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9th IEEE International Conference on Computing, engineering and Design, ICCED 2023

作者： Fariza, Arna Asmara, Rengga Rojaby, Muhammad Oktavian Fajar Astuti, Eha Renwi Putra, Ramadhan Hardani Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Universitas Airlangga Department of Dentistry Surabaya Indonesia

ISBN: (纸本)9798350370126

Accurate tooth numbering is essential for dental procedures, treatment planning, and patient record management. Automatic tooth enumeration in dental panoramic radiographs is crucial in modern dental image analysis and diagnosis. Tooth enumeration in panoramic radiographs has relied on handcrafted features and traditional image processing techniques. Still, these methods often lack the accuracy and robustness required for complex cases and varied image qualities. Despite the advancements in deep learning-based object detection algorithms, a significant research gap remains in the specific domain of automatic tooth enumeration on panoramic radiographs. This research paper presents an innovative approach for automatic tooth enumeration on panoramic radiographs using the state-of-the-art You Only Look Once (YOLO) object detection framework focused on implementing the YOLOv5 library. The YOLOv5 model is evaluated as an efficient system capable of accurately detecting and enumerating individual teeth from panoramic radiographs. The 612 panoramic radiograph images evaluation shows the bounding-box results show the best tooth detection rate in the YOLOv5x model. The YOLOv5 model is generally very good at predicting tooth enumeration on panoramic radiographs in a relatively small dataset. This advancement is expected to enhance dental diagnosis and treatment planning, benefiting dental professionals and patients. © 2023 IEEE.

关键词： Object detection

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Tools Supporting Information Security Risk Management in Practice 9

Tools Supporting Information Security Risk Management in Pra...

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9th International Conference on Socio-Technical Perspective in Information Systems Development, STPIS 2023

作者： Bergström, Erik Department of Computer Science and Informatics School of Engineering Jönköping University Jönköping Sweden

It is well-known that Information Security Risk Management (ISRM) activities can be challenging to perform and that tool support could provide support in different ways, for example, by automating tasks, guiding the user, or helping with documentation. Despite the need for tools, there is a lack of studies investigating ISRM tool usage. This paper contributes by presenting the results from one of the first studies targeting information classification and ISRM tool usage in practice. The study is based on a survey sent to government agencies in Sweden and was answered by 139 respondents (67%). The survey targeted the type of tools used and the perceptions of those tools. Findings include a list of tools perceived to contribute to performing ISRM activities, such as information classification, the reasons why the tools were selected, and how well they fulfil their needs. More specifically, we found that spreadsheets and document templates are the most common tools used – despite not being perceived as fulfilling the needs. We also found that taking on an even more holistic view might be needed when considering functionality in ISRM tools. © 2023 CEUR-WS. All rights reserved.

关键词： Classification (of information)

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Blockchain Integration for Mixed Reality Based Smart Lab Systems 25

Blockchain Integration for Mixed Reality Based Smart Lab Sys...

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25th International Electronics Symposium, IES 2023

作者： Arissabarno, Cahyo Achmad, Zacky Maulana Ruswiansari, Maretha Romadhoni, Achmad Fatrian Satrio, Naufal Adi Budiarti, Rizqi Putri Nourma Sukaridhoto, Sritrusta Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Indonesia Universitas Nahdlatul Ulama Surabaya Indonesia

ISBN: (纸本)9798350314731

Smart lab is a system for controlling electronic equipment in a laboratory room that is possible to be operated remotely via the internet network and integrate mixed reality for a more realistic user interface. In the smart lab system, it is important to know the activity history of each device, particularly in case of malfunctions or misuse. Therefore, the usage of devices in laboratories needs to be traceable. Blockchain is one type of technology that able to address traceability issues. Blockchain's distributed and transparent nature makes it simpler to track each transaction occurrence. In a smart lab system, we are integrating Blockchain as a data storage system for historical smart device usage data. Blockchain has successfully recorded smart devices history data. Based on testing, the transaction throughput on the blockchain is an average of 1 transaction per second with a transaction delay of 1.2 seconds. The REST API developed for the smart lab system able to handle more than 20 requests from 5 users simultaneously with an average response time of 3.75 seconds at a bandwidth ranging from 8 Mbps to 0.5 Mbps. However, there are already failed requests when 10 users use it simultaneously at 8 Mbps. Traceability in the smart lab has been enabled by preserving historical records of smart device usage on the blockchain system. © 2023 IEEE.

关键词： Smart contract

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A Novel Drone-Station Matching model in Smart Cities based on Strict Preferences

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无人系统（英文） 2023年第3期11卷 261-271页

作者： Debolina Nath Anjan Bandyopadhyay Ankit Rana Tarek Gaber Aboul Ella Hassanien Department of Computer Science and Engineering Amity University Kolkata KolkataWest BengalIndia Department of Computer Science and Engineering Gitam University BangaloreKarnatakaIndia School of Science Engineeringand Environment University of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailiaEgypt Scientific Research Group in Egypt(SRGE) Egypt Faculty of Computer and Artificial Intelligence Cairo UniversityCairoEgypt Scientific Research Group in Egypt(SRGE) Egypt

There has been a considerable increase in the use of drones,or unmanned aerial vehicles(UAVs),in recent times,for a wide variety of purposes such as security,surveillance,delivery,search and rescue operations,penetration of inaccessible or unsafe areas,*** increasing number of drones working in an area poses a challenge to finding a suitable charging or resting station for each drone after completing its task or when it goes low on its *** classical methodology followed by drones is to return to their pre-assigned charging station every time it requires a *** approach is found to be inefficient as it leads to an unnecessary waste of time as well as power,which could be easily saved if the drone is allotted a nearby charging station that is ***,we propose a drone-allocation model based on a preference matching algorithm where the drones will be allotted the nearest available station to land if the station is *** problem is modeled as three entities:Drones,system controllers and charging *** matching algorithm was then used to design a Drone-Station Matching *** simulation results of our proposed model showed that there would be considerably less power consumption and more time saving over the conventional *** would save its travel time and power and ensure more efficient use of the drone.

关键词： Unmanned Aerial Vehicle(UAV) stable matching resource allocation smart city matching algorithm

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Improving Open Weather Prediction Data Accuracy Using Machine Learning Techniques 14

Improving Open Weather Prediction Data Accuracy Using Machin...

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Tsipis, Evangelos Banti, Konstantina Louta, Malamati Dimokas, Nikos University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece University of Western Macedonia Department of Informatics Kastoria Greece

ISBN: (纸本)9798350318067

Weather prediction methods have evolved significantly over the past fifty years, including advances in numerical weather prediction, high-performance computing, mesoscale modeling, assimilation of observations from new sources, and ensemble prediction systems. In recent years, machine learning techniques, including Long Short- Term Memory (LSTM) models, have shown promise in improving the accuracy of weather predictions. In this paper, we aim at investigating the use of LSTM models to improve the accuracy of open weather datasets. Our work was conducted as part of the AUGEIAS research project, which aims at capitalizing on research results in the field of Internet of Things (IoT) and Low Power Wide Area Networks (LPWAN). More specifically, the project target is to create a smart ecosystem, utilizing machine learning techniques that will enable and optimize the use of treated wastewater reuse in agriculture. Our research indicates that, within the context of the AUGEIAS project, single-step LSTM models outperform multistep models in enhancing weather prediction accuracy. © 2023 IEEE.

关键词： Internet of things

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A Dynamic Security Assessment Tool for Modern Power Systems with Massive RES Penetration 3

A Dynamic Security Assessment Tool for Modern Power Systems ...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Therapontos, Phivos Charalambous, Charalambos A. Aristidou, Petros Electricity Authority of Cyprus Distribution System Operator Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Cyprus University of Technology Dept. of Electrical & Computer Engineering & Informatics Limassol Cyprus

ISBN: (纸本)9798350375923

Modern power systems are facing significant challenges due to the massive penetration of renewable energy sources (RES). Recently, issues related to frequency security, system strength, and excessive fault levels have been experienced in some power systems. Classical dynamic security assessment (DSA) tools have not been developed to identify and analyze these new challenges that are critical in low-inertia grids. In this paper, a DSA tool has been developed that evaluates frequency security, system strength, and maximum fault levels to allow for secure system planning and operation. Furthermore, to increase the accuracy of the DSA tool, inverter-based resources' (IBRs') protections and capabilities have been incorporated in the DSA tool. A real-time operation and an operational planning application of the DSA tool are showcased using the islanded Cyprus Power System. © 2024 IEEE.

关键词： Dynamic security assessment frequency security IBR modeling low-inertia grids system strength

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Leveraging Transformer-Based Topic Modeling using BERTopic for Publication Venue Recommendation 11

Leveraging Transformer-Based Topic Modeling using BERTopic f...

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11th International Conference on Electrical engineering, computer Science and informatics, EECSI 2024

作者： Badieah Chaerul Haviana, Sam Farisa Mulyono, Sri Salim, Channa Abdullah Adli, Muhammad Fatih Febriansah, Moh Taufik Wiktasari Sulaiman, Noor Suhana Yaqob, Azliza Universitas Islam Sultan Agung Informatics Engineering Department Semarang Indonesia Politeknik Negeri Semarang Computer Engineering Technology Semarang Indonesia University College of TATI Faculty of Computer Media and Technology Management Terengganu Malaysia

ISBN: (纸本)9798350355314

Topic modeling has become essential in a variety of text mining applications, such as document clustering and recommendation systems. This study investigates the potential of BERTopic, a transformer-based method that leverages BERT embeddings to recommend publication venues. Traditional topic modeling methods are made better by BERTopic, which uses the deep contextual embeddings that BERT (Bidirectional Encoder Representations from Transformers) offers. This investigation assesses the efficacy of BERTopic in clustering academic papers according to their content in order to suggest appropriate publication venues. We also compare the performance between different kinds of pre-trained sentence embedding models and input types (title, abstract, and title+abstract). The experimental results indicate that BERTopic performs well with the GARUDA dataset, which comprises 5272 records from three different research subjects. The results emphasize the transformative influence of the transformer-based model. The best performance of all cases was 99.17% mean average precision in the Top 3 position, using Title+Abstract as input data and all-mpnet-base-v2 as the sentence embedding model. © 2024 IEEE.

关键词： Recommender systems

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Clustering High-Dimensional Social Media Datasets sutilizing Graph Mining

Clustering High-Dimensional Social Media Datasets sutilizing...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Kanavos, Andreas Vonitsanos, Gerasimos Mylonas, Phivos Ionian University Department of Informatics Corfu Greece University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9781665480451

Social networks are an essential component of people' daily lives, and as a result, much academic attention has been focused on them. The rapid adoption of machine learning as a problem-solving tool, which simplifies and accelerates numerous tasks while enabling the processing of large volumes of data, has played a significant role in this field of research. This is in contrast to the more traditional approaches that lacked this momentum. Characterization of linkages and cluster identification i n social networks are two of the research community's most well-known issues. The goal of this study is to gather data for a set of users who are then divided into groups based on the hashtags they used in their Twitter postings. The procedure performed generates the numerical data, in following reduces the dimensions, and finally performs the clustering. © 2022 IEEE.

关键词： Social networking (online)

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Towards Threat Modelling of IoT Context-Sharing Platforms 22

Towards Threat Modelling of IoT Context-Sharing Platforms

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22nd International Symposium on Network Computing and Applications, NCA 2024

作者： Goudarzi, Mohammad Shaghaghi, Arash Finn, Simon Stiller, Burkhard Jha, Sanjay School of Computer Science and Engineering UNSW Sydney Australia Australia Communication Systems Group Department of Informatics University of Zurich Switzerland Cisco Australia

ISBN: (纸本)9798331510183

The Internet of Things (IoT) involves complex, interconnected systems and devices that depend on context-sharing platforms for interoperability and information exchange. These platforms are, therefore, critical components of real-world IoT deployments, making their security essential to ensure the resilience and reliability of these "systems of systems." In this paper, we take the first steps toward systematically and comprehensively addressing the security of IoT context-sharing platforms. We propose a framework for threat modelling and security analysis of a generic IoT context-sharing solution, employing the MITRE ATT&CK framework. Through an evaluation of various industry-funded projects and academic research, we identify significant security challenges in the design of IoT context-sharing platforms. Our threat modelling provides an in-depth analysis of the techniques and sub-techniques adversaries may use to exploit these systems, offering valuable insights for future research aimed at developing resilient solutions. Additionally, we have developed an open-source threat analysis tool that incorporates our detailed threat modelling, which can be used to evaluate and enhance the security of existing context-sharing platforms. ©2024 IEEE.

关键词： Cloud platforms

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LIME: Long-Term Forecasting Model for Desalination Membrane Fouling to Estimate the Remaining Useful Life of Membrane 1

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36th International Conference on Industrial, engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2023

作者： Eltanbouly, Sohaila Erradi, Abdelkarim Tantawy, Ashraf Said, Ahmed Ben Shaban, Khaled Qiblawey, Hazim Computer Science and Engineering Department College of Engineering Qatar University Doha Qatar School of Computer Science and Informatics De Montfort University Leicester United Kingdom Chemical Engineering Department College of Engineering Qatar University Doha Qatar

ISBN: (数字)9783031368226

ISBN: (纸本)9783031368219

Membrane fouling is one of the major problems in desalination processes as it can cause a severe drop in the quality and quantity of the permeate water. This paper presents a data-driven approach for long-term forecasting of fouling behavior in membrane-based desalination processes. The proposed Long-term forecastIng ModEl (LIME) consists of two intertwined machine learning models trained separately by historical operating conditions of ultrafiltration for pretreatment of reverse osmosis seawater where transmembrane pressure is used as a fouling indicator. The first model predicts the increase in fouling due to filtration. This output is fed to the second model to predict the fouling reduction due to membrane cleaning. In turn, this output is used as the initial fouling condition for predicting the next filtration cycle. The forecasted fouling is used to estimate the membrane’s remaining useful life (RUL), which ends when cleaning no longer reduces the fouling below a safety threshold. Evaluation results show that the model can predict the membrane fouling for 1400 cycles with an R-squared score of 0.8. Moreover, the RUL is estimated for various thresholds with an average percentage error of 7%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Desalination

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