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A Text Classification Model Based on Gaussian Multi-Head Self Attention Mechanism for Chinese Medical data 2

A Text Classification Model Based on Gaussian Multi-Head Sel...

引用

2nd IEEE international conference on Electrical Engineering, Big data and algorithms, EEBDA 2023

作者： Chen, Mo Yao, Chunlong Li, Xu Shen, Lan Dalian Polytechnic University School of Information Science and Engineering Liaoning Dalian China Dalian Polytechnic University Engineering Training Center Liaoning Dalian China

ISBN: (纸本)9781665462532

In order to improve the matching level of the Q&A system, text classification methods are used to classify user questions. Therefore, this paper proposes a text classification based on a hybrid model of CNN and Transformer in chinese medical dataset. Unlike the existing traditional CNN-based methods, we use a Gaussian multi-head self attention mechanism to obtain the text's spatial features. On the chinese medical dataset ,experimental results show that compared with the baseline model the proposed model performs better in accuracy,recall,precision and F1, in which the accuracy achieves 93.20% and the F1 value achieves 94.66%. © 2023 IEEE.

关键词： Text processing

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Fake News Detection and Classify the Category 14

Fake News Detection and Classify the Category

引用

14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Mitra, Pritha Jacob, Lija Department of Data Science Christ University Pune Lavasa Campus Pune India

A new type of disinformation has emerged: fake news, or untrue stories that have been presented as actual occurrences. We can no longer tell whether the information is true from fraudulent since so much information is published on social media these days. Artificial intelligence algorithms are helpful in resolving the fake news identification issue. In the field of natural language processing, fake news identification is a crucial yet difficult issue (NLP). In this article, we discuss similar duties as well as the difficulties associated with finding bogus news. Based on these findings, we suggest intriguing avenues for future study, such as developing more accurate, thorough, fair, and useful detection models. The average public's life is impacted by mass media since it happens regularly. Because of this, news stories are written that are somewhat true or even entirely untrue. Using online social networking sites, people deliberately promote these fake goods. It is crucial to decide whether the news is false owing to its potential to have detrimental social and national effects. The false news identification process made use of many criteria, including the headline and body content of the news piece. The suggested method works effectively in terms of producing results with excellent accuracy, precision, and memory. Comparing all the models employed in this study, it was discovered that Distillbert and multinomial naïve bayes models perform better than Logistic and others ml models. The credibility of the story may be evaluated using a larger dataset for better results and additional variables like the author and publisher of the news. © Grenze Scientific Society, 2023.

关键词： Natural language processing systems

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Synthetic data for Feature Selection 22nd

Synthetic Data for Feature Selection

引用

22nd international conference on Artificial Intelligence and Soft computing (ICAISC)

作者： Kamalov, Firuz Sulieman, Hana Cherukuri, Aswani Kumar Canadian Univ Dubai Dept Elect Engn Dubai U Arab Emirates Amer Univ Sharjah Dept Math & Stat Sharjah U Arab Emirates Vellore Inst Technol Sch IT & Engn Vellore Tamil Nadu India

ISBN: (纸本)9783031425073;9783031425080

Feature selection is an important and active field of research in machine learning and data science. Our goal in this paper is to propose a collection of synthetic datasets that can be used as a common reference point for feature selection algorithms. Synthetic datasets allow for precise evaluation of selected features and control of the data parameters for comprehensive assessment. The proposed datasets are based on applications from electronics in order to mimic real life scenarios. To illustrate the utility of the proposed data we employ one of the datasets to test several popular feature selection algorithms. The datasets are made publicly available on GitHub and can be used by researchers to evaluate feature selection algorithms.

关键词： feature selection synthetic data electronics

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Filtering Influential Features for Adolescent Positive Mental Health 20

Filtering Influential Features for Adolescent Positive Menta...

引用

20th international Computer conference on Wavelet Active Media Technology and Information processing, ICCWAMTIP 2023

作者： Yi, Cao Shimin, Cai Tao, Zhou University of Electronic Science and Technology of China Complex Lab Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

Adolescents' positive mental health is deeply associated with their growth. Identifying the factors that contribute to the positive mental health of junior and senior high school students is crucial for supporting youth development in the long run. Leveraging questionnaire data, this paper utilizes machine learning algorithms and fusion methods for feature selection to examine the factors that influence the onset of adolescent positive mental health. The influential features are ranked by importance. Then, machine learning algorithms in a heuristic form are employed to assess the predictive power of the fused features. The best f1-score of the classifiers reaches 0.844. It illustrates the effectiveness of feature filtering and provides valuable references for educational administrators to potential interventions. © 2023 IEEE.

关键词： Heuristic methods

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Ensuring the data Security and Integrity over Cloud computing Environment using Novel Cipher Strategy

Ensuring the Data Security and Integrity over Cloud Computin...

引用

2023 Annual international conference on Emerging Research Areas: international conference on Intelligent Systems, AICERA/ICIS 2023

作者： Praveen Kumar, E. Yasotha, B. Narmadha, P.G. Chandrakala, P. Ushapriya, C. Chamundeeswari, G. Tamil Nadu Coimbatore India Srmist Department of Data Science and Business Systems Tamil Nadu kattankulathur India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India St. Martin's Engineering College Department of Artificial Intelligence and Machine Learning Telangana India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350303452

The advent of cloud computing has revolutionized the Internet. Users may effortlessly collaborate, back up, and access their information from any location thanks to cloud computing. When it comes to providing IT enabled services via the internet, cloud computing is the clear winner. Cloud computing allows a large number of people to share resources like computers, programs, and servers. Users of cloud services often have no idea where their data actually resides or where it came from. Users of cloud computing services are understandably concerned about the safety of their data once it has been uploaded to the cloud with respect to service providers. Privilege control, comprehensive encryption algorithms, management of keys, provenance instances and global consistency verification are only some of the numerous data privacy and security issues that occur as a result. This study introduces a novel cloud security strategy, Cloud based Novel Cipher Strategy (CNCS), and analyzes issues related to the safety and integrity of data stored in the cloud. We're looking into many data safety related factors here. In this article, we'll look closely at a selection of data protection strategies now in use throughout the globe to guarantee the highest level of security possible. Later, the study delves into the various methods of cloud based data security. To prevent unwanted access, alteration, or deletion of dynamic as well as static information, a procedure for validating their integrity is outlined. The proposed system CNCS efficiency is evaluated in the resulting section through the following metrics such as: integrity level, data storage efficiency and security level. All these metrics are evaluated based on the size of data, in which it is tested with respect to number of days the proposed scheme is tested over the industrial environment to prove the performance and efficiency of the proposed approach. © 2023 IEEE.

关键词： Cloud security

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Assessing the Performance of Small and Medium-Sized Enterprises Through data processing Techniques and GRBT Model 2

Assessing the Performance of Small and Medium-Sized Enterpri...

引用

2nd international conference on Machine Learning, Cloud computing, and Intelligent Mining, MLCCIM 2023

作者： Yu, Yi Han, Boyu Gao, Qiushi He, Weiwei Liang, Jieer Li, Yufei Liang, Zijun University of Macau Faculty of Science and Technology FST Macau China Lingnan University Hong Kong Master of Science in Finance Hong Kong Hong Kong Zhuhai College of Jilin University Ali Cloud School of Big Data Application Guangdong Province Zhuhai China Beijing Normal University-Hong Kong Baptist University United International College School of Communication Guangdong Province Zhuhai China Zhuhai College of Jilin University School of Finance and International Trade Guangdong Province Zhuhai China Macao Polytechnic University Faculty of Applied Sciences Macau China

ISBN: (纸本)9798350328530

The evaluation of small and medium-sized enterprises (SMEs) is of utmost importance given their significant role in the global economy. As such, a reliable and accurate indicator system is essential to effectively assess their performance. Currently, the existing index system for evaluating the growth of SMEs is predominantly a single index system or a composite index system designed for direct evaluation. This paper proposes a growth evaluation model for SMEs based on the growth evaluation system theory, data processing, and machine learning algorithm models. The proposed model examines the impact of various dimensions on enterprise growth. To validate its utility and feasibility, we evaluate the SMEs listed in the National Equities Exchange and Quotations (NEEQ), utilizing data processing techniques, Gradient Boosted Regression Trees (GBRT) algorithm in machine learning, and empirical analog verification to assess their growth potential. © 2023 IEEE.

关键词： Trees (mathematics)

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Predicting Student Grade Point Average: Comparison of Machine Learning Regression algorithms 24

Predicting Student Grade Point Average: Comparison of Machin...

引用

24th international Arab conference on Information Technology, ACIT 2023

作者： Mohammed, Elfadil A. Naaj, Mahmoud Abou Nachouki, Mirna Mehdi, Riyadh Department of Information Technology Ajman United Arab Emirates Ajman United Arab Emirates

ISBN: (纸本)9798350384307

Education data mining has emerged as a powerful technique for uncovering hidden patterns in educational data, forecasting academic achievement, and increasing retention rates. In this work, the performance of nine regression algorithms has been evaluated in predicting students' academic success. Information from 650 students enrolled in three different computing majors has been assembled into a dataset. The following input attributes were chosen: attendance rate, course grade, gender, course category, delivery mode, school type, and high school score;the grade point average was the target variable. Findings indicate that Random Forest Regressor, Light Gradient Boosting Machine, Gradient Boosting Regressor, and Extra Tree are the four most effective regression algorithms in the order given. Except for the Light Gradient Boosting Machine approach, the other three algorithms showed that course grade is the most important predictor of a student's GPA, followed by high school score. All four algorithms showed that gender is the least reliable indicator of GPA. Future work will conduct sensitivity analysis to evaluate the impact of individual attributes on predictions to gain more insight into the factors affecting students' performance. The effect of social, economic, and demographic features will also be investigated © 2023 IEEE.

关键词： Educational data Mining Machine Learning Regression Models Student Success Prediction

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Covid-19 Outbreak data Analysis and Prediction 5

Covid-19 Outbreak Data Analysis and Prediction

引用

5th IEEE international conference on Advances in computing, Communication Control and Networking, ICAC3N 2023

作者： Kumar, Rishabh Sonkar, Prashant Galgotias University School Of Computing Sciences And Engineering India

ISBN: (纸本)9798350330861

Resolving the crucial issue of identifying patients requiring ICU admissions in the early stages of COVID-19 management, specially when medical resources are limited, is of paramount importance. Delayed ICU admissions can lead to unfavorable outcomes such as increased mortality and higher costs. identification of patients at failure is essential to prevent complications, improve risk stratification, and enhance the outcomes of severely ill hospitalized individuals. This research paper presents a developed model that accurately predicts the need for ICU admissions using patient characteristics and information collected upon admission and during the initial hospitalization period. The model utilizes data organized in a window-based format, as provided by the hospital team and published on Kaggle. Preprocessing techniques, including imputation, cleaning, and feature selection, are applied to the data. In the cleaning process, features with zero variance, unnecessary features, and highly correlated features are removed to streamline the dataset. Various algorithms, such as K-Nearest Neighbors (KNN), Logistic Regression, Random Forest, and Decision Tree Classifier with early stopping, are employed as predictors in the developed model. It is observed that the Decision Tree Classifier yields the highest accuracy among the algorithms. The results obtained from the developed model demonstrate a sensitivity of 92.5%, specificity of 93.44%, and an AUC score of 80%. These performance metrics highlight the model's capability to effectively identify patients who will require ICU admissions, contributing to improved decision-making and resource allocation in the early stages of COVID-19 management. © 2023 IEEE.

关键词： COVID-19

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Combining Computer Vision and Word processing to Classify Film Genres 25

Combining Computer Vision and Word Processing to Classify Fi...

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25th international conference on Digital Signal processing and its Applications, DSPA 2023

作者： Andriyanov, Nikita Dementev, Vitaly Financial University under the Government of the Russian Federation Data Analysis and Machine Learning Department Moscow Russia Ulyanovsk State Technical University Radio Engineering Telecommunications and Information Security Department Ulyanovsk Russia

ISBN: (纸本)9798350337235

The paper investigates various deep learning approaches for classifying movies by genre. To prepare the initial dataset, a system for parsing textual information and images from the Internet Movie database, IMDB (https://***/) was implemented. Pandas (Python library) methods were applied to generate the dataset. Particular attention was paid to the preparation of target information and the choice of the learning loss function due to the fact that the problem was reduced to a multi-label classification. A number of transfer learning algorithms have been implemented for convolutional neural network models and transformer architectures when processing movie posters, as well as simple machine learning models and deep learning transformer-type models for text classification. The paper proposes a method for complexing extracted features from heterogeneous data in another neural network to improve classification accuracy. It was possible to achieve an increase in the average cosine distance for the test sample by 3% higher than when using standard models and about 7% in sense of intersection over union metric. © 2023 IEEE.

关键词： Learning algorithms

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Research on Cross-Border E-Commerce Customer Service System under the Integration of Computer Big data 2

Research on Cross-Border E-Commerce Customer Service System ...

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2nd IEEE international conference on Electrical Engineering, Big data and algorithms, EEBDA 2023

作者： Gu, Shanyu Guangxi Vocational and Technical College of Communications Guangxi Nanning530001 China

ISBN: (纸本)9781665462532

Today's cross-border e-commerce already has new development conditions. It not only helps enterprises to reduce production costs but also dramatically improves the economic benefits enterprises. In international e-commerce, how to make consumers inquire about the logistics status of orders more conveniently and quickly is the crucial point that affects the user's shopping experience. Combined with the distributed file storage technology in the Hadoop framework, this paper designs a cross-border e-commerce user behavior analysis system. This paper first establishes cross-border e-commerce customer profiles according to data characteristics and then analyzes user access behavior data offline. Calculate the conversion rate of website pages and statistics of popular products, and finally analyze the real-time advertising data. The test results show that the e-commerce user behavior analysis system based on the Spark framework can significantly improve the running speed and stability and increase the data processing efficiency. © 2023 IEEE.

关键词： Big data

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