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Modeling Parking Occupancy Using Algorithm of 3D Visibility Network

IEEE Access 2025年 13卷 84424-84434页

作者： Matej Babič Branko Šter Ladislav Hluchý Giuliana Baiamonte Michele Calì Faculty of Information Studies Novo Mesto Slovenia Faculty of Computer and Informatics University of Ljubljana Ljubljana Slovenia Slovak Academy of Sciences Institute of Informatics Bratislava Slovakia Electric Electronics and Computer Engineering Department University of Catania Catania Italy

The modernization of the information communication infrastructure of the regional data transmission network has advanced in order to increase the maximum transmission speed of existing transport routes, ensuring the quality of the service and its reliability. In the present research work, a new approach for the 3D visibility network algorithm has been developed, showing how graph theory applied to wireless sensor networks (WSNs) enables technological development for new solutions in areas such as public infrastructure. The possibility of determining in such networks whether an area of interest is sufficiently covered by a given set of sensors by means of the Voronoi diagram is discussed. The parking dynamics and parking system were modeled with cellular neural networks (CNNs) based on weather conditions, and magnetic parking sensors were replaced with pillar sensors. The proposed method has proven its effectiveness in determining the position of the minimum sensors covering the area of interest, in order to find a solution in the occupation of parking spaces in the presence of different weather conditions. The proposed approach and experimental results offer potential applications in various fields such as lighting and rendering, motion planning, pattern recognition, computer graphics and computational geometry, in order to conduct studies on problems and perspectives of pillar sensor technology while reducing costs compared to magnetic ones.

关键词： Sensors Magnetic sensors Three-dimensional displays Wireless sensor networks Meteorology Computational modeling Automata Path planning Urban areas Sensor phenomena and characterization

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Blockchain-based cross-domain authorization system for user-centric resource sharing

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Blockchain(Research and Applications) 2023年第2期4卷 67-81页

作者： Yuki Ezawa Shohei Kakei Yoshiaki Shiraishi Masami Mohri Masakatu Morii Department of Electrical and Electronic Engineering Kobe UniversityKobeHyogo657-8501Japan Department of Computer Science Nagoya Institute of TechnologyNagoyaAichi466-8555Japan Faculty of Informatics Kindai UniversityHigashi-OsakaOsaka577-8502Japan

User-centric data sharing is essential to encourage citizens'active participation in the digital *** key to smart cities,a form of the digital economy,is the promotion of public use of citizen ***,it is not easy to utilize data without citizens’*** this study,we took a technological approach to these *** access(UMA)is a well-known framework for delegating resource access rights to others on the *** UMA,authorization mechanisms are designed to be centralized so that resource owners can centrally manage access rights for various resources stored in different ***,the lack of transparency in the authorization mechanism is a barrier to its implementation in large-scale systems such as smart *** this study,we developed a blockchain-based cross-domain authorization architecture that enables a resource-sharing ecosystem in which organizations that wish to utilize data can freely trade with each *** proposed architecture solves the transparency problem that conventional authorization systems have had by designing the authorization mechanism on blockchain *** implemented the proposed architecture as smart contracts and evaluated its processing *** resultant time required for delegating access rights and accessing resources was less than 500 ***,we found that the fluctuation in the processing time overhead was *** on these results,we concluded that performance degradation with the proposed architecture is minor.

关键词： Access control Authorization Blockchain Hyperledger fabric User-managed access

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Private Computation of Boolean Functions Using Single Qubits 15th

Private Computation of Boolean Functions Using Single Qubit...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Rahmani, Zeinab Pinto, Armando N. Barbosa, Luis S. Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal Instituto de Telecomunicações Aveiro Portugal International Iberian Nanotechnology Laboratory Braga Portugal Department of Computer Science University of Minho Braga Portugal Institute of Systems and Computer Engineering Technology and Science Braga Portugal

ISBN: (纸本)9783031856990

Secure Multiparty Computation (SMC) facilitates secure collaboration among multiple parties while safeguarding the privacy of their confidential data. This paper introduces a two-party quantum SMC protocol designed for evaluating binary Boolean functions using single qubits. Complexity analyses demonstrate a reduction of 66.7% in required quantum resources, achieved by utilizing single qubits instead of multi-particle entangled states. However, the quantum communication cost has increased by 40% due to the amplified exchange of qubits among participants. Furthermore, we bolster security by performing additional quantum operations along the y-axis of the Bloch sphere, effectively hiding the output from potential adversaries. We design the corresponding quantum circuit and implement the proposed protocol on the IBM Qiskit platform, yielding reliable outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Qubits

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Analysis of Lung Cancer for Developing Smart Healthcare with the Help of BGWO Based TSA-XGBoost Model

Analysis of Lung Cancer for Developing Smart Healthcare with...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Mahaveerakannan, R. Murali Dhar, M.S. Goswami, Sohan Krishna, P. Gopala Jayanthi, K. Kumar, M. Sathish The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Technique Polytechnic Institute Department of Computer Science and Technology West Bengal India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Pollachi Institute of Engineering and Technology Department of Computer Science and Engineering Pollachi Tamil Nadu Coimbatore India Karpagam College of Engineering Department of Cyber Security Tamil Nadu Coimbatore India

ISBN: (纸本)9798350300857

Given the circumstances the healthcare system, the Internet of Things (IoT) is crucial. IoT gadgets offer patient data for the framework of healthcare monitoring. IoT is a key component in every aspect of the health care management system since people can use smart devices to check on their health. Lung cancer is a fatal malignancy, and the likelihood of survival is increased by early identification. Because of the computational difficulty involved in gathering characteristics, it is imperative to design an approach using machine learning techniques for categorising cancer disease because the classification results given by the current methods are inadequate. With the use of the synthetic minority oversampling methods (SMOTE) methodology, the incoming data is pre-processed and balanced. With the help of the binary grey wolf optimisation algorithm (BGWOA), the pertinent features are best chosen. Finally, the suggested model's hyper-parameters are best chosen by the tunicate swarm optimisation (TSA) model, and the classification is carried out by the extreme gradient boosting (XGBoost) model. The experimental analysis demonstrates that the suggested model attained accuracy and recall values of 98% and 95%, respectively, compared to 95% and 95%, respectively, for the identical proposed model without the feature selection (FS) method. © 2023 IEEE.

关键词： Internet of things

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Efficient Cloud Resource Scheduling with an Optimized Throttled Load Balancing Approach

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computers, Materials & Continua 2023年第11期77卷 2179-2188页

作者： V.Dhilip Kumar J.Praveenchandar Muhammad Arif Adrian Brezulianu Oana Geman Atif Ikram Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Science and Engineering Karunya Institute of Technology and SciencesCoimbatoreTamil Nadu641114India Department of Computer Science Superior UniversityLahorePakistan Faculty of Electronics Telecommunications and Information Technology Gheorghe Asachi Technical University of IasiIa¸si700050Romania Department of Health and Human Development Stefan cel Mare University of SuceavaSuceava720229Romania Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia

Cloud Technology is a new platform that offers on-demand computing Peripheral such as storage,processing power,and other computer system *** is also referred to as a system that will let the consumers utilize computational resources like databases,servers,storage,and intelligence over the *** a cloud network,load balancing is the process of dividing network traffic among a cluster of available servers to increase *** is also known as a server pool or server *** a single node is overwhelmed,balancing the workload is needed to manage unpredictable *** load balancer sends the load to another free node in this *** focus on the Balancing of workflows with the proposed approach,and we present a novel method to balance the load that manages the dynamic scheduling *** of the preexisting load balancing techniques is considered,however it is somewhat modified to fit the scenario at *** on the experimentation’s findings,it is concluded that this suggested approach improves load balancing consistency,response time,and throughput by 6%.

关键词： Load balancing throttled algorithm efficient resource allocation

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Genera: A Serious Game Leveraging Blockchain Technology for Renewable Energy Education 14

Genera: A Serious Game Leveraging Blockchain Technology for ...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Stamatakis, Dimitris Kogias, Dimitrios G. Leligou, Helen C. Karkazis, Panagiotis A. Penalvo, Elisa Department of Industrial Design and Production Engineering University of West Attica Petrou Ralli Athens12243 Greece Department of Informatics and Computer Engineering University of West Attica Ag. Spyridonos Athens12210 Greece Institute of Energy Engineering U. Politecnica de Valencia Spain

ISBN: (纸本)9781837242689

The GENERA Card Game combines serious gaming and blockchain technology to raise awareness on renewable energy. As part of the GENERA project, this multiplayer game engages players by assigning them the role of a town's mayor tasked with expanding their town using sustainable energy sources. The game integrates educational content with interactive gameplay, utilizing NFTs for the in-game assets and ERC-20 tokens as rewards. Developed using web technologies, the game benefits from extensive accessibility while maintaining high-quality features. A survey of 45 players indicated high satisfaction with the game's educational impact and accessibility, though it highlighted areas for improvement in user experience and mechanics. The study underscores the potential of serious games in promoting environmental awareness and the benefits of blockchain technology in enhancing educational tools. Future updates will focus on adding new features, improving user interface and experience, and integrating realistic data on renewable energy sources to further enrich the educational content. The findings suggest that serious games like GENERA can effectively raise awareness and foster a deeper understanding of renewable energy, thereby supporting global sustainability efforts. © The Institution of engineering & Technology 2024.

关键词： Clean energy

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Language Models for Online Depression Detection: A Review and Benchmark Analysis on Remote Interviews

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ACM Transactions on Management Information Systems 2025年第2期16卷 1-35页

作者： Qin, Ruiyang Yang, Kai Abbasi, Ahmed Dobolyi, David Seyedi, Salman Griner, Emily Kwon, Hyeokhyen Cotes, Robert Jiang, Zifan Clifford, Gari Cook, Ryan A. Computer Science University of Notre Dame Notre Dame United States College of Economics Shenzhen University Shenzhen China IT Analytics and Operations University of Notre Dame Notre Dame United States University of Colorado Boulder Boulder United States Emory University Atlanta United States Biomedical Engineering Georgia Institute of Technology Atlanta United States Biomedical Informatics Emory University Atlanta United States Biomedical Engineering Georgia Institute of Technology Atlanta United States

The use of machine learning (ML) to detect depression in online settings has emerged as an important health and wellness use case. In particular, the use of deep learning methods for depression detection from textual content posted on social media has garnered considerable attention. Conversely, there has been relatively limited evaluation of depression detection in clinical environments involving text generated from remote interviews. In this research, we review state-of-the-art feature-based ML, deep learning, and large language models for depression detection. We use a multidimensional analysis framework to benchmark various language models on a novel testbed comprising speech-to-text transcriptions of remote interviews. Our framework considers the impact of different transcription types and interview segments on depression detection performance. Finally, we summarize the key trends and takeaways from the review and benchmark evaluation and provide suggestions to guide the design of future detection methods. © 2025 Copyright held by the owner/author(s).

关键词： mHealth

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AI-based Models to Predict the Heart Rate Using PPG and Accelerometer Signals During Physical Exercise

AI-based Models to Predict the Heart Rate Using PPG and Acce...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Ribeiro, Lucas Oliveira, Helder P. Hu, Xiao Pereira, Tania University of Porto Inesc Tec Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Feup - Faculty of Engineering Porto Portugal University of Porto Inesc Tec - Institute for Systems and Computer Engineering Technology and Science Fcup - Faculty of Science Porto Portugal Emory University Nell Hodgson Woodruff School of Nursing Department of Biomedical Informatics School of Medicine Department of Computer Science College of Arts and Sciences Atlanta United States Technology and Science Inesc Tec - Institute for Systems and Computer Engineering Porto Portugal

ISBN: (纸本)9798350337488

PPG signal is a valuable resource for continuous heart rate monitoring;however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events. © 2023 IEEE.

关键词： Convolutional Neural Network Heart Rate Estimation Long Short Term Memory Motion Artifacts Photoplethysmography

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Improving Power Transformer Lifetime Prediction using Hyperparameter Optimization 24

Improving Power Transformer Lifetime Prediction using Hyperp...

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24th International Electronics Symposium, IES 2022

作者： Ningrum, Ayu Ahadi Maulida, Ihdalhubbi Ansari, Rudy Syarif, Iwan Universitas Muhammadiyah Banjarmasin Department of Informatics Banjarmasin Indonesia Electronic Engineering Polytechnic Institute of Surabaya Department of Informatic and Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

Since the global pandemic has significantly impacted human life, technology has become a vital role in various sectors. The more technology used, the more we need the electricity supply. The stability of the electricity supply is an absolute thing to customers. Power Transformer is essential equipment for delivering electricity to customers. So the condition of the power transformers should be an essential thing that must be considered. Deep Learning is part of artificial intelligence that is widely applied to facilitate the human need. Based on its role, accuracy in the prediction results will be absolute. Hyperparameter optimization is the essential methods in the machine learning process. Errors in assigning hypermeter values can harm producing predictive values. This study discusses how to optimize the prediction results of the lifetime prediction on a power transformer. With optimal prediction results, it can help electricity management companies monitor conditions. Thereby minimizing the risk of disruption of electricity supply to customers. Models are tested and verified using a real dataset from a power transformer in several locations. The best hyper-parameter for this dataset is Bayesian Search, producing 0,001343 for Mean Absolute Error (MAE). © 2022 IEEE.

关键词： Power transformers

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DsetGenS: An Automated Technique for Building Dataset From Speech with respect to Gujarati-English 11

DsetGenS: An Automated Technique for Building Dataset From S...

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11th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2022

作者： Patel, Margi Joshi, Brijendra Kumar Indore Institute of Science and Technology Department of Computer Science and Engineering Indore India Military College of Telecommunication Engineering Department of Informatics Mhow India

ISBN: (纸本)9781665480383

The computer has seen significant evolution in recent years, with applications in variety of disciplines like Machine Learning, Deep Learning etc. Machine Translation (MT) technology has advanced significantly as a subfield, with many methodologies and techniques. The number of individuals using the internet has risen tremendously. Most documents are written in English since that is the most extensively used language on the internet. If a user's first dialect is Gujarati, he or she will naturally prefer to access the information in Gujarati whenever possible. Even though there are already various MT systems and tools that support Indian languages;however, the translation's quality is mediocre and might be improved. As observed, when models are trained on limited quantities of parallel data, their performance declines. Learned models often have limited performance (inaccurate translations and feature scores) along with low coverage (high out-of-vocabulary rates). Furthermore, the researchers were driven to present novel methodologies and solutions that would automatically construct Datasets for MT due to the increased demand for effective technologies to process and translate information from/to Gujarati Language. Our objective of generating a Gujarati-English dataset has been met in two ways. We have already introduced GEDset, that is automatic Dataset Builder for Machine Translation System with Specific Reference to Gujarati-English [1]. Here, in this paper we are proposing a model to automatically build Gujarati-English dataset from audio that is available in Gujarati Language through Speech Processing. © 2022 IEEE.

关键词： Deep learning

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