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1st International Conference on Data Science, Machine Learning and Artificial Intelligence, DSMLAI 2021

作者： Khasnis, Namratha S Sen, Snigdha Khasnis, Shubhangi S Department of Computer Science and Engineering Global Academy of Technology Karnataka Bangalore India Software Engineering Department Sabre India Karnataka Bangalore India

ISBN: (纸本)9781450387637

During the pandemic, when fresh news content is generated every minute about the widespread of the virus, many conversations revolve around the spread and cure of the contagion. At the hands of a commoner who posts news about COVID-19 on social media, the news may manifest itself to accommodate the said person's fear or negative propaganda which can potentially trigger a mass panic outbreak or can disrupt the mental health of a reader. This research discusses the application of Machine Learning in Sentiment Analysis to classify Tweets about Coronavirus as fear sentiment or panic sentiment. It proposes the idea of a web-based application that caters to filter out the fear-inducing sentiment from a user's daily Twitter feed, thus giving the user accurate and well-spirited information. Textual analysis is performed along with necessary textual data visualization. A substantial accuracy of 91% is achieved in the classification of brief Tweets using the Naïve Bayes method. An accuracy of 74% is achieved using the Logistic Regression classification method for brief tweets. This depicts the advancements in the field of sentimental analysis and sheds light on how it can be employed amidst a challenging situation like the pandemic to preserve mental health. © 2021 ACM.

关键词： Coronavirus

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Architecture design of emotion recognition early warning system based on AE-TAN algorithm 6

Architecture design of emotion recognition early warning sys...

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6th Annual International Conference on Network and Information Systems for Computers, ICNISC 2020

作者： Zhang, Yongxiong Huang, Xiangzheng Wang, Liangming Department of Economics and Trade GuangZhou College of Technology and Business Guangzhou China School of Software Engineering South China University of Technology Guangzhou China

In order to improve the analysis effect of human emotion text data, this paper proposed NB algorithm, TAN algorithm and AE-TAN algorithm model method. In addition, three index parameters, precision, recall and F1 value, are compared and analyzed under three different algorithm model scenarios. The experimental results show that the AE-TAN algorithm is superior to the other two algorithms in Chinese text emotion analysis. Based on the AE-TAN algorithm, the architecture design scheme of emotion recognition early warning system was proposed in this paper, which has the characteristics of high concurrency and high availability, thus laying a technical foundation for the system that can quickly and accurately warn people of psychological problems. © 2020 Journal of Physics: Conference Series.

关键词： Emotion Recognition

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Research on an Encryption Algorithm of H.265 Video File 8

Research on an Encryption Algorithm of H.265 Video File

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zheng, Jian Huang, Hao Ma, Xuan School of Software Engineering HuaZhong University of Science & Technology Wuhan China Shenzhen Research Institute Huazhong University of Science & Technology Shenzhen China

ISBN: (纸本)9781665450515

The paper research an encryption algorithm for H.265 video files based on content-associated key. By analyzing the coding structure of H.265 video file, the algorithm divides the content of H.265 files, and extracts various parts of data according to the division results to realize the encryption. The implementation of the algorithm is divided into two parts: data area positioning and data extraction. Data area positioning is used to determine the specific location of the data required for encryption processing in the video file data flow, and data extraction is to extract the data according to certain rules after determining the location of each data area. According to the playback effect of encrypted H.265 video files, the algorithm is effective. © 2022 IEEE.

关键词： Cryptography

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Anomaly Detection of EEG in patients with epilepsy based on Graph Deviation Network 6

Anomaly Detection of EEG in patients with epilepsy based on ...

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6th International Conference on Computer Information Science and Application technology, CISAT 2023

作者： Chen, Ming Liu, Changyu Gao, Huaijin Tao, Haomin Lin, Siyuan Computer Technology and Software Engineering Wuhan Polytechnic Wuhan430074 China Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781510668546

A chronic neurological condition called epilepsy affects about 50 million people globally. Although there has been a lot of study on deep neural networks to identify seizures, it is still difficult to precisely define the structural relations between electrodes in EEG signals. The difficulty involved may lead to less accurate prediction. We provide a novel method that combines structured learning methods with graph neural networks and incorporates graph convolution to provide explanations for detected anomalies in EEG signals in order to overcome this constraint. Our investigations on the CHB-MIT dataset show that our approach is more effective and efficient than baseline methods and conventional neural network models at identifying the onset of seizures. © 2023 SPIE.

关键词： Anomaly detection

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IEARACDA: Predicting circRNA-disease associations based on information enhancement and arctangent rank approximation

IEARACDA: Predicting circRNA-disease associations based on i...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Song, Zheqi Liu, Tiyao Wu, Wenhao Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Circular RNAs (circRNAs) are non-coding RNA molecules that play a significant role in cell regulation and disease occurrence. In recent years, the use of computational methods to predict circRNAs associated with diseases has become a research focus. However, these methods are often constrained by the noise interference and an insufficient exploration of the underlying information in known associations. Therefore, this paper proposes an information enhancement and arctangent rank approximation based method to predict potential circRNA-disease associations (IEARACDA). Firstly, information enhancement methods are used to make circRNA and disease similarity information more complete and to reduce the sparsity of known association. Subsequently, the arctangent rank approximation method is employed to mitigate the potential bias issues that may arise from the nuclear norm, and optimization is carried out using the inexact augmented Lagrange multiplier (IALM) method. Finally, potential associations are predicted through heterogeneous graph inference method. The experimental results demonstrate that IEARACDA exhibits superior performance compared to existing methods. Furthermore, case studies for hepatocellular carcinoma and lung cancer provide additional confirmation of the accuracy and practical significance of the model predictions. © 2024 IEEE.

关键词： Lung cancer

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Reliability and performance of IEEE 802.11n for vehicle networks with multiple nodes

Reliability and performance of IEEE 802.11n for vehicle netw...

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2012 International Conference on Computing, Networking and Communications, ICNC'12

作者： Lee, Seungbae Lim, Alvin Department of Computer Science and Software Engineering 3101 Shelby Center for Engineering Technology Auburn University Auburn AL 36849-5347 United States

ISBN: (纸本)9781467300094

Vehicular Ad hoc Networks (VANETs) have generated considerable research interest in recent years since VANETs can be used to effectively support Intelligent Transportation Systems (ITS) applications and can potentially maximize their performance and reduce their implementation cost. However, the majority of VANET studies have been carried out in network simulators which may not represent real vehicular environments. Experimental studies, on the other hand, can produce more realistic results, although they involve higher experimental cost. This paper describes common unrealistic assumptions and misconceptions in VANET simulations and discusses challenging implementation issues, focusing on reliability and performance of typical VANET configurations in empirical case studies. Our experimental results show that interference from multi-hop routing and broadcast can reduce throughput performance, increase packet loss rate, and jitter. © 2012 IEEE.

关键词： Vehicular ad hoc networks

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AdaBoost-based Refused Bequest Code Smell Detection with Synthetic Instances 7

AdaBoost-based Refused Bequest Code Smell Detection with Syn...

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7th International Conference on Dependable Systems and Their Applications, DSA 2020

作者： Chen, Hao Ren, Zhilei Qiao, Lei Zhou, Zhide Gao, Guojun Ma, Yue Jiang, He Dalian University of Technology School of Software Dalian China Beijing Institute of Control Engineering Beijing China Taiyuan University of Technology Taiyuan China

ISBN: (纸本)9780738124223

software requirements are constantly changing. Consequently, the development process is frequently under time pressure, which results in technical debt. To illustrate the symptoms of technical debt, 22 code smells have been introduced to indicate the poor design in code fragment, among which refused bequest is one of the most harmful smells and with high diffuseness. However, refused bequest is rarely taken into account because there is a lack of dataset. Moreover, it is difficult to design the detection rules for refused bequest compared with other popular *** this paper, we propose a machine-learning-based refused bequest smell detection framework SEADART, which features the utilization of a set of synthetic smelly instances. Specifically, SEADART comprises three components: (1) a smell generation approach, and (2) a model training strategy, and (3) an AdaBoost-based detection model. We evaluate the performance of the proposed framework. The evaluation results suggest that the generated smelly instances are reliable, and the trained AdaBoost model significantly outperforms the state-of-the-art over a real-world dataset. © 2020 IEEE.

关键词： Adaptive boosting

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Proposed Square Lattice Photonic Crystal Fiber for Extremely High Nonlinearity, Birefringence and Ultra-High Negative Dispersion Compensation

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Journal of Optical Communications 2019年第4期40卷 401-410页

作者： Islam, Md. Ibadul Ahmed, Kawsar Sen, Shuvo Paul, Bikash Kumar Islam, Md. Shadidul Chowdhury, Sawrab Hasan, Md. Rabiul Uddin, Muhammad Shahin Asaduzzaman, Sayed Bahar, Ali Newaz Santosh-1902 Tangail Bangladesh Group of Bio-photomatix Santosh-1902 Tangail Bangladesh Department of Electronics and Telecommunication Engineering Rajshahi University of Engineering and Technology Rajshahi6204 Bangladesh Department of Software Engineering Daffodil International University Sukrabad Dhanmondi-1207 Dhaka Bangladesh

A photonic crystal fiber in square lattice architecture is numerically investigated and proposed for broadband dispersion compensation in optical transmission system. Simulation results reveal that it is possible to obtain an ultra-high negative dispersion of about -571.7 to -1889.7 (ps/***) in the wavelength range of 1340 nm to 1640 nm. Experimentally it is demonstrated that the design fiber covers a high birefringence of order 4.74 × 10-3at the wavelength of 1550 nm. Here, numerical investigation of guiding properties and geometrical properties of the proposed PCF are conducted using the finite element method (FEM) with perfectly match layers. Moreover, it is established more firmly that the proposed fiber successfully compensates the chromatic dispersion of standard single mode in entire band of interest. Our result is attractive due to successfully achieve ultra-high negative dispersion that is more promisor than the prior best results. © 2019 De Gruyter. All rights reserved.

关键词： Dispersion compensation

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Towards Edge Computing for 6G Internet of Everything: Challenges and Opportunities 1

Towards Edge Computing for 6G Internet of Everything: Challe...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Alawadhi, Abdulwadood Almogahed, Abdullah Azrag, Etidal Universiti Utara Malaysia School of Computing Kedah Malaysia Taiz University Faculty of Engineering and Information Technology Department of Software Engineering Taiz Yemen

ISBN: (纸本)9781665472753

The Internet of Everything (IoE)-based smart services are becoming more prominent as a result of the growing demands placed on wireless networks. Despite 5G having the ability to serve a wide range of IoE-based applications, they are unable to fully satisfy the needs of the newest intelligent systems. Therefore, 6G is the next generation for the IoT, IoE, and cellular networks, which aim to significantly improve smart services quality, such as maximum throughput and reduced latency. The number of internet-connected smart devices grows exponentially on a daily basis, resulting in Big Data. Several other cloud-based implementations use data centers as centralized servers for handling data collected by edge devices. This model places ever-increasing demands on computational infrastructure and communication, with unavoidable consequences for Experience and Quality-of-Service. By attempting to obtain cloud abilities closer to end users, edge computing is a novel technology which enables the development for 6G by attempting to overcome typical cloud weaknesses such as high latency and a lack of security. The purpose of this paper is to discuss challenges and opportunities in edge computing technology in 6G IoE. © 2023 IEEE.

关键词： Internet of Everything

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A Graph Neural Network with Type-Feature Attention for Node Classification on Heterogeneous Graphs

A Graph Neural Network with Type-Feature Attention for Node ...

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2022 International Conference on Automation, Robotics and Computer engineering, ICARCE 2022

作者： Chen, Kang Li, Xueying Gong, Tao Qiu, Dehong Huazhong University of Science and Technology School of Software Engineering Wuhan China Huazhong University of Science and Technology Cyberspace Security Academy Wuhan China

ISBN: (纸本)9781665475488

Heterogeneous graphs are emerging as a prevalent form of data representation to capture complex structures and different relationships between a set of different types of objects in diverse disciplines. Node classification on heterogeneous graphs is a basic and critical task that remains unaddressed until the present day. Graph Neural Network is a powerful tool and has demonstrated remarkable performance in various tasks on graphs. However, most existing graph neural networks are based on the homophily assumption, which may be unsuitable for heterogeneous graphs. In this paper, we propose a graph neural network with type-feature attention mechanism to solve the problem of node classification on heterogeneous graphs. As a heterogeneous graph is composed of a group of edges between different types of nodes, it is reasonable to assume that each type of edge plays a different role in message propagation with different importance. An attention mechanism that considers the edge type and the features of the end nodes of the corresponding edge is built and incorporated into the process of message propagation of the graph neural network, by which the different heterogeneous information of nodes and edges is used jointly in solving the problem of node classification. We evaluate the proposed method on two public real-world heterogeneous graphs and the experimental results demonstrate the effectiveness of the proposed method. © 2022 IEEE.

关键词： Classification (of information)

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