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Aspect-Level Sentiment Analysis Based on Graph Attention Fusion Networks 24

Aspect-Level Sentiment Analysis Based on Graph Attention Fus...

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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： Yu, Wenting Wang, Xiaoye Yang, Peng Xiao, Yingyuan Wang, Jinsong School of Computer Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350319934

The goal of aspect-based sentiment analysis is to predict the sentiment polarity of various aspect terms. In aspect-based sentiment classification, the most advanced approach uses the graph convolutional network (GCN) based on integrating the syntactic and semantic structures of sentences. When dealing with word relations, however, GCN only apply the same weight to the edges between words. In contrast, graph attention network (GAT) can assign different weights to different edges according to the importance of the words through an attention mechanism. Therefore, we propose a Graph Attention Fusion Network: in order to establish word dependencies, a Syntactic Graph Attention Network (SyGAT) module with rich syntactic knowledge is designed. A Semantic Graph Attention Network (SeGAT) module with Self-attention is developed to capture the semantic associations of words. Syntactic and semantic features are exchanged through Transitional Graph Attention Network (TraGAT). SyGAT, SeGAT and TraGAT are fused into a new GAT. A convolutional layer is also added to increase the learning ability of n-gram words features. Finally, the fused GAT is merged with the convolutional layer features for sentiment prediction. Experimental results on three benchmark datasets demonstrate the effectiveness of the model in this paper. © 2022 IEEE.

关键词： Sentiment analysis

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Deep Learning Based Privacy Preserving for Object Classification Using High-Resolution Satellite Images 1

Deep Learning Based Privacy Preserving for Object Classifica...

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1st IEEE international conference on Ambient Intelligence, knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Mishra, Priti Mathapati, Mahantesh Nalinakshi, N. Kotagi, Veeranna Atria Institute of Technology Information Science and Engineering Bengaluru India Dept. of Cse Bengaluru India Atria Institute of Technology Mathematics Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Ise Bengaluru India

ISBN: (纸本)9798350316469

In remote sensing (RS), deep learning (DL) has demonstrated exceptional performance. The field of RS is finding that DL is becoming increasingly useful. The proliferation of cloud platforms that grant access to massive amounts of processing power has made the deployment of DL systems as a service feasible. This, however, has given rise to fresh issues with data privacy and security. Several levels of confidentiality are necessary for the RS data required to teach DL algorithms. High-resolution satellite photos relating to public safety, for example, must be treated with the utmost secrecy. The owner of a satellite picture may also refuse to allow anybody else to use it on the grounds that it is copyright protected. Consequently, privacy-preserving deep learning (PPDL) methods might be the answer. To train DL without exposing the original plaintext, PPDL allows for the use of encrypted data. In order to classify objects in very high-resolution satellite photos, a hybrid PPDL method is proposed in this research. For privacy-preserving to be designed, existing deep learning models and algorithms must be rethought and redesigned from the ground up to meet the stringent technological and algorithmic restrictions of HE. This study presents a primer on the topic of privacy-preserving convolutional neural networks (CNNs) and a technique for their creation. High-resolution photos captured by the SPOT6 and SPOT7 satellites were used in the experiments. Using the suggested encryption approach resulted in a loss of classification accuracy of 2-3.5 percent. © 2023 IEEE.

关键词： Convolutional neural networks Deep learning High-resolution satellite images Privacy-preserving data Remote sensing

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Navigating Flood Prediction Complexities: Harnessing Fuzzy Expert systems and Real-Time Sensor Integration 7

Navigating Flood Prediction Complexities: Harnessing Fuzzy E...

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7th international conference on Image Information Processing, ICIIP 2023

作者： Madasamy, Sridhar Praveena, K.V.S. Sreedevi, Vaitla Basi Reddy, A. Nirosha, Ch. Naik, S. K. Lokesh Cognizant Technology Solution 500 Frank W. Burr Boulevard TeaneckNJ07666 United States Vignan's Institute of Information Technology Department of Management Studies Andhra Pradesh Visakhapatnam India Department of Cse Maisammaguda Kompally Telagana State Secunderabad500100 India Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India St. Martin's Engineering College Department of Eee Secunderabad Telangana Dhulapally India Mlr Institute of Technology Department of Cse Hyderabad Dundigal India

ISBN: (纸本)9798350371406

Fuzzy Expert systems (FESs) stand at the forefront as a pioneering amalgamation of fuzzy logic and expert systems, constituting a robust framework primed for intricate decision-making processes. Within this context, this work presents an innovative paradigm that harnesses the profound capabilities of FESs to elevate the precision of flood prediction through the seamless integration of real-time sensor data. The domain of flood prediction takes on paramount significance in the realm of disaster management, necessitating the timely and accurate identification of potential risks. By orchestrating the nuanced cognitive processes of fuzzy logic with the specialized knowledge inherent to expert systems, the methodology introduced herein serves as a tangible demonstration of FESs' potential to fundamentally reshape the landscape of flood prediction strategies. This pioneering approach, empowered by the dynamic fusion of disparate fields, stands as a testament to the evolving nature of predictive analytics. By effectively leveraging real-time sensor data, the developed system adeptly maneuvers through the intricate dynamics of flooding events, culminating in predictions that transcend traditional forecasts. The outcomes of these predictions, actionable in nature, engender a profound sense of empowerment in the realm of proactive decision-making, thereby minimizing the potential ramifications of impending flood disasters. Within this work, a thorough examination unfolds, encompassing the foundational underpinnings, the meticulous design, and the execution of the fuzzy expert system. Furthermore, the implications of this approach are rigorously tested through its application in real-world flood scenarios, providing tangible evidence of its efficacy. This holistic exploration thus unravels the transformative potential of FESs in the context of augmenting flood prediction precision, thus forming a cornerstone of disaster management frameworks that embrace resilience and adaptab

关键词： Decision making

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Determination of Radiation Efficiencies for a Prototype with Impedance Bandwidth of 400 MHz in Free Space and 480 MHz on a Metallic Surface

Determination of Radiation Efficiencies for a Prototype with...

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2023 international conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Ahanaf, Tahamid Rahman Ahad, Md Akhlakur Hussain, Imtiaz Jahan Eva, Mosammat Israt Khair, Aqib Abul Alam, Md Zahangir Department of Electrical and Electronic Engineering Dhaka1212 Bangladesh Department of Maritime Science and Engineering Batu78200 Malaysia

ISBN: (纸本)9798350369359

RFID technology is widely used in a variety of service industries, including distribution logistics, manufacturing firms, and commodities flow systems. This is because RFID is versatile, as it can help with tracking, identification, automatic data collecting, and access control. This work presents a small and efficient antenna that can be used for active radio frequency identification (RFID) tags. The suggested antenna comprises a slotted patch positioned on an FR4 substrate, a microstrip line (feeding), and a foam spacer with a volume of merely 60.02mm × 50.33 mm × 6.09 mm(0.48λ0 × 0.40λ0 × 0.048λ0)where λ0 represents the wavelength at the center frequency. When positioned on a metallic surface, it functions as a patch, whereas in free space it acts as a dipole. A prototype with an impedance bandwidth of 400 MHz (2.29-2.65 GHz) in free space and 480 MHz (2.30-2.77 GHz) on a metallic surface has been constructed and evaluated. The radiation patterns, peak gains, and radiation efficiencies recorded in these two circumstances are given. © 2023 IEEE.

关键词： Radiation efficiency

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A Preliminary Fuzzy Markup Language based Approach for the Queue Buffer Size Optimization in Fog Nodes for Stream Processing

A Preliminary Fuzzy Markup Language based Approach for the Q...

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IEEE international conference on Fuzzy systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / international Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Corpas-Prieto, Gregorio Leon-Garcia, Fernando Gamez-Granados, Juan Carlos Manuel Palomares, Jose Olivares, Joaquin Soto-Hidalgo, Jose Manuel Univ Cordoba Dept Elect & Comp Engn Cordoba Spain Univ Granada Dept Comp Architecture & Technol Granada Spain

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

The Internet of Things (IoT) is usually divided in three layers: Edge, Fog, and Cloud layers. The whole IoT infrastructure deals with large amount of data between layers. Focusing on the Fog layer, the sending/receiving data and further cascade processing of those data in the Fog layer enable the Stream Processing paradigm. Thus, aspects such as the number of connections, delays, buffer size, memory usage, among others, have to be considered to optimize the network traffic. Moreover, these characteristics are affected by uncertainty and imprecision since, for example, the number of connections or the buffer size may be considered low in some cases and high in others. Fuzzy Rule -Based systems (FRBS) are suitable for addressing complex data and managing their imprecision. The objective of this paper is to propose an approach that optimizes network traffic with the main goal of dynamically and automatically adjusting the queue buffer size in a node to avoid network collapse. The IEEE std 1855-2016 for Fuzzy Markup Language and the open source library JFML are used for their flexibility and interoperability offered by these technologies. The proposal has been simulated in three basic different scenarios involving several network traffic states in a fog infrastructure.

关键词： Fuzzy logic knowledge engineering Uncertainty Markup languages Telecommunication traffic IEEE Standards Real-time systems

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Research on the Influence of Visual Merchandising on Customer Value in Mobile Recommendation System-Based on the Intermediary Effect of Brand Image 11

Research on the Influence of Visual Merchandising on Custome...

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11th international conference on Innovation, knowledge, and Management, ICIKM 2022

作者： Li, Siying Xu, Yongxin Li, Qianying Lu, Hangdong Zhuo, Yajing Qiu, Huizhang Software Engineering Institute of Guangzhou Guangzhou China Guangzhou Open University Guangzhou China

ISBN: (数字)9781665461504

ISBN: (纸本)9781665461504

With the development of e-commerce and big data, mobile recommendation systems are closely connected with consumers' daily consuming behaviors. The visual merchandising plays a pivotal role in e-commerce and other fields. Based on the mobile recommendation system and rated the brand image as intermediary, this paper discusses and verifies the impact of visual merchandising on customer value through questionnaires and experimental methods. Besides, this research provides feasible suggestions for business practices. © 2022 IEEE.

关键词： Recommender systems

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Enhanced Structural Image Classification using Hybrid CNN-GNN Model

Enhanced Structural Image Classification using Hybrid CNN-GN...

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knowledge engineering and Communication systems (ICKECS), international conference on

作者： P Thangamariappan Manish Singh M Arthi T Kannan S Saraswathy Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchengode India Computer Science and Business Systems Sri Sai Ram Engineering College Chennai India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

The availability of structural image data has significantly increased in recent years due to the widespread use of improved imaging techniques. The increase in the amount of data has required the creation of strong and effective classification algorithms to extract valuable information from these photos. Convolutional Neural Networks (CNNs) have exhibited exceptional efficacy in image classification tasks by capturing hierarchical characteristics. Graph Neural Networks (GNNs) have demonstrated potential in representing intricate connections within graph-structured data. This research presents a new hybrid model that combines the advantages of both CNNs and GNNs to improve the categorization of structural images. Our model combines the spatial locality acquired by CNNs with the relational information stored by GNNs to obtain better classification performance. We perform extensive experiments on standard structural picture datasets to showcase the efficacy of our suggested methodology. The findings demonstrate that our hybrid CNN-GNN model surpasses current approaches, underscoring its capacity to enhance structural picture categorization tasks.

关键词： knowledge engineering Quality control Feature extraction Graph neural networks Convolutional neural networks Task analysis Standards

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Mixed Reality Platform Supporting Human-Robot Interaction

Mixed Reality Platform Supporting Human-Robot Interaction

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international Joint conference on Mechanics, Design engineering and Advanced manufacturing, JCM 2022

作者： Calzone, Nicolas Sileo, Monica Mozzillo, Rocco Pierri, Francesco Caccavale, Fabrizio School of Engineering Basilicata University Via dell’Ateneo Lucano 10 Potenza85100 Italy

ISBN: (纸本)9783031159275

Human-Robot Interaction (HRI) is an interdisciplinary research field aiming to study and develop platform in which robots (and in particular collaborative robots, also called cobots) can interact and collaborate with humans to execute dedicated tasks. Usually, cobots are intended as passive robotic devices for direct collaboration with a human operator within a shared workspace. They are designed to be used in open and uncontrolled environments;the robot shall be able to adapt its behaviors to the dynamic input of the surrounding environment. In this optic, Mixed Reality (MR) can play a crucial role supporting the flow of data between the actors (cobot and human) working in the shared environment, it can offer a simply, but remarkably advanced, communication interface between human and robot. Thanks to MR, tools to allow human operators, without particular experience or knowledge of robotics, to easily interact with the cobot can be developed. Our work is focused on development of a MR platform that integrates cutting-edge technologies, i.e. a Head Mounted Display (HMD), and a cobot in a shared environment. The experimental setup includes the Microsoft’s Mixed Reality HMD HoloLens 2 and the Franka Emika Robot System. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Helmet mounted displays

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Secure and Energy-Efficient Framework for Internet of Medical Things (IoMT)-Based Healthcare System

Secure and Energy-Efficient Framework for Internet of Medica...

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international conference on data Science and Network engineering, ICDSNE 2023

作者： Dewan, Ritu Nagpal, Tapsi Ahmad, Sharik Rana, Arun Kumar Islam, Sardar M. N. Department of Computer Science and Engineering Lingaya’s Vidyapeeth Faridabad India Department of Computer Science and Engineering Galgotias College of Engg. and Technology Greater Noida India SILC and Decision Sciences and Modelling Program Victoria University Footscray Australia

ISBN: (纸本)9789819967544

manufacturing, energy, finance, education, transportation, smart home, and medicine employ IoT technology. IoT solutions can efficiently manage hospital patients and mobile assets to provide high-quality medical services. The Internet of Medical Things (IoMT) integrates IoT with medical equipment to increase patient comfort, cost-effective medical solutions, hospital treatment speed, and personalized healthcare. This work uses Constrained Application Protocol to secure remote patient health data (CoAP). Nevertheless, the CoAP DTLS layer lacks key control, session establishment, and multicast message exchange. Hence, IoMT communication requires an efficient protocol for safe CoAP session formation. Consequently, to address key management and multicast security issues in CoAP, we presented an efficient and secure communication method to establish a secure session key between IoMT devices and distant servers utilizing lightweight, energy efficient, and Secure CoAP Elliptic Curve Cryptography (E2SCEC2). E2SCEC2 can use a smaller key size than Rivest-Shamir-Adleman (RSA) due to its tiny key size. To determine if these algorithms are compatible in limited contexts, the paper examines key creation, signature generation, and verification of E2SCEC2 and RSA algorithms, energy consumption, and radio duty cycle. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Hospitals

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Analysis of Educational Recommender System Techniques for Enhancing Student's Learning Outcomes 3

Analysis of Educational Recommender System Techniques for En...

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3rd international conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Pal, Neeti Dahiya, Omdev School of Computer Applications Lovely Professional University Jalandhar India School of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9798350336238

Recommender System was widely used in commercial websites for the past few years. These systems track past activities of customers and recommend them the relevant items. The emergence of E-learning activities over a few decades develops a variety of E-learning content available for virtual learning environments (VLE). A large amount of learning objects is present in E-learning repositories. Dealing with problems of the diversity of data, Educational Recommender System (ERS) plays a vital role in the educational sector. Educational recommender systems track the learners' past activities, know the users' preferences, assist the educators and learners, provide relevant content to learners, and enhance their learning outcomes. A personalized recommender system will intensify the learners' interest in particular content and reduce the course dropout rate. The recommender system makes the decision-making process for choosing the appropriate content easy for learners. ERS uses various approaches and technologies for assisting the learners' and helps them to run their learning process smoothly. Collaborative filtering, Content-based, and knowledge-based are the basic techniques of recommender systems. The research shows that a combination of these approaches will give more effective and efficient results. This mixing of approaches refers to the hybridization techniques. Traditional approaches with deep learning networks will improve the recommendations and provide results with higher accuracy. This paper provides how E-learning support recommender systems produce recommendations using different techniques. The technical results of recommender techniques help to find the best approach for making a recommender system in the future. © 2023 IEEE.

关键词： Recommender systems

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