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Cultural Heritage Monitoring and Predictive Maintenance using Internet of Things: Assessment and Future Aspects

Journal of Mobile Multimedia 2024年第6期20卷 1211-1250页

作者： Um-E-habiba Shaikh, Faisal Karim Chowdhry, Bhawani Shankar Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Hyderabad Pakistan Telecommunication Engineering Department Mehran University of Engineering and Technology Jamshoro Pakistan Faculty of Electrical Electronics and Computer Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

This study presents a detailed survey on the use of the Internet of Things (IoT) for predictive maintenance and monitoring of cultural heritage, focusing on museums and exhibitions. The integration of IoT in these fields offers significant potential for optimizing maintenance and supervision of heritage resources while also enhancing maintenance strategies. This review examines the application of IoT devices, data analysis, and predictive algorithms in monitoring the state of heritage resources to improve tourist experiences and conserve cultural resources. Key enabling technologies can enable adaptive interactions and engagement with enhanced navigation. Visualization technologies are also common, offering hands-on displays. The study also emphasizes the value of accessibility and interactivity, providing personalization and content sharing features. Several initiatives prioritize the delivery of digital content through mobile applications, internet browsers, and downloadable *** study examines how IoT-enabled predictive maintenance systems can recognize potential hazards, like resource decay or environmental dangers, enabling immediate action. This survey also highlights trends, challenges, and future research in IoT and cultural heritage monitoring. © 2025 River Publishers.

关键词： heritage monitoring IoTs predictive maintenance trends

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Sorting of Fresh and Damaged Apple Fruits using Machine learning Approach 5

Sorting of Fresh and Damaged Apple Fruits using Machine lear...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Ghodeswar, Ujwala Puri, Chetan Shingade, Sharvari Waware, Teena Ladhe, Ajinkya Durge, Tejas Department of Electronics Engineering Yeshwantrao chavan College of Engineering Nagpur India Department of Computer Science & Design Faculty of Engineeering & Technology Wardha India

ISBN: (纸本)9798350361155

The research investigates how machine learning algorithms and Internet of Things (IoT) technologies might be combined to solve the crucial problem of separating fresh fruits from damaged apple fruits. Using Internet of Things (IoT) devices with sensors, the system collects extensive data on important characteristics, such as real-time apple picture analysis and available apple database. Convolutional neural networks (CNNs) and YOLO are two examples of sophisticated machine learning models that are used with this data to accurately classify the apples into different groups according to their quality. This initiative fosters enhanced efficiency, production, and profitability in the agricultural supply chain while providing a sustainable solution to the problems connected with fruit sorting. It represents a synergistic convergence of IoT, machine learning, and agriculture. © 2024 IEEE.

关键词： Internet of things

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Access Management in IoT: Implementing a Secure Access Control System 15

Access Management in IoT: Implementing a Secure Access Contr...

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15th International Conference on Communications, COMM 2024

作者： Neculai, Razvan-Sebastian Bogdan, Ioana-Corina Alexandru, Marian Robu, Dan-Nicolae Transilvania University of Brasov & Eviden Faculty of Electrical Engineering and Computer Science Department of Cybersecurity Braşov Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Electronics and Computers Braşov Romania

ISBN: (纸本)9798350355956

This paper aims to implement and use the Spring Security framework to secure and authenticate the connection between a web application and the ESP32 device. In the context of a web application, by analyzing the integration of the Spring Security framework, it investigates the ways in which it manages the complex process of authentication and authorization, thus providing a secure and protected connection to an IoT device. Additionally, this study investigates the integration and use of the RTOS operating system to manage physical access in an IoT environment, in particular by implementing the opening of a door using tags. The mechanism described above leads to an additional level of security and control over physical access in an IoT ecosystem. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Remodeling E-Commerce Through Decentralization: A Study of Trust, Security and Efficiency 1

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Ahmed, Adnan Shakeel Rizvi, Danish Raza Prasad, Dinesh Khan, Amber Department of Electronics and Communications Engineering Faculty of Engineering and Technology Jamia Millia Islamia New Delhi110025 India Department of Computer Engineering Faculty of Engineering and Technology Jamia Millia Islamia New Delhi110025 India

ISBN: (数字)9789819995622

ISBN: (纸本)9789819995615

While there has been a massive growth in online shopping platforms. There data has been mostly stored in centralized storages which mean they can be an easy target to fraud and theft. It is important to preserve the data of the product and the user in a secure and decentralized manner. With the advent of blockchain technology across the horizon, it is necessary to build a marketplace model that works in a reliable way on this technology while combating the shortcomings of the previous models. Aim of this paper is to address the issues and propose a model that can get reduced fees costs while uploading the data to the blockchain that is uploading the item for sale and while purchasing the item, we also as well as find a solution to collusion from different parties. The end result of this paper should be a fully functional application that is capable of reducing gas fees and increases user reliability on the platform. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Deep Reinforcement Learning Algorithms for Location Optimization in Multi-RAT UAV-Assisted Heterogeneous Networks 5

Deep Reinforcement Learning Algorithms for Location Optimiza...

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5th International IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2023

作者： Anany, M.G. Elmesalawy, Mahmoud M. Ibrahim, Ibrahim I. El-Haleem, Ahmed M. Abd Department of Electronics and Communications Cairo Egypt Helwan University Communications and Computer Faculty of Engineering Department of Electronics Cairo Egypt

ISBN: (纸本)9798350381030

Due to the unprecedented growth in wireless mobile connectivity, the next generations of wireless networks are required to meet the different requirements of the diverse devices. One of the promising solutions for the wireless network design is the Unmanned Aerial Vehicles (UAVs), which provide flexibility and dynamic mobility, that can be exploited to extend capacity for unexpected and unplanned increases in demand. In this paper, different Deep Reinforcement Learning (DRL) approaches along with different reward functions are compared, to optimize the 3D location of an Unmanned Aerial Vehicle (UAV) carrying Multiple Radio AccessTechnologies (Multi-RAT) base stations in a Multi-RAT Heterogeneous Network (HetNet) environment, based on the network conditions and users' demands. The idea of deploying Multi-RAT base stations on a UAV will increase the system capacity by exploiting the same resources, however, it will be more challenging to optimize the complex UAV's 3D location to maximize the users' experience. Extensive simulations are conducted to evaluate and compare the performance of the different DRL approaches, and the different reward functions, which revealed that the instantaneous reward function and the Double Deep Q-Network (DDQN) algorithm are more effective in terms of average user satisfaction and stability in solving the proposed optimization problem. © 2023 IEEE.

关键词： Heterogeneous networks

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Analysis of Selected Electricity Quality Parameters in Low-Voltage Power Grids with Attached Photovoltaic Micro-Installations

Analysis of Selected Electricity Quality Parameters in Low-V...

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2023 Progress in Applied Electrical engineering, PAEE 2023

作者： Latka, Mlgorzata Faculty of Electrical and Computer Engineering Rzeszow University of Technology Department of Power Electronics and Power Engineering Rzeszow Poland

ISBN: (纸本)9798350316254

The article presents selected parameters of power quality in low-voltage power grids with connected photovoltaic micro-installations. The measurements and its analysis have been conducted for power quality evaluation due to frequent complaints made by recipients. Both recipients have been unsatisfied, those with photovoltaic panels installed and those attached to such power lines with PV panels. © 2023 IEEE.

关键词： Power quality

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Machine Learning Techniques for Detecting Fraud in Credit Card Transactions 31

Machine Learning Techniques for Detecting Fraud in Credit Ca...

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31st Symposium of Advanced Database Systems, SEBD 2023

作者： Mahmood, Tariq Hashemi, Seyedeh Khadijeh Mirtaheri, Seyedeh Leili Greco, Sergio Department of Computer Science Modeling Electronics and Systems Engineering University of Calabria Italy Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran

Credit cards became one of the most popular payment methods as technology advanced and e-commerce services expanded, resulting in an increase in the volume of banking transactions. Furthermore, the significant increase in the number of frauds necessitates high banking transaction costs. As a result, detecting fraudulent activities has become an intriguing topic, attracting a large number of researchers. We consider the use of class weight-tuning hyperparameters to control the weight of fraudulent and legitimate transactions in this study. We use Bayesian optimization, in particular, to optimize the hyperparameters while taking into account practical issues such as unbalanced data. We propose weight-tuning as a pre-process for unbalanced data, as well as using CatBoost and XGBoost to improve the performance of the LightGBM method by taking the voting mechanism into account. Finally, we use deep learning to fine-tune the hyperparameters, particularly our proposed weight-tuning one, in order to improve performance even further. We conducted some experiments to evaluate the proposed methods on real-world data. We use recall-precision metrics in addition to the common ROC-AUC to better cover unbalanced datasets. We use a 5-fold cross-validation method to test CatBoost, LightGBM, and XGBoost separately. Furthermore, the performance of the combined algorithms is evaluated using the majority voting ensemble learning method. According to the results, LightGBM and XGBoost achieve the best level criteria of ROC-AUC = 0.95, precision 0.79, recall 0.80, F1 score 0.79, and MCC 0.79. Also, by using deep learning and the Bayesian optimization method to tune the hyperparameters, we meet the following criteria: ROC-AUC = 0.94, precision = 0.80, recall = 0.82, F1 score = 0.81, and MCC = 0.81. This is a big improvement over the state-of-the-art methods we compared it to. © 2023 CEUR-WS. All rights reserved.

关键词： Learning systems

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Wind Power Prediction Based on Machine Learning and Deep Learning Models

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computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams Ahmed M.Elshewey El-Sayed M.El-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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