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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Imre Farkas Attila Kovari Mónika Rajcsanyi-Molnar Department of Computer Systems and Control Technology Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Electronics and Communication Systems Kandó Kalmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Economics and Management Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes.

关键词： Ethics Power engineering Large language models Education Collaboration Learning (artificial intelligence) Reliability Artificial intelligence Monitoring Guidelines

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Efficiency of Public Wireless Sensors Applied to Spatial Crowd Monitoring in Buildings 15th

Efficiency of Public Wireless Sensors Applied to Spatial Cro...

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15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020

作者： Kamińska-Chuchmala, Anna Department of Computer Science and Systems Engineering Wroclaw University of Science and Technology Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

ISBN: (纸本)9783030578015

Contemporary world rest on wireless connections via networks developed rapidly. Majority of people using wireless networks i.e. WiFi at: work, home, and also when they are outside. A central issue in wireless networks is efficiency and stable connection to Internet. Additional challenge is to create and maintain reliable WiFi network with full open access, videlicet every user could connect with that network if only have a range. Exemplary aforesaid network is university network where users are very specific group. An innovative approach is to treat and use Access Points (APs) as wireless sensors network to crowd monitoring in buildings where such network is located. Thanks to this we receive accurate information about the number of users (people) without interfering with their sensitive data. The aim of this research is to use geostatistical methods to prepare spatial models and predict behaviour of crowd connected with wireless sensors on full of range considered area. The investigation has tended to focus on efficiency of wireless sensors belong to open WiFi network named PWR-WiFi located in building at the main campus of Wroclaw University of science and Technology (WUST) in Poland. The data gathered for analysis are acquired from three consecutive years 2014–2016 to better show the dynamic growth of number of PWR-WiFi network users it means number of people crowded in building. Parameter, which most reflecting behaviour of PWR-WiFi network is the number of users, obtained from APs, that was investigated during research. Preliminary and structural analysis with approximation of variogram models were made and as the next step spatial prediction models of wireless sensors network efficiency were performed by Turning Bands geostatistical simulation method. Three models of spatial prediction were prepared for three subsequent years 2014, 2015 and 2016. Following, the results were compared with spatial prediction models created previously by ordinary kriging estimati

关键词： Wireless sensor networks

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： computer vision

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Design and Comparison of Collaborative Filtering Technology for Product Suggestions in E-Commerce 1

Design and Comparison of Collaborative Filtering Technology ...

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1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRS 2022

作者： Rajasekar, P. Mohanraj, B. Padhi, S.N. Sivakumar, N. Lavanya, J. Charles Prabu, V. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Information Technology Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Tamil Nadu Chennai600029 India Department of Fashion Designing SRM Institute of Science and Technology CSH Kattankulathur campus Tamil Nadu Chennai603203 India Department of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600 062 India

ISBN: (纸本)9781665460842

E-commerce websites and internet purchasing have never been more necessary. Product recommendations are a key tactic employed by online buying platforms. This study compares two different filtering algorithms that are used in product recommendationson websites like Amazon and Flipkart. Reviews of electrical devices, user data, and timestamps were all gathered from Kaggle and put into a dataset. These specifics then undergo several preprocessing steps. The steps are data visualization, duplicate value analysis, missing value analysis, and datatype analysis. The exhibited data is then used to train and test the machine learning models. For this purpose, two machine learning models are developed using two different algorithms. The algorithms K-Nearest Neighbor(KNN) and Support Vector Decomposition (SVD) are employed. The models are then trained to acquire used in the prediction procedure after that. The best models for the prediction process are then determined by scrutinizing the results of both models. The Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) values are located and compared because the findings of the analysis weren't *** erroneous data are plotted into a graph and evaluated to further demonstrate the point. As the SVD method is discovered to be the best of the group, the algorithm's greatest and worst predictions are discovered and examined. Even the poorest model constructed for the study's predictions turned out to be superior to several other prediction algorithms used for product suggestions. This algorithm can be deployed into the backend of any e-commerce website making it more effective. © 2022 IEEE

关键词： Value engineering

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Electronic Preresonance Stimulated Raman Scattering Spectromicroscopy Using Multiple-Plate Continuum

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Journal of Physical Chemistry B 2023年第31期127卷 6896-6902页

作者： Huang, Guan-Jie Li, Cheng-Wei Lee, Po-Yi Su, Jia-Xuan Chao, Kuo-Chuan Chu, Li-An Chiang, Ann-Shyn Cheng, Ji-Xin Chen, Bo-Han Lu, Chih-Hsuan Chu, Shi-Wei Yang, Shang-Da Department of Physics National Taiwan University Taipei10617 Taiwan Institute of Photonics Technologies National Tsing Hua University Hsinchu300044 Taiwan Brain Research Center National Tsing Hua University Hsinchu300044 Taiwan Department of Biomedical Engineering & Environmental Sciences National Tsing Hua University Hsinchu30044 Taiwan Institute of Systems Neuroscience and Department of Life Science National Tsing Hua University Hsinchu30044 Taiwan Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Molecular Imaging Center National Taiwan University Taipei10617 Taiwan

Stimulated Raman scattering (SRS) spectromicroscopy is a powerful technique that enables label-free detection of chemical bonds with high specificity. However, the low Raman cross section due to typical far-electronic resonance excitation seriously restricts the sensitivity and undermines its application to bio-imaging. To address this bottleneck, the electronic preresonance (EPR) SRS technique has been developed to enhance the Raman signals by shifting the excitation frequency toward the molecular absorption. A fundamental weakness of the previous demonstration is the lack of dual-wavelength tunability, making EPR-SRS only applicable to a limited number of species in the proof-of-concept experiment. Here, we demonstrate the EPR-SRS spectromicroscopy using a multiple-plate continuum (MPC) light source able to examine a single vibration mode with independently adjustable pump and Stokes wavelengths. In our experiments, the C═C vibration mode of Alexa 635 is interrogated by continuously scanning the pump-to-absorption frequency detuning throughout the entire EPR region enabled by MPC. The results exhibit 150-fold SRS signal enhancement and good agreement with the Albrecht A-term preresonance model. Signal enhancement is also observed in EPR-SRS images of the whole Drosophila brain stained with Alexa 635. With the improved sensitivity and potential to implement hyperspectral measurement, we envision that MPC-EPR-SRS spectromicroscopy can bring the Raman techniques closer to a routine in bio-imaging. © 2023 American Chemical Society.

关键词： Stimulated Raman scattering

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

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Frontiers of Medicine 2022年第3期16卷 496-506页

作者： Yaxin Chen Tianyi Yang Xiaofeng Gao Ajing Xu Department of Pharmacy Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai 200240China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China Clinical Research Unit Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai 200240China

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia,usage of diabetes drugs,changes in insulin levels,and excretion,and this risk begins as early as *** factors including demographic data(such as age,height,weight,and gender),medical history(such as smoking,drinking,and menopause),and examination(such as bone mineral density,blood routine,and urine routine)may be related to bone metabolism in patients with ***,most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of *** addition,the fracture risk of patients with diabetes and osteoporosis has not been further studied *** this paper,a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis,and investigate the effect of patients’physiological factors on fracture risk.A total of 147 raw input features are considered in our *** presented model is compared with several benchmarks based on various metrics to prove its ***,the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost deep neural network healthcare risk prediction

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Blockchain-based Edge Computing Service with Dynamic Entry and Exit Mechanism

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IEEE Transactions on Mobile Computing 2025年

作者： He, Qiang Feng, Zheng Fang, Hui Wang, Xingwei Zhao, Liang Yu, Keping Choo, Kim-Kwang Raymond Northeastern University College of Medicine and Biological Information Engineering China Shanghai University of Finance and Economics Research Institute for Interdisciplinary Science School of Information Management and Engineering China Northeastern University College of Computer Science and Engineering Shenyang110169 China Shenyang Aerospace University College of Computer Science and Engineering Shenyang110136 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

With the widespread application of 5G and artificial intelligence (AI) technology, the Internet of Things (IoT) has been expanding and integrated into various aspects of our daily lives. However, this also poses challenges such as the ubiquitous demand for communication and computing resources, and data privacy issues. Considering its flexible deployment, high security, and ease of scalability, blockchain-enabled edge computing IoT network (BECIN) has become a promising solution to provide secure and fast communication and computing services. However, existing research on computation offloading in edge computing largely overlooks the stochastic arrival of computational tasks and the potential variability in the number, locations, and resource provisions of edge computing service providers. Therefore, we propose a dynamic, self-adjusting BECIN framework aimed at providing long-term stable, efficient, and secure edge computing data offloading services for ground users in a specific region. This framework supports the dynamic entry and exit of edge computing service providers. Additionally, we introduce a novel dynamic Dueling DDQN approach to update the offloading and resource management policies based on changes in resource provisioning. Experimental results demonstrate the feasibility and superior performance of our framework on system cost and system latency. © 2002-2012 IEEE.

关键词： Reinforcement learning

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Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement

arXiv

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arXiv 2024年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia1678 Cyprus Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden

In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node’s local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through 3-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only 3-bits). Our proposed algorithm respects the network’s bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm’s operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm’s unique characteristics. © 2024, CC BY.

关键词： Optimization algorithms

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Map Building of Indoor Environment with Sensors using Neural Network

Map Building of Indoor Environment with Sensors using Neural...

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Smart Computing and Networks (WiSSCoN), Winter Summit on

作者： S Angel Latha Mary K. Ulagapriya A Poonguzhali R. Menaha Beaulah David T.R. Priyadharshini Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Vels Institute of Science Technology and advanced studies Chennai India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Bangalore India Department of Information Technology Sri Eshwar College of Engineering Coimbatore India Department of Information Technology Hindusthan College of Engineering and Technology Coimbatore India

The necessity of a blueprint of a building structure is a mandatory requirement for any reconnaissance or rescue operations. In our project, we build a modular system combining sensors related to sonar, laser, micro-wave to read sensory values and generate a 2D path of any building. The data is fetched and stored to feed to anOptimal Neural Network (ONN)-based computing system to create a 2D route with minimal discrepancies of error. Here the NN architecture is fine-tuned using Modified Dolphin Partner Optimization (MDPO) Exploration of unknown environments and space using autonomous vehicles has recently gained good attention in the field of Robotic Mapping. The recent advancements in the Internet of Things have enabled us to establish an ideal frame of reference for sonar and lidar-based systems. New effects are displayed by the sensors according to the physical characteristics of a room. The range data from sensors in various surroundings are interpreted by NNs. The distorted errors due to the material medium, particles, and moving objects present in the environment pose a threat to building a high-quality path map. The sensor fusion technique is applied to the rotatable modular array sensor to minimalize discrepancies caused by cloth materials during sonar readings, particle noise in an environment for Lidar reading, and moving human bodies present in the environment for path building and obstacle detection.

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Secure Communication in Dynamic RDARS-Driven systems

arXiv

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arXiv 2025年

作者： Pei, Ziqian Wang, Jintao Zhang, Pingping Shi, Zheng Yang, Guanghua Ma, Shaodan State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau Macau999078 China School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

In this letter, we investigate a dynamic reconfigurable distributed antenna and reflection surface (RDARS)driven secure communication system, where the working mode of the RDARS can be flexibly configured. We aim to maximize the secrecy rate by jointly designing the active beamforming vectors, reflection coefficients, and the channel-aware mode selection matrix. To address the non-convex binary and cardinality constraints introduced by dynamic mode selection, we propose an efficient alternating optimization (AO) framework that employs penalty-based fractional programming (FP) and successive convex approximation (SCA) transformations. Simulation results demonstrate the potential of RDARS in enhancing the secrecy rate and show its superiority compared to existing reflection surface-based schemes. Copyright © 2025, The Authors. All rights reserved.

关键词： Secure communication

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