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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6844 条记录，以下是611-620 订阅

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Secure and Robust User Authentication Using Transfer learning and CTGAN-based Keystroke Dynamics 3

Secure and Robust User Authentication Using Transfer Learnin...

引用

3rd international conference on Computing and machine Intelligence (ICMI)

作者： AbdelRaouf, Hussien Fouda, Mostafa M. Ibrahem, Mohamed I. Augusta Univ Sch Comp & Cyber Sci Augusta GA 30912 USA Idaho State Univ Dept Elect & Comp Engn Pocatello ID 83209 USA Benha Univ Fac Engn Shoubra Dept Elect Engn Cairo Egypt

ISBN: (纸本)9798350372977;9798350372984

Due to keystroke dynamics's reliability and efficiency, it has been widely used in multi-factor authentication schemes by enterprises for robust user authentication, hence enhancing online service security. Therefore, in this paper, we propose a novel methodology designed not only to enhance user authentication but also to detect imposter users who are trying to get unauthorized access. Our methodology uses transfer learning and conditional tabular generative adversarial networks (CTGAN)-based keystroke dynamics, and consists of a four-step process;mitigating outliers that hinder the model's performance through utilizing quantile transformation (QT), employing data augmentation through CTGAN, converting CSV data into 3D images, and concatenating features from three accurate transfer learning models (VGG16, ResNet50, and DenseNet121) followed by dense layers to detecting imposter users accurately and hence making precise decisions to enhance the user authentication process. We conducted extensive experiments to evaluate our methodology using the Carnegie Mellon University (CMU) keystroke dynamics benchmark dataset that contains real typing patterns, and the results demonstrate superior security performance and robustness using our methodology by achieving an accuracy of 99.99% and equal error rate (EER) of 1%, thereby, outperforming the state-of-the-art.

关键词： User authentication keystroke dynamics conditional tabular generative adversarial networks (CTGAN)

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Progress in Cross Language Text Sentiment Analysis Based on Deep learning 3

Progress in Cross Language Text Sentiment Analysis Based on ...

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3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Xu, Na Wu, Yu Department of Foreign Languages Dalian University of Science and Technology Liaoning Dalian China

ISBN: (纸本)9798331519032

With the acceleration of globalization, cross-language information processing technology is getting more and more attention. Among them, cross-lingual text sentiment analysis, as an important branch in the field of sentiment computing, understands and analyzes the user's emotional state in a multilingual environment, which is of great significance for improving user experience and optimizing decision making. In recent years, cross-lingual sentiment analysis methods based on deep learning have made significant progress. This paper summarizes the recent research progress in this field. Firstly, we introduce the research background and challenges of cross-lingual text sentiment analysis, including language differences, domain differences, data scarcity and other issues. Then, we focus on the application of deep learning-based methods, such as cross-language word embedding, adversarial transfer learning, meta-learning and other techniques in cross-lingual sentiment analysis. It is found that these methods can effectively capture the semantic and emotional links between different languages and significantly improve the accuracy of cross-lingual sentiment analysis. This paper discusses future research directions in this area, including fusing multimodal information, enhancing the generalization ability of models, and combining deep learning techniques with traditional knowledge-based methods. © 2024 IEEE.

关键词： Adversarial machine learning

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machine learning Algorithms for Binary and Multiclass Classification of IoT Network Traffic in Smart Cities 3

Machine Learning Algorithms for Binary and Multiclass Classi...

引用

3rd international conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Kumar, V Santhosh Sunehra, Dhiraj JNTUH Univ. College of Engg. Sc.& Tech. Department of ECE Hyderabad India JNTUH Univ. College of Engg Rajanna SircillaT.S. Department of ECE India

ISBN: (纸本)9798350372120

Internet of Things describes variety of embedded devices that are connected to the internet. This network of devices are used to build smart environment for smart city applications and used by many organizations. While data is constantly exchanged among various IoT devices, the detection of malicious attacks is essential for effective quality of service implementation in IoT networks. In this research, the University of New South Wales TON_IOT Train-Test dataset is used to classify network traffic. State of art machine learning methods such as Random Forest (RF), Decision Tree (DT), and Gradient Boosting (GB) are used to detect malicious IoT traffic. There are two classification types are considered in this study-binary and multi-label. When compared to the other two models, the Random Forest model gets the best accuracy of 99% in both binary and multi class classification. © 2024 IEEE.

关键词： Smart city

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Enhancing the Optimization Efficiency of Modal Filters Based on Microstrip Lines Using Regression machine learning Models 3

Enhancing the Optimization Efficiency of Modal Filters Based...

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3rd IEEE international conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Kim, Georgiy Yu. Belousov, Anton O. Gordeyeva, Viktoriya O. Tomsk State University of Control Systems and Radioelectronics Television and Control Department Tomsk Russia

ISBN: (纸本)9798331516321

This work presents the first implementation of three machine learning models to enhance the optimization efficiency of a modal filter based on a three-conductor microstrip line. The training was conducted on a comprehensive dataset (including intermediate and final results) obtained from the optimization of the modal filter using a simple genetic algorithm aimed at minimizing the maximum output voltage of the modal filter (Umax). The machine learning models considered include random forest, gradient boosting, and ridge regression. A range of key metrics was employed to evaluate the quality of the trained models, providing a comprehensive analysis of their accuracy, reliability, and efficiency. In particular, the coefficient of determination (R2), mean absolute error, and root mean square error were examined. A significant reduction in the time required to find optimal parameters was demonstrated (running a simple genetic algorithm with 5000 evaluations takes approximately 12,000 seconds) while maintaining the accuracy of the results obtained. Specifically, the performance of the three trained models on a separate dataset, not used during training, showed the ability to predict the value of Umax at modal filter output with an accuracy of up to 1.75%, 0.77%, and 7.98% for the random forest, gradient boosting, and ridge regression models, respectively. A detailed analysis of the obtained results was performed, highlighting the advantages and limitations of the trained machine learning models in the design tasks of the modal filter. © 2024 IEEE.

关键词： Genetic algorithms

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Research on the Construction and Management Monitoring of Big data Platform Based on Artificial Intelligence 24

Research on the Construction and Management Monitoring of Bi...

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3rd international conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Zheng, Yan Wuhan Polytech Univ Sch Artificial Intelligence Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9798400718212

The introduction of big data technology can bring significant changes to engineering project management, greatly improving the efficiency of various aspects of project management, providing reference for scientific decision-making, and thus achieving the improvement of project benefits. In the analysis of engineering construction, the significance of monitoring in engineering construction involves the application of many new technologies, while analysis and prediction involve many interdisciplinary methods and theories. This article conducts research on the construction and management monitoring of big data platforms based on AI (Artificial Intelligence). This article applies DL (Deep learning) to the research of this article. The research results show that the average relative error rates of interval 1 and interval 39 are 18.3%, respectively. It can be concluded that the average relative error of the quality distribution of each particle size grading in engineering filling stone mining based on AI is <= 25%. It can effectively optimize the blasting parameters of sand and stone mining, control the particle size grading of stone materials, and meet the mining grading requirements. It provides technical reference for the design of filling stone mining in embankment engineering and ensures the quality of embankment engineering. Through the construction and management monitoring of this platform, it is beneficial to reduce resource waste during the construction process and reduce construction costs for enterprises.

关键词： Artificial intelligence Construction of big data platforms Management monitoring

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Email Spam Filteration with machine learning 3

Email Spam Filteration with Machine Learning

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3rd IEEE World conference on Applied Intelligence and Computing, AIC 2024

作者： Sajwan, Manmohan Singh Singh Mahar, Abhinav Rawat, Priyanshu Sharma, Khushi Das, Purushottam Graphic Era Hill University Computer Science Engineering Dehradun India R/S Graphic Era Deemed to Be University Dehradun India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350384598

Email spam has become a significant contemporary issue, mainly propelled by the rapid expansion of internet users. Correspondingly, the incidence of email spam is on the rise. These unsolicited communications serve as vehicles for nefarious and unethical purposes, including phishing and fraudulent activities. Using sophisticated techniques spammers create malicious links capable of compromising system security and infiltrating personal data. Crafting counterfeit profiles and email accounts is effortlessly achieved by spammers, enabling them to masquerade as genuine entities in their deceptive communications. Especially vulnerable individuals are unaware of such schemes, thus emphasizing the critical need to identify and mitigate such deceptive emails. This project aims to detect fraud email spam through the application of various machine learning algorithms. Numerous machine learning algorithms such as Naïve Bayes, SVMs, KNN & Decision Trees were used with specific datasets for this project. Integration has been done for the selection of the most effective algorithm for email spam detection as per definitive metric standards. Thus, our key findings indicate that Multinomial Naïve Bayes Algorithm is most suitable for spam filtration. © 2024 IEEE.

关键词： Adversarial machine learning

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machine learning-based modeling of the mother-infant health relationship 3

Machine learning-based modeling of the mother-infant health ...

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3rd international conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Li, Yanmin University of Shanghai for Science and Technology 516 Jungong Road Shanghai China

ISBN: (纸本)9781510681279

The aim of this study is to investigate the effects of mothers' physical and mental health on infants' behavior. The model of the relationship between infants' behavioral characteristics and mothers' physical and mental indicators was established by machine learning classification methods. Eventually, 20 groups of infant behavioral characteristic types were identified. The results of the study show that the physical and mental health of mothers has an important impact on the behavioral characteristics of infants, and the machine learning method can effectively predict the behavioral characteristics of infants and provide a basis for the development of a personalized treatment plan, which is of great significance for optimizing infant development and improving the health of mothers and infants. © 2024 SPIE.

关键词： Heuristic algorithms

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Parameter recommendation system enhanced by RAG based on process knowledge 3

Parameter recommendation system enhanced by RAG based on pro...

引用

3rd international conference on Electronics and Information Technology, EIT 2024

作者： Su, Aihua Sun, Jiawei Dong, Kejing Ling, Weiqing Co. Ltd. Information Technology Center Shanghai China Tongji University Cims Research Center School of Electronic and Information Engineering Shanghai China

ISBN: (纸本)9798350369151

This article proposes an innovative method that utilizes the Neo4j graph database as a vector database to segment text, images, and table contents in process files and convert them into vector form for storage. On this basis, a parameter recommendation system was designed and implemented. The system efficiently retrieves parameter reference questions raised by users through vector similarity matching technology, and passes the matched relevant results to advanced machine learning models for further analysis. Ultimately, the system is able to accurately answer users' questions and recommend corresponding parameter ranges, significantly improving the efficiency and accuracy of enterprises in obtaining specific parameter requirements. © 2024 IEEE.

关键词： Contrastive learning

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Innovative Approaches of machine learning to Enhance Predictive Analytics Based on Banking Institution Strategies 3

Innovative Approaches of Machine Learning to Enhance Predict...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Dixit, Anuj Kumar Kokila, R. Dayananda Sagar University School of Computer Applications Kudlu Campus Bangalore68 India

ISBN: (纸本)9798350354171

Banking institutions are vital to the modern economy, serve as guardians of financial transactions and facilitators of capital flow. The proposed scheme explores innovative machine learning to bolster predictive analytics specifically for banking institutions strategies. The aim is to provide better customer insights by accurately predicting the likelihood of term deposit subscriptions, thereby enhance direct marketing strategies and optimizing resource allocation for utilizing a dataset sourced from Portuguese banking institutions, this concept delves into various innovative approaches techniques such as feature engineering, model tuning, ensemble techniques, cross validation. The primary objective is to refine the performance of existing classification models, enabling more precise predictions through experimentation and analysis, providing to uncover insights that can optimize banking strategies, greater efficiency and effectiveness in financial decision-making processes. The research is employs various machine learning algorithms, including K-Nearest Neighbors and logistic model, and conducts a comparative analysis with classification algorithms such as Gaussian NB, Decision Tree Classifier, Random Forest Classifier, XGBoost Classifier, and CatBoost Classifier, achieving a highest accuracy of 90.36%. Additionally, ensemble techniques like AdaBoost and Stacking are used to combine predictions from different classifiers. © 2024 IEEE.

关键词： Predictive analytics

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An Enhanced Diagnostic Framework for Hepatitis C Prediction Using machine learning with Advanced Feature Selection Techniques 3

An Enhanced Diagnostic Framework for Hepatitis C Prediction ...

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3rd international conference on Embedded Systems and Artificial Intelligence, ESAI 2024

作者： Adib, Asif Ur Rahman Jahin, Md. Asib Rahman Nafiz, Abdullah Al Mahmud Newaz, Asif Islamic University of Technology Department of Electrical and Electronic Engineering Gazipur1704 Bangladesh

ISBN: (纸本)9798331522056

Hepatitis C is a major global health concern and one of the leading causes of chronic liver disease, liver cirrhosis, and liver cancer worldwide. In this paper, an intelligent machine-learning algorithm-based Hepatitis C diagnosis system has been proposed. A publicly available Hepatitis C dataset has been utilized to develop the prediction framework. The dataset shows a significant imbalance between positive and negative cases, which complicates the classification process, a common challenge in healthcare datasets. Appropriate strategies have been undertaken to achieve a reliable decision framework. Two feature selection techniques, Sequential Feature Selection (SFS) and Recursive Feature Elimination (RFE), were incorporated to identify the most representative set of attributes and improve the system's diagnostic performance. Six different classifiers were employed for prediction, among which Adaboost with the SFS algorithm achieved the highest MCC score of9 2. 6%, providing the most effective prediction of Hepatitis C. This framework offers a robust approach for early diagnosis, potentially benefiting patient care and optimizing healthcare resources. © 2024 IEEE.

关键词： Adversarial machine learning

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