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Machine Learning data Markets: Trading data using a Multi-Agent System

Machine Learning Data Markets: Trading Data using a Multi-Ag...

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IEEE/WIC/ACM international Joint conference on Web Intelligence and intelligent Agent Technology (WI-IAT)

作者： Baghcheband, Hajar Soares, Carlos Reis, Luis Paulo Univ Porto FEUP Fac Engn LIACC Artificial Intelligence & Comp Sci Lab LASI LA Porto Portugal

ISBN: (纸本)9781665494021

The amount of data produced by distributed devices, such as smart devices and the IoT, is increasing continuously. The cost of transmitting data and also distributed computing power raise interest in distributed data mining (DDM). However, in a pure DDM scenario, data availability may not be enough to generate reliable models in a distributed environment. So, the ability to exchange data efficiently and effectively will become a crucial component of DDM. In this paper, we propose the concept of the Machine Learning data Market (MLDM), a framework for the exchange of data among autonomous agents. We consider a set of learning agents in a cooperative distributed ML, where agents negotiate data to improve the models they use locally. In the proposed data market, the system's predictive accuracy is investigated, as well as the economic value of data. The question addressed in this paper is: How data exchange among the agents will improve the accuracy of the learning model. Agent budget is defined as a limitation of negotiation. We defined a multi-agent system with negotiation and assessed it against the multi-agent system baseline and the single-agent system. The proposed framework is analyzed based on the different sizes of batch data collected over time to find out how this changes the effect of the negotiation on the accuracy of the model. The results indicate that even simple negotiation among agents increases their learning accuracy.

关键词： Machine Learning data Market data Exchange data Trading Multi-Agent Systems

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Intelli-farm: IoT based smart farming using Machine learning approaches

Intelli-farm: IoT based Smart farming using Machine learning...

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Business analytics for Technology and Security (ICBATS), international conference on

作者： Sidra Tahir Yaser Hafeez Farkhanda Qamar Ghadah Naif Alwakid University Institute of Information Technology University of Arid Agriculture Rawalpindi Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia

Internet of Things (IoT) technology has transformed every facet of everyday life by making everything smarter. Among the large spectrum of IoT applications, IoT based smart agriculture has interested many researchers and has employed Machine Learning(ML) and IoT technology to undertake unique contributions. IoT based data-driven farm management approaches can assist enhance agricultural yields by managing input costs, decreasing losses, and using resources especially water more efficiently. The IoTs creates a large volume of data with varying properties dependent on location and time. To increase agricultural output through intelligent farm management, data must be thoroughly evaluated and processed. High-performance computing power in ML brings up new options for data-intensive science as the amount of data gathered rises; ML methods might be employed to further enhance application intelligence and usefulness. In this paper, a novel framework named Intelli-farm is proposed which is based on IoT and ML collectively and produces higher accuracy for detecting need of water in a particular farm. Experiments conducted with different training and testing ratio provided an average accuracy of 93.87%.

关键词： smart agriculture Training Irrigation Costs Machine learning Manuals Internet of Things

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Analysis of Digital Twins Implementation in smart City using Big data and Deep Learning

Analysis of Digital Twins Implementation in Smart City using...

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international conference on ent computing and Control Systems (ICICCS)

作者： Chinmaya Kumar Nayak S. Karunakaran P. Yamunaa S. Kayalvili Mohit Tiwari Manu Vasudevan Unni Faculty of Emerging Technologies Sri Sri University Sri Sri Vihar Ward No - 3 Godi Sahi Barang Odisha Computer Technology Kongu Engineering College Erode Tamilnadu India Department of EEE Peri Institue of Technology Computer Science & Engineering Velalar College of Engineering and Technology Erode Tamil Nadu Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering A-4 Rohtak Road Paschim Vihar Delhi St. Claret College P. O. Box 1355 MES Ring Rd Sharadhamba Nagar Jalahalli Bengaluru Karnataka

Digital twins are becoming more relevant for business and academic users due to advances in IoT, AI, and Big data. Due to global urbanization, pollution, public safety, traffic congestion, and other challenges have arisen. New technologies make cities smarter to keep up with growth. In the Internet of Things (IoT) age, many sensing devices acquire and/or produce a broad range of sensory data over long periods of time for a variety of businesses and applications. The use case determines the device’s data stream volume and speed. The efficacy of the analytics process used to analyze these streams of data to learn, predict, and act determines IoT’s worth as a business paradigm changer and quality-of-life technology. This study introduces Deep Learning (DL), a family of advanced machine learning techniques, to enhance IoT analytics and teaching. Introducing new results, challenges, and research opportunities. This study may assist academics and newbies comprehend how to use DL to smart cities. Analyzing and summarizing major IoT DL research initiatives. Check out smart IoT devices with DL embedded into their AI. Ultimately, the study will identify issues and suggest additional research. Each chapter concludes with experimental findings and the newest literature review.

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The Impact of ChatGPT on Streaming Media: A Crowdsourced and data-Driven Analysis using Twitter and Reddit

The Impact of ChatGPT on Streaming Media: A Crowdsourced and...

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IEEE international conference on Big data Security on Cloud (BigdataSecurity), High Performance and smart computing (HPSC) and intelligent data and Security (IDS)

作者： Yunhe Feng Pradhyumna Poralla Swagatika Dash Kaicheng Li Vrushabh Desai Meikang Qiu University of North Texas Ohio State University Dakota State University

ChatGPT, a general-purpose text generation AI model, is reshaping various domains ranging from education and software development to legal defense and novel writing. Despite its potential impact, there is a lack of research on how ChatGPT might influence streaming media, which is an essential part of everyday entertainment. As a result, it remains unclear how ChatGPT is changing the future of streaming media. To bridge such a research gap, in this paper, we propose a crowdsourced, data-driven framework that leverages two social media platforms, Twitter and Reddit, to explore the impact of ChatGPT on streaming media. Through extensive analysis of social media data collected from Twitter and Reddit, we reveal how ChatGPT is transforming streaming media from diverse perspectives. Our data analytics demonstrates that ChatGPT is sparking both fear and excitement in the context of the streaming media and enhancing the downstream visual generative models, such as DALLE-2 and Stable Diffusion Videos. To the best of our knowledge, this study is the first large-scale and systematical investigation into the effects of ChatGPT on streaming media. Hope our findings will inspire further research and discussions on this topic across academia and industry.

关键词： Analytical models Visualization Social networking (online) Blogs Streaming media Media Writing

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Construction and Development of High-Tech smart City

Construction and Development of High-Tech Smart City

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international conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT 2020

作者： Liu, Quan Guangzhou College of Commerce GuangzhouGuangdong China

ISBN: (纸本)9783030627454

Information technology is becoming more and more mature, and the construction of smart cities can achieve accurate economic and social development. Modern electronic information technology, represented by Internet of things technology, is the key force and core technology for building smart cities. This paper firstly studies the characteristics and connotation of smart cities, and then draws the main characteristics of smart city development. Then it focuses on the IoT from the aspects of architecture, transportation, tourism, and medical care briefly analyze the technology in building smart cities. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： smart city

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An Illicit Bitcoin Address Analysis Scheme Based on Subgraph Evolution 24

An Illicit Bitcoin Address Analysis Scheme Based on Subgraph...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on data Science and Systems, 20th IEEE international conference on smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Application, HPCC/DSS/smartCity/DependSys 2022

作者： Yang, Qingqing Xiang, Yuexin Liu, Wenmao Ren, Wei School of Computer Science China University of Geosciences Wuhan430074 China China University of Geosciences Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan430078 China Nsfocus Technologies Group Co. Ltd. Beijing China

ISBN: (纸本)9798350319934

Bitcoin, due to its decentralization and anonymity, is more frequently utilized in cybercrimes such as money laundering, darknet market, and blackmail. In order to maintain the development of cryptocurrency communities, it is vital to fight against these cryptocurrency-related (e.g., Bitcoin) cybercrimes. To better monitor cryptocurrency-based cybercrime, it is essential for a deeper understanding of the patterns that exist in Bitcoin transactions. In this paper, we propose a novel framework consisting of empirical analysis and machine learning-based analysis for studying three typical categories of Bitcoin addresses from a dynamic perspective of subgraph evolution. Specifically, we collect 22,001 continuous blocks to build a Bitcoin transaction graph and extract subgraphs from the constructed graph for each address in different periods. In the empirical analysis, we have several important findings from three perspectives. While in the machine learning-based analysis, we propose a 110-dimensional feature set for classification tasks among these three types of Bitcoin addresses, where decision tree, k-nearest neighbor algorithm, and random forest are applied. The experimental results illustrate that metrics of all classifiers can achieve 0.88, and the highest accuracy rate reaches 0.95 in the random forest classifier. In addition, we explore the feature importance in the random forest using the SHAP library and discuss several interesting phenomena that appear in the evolution process of the subgraphs. © 2022 IEEE.

关键词： Bitcoin

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ICAC 2021 - 3rd international conference on Advancements in computing, Proceedings

ICAC 2021 - 3rd International Conference on Advancements in ...

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3rd international conference on Advancements in computing, ICAC 2021

ISBN: (纸本)9781665408622

The proceedings contain 83 papers. The topics discussed include: event-driven malicious URL extractor;APlus: an intelligent lecture delivery system using artificial intelligence learning analytics;smart intelligent floriculture assistant agent (SIFAA);smart monitoring and disease detection for robotic harvesting of tomatoes;SentinelPlus: a cost-effective cyber security solution for healthcare organizations;e-learn detector: smart behavior monitoring system to analyze student behaviors during online educational activities;automated crop harvesting, growth monitoring and disease detection system for vertical farming greenhouse;and a sensitive data leakage detection and privacy policy analyzing application for android systems (PriVot).

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A COLLABORATIVE EDGE computing FRAMEWORK FOR PREDICTIVE MAINTENANCE IN NUCLEAR POWER PLANTS 29

A COLLABORATIVE EDGE COMPUTING FRAMEWORK FOR PREDICTIVE MAIN...

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2022 29th international conference on Nuclear Engineering, ICONE 2022

作者： Feng, Yixiong Wang, Yong Hu, Bingtao Hong, Zhaoxi Tan, Jianrong State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province Zhejiang University Hangzhou310027 China

ISBN: (纸本)9780791886472

Nuclear power is an indispensable part of modern energy systems. To operate the nuclear power plants safely and reliably, it is crucial to greatly develop the predictive maintenance of nuclear infrastructure with the support of various smart sensors and big data analytics. To this end, this paper proposes a novel collaborative edge computing-enabled solution for predictive maintenance in nuclear power plants, from which a key problem of efficiently allocating some edge computing tasks is formulated. Specifically, considering huge amounts of industrial data are continuously generated during plant operations, we first present a three-layer predictive maintenance computing framework for nuclear power plants. Subsequently, to timely process these data in some distributed and heterogeneous industrial computing nodes, a complicated scheduling optimization model with some interdependent computational tasks is established. To lower the size of model, we also introduce some reduction strategies. Finally, an actual predictive maintenance scenario in nuclear power plant is chosen and some algorithms are taken for comparisons. Copyright © 2022 by ASME.

关键词： Nuclear power plants

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Performance Analysis of Clan Updated Grey Wolf Optimization (CU-GWO) based Deep Ensemble Technique for Identifying Sarcasm from Social Media data

Performance Analysis of Clan Updated Grey Wolf Optimization ...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Geeta Abakash Sahu Manoj Hudnurkar Faculty of Computer Studies (FoCS) Symbiosis Center for Research and Innovation(SCRI) Symbiosis International (Deemed University)(SIU) Pune Maharashtra India Department of Information Technology and Data Science Vidyalankar School of Information Technology (VSIT) Mumbai India Operations and Analytics Symbiosis Centre for Management and Human Resource Development(SCMHRD) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India

Sarcasm is often considered as a prevalent linguistic element in online content that conveys strongly held subjectivity and viewpoints. The detection of sarcasm is a significant consideration over the dominant NLP appliances as sentiment analysis, which is utilizing for advertising and opinion mining is a precious tool for figuring out the people sentiments and attitudes. The proposed approach describes automatic detection of sarcasm as an ease classification of text challenges. This paper aims to develop a sarcasm detection model that categorizes words into either sarcastic or non-sarcastic expressions. Initially, the text input involves processing, here the stop word removal and tokenization process are carried out. Subsequently, various features like information gain, chisquare, mutual information, and symmetrical uncertaintybased features are extracted from the preprocessed data. The curse of dimensionality is a crucial issue, thereby the selection of optimal features is conducted by employing hybrid optimization model called Clan Updated Grey Wolf Optimization (CU-GWO). The optimal features are utilized for sarcasm detection by using ensemble technique. The ensemble techniques involve the classifiers such as NN, SVM, RF and DCNN. The final output of DCNN indicates whether the sarcasm is present. The effectiveness of the proposed approach demonstrated through diverse metrices.

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4th international conference on data Management, analytics and Innovation, ICDMAI 2020

4th International Conference on Data Management, Analytics a...

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4th international conference on data Management, analytics and Innovation, ICDMAI 2020

ISBN: (纸本)9789811556159

The proceedings contain 33 papers. The special focus in this conference is on data Management, analytics and Innovation. The topics include: A Novel Framework for data Acquisition and Retrieval Using Hierarchical Schema Over Structured Big data;Predicting the Cricket Match Outcome Using ANFIS Classifier for Viewers Opinions on Twitter data;Extraction of Tabular data from PDF to CSV Files;railway Complaint Tweets Identification;identification of Entities in Scientific Documents;a Machine Learning Model for Review Rating Inconsistency in E-commerce Websites;NLIDB Systems for Enterprise databases: A Metadata Based Approach;a data Science Approach to Analysis of Tweets Based on Cyclone Fani;A GPU Unified Platform to Secure Big data Transportation Using an Error-Prone Elliptic Curve Cryptography;an IoT-Based smart Parking Framework for smart Cities;an Empirical Analysis of Classifiers Using Ensemble Techniques;force of Gravity Oriented Classification Technique in Machine Learning;machine Learning Classifiers for Android Malware Detection;Designing a Model to Handle Imbalance data Classification Using SMOTE and Optimized Classifier;an Analysis of Computational Complexity and Accuracy of Two Supervised Machine Learning Algorithms—K-Nearest Neighbor and Support Vector Machine;a Survey on Application of Machine Learning Algorithms in Cancer Prediction and Prognosis;augmented Reality Building Surveillance System;interpretation and Segmentation of Chart Images Using h-Means Image Clustering Algorithm;innovative Techniques for Student Engagement in Cybersecurity Education;a Feasibility Study of Service Level Agreement Compliance for Start-Ups in Cloud computing;open-Source Software Challenges and Opportunities;smart Waste Monitoring Using Internet of Things;security Issue in Internet of Things.

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