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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33943 条记录，以下是651-660 订阅

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Moth-flame optimization for critical node detection 3

Moth-flame optimization for critical node detection

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3rd international conference on machine Vision, Automatic Identification, and Detection, MVAID 2024

作者： Zhao, Xuehua Chen, Dengfeng Shenzhen Institute of Information Technology Guangdong Shenzhen518172 China State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou550025 China

ISBN: (纸本)9781510681880

The critical node detection (CND) is after removing these critical nodes of a given number, there is the least network connectivity on a predefined measure for the residual network. The CND arouses great attention for its extensive practical applications, however, the CND is still computationally challenging until now. In this paper, we present an improved moth-flame optimization for CND, in which opposition-based learning and fast population evolution strategy are implemented to generate solutions with diversity and accelerate the convergence, and hybrid Gaussian evolution method helps the proposed algorithm to get rid of local optima traps and enhance its exploration ability. In our experiments, the performance of proposed algorithm is evaluated on the synthetic networks and real-world networks, and we also make comparisons with other algorithms. The experimental results illustrate that the proposed algorithm outperforms other current algorithms. © 2024 SPIE.

关键词： Premixed flames

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When social networks meet payment: a security perspective 17

When social networks meet payment: a security perspective

引用

17th international conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Singh, Nivedita Alawami, Mohsen Ali Kim, Hyoungshick Sungkyunkwan University Computer Science and Engineering Korea Republic of Sungkyunkwan University Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9781665453486

In the big data arena, opportunities and challenges are mixed. The volume of data in the financial institution is proliferating, which imposes a challenge to big data analytics to ensure safety during each transaction. Moreover, as more and more social networking sites (SNS) are integrating an inbuilt online payment system into their domain, an exponential surge in financial scams is expected in the upcoming days. These scenarios are alarming, and with the rapid growth in the daily addition of new end users and their increasing time spent on SNS, the situations become more vulnerable. With the existing trend of data mobilizations and rapid increase in volume, variety, and velocity of data being produced, big data has a significant role in detecting fraud incidents in financial transactions. However, in the framework of present followed international standards, there is a voluntary compliance obligation on domestic governing bodies, which is a significant source for such voluminous financial frauds on SNS. In order to strengthen the enforcement of international standards to combat financial transactions on SNS, the paper proposes that domestic legislation should comply with international standards with the further addition of machine learning encircled by domestic banking legislation. Eventually, this could solve the security and privacy governance difficulties arising from these financial frauds over SNS. We believe that with our approach of three-layer security i.e. by international standards, domestic legislation, and machine learning, the finan-cial fraud arising due to the SNS payment system will be reduced to a larger extent. © 2023 IEEE.

关键词： machine learning

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Sentiment Analysis based Emotion Extraction for COVID-19 Using Crawled Tweets and Global Statistics for Mental Health

Sentiment Analysis based Emotion Extraction for COVID-19 Usi...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Nandal, Neha Tanwar, Rohit Pathan, Al-Sakib Khan Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology India School of Computer Science University of Petroleum and Energy Studies India Department of Computer Science and Engineering United International University Bangladesh

The unpredictable and crucial challenges that occurred because of the COVID-19 pandemic disease have taken a gradual upsurge impacting over 213 countries across the globe. Different countries have taken several measures to get control over it like Lockdown, Curfews, Travel ban, etc. but still the cases were increasing and the situation was getting worse globally during some period of time. The impacts on the financial, social, and physical aspects of several citizens resulted in their psychological and mental health issues. In this work, we have quantitatively analyzed the depression, stress, and suicide cases during the period of COVID-19 globally and especially, in India. The global data including tweets (collected using a Scraper) is used for analysis. The data have been analyzed on Tableau and;sentiment analysis for extracting emotions in tweets has been performed using Python. Tweets are analyzed to extract the emotion of people in terms of Fear, Sadness, Anger, and Happiness. With total collected Tweets of 819678 from Jan 2020 to March 2022, it is found that people are more into Fear and Sadness with 59.3% and 28.9% scores respectively. © 2023 The Authors. Published by Elsevier B.V.

关键词： COVID-19

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Bibliographic Exploration of Application of machine learning and Artificial Intelligence in Solar Energy

Bibliographic Exploration of Application of Machine Learning...

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2024 international conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ajibade, Samuel-Soma M. Bashir, Faizah Mohammed Dodo, Yakubu Aminu Oyebode, Oluwadare Joshua Culpable, Rex V. De La Calzada, Limic M. Adediran, Anthonia Oluwatosin Ajibade, Temiloluwa Iyanuoluwa Sunway University Dept of Computing and Information Systems Selangor Malaysia Istanbul Ticaret Univ Dept of Comp. Engineering Turkey University of Hail Department of Decoration and Interior Design Hail55467 Saudi Arabia Najran University Architectural Engineering Department Najran66426 Saudi Arabia Afe Babalola University Civil and Environmental Engineering Ekiti State Ado-Ekiti Nigeria Cebu Technological University College of Technology Cebu Philippines Cebu Technological University College of Engineering Cebu Philippines Federal University of Uberlandia Faculty of Architecture and Urban Design Brazil Scholar School System Dept of Business Management Birmingham United Kingdom

ISBN: (纸本)9798350358155

Solar energy could mitigate global warming and climate change. Solar energy faces economic, environmental, and technical challenges. machine learning solves these technical issues. Despite several studies, machine learning in photovoltaics and solar energy is understudied. This study examines publishing patterns and bibliometrics to critically evaluate machine learning applications in photovoltaics and solar energy research. Scopus uses PRISMA. international publishing, citations, and collaboration are high. The Chinese Ministry of Education employs famous scholars like G. E. Georghiou and Haibo Ma. China is most active due to funding schemes like the National Natural Science Foundation and the National Key Research and Development Programme. This study examines publication patterns by country, institution, and funding organisation from 2014 to 2022, spanning topic categories and indicators. Examining author-keyword data to group publishing themes and identify influential journals. Increasing understanding of machine learning applications in photovoltaics and solar energy research. This project will examine the potential for significant development and the hurdles that must be overcome to leverage Cognitive Computing's benefits in cancer and tumour research. In response to the rising amount of malware, phishing, and intrusion attacks on global energy and grid infrastructure, photovoltaic and solar energy system cybersecurity may be studied. © 2024 IEEE.

关键词： Malware

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A Digital Twin Architecture for Minimizing Injuries Risks with Personalized Regimens via IoT and machine learning 9

A Digital Twin Architecture for Minimizing Injuries Risks wi...

引用

9th international conference on Smart and Sustainable Technologies (SpliTech)

作者： Mamen, Abdelkarim Kovaci, Sara Montanaro, Teodoro Sergi, Ilaria Patrono, Luigi Univ Salento Dept Innovat Engn Lecce Italy

ISBN: (纸本)9798350390797;9789532901351

Within the sports domain, the athlete needs to prioritize and maximize performance while minimizing injuries and incidents. With this aim, in recent years, the research community has started to investigate how innovative solutions like the Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twins could be utilized to monitor and improve the level of customization in athletes' training. This paper presents a Digital Twin architecture that combines IoT and AI techniques to create a dynamic digital copy of the athlete. By using physiological data captured by the wearable sensors, the proposed system can implement a machine learning(ML) layer of classification aiding in the detection of fatigue levels, one of the most important parameters that influence athletes' performances. The collected data can be used to enrich the dynamic digital copies of the athletes in order to create more realistic simulations and help the interested stakeholders in their mission of enabling athletes to reach their best performance without sacrificing their overall health. The system we propose enhances fatigue management research by utilizing non-intrusive digital scenarios.

关键词： IoT Digital Twin machine learning Artificial Intelligence fatigue sports

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Automated Spelling Error Detection in Assamese Texts using Deep learning Approaches 2

Automated Spelling Error Detection in Assamese Texts using D...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Phukan, Rituraj Neog, Mandira Goutom, Pritom Jyoti Baruah, Nomi Dibrugarh University Dibrugarh786004 India

The growing prevalence of regional languages, including Assamese, in user-generated content on social media platforms, has brought the problem of spelling errors into focus. In the realm of natural language processing (NLP) tasks, especially in hate speech and toxic comment detection models, the accuracy of spelling becomes of utmost importance to ensure that misspelled terms do not slip through undetected. This highlights the need for effective approaches to address spelling issues in such language-based applications. This research work suggests a novel approach for identifying word-level spelling errors in Assamese text. The approach employs two deep learning techniques, Long-short-term memory (LSTM) and Bidirectional Long-short-term memory (BiLSTM), to accurately detect misspelled words by analyzing the context of each word inside a sentence. The models are trained using a dataset of 2677 Assamese sentences that include both correctly and incorrectly spelled words. The LSTM and Bi-LSTM models have shown significant improvement over previous spelling detection research in Assamese. LSTM achieved an accuracy of 92.72%, while Bi-LSTM achieved 94.59% accuracy. These results highlight the efficacy of deep learning methods in identifying spelling errors in the Assamese language. By ensuring precise spelling detection and reducing misunderstandings caused by misspelled words, the proposed approach can be implemented to improve the reliability of NLP tasks such as identifying toxic comments and hate speech. © 2024 Elsevier B.V.. All rights reserved.

关键词： Long short-term memory

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A Comprehensive Study on the Role of machine learning in Hybrid Biometric Recognition

A Comprehensive Study on the Role of Machine Learning in Hyb...

引用

2022 international conference on Sustainable Computing and data Communication Systems, ICSCDS 2022

作者： Shand, Shipra Rahul Delhi Technological University Department of Software Engineering New Delhi India

ISBN: (纸本)9781665478847

There is a potential issue with unimodal biometric recognition systems. If the system is only able to use a single type of biometric, such as fingerprints, there is a risk that the system could be fooled by a fake fingerprint. As a result, this research study investigates how a hybrid biometric recognition system (HBRS) can overcome the problem with biometrics that rely on a single attribute for recognition. A HBRS is a system that uses more than one type of biometric recognition technology. machine learning (ML) is used in biometric recognition to improve the accuracy of the recognition system. Therefore, this research study explore the role of machine learning in this identification process, as well as several machine learning algorithms that aid in the construction of HBRS systems, to see which methods provide the best accuracy. © 2022 IEEE.

关键词： machine learning

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Portable data Collection Unit 3

Portable Data Collection Unit

引用

3rd international conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Guo, Zhenyan Zhu, Yongcan Bing, Kaiyang He, Jiacheng Xi'an Polytechnic University College of Electronics and Information Xi'an China

ISBN: (纸本)9798350352658

This study aims to address the challenge of inconsistent and difficult-to-unify archiving and analysis of data from live detection and preventive testing of electrical equipment in the intelligent operation and maintenance system of the power grid. To tackle this issue, we propose a portable data aggregation unit. This unit enables seamless integration of live detection and preventive testing instruments, and possesses the capability to extract data from various instruments, regardless of whether they have interfaces or not. Through communication interfaces and protocols of existing instrument devices, the unit collects device data, while for devices without interfaces, deep learning and machine learning algorithms are employed for image differentiation to achieve data recognition and communication. Consequently, collected device data can be transmitted in real-time to a platform server, resolving the problem of devices without interfaces being inaccessible and facilitating online data processing. Additionally, the collected data is utilized for transformer fault diagnosis under multiple feature data conditions. In summary, our research brings substantial improvements to the intelligent operation and maintenance system of the power grid, addressing issues of data recording and analysis inconsistency, and providing crucial support for fault prediction and health management of electrical equipment. © 2024 IEEE.

关键词： Instrument testing

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Benchmarking Unsupervised machine learning for Mobile Network Anomaly Detection

Benchmarking Unsupervised Machine Learning for Mobile Networ...

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2022 international conference on Innovations in Science, engineering and Technology, ICISET 2022

作者： Ahasan, Md Rakibul Momen, Mohammed Fahim Haque, Mirza Sanita Akram, Mohammad Rubbyat Alam, Golam Rabiul Uddin, Md Zia Brac University Bangladesh Robi Axiata Limited Bangladesh Miami University United States Sintef Digital Norway

ISBN: (纸本)9781665483971

A mobile network is a type of communication system that reaches end-users with high speed, availability and provides many services without any delay. The type of services is in the health sector, emerging market, emergency services, and many more. To ensure service availability mobile network monitoring in now gone to another level by introducing many automated ways, introducing advanced analytic. In a mobile network, multiple nodes are interconnected to each other. Those nodes are spread across various network domains and often produce a data type like network performance data. Over this huge data set, machine learning is useful to detect an anomaly for a different domain. But implementing supervised machine learning over large data will be difficult to use because it requires ground truth determination. On the other hand, unsupervised machine learning will cluster various data volumes without any prerequisite. If proper tuning is imposed on this model, it will give an outcome of efficient anomaly detection. The key content of this paper is to identify unsupervised machine learning which is best suited for mobile network anomaly detection. To do that a benchmark approach is performed over three unsupervised machine learning and these are K-means, DBSCAN, and HDBSCAN. The thumb rule of the benchmark is followed as converting the unsupervised machine learning output into a classification problem and then measuring the model performance. © 2022 IEEE.

关键词： Anomaly detection

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A study of machine learning based credit card potential default customer identification 2

A study of machine learning based credit card potential defa...

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2nd international conference on Digital Society and Intelligent Systems, DSInS 2022

作者： Xu, Sijie Lin, Peixin Luo, Wanqi Yang, Wenjun Jia, Yuntao Beijing Institute of Technology Zhuhai Campus Zhuhai519088 China

ISBN: (纸本)9781510663244

Fintech is continuously driving the overall upgrade of payment methods. Technologies such as Big data, the Internet of Things, and Artificial Intelligence continue to be applied in the payment field and significantly impact the payment industry. Based on the fusion of multiple machine-learning models, the problem of identifying potential default credit card customers is investigated in this paper. The customers are mined and classified based on their bill amount, education level, marital status and other characteristic information. The various models predict using the AutoML framework, then fused and optimized by bagging and stacking methods, and the models are evaluated using evaluation metrics such as F1 values. The test results show that the F1 value of the integrated model after multiple stacks reaches 54.3%, which is better than that of a single algorithm. © 2023 SPIE.

关键词： machine learning

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