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检索条件"任意字段=2nd International Conference on Computer Science and Software Engineering, CSASE 2022"

共 8423 条记录，以下是101-110 订阅

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Detecting Fake Political News on Social Media by Support Vector Machine Compared to Naive Bayes 2

Detecting Fake Political News on Social Media by Support Vec...

引用

2nd international conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Sireesha, M. Tamilselvan, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cloud Computing and Image Processing Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The primary objective is to enhance the precision of the Support Vector Machine and evaluate it in comparison to the Naive Bayes algorithm in terms of its ability to forecast fake political news on social media networks. This research work focuses on two groups: Support Vector Machine and Naive Bayes Algorithm, where each group is executed for 10 times. The ClinCalc software was used in calculating samples in which the alpha value is considered as 0.05, and the confidence interval of 95%. Result: There is a statistically significant difference between the algorithm used by Improved Support Vector Machine and Naive Baye with P value as 0.001 (Independent Sample t-Test P © 2024 IEEE.

关键词： Cloud Computing Innovative Fake News Detection Internet Access Naive Bayes Support Vector Machine

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Middle School Architect: Programmatic Modeling and Construction via a 3D Graphics Platform in Elm 2

Middle School Architect: Programmatic Modeling and Construct...

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2nd international conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Parcha, Anurag Mishra, Sanjana Anand, Christopher Kumar Pushparaj, Jagalingam Simon Fraser University BurnabyBC Canada McMaster University Computing and Software HamiltonON Canada School of Computer Science and Engineering Vellore Institute of Technology TN Vellore India

ISBN: (纸本)9798350328202

Today, school students are first introduced to geom-etry and advanced mathematical concepts by plotting a graph in 2D and they struggle with 3D concepts since current technologies are either too difficult to grasp or too simple with only drag-drop functionalities disconnected from coordinates. This article presents a 3D graphics library and an interactive, educational system for visualization of a 3D environment where planks of Cross Laminated Timber (CLT) can be manipulated by geometric and mathematical functions to produce a 3D building. Throughout, there is a focus on functions, 3D geometry and other underlying mathematics, and links are made with the world of computing, specifically computer graphics, to increase students' interest and confidence. Activities built on this library simultaneously meet multiple curricular objectives: mathematics (including 3D geometry), programming (in Elm, a web program-ming language used commercially), and sustainable development (using a negative embodied carbon building material). © 2024 IEEE.

关键词： Sustainable development

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Study on Networking Models of SDN using Mininet and MiniEdit 2

Study on Networking Models of SDN using Mininet and MiniEdit

引用

2nd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Bhaskaran, Sreebha Supriya, M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350327533

software Defined Networking (SDN) has brought about a revolutionary shift in network management and control. The driving force behind SDN's development is the need for enhanced network programmability and scalability to accommodate rapidly changing applications and services. By decoupling the control plane from the data plane, network managers gain centralized control and programmability over resources. This study explores the architecture, encompassing application, control layer, and data layer, while highlighting the pivotal role of controllers. In addition, this work also explores SDN using Mininet and MiniEdit emulators for designing custom topologies built based on real time scenarios. Consequently, the research aims to pinpoint the optimal topology suited for various applications. © 2024 IEEE.

关键词： Controllers

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From Data to Diagnosis: Investigating Approaches in Mental Illness Detection 2

From Data to Diagnosis: Investigating Approaches in Mental I...

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2nd international conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Montasser, Reem Kadry Mazen, Sherif A. Helal, Iman M. A. October University for Modern Sciences and Arts Faculty of Computer Science Software Engineering Department Giza Egypt Cairo University Faculty of Computers and Artificial Intelligence Information Systems Department Giza Egypt

ISBN: (纸本)9798350362633

Mental illness is a considerable global public health problem, impacting both individual well-being and society's health. The growing popularity of social media and the increase of other data sources led to more research interest in detecting mental illness with a particular focus on utilizing and fusing emotional information. Artificial intelligence techniques like natural language processing and others, have shown encouraging improvements in recognizing complex relationships in a wide range of textual data, including clinical notes, social media posts, and interviews. In this paper, we assess the different branches of artificial intelligence based on previous research in mental illness detection, discussing methods, trends, challenges, and future research directions. © 2024 IEEE.

关键词： Adversarial machine learning

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A Comprehensive Analysis on using Multifactor Authentication System for Three Level Security 2

A Comprehensive Analysis on using Multifactor Authentication...

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2nd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Kantipudi, Revathi Kumar Mallavarapu, Ajay Sai Rajagopal, Shinu M Kagolanu, Madhav Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350327533

In the era of digitalization, authentication is a vital part of modern society's security. Multi-factor authentication (MFA) is a vital solution that offers fast, simple, and trustworthy authentication in a range of scenarios, such as communications, online payments, and access rights management. It is specifically made for human-to-everything interactions. This research study investigates MFA, a constantly evolving field in authentication systems. The conventional password-based method of securing account access is susceptible to a growing number of attacks, including smudge, guessing, and shoulder-surfing. This research validates the need for a complete software solution that is reliable and easy to use, supporting Three Level Security. It addresses the shortcomings of traditional authentication methods as well as the challenges posed by ever- evolving security threats. © 2024 IEEE.

关键词： Authentication

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Research on Human Resource Management and Prediction Model Based on Neural Network Optimized by Genetic Algorithm 2

Research on Human Resource Management and Prediction Model B...

引用

2nd IEEE international conference on Image Processing and computer Applications, ICIPCA 2024

作者： Qi, Zexuan Yang, Sihan He, Xinjian Shi, Desheng Xinjiang University of Science & Technology Xinjiang Korla China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Engineering Guangzhou Huali College Guangzhou China Modern Agricultural College Honghe Vocational and Technical College Yunnan Mengzi China

ISBN: (纸本)9798350360240

This paper investigates the application of artificial neural networks optimized based on genetic algorithms in human resource management in hospital enterprises and constructs a human resource management and predictive analysis model. The model aims to support enterprises in achieving the optimal allocation of human resources by mining their internal human resource data and revealing the intrinsic correlation between personnel structure, business type and future development strategy. The study adopts multiple regression analysis method and combines the artificial neural network optimized by genetic algorithm to construct a human resource demand prediction model. Taking the operational data of Hospital X as an example, the model predicts the number of human resources in the hospital and analyzes in depth the demand for staff types in each department. The results show that the model has high accuracy and reliability in predicting the variables of hospital revenue, wage level, R&D investment and number of personnel, with a prediction error range between 0.09% and 1.27%. The research in this paper provides strong data support for enterprises, which helps them to realize the rational allocation of human resources and strategic transformation, and then maintain their competitive position in the market. © 2024 IEEE.

关键词： Prediction models

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Prediction of Brain Tumor Severity with Magnetic Resonance Imaging using Ridge Regression Over Recurrent Neural Network 2

Prediction of Brain Tumor Severity with Magnetic Resonance I...

引用

2nd IEEE international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Bilal, D. Mohamed Tamilselvan, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350306415

The goal of the research work is to predict Brain tumors using the Ridge Regression algorithm and compare it with the Recurrent Neural Network. The research study involves two groups, Ridge Regression and Recurrent Neural Network, which were both examined using a sample size of 10 calculated with an alpha value of 0.05 and a confidence interval of 95% using the ClinCalc software. The image analysis dataset used in this study contains 82,347 tuples and was obtained from ***. The Ridge Regression algorithm obtained an accuracy of 96.47%, while the Recurrent Neural Network classifier achieved an accuracy of 90.11%. An independent sample t-test conducted in SPSS statistical analysis indicated a statistically significant difference between the two algorithms with a p-value of 0.002 (p © 2024 IEEE.

关键词： Brain Tumor Human Machine Learning Magnetic Resonance Imaging Recurrent Neural Network Ridge Regression

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Stock price prediction research in steel and food industries using random forest algorithm 2

Stock price prediction research in steel and food industries...

引用

2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Jiang, Yuxiao Yang, Lei Du, Shipei Sun, Yu Guangdong University of Science and Technology Dongguan China Dongguan Key Laboratory of Industrial Software for Intelligent Manufacturing Dongguan China

ISBN: (纸本)9781510689053

The application of computer technology in the financial field and the utilization of computer algorithms to enhance the accuracy of stock price prediction have drawn the attention of numerous researchers. Against this backdrop, this study aims to precisely predict the stock prices in the steel and food industries by leveraging the random forest algorithm in the computer domain, so as to provide valuable reference for investors. This study selected the historical trading data of six listed A-shares as the basic data source. Firstly, the dataset was divided into the training set and the testing set according to a scientific proportion. Subsequently, the parameters of the random forest model were reasonably set, and thus the random forest model was constructed for subsequent analysis. Through the practical application of this model to predict the daily closing prices of stocks, it was found that the model demonstrated excellent capabilities and adaptability, with relatively small deviations between the predicted values and the actual values. This indicates that the model has relatively high reliability and practical value in predicting the stock prices of the steel and food industries. In conclusion, the application of the random forest algorithm in predicting the stock prices of the steel and food industries is an effective method, which has a promising application prospect and is worthy of further in-depth research and promotion. © 2025 SPIE.

关键词： Food industry

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Performance Evaluation of Digital Traffic Microservice Chain Application of YOLOv5 Model 2

Performance Evaluation of Digital Traffic Microservice Chain...

引用

2nd international conference on Cloud Computing, Big Data Application and software engineering, CBASE 2023

作者： Zhang, Hang Changchun University of Science and Technology School of Computer Science and Technology Changchun China

ISBN: (纸本)9798350331448

In the realm of digital transformation, the utilization of AI-powered digital technology encounters a multitude of challenges, among which lies the imperative need to restructure the IT framework. This endeavor centers on a digital intelligent transportation system, leveraging YOLOv5, Kafka, and Flink big data technologies as its pillars of support. Its core objective is to conceptualize a microservices architecture tailored for digital technology applications while introducing the 3D (Dimensional Factors in software Architecture) architectural model for a comprehensive assessment of microservice interconnections. Empirical findings underscore that the architectural design and execution of digital technology applications unfold on a substantial scale. Moreover, it culminated in the successful evaluation of both individual microservice performance and the holistic performance of interlinked microservices, thereby offering pragmatic insights for the efficient deployment of digital technologies. © 2023 IEEE.

关键词： Big data

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A Study on Financial Time Series Forecasting Based on LSTM-GARCH Modeling 2

A Study on Financial Time Series Forecasting Based on LSTM-G...

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2nd IEEE international conference on Electrical, Automation and computer engineering, ICEACE 2024

作者： Yang, Liu Wu, Qi Liang, Shenrui Mu, Sen Xue, Zilian Yang, Yixuan Peking University School of Software & Microelectronics Beijing China Nanjing University of Information Science and Technology Nanjing China University of Macao China Northeastern University at Qinhuangdao Qinhuangdao China University of Sydney Sydney Australia University of International Business and Economics Beijing China

ISBN: (纸本)9798350368208

This paper provides an in-depth study on financial time series forecasting and risk management, utilizing a hybrid approach of LSTM and GARCH models to enhance the forecasting accuracy of financial data volatility. First, through empirical analysis of multiple financial datasets, it is demonstrated that the hybrid model performs excellently in predicting future volatility with a prediction accuracy of 94.3%, which is significantly better than that of the traditional GARCH model (85.7%) and the single LSTM model (89.5%). Second, the model is particularly good at capturing extreme market volatility, and is able to more accurately predict risk during periods of high market volatility. Finally, in the application of portfolio management, the model verifies its effectiveness through backtesting analysis, with a 12.4% reduction in maximum retracement and a 0.15 improvement in the Sharpe ratio, showing a significant increase in risk-adjusted returns. The study shows that combining deep learning with traditional financial models not only improves prediction accuracy, but also achieves better control and return optimization in risk management. © 2024 IEEE.

关键词： Decentralized finance

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