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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4173 条记录，以下是281-290 订阅

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Using machine learning to Detect and Classify URLs: A Phishing Detection Approach 4

Using Machine Learning to Detect and Classify URLs: A Phishi...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mahesh Ananth Dheepthi Hindustan Institute of Technology and Science Computer Science and Engineering India

ISBN: (纸本)9798350300093

It has become absolutely necessary to identify malicious URLs in real time due to the growing number of cyber-attacks and fraudulent activities that take place on the internet. Within the scope of this study, proposing a method that makes use of machine learning to identify four distinct categories of URLs: phishing, malware, benign, and defacement. the training and testing dataset using for our models contains over 651,191 URLs with a variety of features, such as the length of the URL, the presence or absence of symbols, the length of the hostname, the length of the path, and many more. In order to find the machine learning algorithm and architecture that produces the best results for the classification task, by investigating a variety of options. Based on the results of our experiments, a multi-layer perceptron (MLP) architecture performs significantly better than other models, achieving an accuracy of 95.6percent. this study has implemented a parallel data processing pipeline so that handle the large dataset. this pipeline preprocesses and extracts features from URLs in parallel, which significantly reduces the amount of time needed for training. Our proposed method offers a practical answer to the problem of identifying potentially harmful URLs and is adaptable enough to be incorporated into existing infrastructure in order to improve the safety of internet users. © 2023 IEEE.

关键词： Statistical tests

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Advanced applications of machine learning Techniques in FOITS 4

Advanced Applications of Machine Learning Techniques in FOIT...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Davronbekov, Dilmurod Juraeva, Nafisa Boboev, Asilbek Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Mobile Communication Technologies Tashkent Uzbekistan New Uzbekistan University Department of Computer Science Tashkent Uzbekistan

ISBN: (纸本)9798350387490

the use of machine learning methods in fiberoptic information transmission systems (FOITS) is considered. the article discusses the basic operating principles of fiber optic systems and the problems they face, such as noise, nonlinear effects, and degradation of transmitted information. Describes various machine learning techniques used in fiber optic communication systems to control and monitor performance, prevent intelligent decisions, and suppress nonlinear fiber optic noise. Approaches used in machine learning are presented, such as neural networks, classification and regression algorithms, their application in the analysis and optimization of fiber optic systems, such as neural networks, support vector machines, classification and regression algorithms, their application in the analysis and optimization of fiber optic systems. this paper proposes a method for monitoring performance and predicting failures in optical networks based on machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences 23

Towards a Foundation Purchasing Model: Pretrained Generative...

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4th ACM international conference on AI in Finance (ICAIF)

作者： Skalski, Piotr Sutton, David Burrell, Stuart Perez, Iker Wong, Jason Featurespace Innovat Lab Cambridge England

ISBN: (纸本)9798400702402

machine learning models underpin many modern financial systems for use cases such as fraud detection and churn prediction. Most are based on supervised learning with hand-engineered features, which relies heavily on the availability of labelled data. Large self-supervised generative models have shown tremendous success in natural language processing and computer vision, yet so far they haven't been adapted to multivariate time series of financial transactions. In this paper, we present a generative pretraining method that can be used to obtain contextualised embeddings of financial transactions. Benchmarks on public datasets demonstrate that it outperforms state-of-the-art self-supervised methods on a range of downstream tasks. We additionally perform large-scale pretraining of an embedding model using a corpus of data from 180 issuing banks containing 5.1 billion transactions and apply it to the card fraud detection problem on hold-out datasets. the embedding model significantly improves value detection rate at high precision thresholds and transfers well to out-of-domain distributions.

关键词： transaction embeddings self-supervised learning generative modelling multivariate time series fraud detection

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application for Early Heart Disease Prediction Based on Data Mining Approach 4

Application for Early Heart Disease Prediction Based on Data...

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4th IEEE international conference on computer and Informatics Engineering (IC2IE)

作者： Miranda, Eka Aryuni, Mediana Bernando, Charles Hartanto, Andrian Bina Nusantara Univ Sch Informat Syst Informat Syst Dept Jakarta 11480 Indonesia

ISBN: (纸本)9781665442886

the purpose of this study was to develop an application for early heart disease prediction using a data mining approach. A clinical dataset of 303 patients was gathered from the UCI repository that was available at http://***/ml/datasets/Heart+Disease. there were 13 attributes that were used to classify the patient into two class predictions namely No presence or Have heart disease. machine learning with CART algorithm used for backend. Flask micro web framework used for middleware and Bootstrap web framework used for frontend. the experiment result performed the application for early heart disease prediction with CART achieved performance for accuracy 88.33%, precision 88.00%, F Measure 86.28%, recall 84.62%.

关键词： heart disease CART machine learning

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Incorporating Large Language Models into Named Entity Recognition: Opportunities and Challenges 4

Incorporating Large Language Models into Named Entity Recogn...

引用

4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Luo, Zhizhao Yang, Yuting Qi, Rui Fang, Zhi Guo, Yikai Wang, Youchen Beijing Institute of Computer Technology and Application Beijing China China Life Property & Casualty Insurance Company Limited Changsha China

ISBN: (纸本)9798350373233

Named entity recognition (NER) is a critical task in natural language processing, involving the identification and classification of entities in text. Traditional approaches to NER, such as rule-based methods and supervised machine learning, faced limitations in adapting to diverse domains and languages due to the need for large labeled datasets. Large language models (LLMs) such as GPT3, which undergo vast pretraining, demonstrate the promising ability to substantially improve NER beyond supervised baselines as evidenced by over a 100% increase in F1 score on domain-specific evaluation *** survey comprehensively examines the pivotal roles and methodologies of LLMs in NER. It provides a thorough analysis of two key techniques: in-context learning and fine-tuning. In-context learning enables quick NER task resolution without major model changes, while fine-tuning offers significant improvements, especially in domain-specific applications. the survey also covers essential resources, including NER corpora, LLM types, and methods of fine-tuning. It addresses challenges related to computational resources and model size. the survey concludes by outlining potential future research directions, emphasizing LLMs' contributions to advancing NER technologies. © 2023 IEEE.

关键词： Supervised learning

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Opening the Black Box: Interpretable machine learning Reveals the Relationship between Lexical Diversity and Writing Quality 4

Opening the Black Box: Interpretable Machine Learning Reveal...

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4th international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2023

作者： Li, Minzi Lin, Zhiqing Center for Linguistics and Applied Linguistics Guangdong University of Foreign Studies Guangzhou China School of English Studies Shanghai International Studies University Shanghai China

ISBN: (纸本)9798350343113

machine learning serves as the core component of artificial intelligence (AI), however, there is scant interdisciplinary research between AI and applied linguistics. Very few studies touched upon the aspect of how to leverage interpretable machine learning to solve the problem in language learning, teaching, and assessment. this study aims to lift the veil and fill this gap by employing interpretable machine-learning approaches to explore the relationship between lexical diversity and writing quality. the findings contributed to providing a finer-grained understanding of the CET-4 writing construct and its rating scale. Furthermore, it may lend empirical evidence for the Dynamic Systematic theory (DST) in language acquisition, verify the validity of automatic essay scoring, and advance teachers' language assessment literacy in the long run. © 2023 IEEE.

关键词： machine learning

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proceedings - 2019 4th international conference on Mechanical, Control and computer Engineering, ICMCCE 2019

Proceedings - 2019 4th International Conference on Mechanica...

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4th international conference on Mechanical, Control and computer Engineering, ICMCCE 2019

ISBN: (纸本)9781728146898

the proceedings contain 232 papers. the topics discussed include: optimal design of ultra-thin seats based on ergonomics;research on coverless text steganography based on multi-rule language models alternation;research on wood species recognition method based on hyperspectral image texture features;optimization of public traffic network considering demand satisfaction rate;optimization planning of distribution network structure in Benxi area based on ant colony algorithm;identity-based aggregate signature scheme in vehicle ad-hoc network;tool wear volume and residual life prediction based on extreme learning machine;and application of improved optics algorithm in intelligent lighting system.

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application of Animation of Super Sketch-Pad on Mathematics Education

Application of Animation of Super Sketch-Pad on Mathematics ...

引用

4th international conference on computer Science and Education

作者： Ge, Qiang Peng, Xi-cheng Chen, Mao Huazhong Normal Univ Engn Res Ctr Educ Informat Technol Wuhan 430079 Peoples R China

ISBN: (纸本)9781424435197

the Super Sketch-Pad (SSP) is a dynamic geometry software of China, which is a Chinese version designed for the teaching and learning of mathematics. this paper mainly introduces its motion driving mechanism, some application in dynamic geometry, locus, and trace, and so forth. SSP lead students to understand the geometrical characters of graphs visually, to see the beauty of geometry and to enhance their interests in mathematics. Dynamic geometry softwares and SSP will develop rapidly in mathematics education.

关键词： Animation Super Sketch-Pad Mathematics Education

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Assessment of the Applicability of machine learning Methods for the Detection of Pathology of Internal Human Organs based on the Results of Bioelectrography 4

Assessment of the Applicability of Machine Learning Methods ...

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4th international conference on Neural Networks and Neurotechnologies, NeuroNT 2023

作者： Shichkina, Yulia A. Fatkieva, Roza R. Sychev, Aleksandr Y. Prasad, M.S. Verma, Noida S. Saint Petersburg Electrotechnical University 'Leti' Department of Computer Science and Engineering St.Petersburg Russia Amity Institute of Space Science and Technology Amity University Noida India

ISBN: (纸本)9798350301526

the possibility of using the bioelectrography method for detecting pathology of internal organs is considered. Sets of data sets for machine learning are formed on the basis of ultrasonic diagnostics readings. machine learning methods were analyzed with an assessment of the feasibility of their use. the results of detection of pathology of internal organs with the use of classifiers are presented: Decision tree;KNN;Logistic regression;Random forest;SVC;XGBoost. © 2023 IEEE.

关键词： Pathology

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Veritas: A Sign Language-To-Text Translator Using machine learning and computer Vision 4

Veritas: A Sign Language-To-Text Translator Using Machine Le...

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4th international conference on Computational Intelligence and Intelligent Systems (CIIS)

作者： Njazi, Shaun Ng, Sokchoo Int Univ Malaya Wales Fac Arts & Sci Kuala Lumpur Malaysia

ISBN: (纸本)9781450385930

Language is a vital part of society, as being able to converse with one another allows people to grow advance and share resources. We value the ability of being able to communicate with people and being able to have our lives interact. Just as humanity advances so does language, as well as the inherent language barriers that are associated with it. One communication barrier to be overcome is improving the ability for the non-verbal and verbal speakers to effectively communicate with each other. With the aid of technology, communication between these two parties has improved. this study aims to showcase how to improve the effectiveness of communication between the non-verbal and verbal speakers by using machine learning and computer vision. An application, named Veritas was developed to allow people using sign language to communicate their messages and have that message translated into readable text. Veritas helps people who are struggling with verbal communication communicate better with a non-verbal speaker and vice versa.

关键词： machine learning computer vision Sign language Communication

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