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Securing the Metaverse: The Intersection of ML-Based Oracles and Blockchain Technology

Securing the Metaverse: The Intersection of ML-Based Oracles...

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Intelligent Metaverse Technologies & Applications (iMETA), international conference on

作者： Hajar Moudoud Zakaria Abou El Houda Bouziane Brik Département d'informatique et d'ingénierie Université du Québec en Outaouais (UQO) Gatineau Canada Institut National de la Recherche Scientifique Centre Énergie Matériaux et Télécommunications (INRS-EMT) UMR INRS-UQO Gatineau Quebec Canada Computer Science Department College of Computing and Informatics Sharjah University Sharjah UAE

ISBN: (数字)9798350351514

ISBN: (纸本)9798350351521

The integration of blockchain and machine learning (ML) in decentralized systems has revolutionized the operational potential of smart contracts, especially in dynamic and interactive environments such as the metaverse. Smart contracts leverage blockchain technology to enable secure, decentralized, and transparent automation of agreements without intermediaries. However, their reliability often depends on external data sources, known as blockchain oracles, which are prone to tampering and manipulation due to their operation outside the blockchain. To address these vulnerabilities, we propose a novel architecture, MARL-RSS (Multi-Agent Reinforcement Learning with Ring Signature Scheme), designed to enhance blockchain security and ensure the trustworthiness of oracle networks. MARL-RSS integrates Multi-Agent Reinforcement Learning techniques to dynamically detect and isolate malicious oracles, ensuring data integrity and system resilience. Furthermore, the integration of a Ring Signature Scheme (RSS) guarantees oracle anonymity and authenticity, enabling secure data sharing while preserving privacy. Experimental results demonstrate the effectiveness of MARL- RSS in detecting malicious oracles with high accuracy while maintaining low false positive rates. The RSS mechanism introduces minimal overhead while ensuring privacy and authenticity. MARL- RSS provides a scalable and secure foundation for decentralized systems, enabling efficient and trustworthy operations in blockchain-based applications, including the metaverse.

关键词： Privacy Fault tolerance data privacy Metaverse soft sensors data integrity Smart contracts Reinforcement learning Blockchains Security

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A Critical Review of Road Accident Prediction and Analysis Techniques, data Sources, Challenges and Opportunities in Future Developments

A Critical Review of Road Accident Prediction and Analysis T...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), international conference on

作者： Vasireddy Satish S Gowri Department of CSE Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of CSE School of Computing Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

In today’s era, road accidents are highly increasing across world, leads to cause severe injuries and death on every year. It has also imposed with economic loss, financial crisis for the society. Day by day, the road accident occurrence remains as an inevitable factor. Therefore, the accident prediction model is required to develop and deploy for evading such kind of calamities. Researchers have focused on designing a significant impact of model for analyzing the road accident scenarios. It is accomplished with the help of standard techniques, algorithms. Despite many techniques have explored, the victims count is increased. Also, the identification of cause for road accident is quite challenging task since it holds with large dimensional data or depends with one or multiple factors. As traffic accidents caused by various reasons, the rising of automated model becomes the best solution. In such way, enormous techniques have been implemented to reach the best value as well. Over the decade, the research is going on deeper to forecast the accidents that can influence with the risk of traffic rules and facilities. In this survey, different and existing road accident prediction models are analyzed. For better presentation, the road accident and its problem are given. Several learning methodologies are used for the purpose of enhancing the prediction performances that is also to be analyzed. The prediction techniques suggested for identifying the road accidents in the existing research works is analyzed and categorized. Different types of performance metrics used to verify the performance of each prediction model in existing literatures is checked. Future developments are discussed to improve the capability of road accident prediction models.

关键词： Surveys Analytical models Road accidents Reviews soft sensors Roads Learning (artificial intelligence) Predictive models Prediction algorithms Standards

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Classifying breast invasive carcinoma subtypes: A feature selection-based machine learning approach

Classifying breast invasive carcinoma subtypes: A feature se...

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Electrical Power Engineering, Communication and computing Technology(ODICON), Odisha international conference on

作者： Lutfunnesa Khatun Sk Md Mosaddek Hossain Sumanta Ray Anirban Mukhopadhyay Computer Science & Engineering University of Kalyani Kalyani W.B. India Computer Science & Engineering Aliah University Kolkata W.B. India

ISBN: (纸本)9781665478403

Breast invasive carcinoma (BRCA) is one of the most common breast cancer types with multiple distinct subtypes, each with a distinguished morphological structure. Identifying discriminating genes to classify the subtypes plays an essential role upon the pathogenesis and treatment of carcinomas. In the present work, we propose a machine learning framework to classify BRCA subtypes based on the gene expression profiles of differentially expressed (DE) genes identified from TCGA BRCA data. We have utilized several feature selection algorithms to discover numerous key discriminating DE genes to classify BRCA subtypes through different classification models. The efficacy of the classification framework is evaluated through several performance metrics like accuracy, Matthews correlation coefficient (MCC), logistic loss, precision, recall, and f1-score. Results show that the JMIM feature selection algorithm, in conjunction with the SVM-RBF and the soft voting classifiers, outperforms the other algorithms with high precision. Moreover, we have identified several hub genes most significantly associated with patients' survival in BRCA.

关键词： Measurement Correlation coefficient Power engineering Machine learning algorithms Feature extraction Breast cancer Biology

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Performance modelling and evaluation of in-band control mode in software-defined satellite networks based on queuing theory 21

Performance modelling and evaluation of in-band control mode...

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2nd international conference on computing, Networks and Internet of Things, CNIOT 2021

作者： Guo, Jianming Yang, Lei Liu, Xianfeng Chen, Quan Fan, Chengguang Li, Xuan College of Aerospace Science and Engineering National University of Defense Technology China

ISBN: (纸本)9781450389693

software-defined network (SDN) can provide a flexible, programmable, and controllable way by a logical separation of the control and data planes. Therefore, adopting SDN techniques may benefit satellite networks in terms of network configuration and management. Due to the limited resources on satellites such as bandwidth and capacity, SDN-based satellite networks can still confront challenges. In this paper, we propose an in-band control mode of software-defined satellite network (SDSN) architecture, and then we model and evaluate the performance of SDSN. With the discussion of the dynamic characteristics of satellite networks, the forwarding and processing models of data traffic in switches is developed by adopting the priority-based queuing system M/M/1/m. Based on that, an overall model is then given to evaluate the system performance. Results show that the proposed model provides a good approximation of the SDSN performance. © 2021 Association for computing Machinery. All rights reserved.

关键词： Queueing theory

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Quantile Regression Support Vector Machine (QRSVM) Model for Time Series data Analysis 2nd

Quantile Regression Support Vector Machine (QRSVM) Model for...

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2nd international conference on soft computing and its Engineering Applications, icsoftComp 2020

作者： Patel, Dharmendra Smt. Chandaben Mohanbhai Patel Institute of Computer Applications CHARUSAT Changa Gujarat India

ISBN: (纸本)9789811607073

Analysis of time series information is very interesting as it can be used to understand the past and to forecast the future. Mainly, the data models of the time series are based on the normal least square regression (LSR). For handle the outliers, the least square regression is not efficient. data from the time series contains outliers in a notable quantity that may affect the results of the prediction. The proposed solution will use statistical techniques of quantile regression that robustly gives insights based on different dimensions as well as treats outliers. The advantage of quantile regression is to discover more useful predictive relationships in situations where there is a poor relationship between independent variables. The paper described the statistics of QRSVM model. The paper dealt experiments based on time series data and proved that QRSVM model is superior than LSR model in insights generations and for outlier handling. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Classification of UrbanSound8k: A Study Using Convolutional Neural Network and Multiple data Augmentation Techniques 2nd

Classification of UrbanSound8k: A Study Using Convolutional ...

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2nd international conference on soft computing and its Engineering Applications, icsoftComp 2020

作者： Rahman, Aamer Abdul Angel Arul Jothi, J. Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Dubai Campus Dubai United Arab Emirates Department of Computer Science Birla Institute of Technology and Science Pilani Dubai Campus Dubai United Arab Emirates

ISBN: (纸本)9789811607073

Audio data augmentation methods such as pitch shifting, time stretching and background noise insertion have been proven to improve the performance of deep learning models when it comes to learning discriminative spectro-temporal patterns such as environmental sounds. This work aims to classify the audio samples from the UrbanSound8k dataset using a 5-layer deep convolutional neural network (CNN). The audio samples from the dataset are transformed into their respective mel-spectrogram representation using Short-Time Fourier Transform (STFT). Different audio data augmentation techniques such as pitch shifting, time stretching, background noise and SpecAugment are applied individually and as combinations on the UrbanSound8k dataset and their influence on the performance of the CNN model is studied and compared. 10-fold cross validation is used to ensure the robustness of the results achieved. Results show that the highest accuracy and F1-score of 79.2% and 80.3% respectively is achieved when the CNN model used SpecAugment and time stretching augmented audio data. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Prediction of Influenza A Cases in Tropical Climate Country using Deep Learning Model

Prediction of Influenza A Cases in Tropical Climate Country ...

引用

Biomedical Engineering conference (NBEC), IEEE National

作者： Muhamad Sharifuddin Abd Rahim Fitri Yakub Mas Omar Rasli Abd Ghani Inge Dhamanti Soubraylu Sivakumar WEE Laboratory Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Electronic System Engineering Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Health Policy and Administration Faculty of Public Health Airlangga University (UNAIR) Surabaya Indonesia Computing Technologies SRM Institute of Science and Technology Chennai India

Influenza remains a significant public health concern particularly in tropical climate countries. Accurate prediction of influenza cases is crucial for effective resource allocation and public health planning. This study aimed to design and evaluate deep learning models for the monthly prediction of influenza A cases in a tropical climate country specifically Malaysia. The models considered both univariate and multivariate input configurations incorporating temperature and humidity variables. The study utilized a dataset spanning from January 2006 to December 2019 with training data from January 2006 to December 2016 and test data until December 2019. Various deep learning models such as RNN, LSTM, complex LSTM, GRU, Transformer and Informer were implemented and evaluated. The results demonstrated that the RNN variants specifically LSTM and GRU consistently outperformed the transformer and informer models for both univariate and multivariate prediction. The shorter input sequences (2 months) yielded better performance compared to longer sequences (12 months) with mean square error (MSE) of 0.0069, capturing more relevant temporal patterns and dependencies. The deep learning models produce a better ability to capture complex relationships and temporal patterns in the data. The findings highlight the effectiveness of the RNN variants and the impact of input configurations and sequence lengths on predictive performance. These findings provide valuable insights that can support public health planning and decision-making processes. dataset can be obtained from the https://***/joqxA.

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Feature Representation and Organization Method for Public Opinion Big data Based on Association Analysis 2

Feature Representation and Organization Method for Public Op...

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2nd international conference on computing and data science, CONF-CDS 2021

作者： Wang, Pengju Xue, Huifeng Zhang, Feng School of Automation Northwestern Polytechnical University Shaanxi China School of Information Engineering Yulin University Shaanxi China

In order to improve the data mining level of public opinion big data, by analysing the relationship among semantic web, ontology and RDF (Resource Description Framework), the structure and organization method of public opinion big data association data are separated. Based on the analysis of network public opinion big data organization technology, and then the RDF structure definition of network public opinion knowledge map is given, and the construction method of RDF association analysis map of network public opinion knowledge is realized. Experimental research shows that the proposed method can provide more accurate correlation analysis of public opinion big data, which can effectively realize the analysis and management of public opinion hot events, and provide decision support for the management of network public opinion. © Published under licence by IOP Publishing Ltd.

关键词： data mining

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Crop Yield Estimation Using Machine Learning 2nd

Crop Yield Estimation Using Machine Learning

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2nd international conference on soft computing and its Engineering Applications, icsoftComp 2020

作者： Patel, Nihar Patel, Deep Patel, Samir Patel, Vibha Computer Department Vishwakarma Government Engineering College Ahmedabad India Computer Science and Engineering Pandit Deendayal Petroleum University Gandhinagar India Information Technology Vishwakarma Government Engineering College Ahmedabad India

ISBN: (纸本)9789811607073

The recent area of interest for computer scientists and data analysts working on precision farming has been the use of machine learning and deep learning algorithms to recommend crops to the farmers and predict yield. Improving crop yields not only helps farmers but also seeks to address global problems such as food shortages. Predictions can be made taking into account forecasts of climate, soil and its mineral content, moisture, crop historic performance, rainfall and others. Crop data from six states of India for five crops from 2009–2016 has been used for training and validation of different machine learning regression algorithms to produce a comprehensive study of crop yield estimate. The yields are estimated using Linear models, Support Vector Regressor, K Neighbors Regressor, Tree-based models, Ensemble models and Shallow Neural Networks with R-squared score for evaluation. Test accuracy showed promising results in ensemble models and neural networks. Extra Trees Regressor is the best model with Mean Absolute Error of 351.10 and maximum accuracy of 99.95%. This paper aims at providing state of art implementation on machine learning algorithms to facilitate farmers, governments, economists, banks to estimate the crop yields in Indian states based on specific parameters. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crops

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Link Prediction Computational Models: A Comparative Study 2nd

Link Prediction Computational Models: A Comparative Study

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2nd international conference on Communication, Networks and computing, CNC 2020

作者： Bhawsar, Yachana Ranjan, Rajnish K. Department of Computer Science and Engineering Govt Women’s Polytechnic Bhopal462016 India

ISBN: (纸本)9789811688959

The continuous creation of digital media mainly video has caused a tremendous growth of digital content. Social networks are the way to represent the relationship between entities. Analysis of a social network involves predicting links between actors or finding relationships, finding important nodes or actors, detecting communities of similar actors, etc. The data in social networks, which involves nodes, relations or contents, are huge and dynamic. There is a need of some data mining techniques to analyse such data and perform analysis. Social network analysis from a data mining perspective is also called link mining or link analysis. Earlier social networks are used only for interacting and sharing information, but it has a concept of research work. Now-a-days there are various social networking sites and it’s the choice of the user to select any one of them. The competition to attract many users or actors is always there. Users want more friends or relations suggested by social networks. And users are switching to adopt the one where he/she finds more relations. To this end link prediction became the core and heart of social network analysis. There is lots of work done in social network analysis towards link prediction, but some issues and challenges are there. In this work these issues and challenges are trying to solve out. Uncertainty of social network data is the main issue, and the fuzzy soft set is applied for it. Interval-valued fuzzy soft is another variation of fuzzy soft set, applied to deal with uncertain data. Markov model is used for user’s behavior prediction in the web, the same concept adopted here to find relations. Genetic algorithm-based approach considers the social graph structure into account to predict relation. Scalability is another issue;all the proposed techniques are scalable techniques of link prediction for social network analysis. Although the proposed work is for social network data only, but it can be applied for some other applic

关键词： data mining

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