamwork and knowledge sharing regardless of geographic location. Recently, Virtual Reality (VR) has emerged as a promising tool to address these challenges, offering immersive, three-dimensional environments that enhance collaborative learning experiences. Collaborative Virtual Environments (CVEs) extend VR's potential by enabling users to interact and work together in shared virtual spaces. Moreover, virtual escape rooms have gained traction as an innovative approach to game-based learning, combining the benefits of collaboration, problem-solving, and engagement to create an immersive and interactive educational experience. In view of the above, this paper explores the innovative use of virtual reality (VR) and educational digital escape room games as tools to promote collaborative learning in synchronous distance education and lifelong learning. By designing a cooperative game-based learning environment, the study seeks to leverage the problem-solving and teamwork skills inherent in such games to enhance the learning experience. The impact of these technologies on participants' perceptions was assessed comparing the information emerged from two surveys conducted before and after the intervention. The findings show that the incorporation of virtual escape room into collaborative learning environments effects positively participants' perceptions. © 2024 IEEE.

关键词： Virtual environments

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The Effect of Social Networking-Based Multimedia Repository on Promoting Open Educational Resources 4th

The Effect of Social Networking-Based Multimedia Repository ...

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

作者： Krouska, Akrivi Troussas, Christos Papakostas, Christos Stavrianakou, Efi Mylonas, Phivos Sgouropoulou, Cleo Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

ISBN: (纸本)9783031733437

In recent years, the Open Education movement has emerged as an innovative approach in the field of education. Closely connected to it are the Open Educational Resources (ΟΕRs), which are hosted in Digital Repositories. Despite their unique advantages, global recognition, and increasing numbers, OERs have not yet become fully established. Teachers encounter various challenges in using OERs, including a lack of pedagogical knowledge about their value, usage, production, and integration into teaching. At the same time, many digital repositories are considered inadequate, facing issues such as difficulty in content search, incomplete content, and insufficient assistance for users. As such, this research aims to maximize OERs potential and utilization, introducing a social networking-based multimedia repository. This repository fosters interaction, communication, and collaboration, educating teachers about OERs. The main goal of the research is to determine whether incorporating social networking features into a repository could positively influence users’ experience, access frequency, and the utilization of OERs in teaching. A quantitative research method was employed, showing the positive impact of social networking features on teacher attitude and intention to use OERs in their instruction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Digital repository Multimedia content Open Education Open educational resources Social network

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Enhancing Interaction with Data Lakes Using Digital Twins and Semantic Blueprints 19

Enhancing Interaction with Data Lakes Using Digital Twins an...

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19th International Conference on Evaluation of Novel Approaches to Software engineering, ENASE 2024

作者： Loizou, Spyros Pingos, Michalis Andreou, Andreas S. Department of Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9789897586965

Advanced analytical techniques and sophisticated decision-making strategies are imperative for handling extensive volumes of data. As the quantity, diversity, and speed of data increase, there is a growing lack of confidence in the analytics process and resulting decisions. Despite recent advancements, such as metadata mechanisms in Big Data Processing and Systems of Deep Insight, effectively managing the vast and varied data from diverse sources remains a complex and unresolved challenge. Aiming to enhance interaction with Data Lakes, this paper introduces a framework based on a specialized semantic enrichment mechanism centred around data blueprints. The proposed framework takes into account unique characteristics of the data, guiding the process of locating sources and retrieving data from Data Lakes. More importantly, it facilitates end-user interaction without the need for programming skills or database management techniques. This is performed using Digital Twin functionality which offers model-based simulations and data-driven decision support. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Lakes

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Book Recommender System Using Content-Based Filtering for PNJ Press Website 10

Book Recommender System Using Content-Based Filtering for PN...

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10th International Conference on Education and Technology, ICET 2024

作者： Liliana, Dewi Yanti Nalawati, Rizki Elisa Iswara, Ratna Widya Aisyah, Alifiah Putri Malo, Hana Octavia Trinida Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia

ISBN: (纸本)9798331522483

The vast collections in book publishers and bookstores make it challenging for users to find books on specific topics without knowing the exact titles. An information system with a book recommendation feature can significantly enhance the efficiency of a book search. Recommender systems, which predict or suggest items of interest based on user interactions and queries, are widely used across various platforms to personalize user experiences. This study focuses on developing a Content-Based Filtering recommender system for PNJ Press, a book publisher of the academic community of Jakarta State Polytechnic. The system compares user search queries with the content of book titles and abstracts to provide relevant recommendations. Previous research has demonstrated the effectiveness of Content-Based Filtering in various domains, highlighting its potential for improving book search efficiency on the PNJ Press website. The system achieved a precision of 91.84% and a recall of 97.83%, resulting in an overall accuracy rate of 90%. Additionally, the study found that the combined attributes of book titles and abstracts significantly influence recommendation results. © 2024 IEEE.

关键词： content-based filtering cosine similarity PNJ press website recommender system text mining

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Modeling Educational Strategies in Augmented Reality Learning Using Fuzzy Weights 9

Modeling Educational Strategies in Augmented Reality Learnin...

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9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Papakostas, Christos Troussas, Christos Krouska, Akrivi Mylonas, Phivos Sgouropoulou, Cleo University of West Attica Department of Informatics and Computer Engineering Egaleo Greece

ISBN: (纸本)9798350342482

This paper shows the development and application of a fuzzy weight model methodology for the optimization of educational environments in augmented reality. A technology-based learning process in an augmented reality learning environment, notoriously able to improve spatial abilities and increase user engagement with the material learned as well as knowledge retention, has revolutionary potential in education. However, its effectiveness varies greatly. In this, we collaborated with 13 experts in augmented reality and education to deeply evaluate the following six key in-depth strategies: interactive feedback, gamified leaderboards, challenges, personalized learning path, AR-based simulations, and badges. Thus, strategies were rated upon the impact on the learners' outcome, and the ratings were agreed upon as a fuzzy set, because it is a subjective matter of evaluation. Normalization was done to get a balanced measurement that gave weights on which strategies are to be given higher priority. Such a model has been developed and implemented in a case study of AR-based spatial ability training application, focusing on AR-based simulations supplemented with interactive feedback. Pre- and post-tests among 30 respondents have shown significant improvements in the experimental group using the AR application over the control group using traditional means of spatial ability training. The results strongly support the potential of the model for later implementation in AR educational applications that focus on individual specifics and effective learning. © 2024 IEEE.

关键词： Contrastive Learning

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Practical autoencoder based anomaly detection by using vector reconstruction error

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Cybersecurity 2023年第2期6卷 1-13页

作者： Hasan Torabi Seyedeh Leili Mirtaheri Sergio Greco Department of Electrical and Computer Engineering Faculty of EngineeringKharazmi UniversityTehranIran Department of Informatics ModelingElectronics and System EngineeringUniversity of CalabriaArcavacataItaly.

Nowadays,cloud computing provides easy access to a set of variable and configurable computing resources based on user demand through the *** computing services are available through common internet protocols and network standards.n addition to the unique benefits of cloud computing,insecure communication and attacks on cloud networks cannot be *** are several techniques for dealing with network *** this end,network anomaly detection systems are widely used as an effective countermeasure against network *** anomaly-based approach generally learns normal traffic patterns in various ways and identifies patterns of *** anomaly detection systems have gained much attention in intelligently monitoring network traffic using machine learning *** paper presents an efficient model based on autoencoders for anomaly detection in cloud computing *** autoencoder learns a basic representation of the normal data and its reconstruction with minimum ***,the reconstruction error is used as an anomaly or classification *** addition,to detecting anomaly data from normal data,the classification of anomaly types has also been *** have proposed a new approach by examining an autoencoder's anomaly detection method based on data reconstruction *** the existing autoencoder-based anomaly detection techniques that consider the reconstruction error of all input features as a single value,we assume that the reconstruction error is a *** enables our model to use the reconstruction error of every input feature as an anomaly or classification *** further propose a multi-class classification structure to classify the *** use the CIDDS-001 dataset as a commonly accepted dataset in the *** evaluations show that the performance of the proposed method has improved considerably compared to the existing ones in terms of accuracy,recall,false-positive rate,and F1-score

关键词： Cloud Practical Anomaly detection Autoencoder Reconstruction error Machine learning

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AI and ML Applications Traffic: Designing Challenges for Performance Optimization of Optical Data Center Networks 32

AI and ML Applications Traffic: Designing Challenges for Per...

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Baziana, Peristera Drainakis, Georgios Georgantas, David Bogris, Adonis University of Thessaly Department of Informatics and Telecommunications Greece National Technical University of Athens School of Electrical and Computer Engineering Greece University of West Department of Informatics and Computer Engineering Attica Greece

ISBN: (纸本)9789532901382

Artificial intelligence (AI) and machine learning (ML) are vastly becoming key parts of today's applications, supporting diverse human activities and decision making. New training models are used requiring an explosive traffic exchange among data centers (DCs) servers, putting tremendous pressure on DC network (DCN). Therefore, new DCN design challenges are required to efficiently handle the emerging AI/ML applications traffic. Contrary to existing works that do not account for these challenges, in this paper we introduce an optical intra-rack DCN architecture, as an optical local area network (LAN) paradigm, and an efficient wavelength division multiplexing access (WDMA) algorithm tailored to serve the AI/ML traffic. For the proposed WDMA scheme, we assume diverse AI/ML traffic classes according to the required service time and quality of service (QoS) delivery and we adopt appropriate access rules for each of them to achieve high DCN performance and access fairness. Simulation results show that our proposal reaches high bandwidth utilization (90 % under nominal load 400 Gbps) and low end-to-end latency ( © 2024 University of Split, FESB.

关键词： Quality of service

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