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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Kurniadi, Felix Indra Sutomo, Rudi School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia Universitas Multimedia Nusantara Information Systems Faculty of Engineering & Informatics Banten15810 Indonesia

ISBN: (纸本)9798350307504

Indonesia's tourism sector, a cornerstone of its economy, has seen significant growth with both domestic and international tourists, drawn by its diverse landscapes and cultural sites. In 2022, the country welcomed around 10 million international visitors, indicating the industry's potential. In this context, employing artificial intelligence for personalized travel recommendations is crucial. This study proposes an innovative approach, converting tourism data into a bipartite format focused on the user-place relationship. We introduce the Enhanced Graph Convolutional Neural Network (Enhanced GCN), tailored for the tourism sector's complexities. Compared to the traditional Graph Convolutional Network (GCN), the Enhanced GCN shows superior predictive accuracy, reducing the Mean Squared Error (MSE) to 2.38. This advance represents a significant step in providing more accurate, customized travel suggestions. However, the current MSE suggests room for further improvement. Future research will aim to refine the Enhanced GCN, enhancing its ability to meet and predict tourists' needs in Indonesia's dynamic tourism environment. Ultimately, the goal is to use artificial intelligence to transform how visitors experience Indonesia's cultural and natural offerings, thereby bolstering the tourism industry's growth.. © 2023 IEEE.

关键词： Recommender systems

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Modified U-Net with attention gate for enhanced automated brain tumor segmentation

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Neural Computing and Applications 2025年第7期37卷 5521-5558页

作者： Saifullah, Shoffan Dreżewski, Rafal Yudhana, Anton Wielgosz, Maciej Caesarendra, Wahyu Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia Faculty of Computer Science Electronics and Telecommunications AGH University of Krakow Krakow30-059 Poland Faculty of Mechanical Engineering Opole University of Technology 76 Proszkowska St. Opole45-758 Poland

This study addresses the formidable challenges encountered in automated brain tumor segmentation, including the complexities of irregular shapes, ambiguous boundaries, and intensity variations across MRI modalities. Manual segmentation, plagued by subjectivity and time constraints, further exacerbates the problem. To address these issues, we propose a modified U-Net architecture with an integrated attention gate. The proposed model demonstrates high performance, with notable Dice Similarity Coefficient (DSC) and Jaccard Index (JI) values across various tumor classes, consistently exceeding 0.93 and 0.87, respectively. Incorporating Contrast-Limited Adaptive Histogram Equalization and Histogram Equalization improves segmentation accuracy, particularly in cases of Meningioma. Comparative analyses against established models reveal a DSC of 0.9521 and a JI of 0.9093, underscoring the superiority of our method. Validation in the BraTS 2021 dataset underscores the robustness of the method, achieving high DSC and JI scores in four MRI modalities, with the T2 modality demonstrating the highest performance (DSC: 0.9216, JI: 0.8556). While acknowledging these achievements, we recognize challenges related to dataset specificity and computational intensity associated with the attention gate. Future research efforts should address these issues to improve the generalizability and applicability of the method in real-world scenarios. In addition to presenting a novel automated brain tumor segmentation method, this study contributes comprehensive result values and comparative analyses with previous research, providing valuable insights into the evolving landscape of medical image analysis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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Privacy-preserved learning from non-i.i.d data in fog-assisted IoT:A federated learning approach

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Digital Communications and Networks 2024年第2期10卷 404-415页

作者： Mohamed Abdel-Basset Hossam Hawash Nour Moustafa Imran Razzak Mohamed Abd Elfattah Faculty of Computers and Informatics Zagazig UniversityZagazigSharqiyah44519Egypt School of Engineering and Information Technology University of New South Wales@ADFACanberraACT2600Australia Deakin University Geelong Waurn Ponds CampusAustralia Computer Science Department Misr Higher Institute for Commerce and ComputersMansoura35511Egypt

With the prevalence of the Internet of Things(IoT)systems,smart cities comprise complex networks,including sensors,actuators,appliances,and cyber *** complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks,especially privacy-related attacks such as inference and data poisoning *** Learning(FL)has been regarded as a hopeful method to enable distributed learning with privacypreserved intelligence in IoT *** though the significance of developing privacy-preserving FL has drawn as a great research interest,the current research only concentrates on FL with independent identically distributed(i.i.d)data and few studies have addressed the non-i.i.d *** is known to be vulnerable to Generative Adversarial Network(GAN)attacks,where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other *** paper proposes an innovative Privacy Protection-based Federated Deep Learning(PP-FDL)framework,which accomplishes data protection against privacy-related GAN attacks,along with high classification rates from non-i.i.d ***-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other,where class probabilities are protected utilizing a private identifier generated for each *** PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 *** empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3%–8%as accuracy improvements.

关键词： Privacy preservation Federated learning Deep learning Fog computing Smart cities

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Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

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computers, Materials & Continua 2023年第5期75卷 3993-4008页

作者： Sarkar Hasan Ahmed Adel Al-Zebari Rizgar R.Zebari Subhi R.M.Zeebaree Computer Networks Department Sulaimani Polytechnic UniversitySulaimaniIraq Information Technology Department Technical College of Informatics-AkreDuhok Polytechnic UniversityIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

Remote sensing(RS)presents laser scanning measurements,aerial photos,and high-resolution satellite images,which are utilized for extracting a range of traffic-related and road-related *** has a weakness,such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic *** article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images(ODLTCP-HRRSI)to resolve these *** presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart *** attain this,the presented ODLTCP-HRRSI model performs two major *** the initial stage,the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder(CNN-AE)model for productive and accurate traffic ***,the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization(BADSO)*** experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%.

关键词： Remote sensing images traffic prediction deep learning smart cities intelligent transportation systems

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Public Roads Management and Visualisation

Public Roads Management and Visualisation

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International Symposium on Applied Machine Intelligence and informatics (SAMI)

作者： Jaroslav Marko Anton Baláź Department of Computer and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

The paper deals with the issue of collecting and displaying geographic information about roads in Slovakia with emphasis on information about obstacles on roads. The main objective is to design a solution for collecting and displaying the roads based on the analysis of existing systems and identifying the requirements for an optimal design. The paper starts with an analysis of available solutions and the development of requirements for the most favourable design, followed by the planning of an optimal solution for collecting and displaying road obstruction data. The focus of the paper is on the development of a method for creating and modifying geographic obstacle information, the design of a textual and graphical display of the information, and the design of a three-level model for user access to the application. The paper concludes with a discussion of the proposed solution, comparing the requirements for the optimal design with the developed design.

关键词： Roads Planning Object recognition informatics Machine intelligence

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Internal Validity Index for Fuzzy Clustering Based on Relative Uncertainty

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computers, Materials & Continua 2022年第8期72卷 2909-2926页

作者： Refik Tanju Sirmen Burak Berk Üstündag Graduate School of Science Engineering&Technology Istanbul Technical UniversityIstanbul34469Turkey Faculty of Computer&Informatics Istanbul Technical UniversityIstanbul34469Turkey

Unsupervised clustering and clustering validity are used as essential instruments of data *** clustering being realized under uncertainty,validity indices do not deliver any quantitative evaluation of the uncertainties in the suggested ***,validity measures may be biased towards the underlying clustering ***,neglecting a confidence requirement may result in *** the absence of an error estimate or a confidence parameter,probable clustering errors are forwarded to the later stages of the ***,having an uncertainty margin of the projected labeling can be very fruitful for many applications such as machine ***,the validity issue was approached through estimation of the uncertainty and a novel low complexity index proposed for fuzzy *** involves only uni-dimensional membership weights,regardless of the data dimension,stipulates no specific distribution,and is independent of the underlying similarity *** tests and comparisons returned that it can reliably estimate the optimum number of partitions under different data distributions,besides behaving more robust to over ***,in the comparative correlation analysis between true clustering error rates and some known internal validity indices,the suggested index exhibited the highest strong *** relationship has been also proven stable through additional statistical acceptance *** the provided relative uncertainty measure can be used as a probable error estimate in the clustering as ***,it is the only method known that can exclusively identify data points in dubiety and is adjustable according to the required confidence level.

关键词： Machine learning data science clustering validity fuzzy clustering uncertainty intelligent systems data analytics

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A Tutorial on Hybrid Feedback Control 63

A Tutorial on Hybrid Feedback Control

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Casau, Pedro Ferrante, Francesco Sanfelice, Ricardo G. University of Aveiro Department of Electronics Telecommunications and Informatics Portugal University of Perugia Department of Engineering Italy University of California Faculty of Electrical and Computer Engineering Santa Cruz United States Instituto de Telecomunicações Portugal

ISBN: (纸本)9798350316339

This tutorial paper introduces hybrid feedback control through a self-contained examination of hybrid control systems modeled by the combination of differential and difference equations with constraints. Using multiple examples, it illustrates the power of hybrid feedback control, which stems from the integration of continuous and discrete dynamics, where state variables update instantaneously at specific events while flowing continuously otherwise. The paper defines hybrid closed-loop systems as interconnected hybrid plants and controllers with designated inputs and outputs, and formalizes their solutions. It summarizes key properties of hybrid systems and reviews various control strategies, including supervisory control with logic variables to select feedback controllers, event-triggered control to minimize control input updates, and strategies using multiple Lyapunov-like functions for stabilization. Pointers to further reading and other strategies in the literature are provided. © 2024 IEEE.

关键词： Feedback control

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MobileDNN: Leveraging the Potential of Hybrid MobileNetV2-DNN for Advanced Image Analysis

MobileDNN: Leveraging the Potential of Hybrid MobileNetV2-DN...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ogundokun, Roseline Oluwaseun Maskeliunas, Rytis Damasevicius, Robertas Adebiyi, Marion Olubunmi Landmark University Department of Computer Science Omu Aran Nigeria Kaunas University of Technology Department of Multimedia Engineering Lithuania Kaunas University of Technology Faculty of Informatics Kaunas Lithuania

ISBN: (纸本)9798350358155

computer vision systems designed for posture recognition could offer valuable solutions in the healthcare sector, improving healthy ageing and supporting elderly individuals in their daily routines. One area of interest is the automatic detection of falls, which has garnered attention from computer vision and pattern recognition communities. While many existing methods rely on wearable sensors, which demonstrate high accuracy in fall detection, some individuals may be reluctant to wear such devices. Consequently, alternative approaches, such as vision-based techniques, have been developed. The proposed method utilizes a vision-based system that employs a convolutional neural network (CNN) to recognize and categorize various postures (e.g., sitting, lying, standing) in each frame, if available. This approach begins by detecting the person in the frame, applying background subtraction for human body shape extraction, and associating each daily activity with the extracted shape. The proposed model achieved an accuracy of 93.75%, which shows an outstanding performance over existing models. Subsequently, it estimates bounding boxes encompassing the predicted person's body shape. Overall, the proposed technique shows promising results for indoor monitoring systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Forecasting the Number of Traffic Accidents in Jakarta Using Artificial Neural Network

Forecasting the Number of Traffic Accidents in Jakarta Using...

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2023 International Conference on Information Technology and Computing, ICITCOM 2023

作者： Yulianti, Henny Pranoto, Gatot Tri Yaddarabullah Krishnasari, Erneza Dewi Marchenko, Michael Informatics Engineering Universitas Siber Asia Jakarta Indonesia Informatics Engineering Pelita Bangsa University Bekasi Indonesia Universitas Trilogi Department of Informatics Jakarta Indonesia Universitas Trilogi Department of Visual Communication Design Jakarta Indonesia Dnipro National University Dnipropetrovsk Faculty of Physics Electronics and Computer Systems Oblast Ukraine

ISBN: (纸本)9798350359633

Traffic accidents are a problem confronted by means of each a around the arena, Indonesia isn't any exception. The Indonesian national Police recorded an increase inside the number of traffic accidents in 2019 in comparison to 2018. based totally on POLRI information, there had been 107,500 traffic accidents in 2019, an increase from 103,672 incidents in 2018. in particular in Jakarta, that is the capital city of the Republic of Indonesia, traffic accidents are a common phenomenon, in conjunction with the unexpectedly developing use of motorized motors on the dual carriageway. for that reason, a examine is wanted to determine the variety of traffic accidents with the intention to occur in the future. The observe will forecast the number of traffic accidents for every month based totally on datasets from 2015 to 2020 from the Jakarta Metro Jaya Police Traffic Directorate. The approach used on this examine makes use of facts mining methods by using comparing the artificial Neural community Backpropagation, Deep getting to know and Linear Regression algorithms to decide which algorithm is right for predicting the range of traffic injuries inside the Jakarta region in each following month. This examine concludes that the synthetic Neural community Backpropagation set of rules is the first-rate set of rules to be applied in predicting the number of visitors accidents within the Jakarta vicinity with a Root suggest square mistakes (RMSE) of 29.477. studies trying out with the aid of applying the synthetic Neural network Backpropagation algorithm version has verified to expose the price of forecasting the number of traffic accidents in Jakarta with a mean monthly accuracy cost of 97.34%. © 2023 IEEE.

关键词： Forecasting

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"Check, Check, Check, We Got Those" – Catalogue Use in Information Security Risk Management 17th

"Check, Check, Check, We Got Those" – Catalogue Use in Info...

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17th IFIP WG 11.12 International Symposium on Human Aspects of Information Security and Assurance, HAISA 2023

作者： Bergström, Erik Lundgren, Martin Bernsmed, Karin Bour, Guillaume School of Engineering Department of Computer Science and Informatics Jönköping University Jönköping Sweden School of Informatics University of Skövde Skövde Sweden SINTEF Digital Trondheim Norway

ISBN: (纸本)9783031385292

Information Security Risk Management (ISRM) is fundamental in most organisations today. The literature describes ISRM as a complex activity, and one way of addressing this is to enable knowledge reuse in the shape of catalogues. Catalogues in the ISRM domain can contain lists of, e.g. assets, threats and security controls. In this paper, we focus on three aspects of catalogue use. Why we need catalogues, how catalogue granularity is perceived, and how catalogues help novices in practice. As catalogue use is not yet a widespread practice in the ISRM, we have selected a domain where catalogues are a part of the ISRM work. In this case, the Air Traffic Management (ATM) domain uses a methodology that includes catalogues and is built on ISO/IEC 27005. The results are based on data collected from 19 interviews with ATM professionals that are either experts or novices in ISRM. With this paper, we nuance the view on what catalogues can contribute with. For example, consistency, coherency, a starting point and new viewpoints. At the same time, we identify the need to inform about the aim of the catalogues and the limitations that come with catalogue use in order to leverage the use – especially from a novice perspective. © 2023, IFIP International Federation for Information Processing.

关键词： Risk management

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