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Remaining Cycle Time Prediction in Business Processes with TPOT Regression for Automated Machine Learning 4th

Remaining Cycle Time Prediction in Business Processes with T...

引用

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

Enhancing Collaborative Learning through Virtual Escape Room...

引用

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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Secure Video and Telemetry FPGA Architecture For UAVs 9

Secure Video and Telemetry FPGA Architecture For UAVs

引用

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

作者： Psilias, Dimitrios Milidonis, Athanasios Lentaris, George Voyiatzis, Ioannis University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350342482

Unmanned Aerial Vehicles (UAVs) are used in a wide range of applications. However, since they are vulnerable to cyberattacks security protocols for their data are used. Common existing approaches for data encryption/decryption are based on commercial software platforms which execute their tasks with latency and consume a lot of energy. Moreover, they focus mostly on encrypting/decrypting telemetry data. This paper proposes a Field Programming Gate Array (FPGA)-based architecture for encrypting/decrypting all data exchanged between UAVs and their Ground Station (GS). This architecture uses the Advanced Encryption Standard (AES), known for its robustness, to enhance the confidentiality, integrity, and security. Experimental results show that the proposed architecture performs faster than implementations on well-known software platforms while it consumes less power. © 2024 IEEE.

关键词： Electronic data interchange

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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...

引用

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

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

引用

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

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

引用

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

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

引用

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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Recognition of Dog Motion States: Ensemble vs Deep Learning Models 15

Recognition of Dog Motion States: Ensemble vs Deep Learning ...

引用

15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Davoulos, George Lalakou, Iro Hatzilygeroudis, Ioannis University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Department of Informatics and Telecommunications Tripolis Greece

ISBN: (纸本)9798350368833

Dog activities recognition, especially dog motion status recognition, is an active research area. Although several machine learning and deep learning approaches have been used for dog motion states recognition, use of ensemble learning methods is rather missing as well as a comparison with deep learning ones. This paper focuses on the use of ensemble classifiers and deep learning neural networks in recognizing dog motion states, and their comparison. Our results show a slight superiority in accuracy (94.7% vs 92.8%) of deep learning, which is quite comparable to the state-of-the-art, but at the cost of larger complexity and training time, which makes ensemble techniques still attractive. © 2024 IEEE.

关键词： Contrastive Learning

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Vision Transformer for Audio-Based Depression Detection on Multi-Lingual Audio Data 24

Vision Transformer for Audio-Based Depression Detection on M...

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7th International Conference on Digital Medicine and Image Processing, DMIP 2024

作者： Pratiwi, Monica Sanjaya, Samuel Ady Department of Computer Engineering Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Department of Information System Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia

ISBN: (纸本)9798400709586

Depression has the potential to impact death rates, particularly when it comes to death by suicide. Inadequate diagnosis may result in a delay or unsuitable therapy, which can worsen symptoms of depression. Unaddressed or insufficiently addressed depression can result in deteriorating mental well-being, which includes a higher risk of experiencing suicidal ideation and engaging in self-destructive actions. Voice analysis can be employed to distinguish between individuals with depression and those without depression. However, the research that has been conducted on voice recognition to distinguish between depressed and non-depressed individuals keeps focusing on the use of a single dataset source as input for the classification model that is being developed. This work utilizes the Vision Transformer model and various pre-trained transformers, such as DeiT (Data-Efficient Image Transformer) and Swin Transformer, to detect depression based on voice. The objective was to create a more generic model not restricted to a specific language. Integrating Mel-spectrogram characteristics with a vision transformer-based model can enhance the efficacy of voice recognition models when dealing with multi-language data. The result shows 21% higher accuracy than the previous study that also implemented a cross-dataset test. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Spectrographs

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