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Sentimental Analysis Using Twitter Data Using LSTM, CNN and Extra Tree Classifier Methods

Sentimental Analysis Using Twitter Data Using LSTM, CNN and ...

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Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Arvind Kumar Devender Sharma Deepti Singh School of Computing Science and Engineering Galgotias University KIET Group of Institutions (NCR DELHI) India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Twitter can be said as a Microblogging. One of the most popular microblogging computer sites. Tweets of Twitter can be considered as emotion of users, this is the reason why it is the easiest ways of poignant study. Sentiment analysis can be stated as the process of analysing different opinions and classifying them in types. Opinions are broadly distributed in four groups: neutral, constructive, unhealthy, undesirable. Basically, information mining is used to find pertinent information from websites, particularly from social networking websites. Using an approach that combines several disciplines, such as text mining, human language processing, and device intelligence. This study mainly has highlights that how the tweets are stated emotionally, according to info congregated through Twitter. Earlier the Researchers used device mastering procedures that were already in place aimed at sentimentality assessment, unfortunately it came to know that the outcomes of these methods were not successful in providing superior sentiment category results. As a result, improving them finally lead to sentiment evaluation, where we tend to use collective mechanism knowledge methods to increase the effectiveness and quality of the projected strategy. Tentative fallouts proves that proposed technique offers higher type outcomes in terms of accurateness compared to character classifier.

关键词： Text mining Sentiment analysis Social networking (online) Computational modeling Blogs Organizations Oral communication

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A Brief on Blockchain and cryptocurrency system

A Brief on Blockchain and cryptocurrency system

引用

Advances in computing, Communication Control and networking (ICAC3N), international conference on

作者： Sharvani G. S. Ruquiya Anjum Department of Computer Science and Engineering R.V. College of Engineering Bangalore India

Blockchain is ultra model technique which is making a great impact on the society for its decentralized nature. Cryptocurrency is one of the popular application of blockchain. Blockchain is decentralized in nature and each block is connected with chain using the hashing concept, so that third party will not be able to access it, The blockchain is more secure. Cryptocurrency contain secure distributed ledger and mining concept is important in cryptocurrency. Secure mining algorithm is required for cryptocurrency. This paper describes an overview of an blockchain and cryptocurrency systems.

关键词： distributed ledger Cryptocurrency Blockchains

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Maintenance of Model Resilience in distributed Edge Learning Environments

Maintenance of Model Resilience in Distributed Edge Learning...

引用

international conference on Intelligent Environments (IE)

作者： Qiyuan Wang Christos Anagnostopoulos Jordi Mateo Fornes Kostas Kolomvatsos Andreas Vrachimis School of Computing Science University of Glasgow Department of Computer Science University of Lleida Dept. of Informatics & Telecommunications University of Thessaly School of Computing Science Glasgow University

distributed Machine Learning (DML) at the edge of the network involves model learning and inference across networking nodes over distributed data. One type of model learning could be the delivery of predictive analytics services to formulate intelligent environments, however, those environments heavily rely on real-time inference and are significantly influenced by changes in the underlying data (concept drifts). Moreover, the quality of service and availability in DML environments are directly tied to each node’s reliability, since such environments are highly susceptible to the impact of node failures. Even if such challenges can be tackled with distributed resilience mechanisms, their effectiveness and efficiency, due to concept drifts, should be maintained to ensure continuous and sustained quality of service. DML systems operate in dynamic environments, thus, they require their models to be updated according to the novel trends embedded in the new data they encounter. We, therefore, introduce several model maintenance mechanisms to ensure resilient DML systems in the long term when concept drifts emerge. We provide a comprehensive experimental evaluation of our resilience maintenance mechanisms over synthetic and real data showcasing their importance and applicability in edge learning environments.

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Fake News Detection Using Different Machine Learning Algorithms

Fake News Detection Using Different Machine Learning Algorit...

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Device Intelligence, computing and Communication Technologies, (DICCT), international conference on

作者： Manvi Bohra Indrajeet Kumar Kamred Udham Singh Graphic Era Hill University Dehradun India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

Due to the sheer simple accessibility and exponential rise of data through social media networks, discriminating between false and genuine information has now become challenging. The simplicity by which information may be distributed has resulted in an exponential increase in the amount of information that can be falsified. Where the transmission of fraudulent information is prevalent, the legitimacy of social media networks is also compromised. As a consequence, automatically verifying the information's source, substance, and publisher in order to categorize it as false or real has become a research challenge. Machine learning has proven useful in identifying information, but it has significant limitations. This research describes a variety of machine learning algorithms for identifying bogus and fabricated news. The key goal of this paper is to demonstrate which machine learning algorithm gives maximum accuracy and it was found that the Decision tree gives an accuracy of 99.97 % among all the other algorithms.

关键词： Support vector machines Performance evaluation Machine learning algorithms Social networking (online) Prototypes User interfaces Vectors

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distributed Serverless Chat Bot Networks using Mobile Agents: A distributed Data Base Model for Social networking and Data Analytics 13

Distributed Serverless Chat Bot Networks using Mobile Agents...

引用

13th international conference on Agents and Artificial Intelligence (ICAART)

作者： Bosse, Stefan Univ Bremen Dept Math & Comp Sci D-28359 Bremen Germany

ISBN: (纸本)9789897584848

Today human-machine dialogues performed and moderated by chat bots are ubiquitous. Commonly, centralised and server-based chat bot software is used to implement rule-based and intelligent dialogue robots. Furthermore, human networking is not supported. Rule-based chat bots typically implement an interface to a knowledge data base in a more natural way. The dialogue topics are narrowed and static. Intelligent chat bots aim to improve dialogues and conversational quality over time and user experience. In this work, mobile agents are used to implement a distributed, decentralised, serverless dialogue robot network that enables ad-hoc communication between humans and machines (networks) and between human groups via the chat bot network (supporting personalized and mass communication). I.e., the chat bot networks aims to extend the communication and social interaction range of humans, especially in mobile environments, by a distributed knowledge and data base approach. Additionally, the chat bot network is a sensor data acquisition and data aggregator system enabling large-scale crowd-based analytics. A first proof-of-concept demonstrator is shown identifying the challenges arising with self-organising distributed chat bot networks in resource-constrained mobile networks. The novelty of this work is a hybrid chat bot multi-agent architecture enabling scalable distributed and adaptive communicating chat bot networks.

关键词： Chat Bots Natural Language Processing Human-machine Interface Self-organising MAS Agent-based computing Crowd Sensing

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Enhancing the Performance of an Autonomous distributed Storage System in a Large-Scale Network

Enhancing the Performance of an Autonomous Distributed Stora...

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Summer Virtual conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing (SNPD-Summer), ACIS international

作者： Atsushi Nunome Hiroaki Hirata Faculty of Information and Human Sciences Kyoto Institute of Technology Kyoto Japan

ISBN: (纸本)9798350396386

We are developing an autonomous distributed storage system that autonomously relocates data blocks according to their utilization and the effective performance of storage. This paper proposes an enhancement to the distributed storage system to address large network environments where many network nodes are connected to multiple network switches. In the proposed method, the latency time when a network frame travels on the network is evaluated together with the effective performance of the storage, and this is used to determine the destination of the data block. Simulation evaluation results show that in the networks with cascading multiple network switches and high I/O load on storage, the performance improvement is significantly greater than that of the previous method. This showed that our storage system can provide sufficient performance improvement even in large network environments.

关键词： distributed databases Artificial intelligence Software engineering Load modeling

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Enhancing AI-Generated Content Efficiency Through Adaptive Multi-Edge Collaboration

Enhancing AI-Generated Content Efficiency Through Adaptive M...

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international conference on distributed computing Systems

作者： Changfu Xu Jianxiong Guo Jiandian Zeng Shengguang Meng Xiaowen Chu Jiannong Cao Tian Wang Guangdong Provincial Key Laboratory IRADS BNU-HKBU United International College Zhuhai China Hong Kong Baptist University Hong Kong China Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China Guangdong Donghua Faster Software Co. LTD Zhuhai China The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

The Artificial Intelligence-Generated Content (AIGC) technique has gained significant popularity in creating diverse content. However, the current deployment of AIGC services in a centralized framework leads to high response times. To address this issue, we propose the integration of collaborative Mobile Edge computing (MEC) technology to decrease the processing delay of AIGC services. Nevertheless, existing collaborative MEC methods only facilitate collaborative processing among fixed Edge Servers (ESs), limiting flexibility and resource utilization across heterogeneous ESs for different computing and networking requirements associated with AIGC tasks. This poses challenges for efficient resource allocation. We present an adaptive multi-server collaborative MEC approach tailored for heterogeneous edge environments to achieve efficient AIGC by dynamically allocating task workload across multiple ESs. We formulate our problem as an online linear programming problem aiming to minimize task offloading make-span. This problem is proved to be NP-hard and we propose an online adaptive multi-server selection and allocation algorithm based on deep reinforcement learning that effectively addresses this problem. Additionally, we provide theoretical performance analysis, demonstrating that our algorithm achieves near-optimal solutions within approximate linear time complexity bounds. Finally, experimental results validate the effectiveness of our method by showcasing at least 11.04% reduction in task offloading make-span and a 44.86 % decrease in failure rate compared to state-of-the-art methods.

关键词： Multi-access edge computing Collaboration Approximation algorithms Delays Performance analysis Resource management Time factors

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distributed Incentive Mechanism Based on Hyperledger Fabric 2

Distributed Incentive Mechanism Based on Hyperledger Fabric

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2021 2nd international Symposium on Automation, Information and computing, ISAIC 2021

作者： Li, Baohui Fang, Yadong Xu, Wei Wang, Jiaxing Rui, Lanlan Lei, Min Cyberspace Security Academy Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Lnspur Cloud Information Technology Corporation Shandong Province Jinan China

Crowd sensing is a new type of data acquisition method that can efficiently and diversify the realization of sensing tasks. However, this method currently has some problems, such as data storage being overly dependent on third-party platforms, and there is a lack of reliable data credible evaluation methods. To solve this problem, our paper proposes a distributed incentive mechanism based on Hyperledger Fabric (HF-DIM) in the Crowd sensing scenario. In particular, the following questions are studied: How to achieve distributed incentive to solve the traditional incentive that relies on a centralized platform? How to evaluate the credibility of the sensing data provided by the users? To the former question, we implement a multi-attribute auction algorithm based on smart contracts, and distributed incentives are implemented using blockchain deployed contracts. To the latter question, We propose a K-nearest neighbor outlier detection algorithm based on geographic location and similarity to evaluate the credibility of the data and establish a reputation index based on the credibility of the data. Through simulation experiments using real data set, the feasibility and effectiveness of the proposed framework and algorithm are verified. © Published under licence by IOP Publishing Ltd.

关键词： Smart contract

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distributed Intelligent Framework for Remote Area Observation on Multilayer Non-Terrestrial Networks

Distributed Intelligent Framework for Remote Area Observatio...

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Communications and networking (MeditCom), IEEE international Mediterranean conference on

作者： David Naseh Swapnil Sadashiv Shinde Daniele Tarchi Tomaso DeCola Department of Electrical Electronic and Information Engineering “Guglielmo Marconi” University di Bologna Bologna Italy CNIT - University of Bologna Research Unit Bologna Italy Institute of Communications and Navigation German Aerospace Center (DLR) Weßling Germany

ISBN: (数字)9798350309485

ISBN: (纸本)9798350309492

Satellite-based Remote Area Observation systems are becoming increasingly popular in the upcoming 6 G world. However, traditional Earth Observation (EO) systems suffer from communication requirements, reliability, and data privacy issues. To address these issues, we propose a multilayered Non-Terrestrial Network (NTN) based EO framework for remote area observation purposes. The proposed framework includes the air network along with traditional satellite networks for reliable and low-cost EO services. Additionally, with onboard edge computing facilities, the proposed EO framework can process data in space. Next, given the importance of intelligent services in the 6 G world, we extend the multi-layered EO framework and propose a novel distributed Learning (DL) solution for federated training. The proposed framework is defined as Generalized Federated Split Transfer Learning (GFSTL), which can induce split and transfer learning tools into a federated learning framework for improving overall training performance and accuracy. Moreover, GFSTL uses Unmanned Aerial Vehicles (UAVs) for improved data accuracy and image quality in challenging terrains, ensuring increased accuracy in EO applications, and establishes a resilient model for efficient and secure training across distributed platforms, making it both efficient and accurate. In addition, SL helps resource-constrained UAVs perform the task efficiently, enhancing scalability and extensibility. Finally, we conduct experiments to provide theoretical and numerical insight into the performance of the proposed method.

关键词： Training Image quality Accuracy Satellites Scalability Transfer learning Nonhomogeneous media

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A Generic Blockchain Framework to Secure Decentralized Applications

A Generic Blockchain Framework to Secure Decentralized Appli...

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IEEE international conference on Communications (ICC)

作者： Fan, Wenjun Hong, Hsiang-Jen Zhou, Xiaobo Chang, Sang-Yoon Univ Colorado Coll Engn & Appl Sci Comp Sci Dept Colorado Springs CO 80918 USA

ISBN: (纸本)9781728171227

Blockchain technology is gaining popularity in industries and governments for information monitoring, distribution, and tracking. Thanks to the built-in security properties, blockchain provides security integrity to various decentralized applications (dApps) involving distributed operations including supply chain, healthcare, banking, internet of things (IoT), and networking. In this paper, we propose a generic blockchain framework (GBF) for applying two blockchains to the dApp systems, one to establish trust and the other to use the trust for securing applications. GBF provides a generally applicable framework and addresses the foundational questions of the blockchain objectives, participants, and the underlying distributed consensus protocol in use. We apply GBF to various case studies from the recent blockchain research to show its effectiveness and generality. We also prototype GBF using smart contract and experiment on CloudLab for preliminary evaluations focusing on the application-general metrics. We propose GBF to facilitate blockchain/dApp research and development by providing the initial framework and enable the preliminary analyses so that the decentralized applications with specific aims can build on GBF.

关键词： Blockchain distributed Ledger Technology Permissioned Blockchain distributed computing distributed PKI Smart Contract

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