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检索条件"任意字段=5th IEEE International Conference on Big Data Intelligence and Computing, DataCom 2019"

共 603 条记录，以下是51-60 订阅

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Exploring Robust DDoS Detection: A Machine Learning Analysis with the CICDDoS2019 dataset 5

Exploring Robust DDoS Detection: A Machine Learning Analysis...

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5th ieee India Council international Subsections conference, INDISCON 2024

作者： Saluja, Kamal Bagchi, Susama Solanki, Vikas Khan, Muhammad Numan Ali Dhamija, Esha Debnath, Sanjoy Kumar Chitkara University Institite of Engineering and Technology Chitkara University Punjab India University of Technology and Applied Sciences College of Computing and Information Sciences Shinas Oman

ISBN: (纸本)9798350376753

the ever-present threat posed by Distributed Denial of Service attack is highlighted. the significance of building robust cybersecurity measures that are not just inventive but also flexible. the purpose of this study paper is to investigate the effectiveness of an algorithm based on machine learning in detecting distributed denial of service assault to strengthen the resilience of Internet infrastructure. Random forest, decision tree, naïve bayes, and KNN are some of the machine learning techniques that are investigated in this study. the dataset used for this investigation is the CICDDoS 2019 data, Moreover, the research broadened its approach to identify the role of Artificial intelligence in the Cybersecurity Framework. It does this by providing insight into the relative merits of various algorithms. the study, in its essence, highlights the critical role that adaptive and intelligent defense systems play in decreasing the impact of distributed denial of service assaults (DDoS). this provides a foundation for prepared strategies in the constantly shifting world of cybersecurity. © 2024 ieee.

关键词： Cybersecurity

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Research on Face Attribute Privacy Protection Method Based on Multi task Learning 5

Research on Face Attribute Privacy Protection Method Based o...

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5th international conference on Machine Learning, big data and Business intelligence, MLBDBI 2023

作者： Gu, Fenfei Blancaflor, Eric Abisado, Mideth National University College of Computing and Information Technologies Manila Philippines Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei China Mapua University School of Information Technology Manila Philippines

ISBN: (纸本)9798350359930

With the widespread application of facial image recognition in fields such as finance, social media, and transportation, the privacy data of facial images is also frequently leaked. In order to effectively protect facial images, the protection of facial attributes is particularly important. therefore, this article combines the method of multi task learning to divide deep learning models into low-level models and top-level models. the underlying model serves as a data feature extractor;the top-level model serves as a classifier, specifically responsible for three tasks in multi task learning: business classification, privacy protection, and image restoration. the goal of the business classification task is to identify user facial attributes;the goal of privacy protection tasks is to minimize the inclusion of privacy related feature information in data features as much as possible;the goal of image restoration task is to make it difficult to recover the original image containing privacy attributes from the extracted data features. In order to measure the privacy of the data feature extractor, a loss function is set for each task, and finally, the loss functions of these three tasks are fused to obtain a data feature extractor that can protect privacy. Finally, through experimental data analysis, this method ensures both the accuracy of facial image recognition and the leakage of facial attribute privacy data. © 2023 ieee.

关键词： data privacy

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A Strategic Innovations for Stock Market Optimization by Using data Science & Explainable AI in High Frequency Trading 5

A Strategic Innovations for Stock Market Optimization by Usi...

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5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Devi, Pooja Desai, Swati Subhash Kumar, Rakesh Naruka, Mahaveer Singh Tripathi, Vikas Uttaranchal Institute of Management Uttaranchal University India SJJTU Department of Statistics Rajasthan Jhunjhunu India Graphic Era Deemed to Be University Department of Management Studies Dehradun India G.L. Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9798331518097

In the era of technology, financial markets especially stock market has been completely transformed through the integration of data science and explainable artificial intelligence. this paper presents a comprehensive analysis of evolution in the high frequency trading with the support of XAI and data science which made it possible to do financial transactions in extremely high speeds, often within milliseconds. But with the execution of these techniques HFT raises few concerns regarding transparency, interpretability and complexity. this study explores how data science shapes the strategies of HFT such as market making, arbitrage strategies: simple arbitrage, triangle arbitrage, statistical arbitrage etc. this study also explores how XAI and data science techniques such as machine learning, big data analytics, momentum trading and quantum computing are being employed in optimizing trading strategies to improve the decision making and liquidity in stock market. It includes regulations of high frequency trading at global level and challenge faced by financial sector in adopting these technologies. this study investigates the qualitative Scopus indexed journal papers and ieee explores paper for the referencing the data to find out the authenticated literature review. this study will help out to market participants such as traders, investors, policymakers who aims to navigate the future of finance in India. © 2024 ieee.

关键词： Marketplaces

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Cell Segmentation Algorithm of Fully Connected U-Net ++ Network 5

Cell Segmentation Algorithm of Fully Connected U-Net ++ Netw...

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5th international conference on Machine Learning, big data and Business intelligence, MLBDBI 2023

作者： Yuan, Shuping Mariano, Vladimir Y. National University College of Computing and Information Technologies Manila Philippines AnHui XinHua University School of Big Data and Artificial Intelligence Hefei China

ISBN: (纸本)9798350359930

With the continuous emergence and wide application of new medical imaging technologies, doctors can easily use them to provide key and reliable information for the diagnosis of lesions in real time. At the same time, with the continuous development of computer vision, deep learning has derived many model structures in the field of medical image segmentation. Among them, U-net network structure has become one of the important segmentation models in the field of medical image because of its clever skip connection, which can retain complete features while locating clear boundaries. Algorithms based on deep learning often rely on the training of massive data sets. In the field of cell images, dataset calibration is very difficult. Especially in multi-cell images, the position and boundary of nucleus and cytoplasm are ambiguous, and can not be correctly calibrated even by a large amount of manpower. And from the perspective of imaging principles, medical image data is complex and not informative enough. Based on the comparison between the improved fully connected U-net++ network and FCN, SegNet, U-Net and other network models, this paper proposes a data augmentation method for the selection and splicing of cell sample data from the perspective of data sampling and augmentation, which can effectively transform the weak supervised data containing part of the label into strong supervised data for training. © 2023 ieee.

关键词： Cytology

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Applying Machine Learning to Enhance Human Resource Management Strategies for Improved Organisational Results 2

Applying Machine Learning to Enhance Human Resource Manageme...

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2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Joshi, Meeta Misal, Ashutosh N Gosavi, Pranita R Gautam, Sharad Lourens, Melanie Mungekar, Pratik Rajan Marwadi University Faculty of Management Studies India Indira Institute of Management Pune India Waltham International College London United Kingdom Graphic Era Hill University School of Management Haldwani Campus India Durban University of Technology Faculty of Management Sciences South Africa London United Kingdom

ISBN: (纸本)9798350384277

Organizations can undoubtedly count on technologies to continue to have an important part in human resource management in the period of the 5th industrial transformation. this transformation is being made possible by a number of innovations, including robotics, Internet of things (IOT), cloud services, big data, and artificial intelligence, or AI. the retention rate is rather high in staff members that fall into such group since only contented workers are productive workers. Finding the elements that contribute worker happiness will undoubtedly assist management include workable elements within the ones that are already discovered. this is the application of machine learning. Efficient prediction-making and data analysis depend heavily on machine learning methods such as grouping and segmentation. In an effort to reduce worker turnover, this article attempts to explore how machine learning affects human resource management. © 2024 ieee.

关键词： Adversarial machine learning

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Hybrid-Asynchronous Low Powered Multicore Architecture for improved battery life in future mobile computing devices 5

Hybrid-Asynchronous Low Powered Multicore Architecture for i...

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5th international conference on Artificial intelligence, big data, computing and data Communication Systems (ICABCD)

作者： Mogale, Hope Esiefarienrhe, Bukohwo Michael North West Univ Private Bag Dept Comp Sci Private Bag X2046 ZA-2735 Mafikeng South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

In this paper we present a novel Hybrid-Asynchronous Low Powered Multicore Architecture which can be adopted for improved battery life in future mobile computing devices. Most modern mobile computing devices today are powered by traditional multicore microprocessors which consume most of the energy and battery life in mobile devices. At the heart of these multicore microprocessors lies the traditional multicore architecture which is prone to problems such as dark silicon. With a novel approach this paper presents Hybrid-Asynchronous Low Powered Multicore Architecture which utilizes Hybrid-Asynchronous cores which are energy efficient and consume less energy in battery powered mobile computing devices. A first step towards reliable battery life in mobile computing devices.

关键词： Asynchronous Cores Silicon Multicore Architectures Eco-friendly

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Innovative Strategies for Biological Resource Management and Sustainable Development in the Era of big data 5

Innovative Strategies for Biological Resource Management and...

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5th international conference on Industrial IoT, big data and Supply Chain, IIoTBDSC 2024

作者： Yang, Shuxiang School Nursing and Health Management Wuhan Donghu University Hubei Wuhan China

ISBN: (纸本)9798331539818

In the era of big data, biological resource management faces unprecedented opportunities and challenges. this paper explores how to utilize big data technology to enhance the management efficiency of biological resources and promote sustainable development. First, it analyzes the application of big data in the monitoring, assessment, and decision support of biological resources. through data mining and analysis tools, a large volume of ecological data can be obtained and processed in real-time to improve resource utilization efficiency. It also proposes innovative management model strategies that combine technologies such as the Internet of things and artificial intelligence to promote the sustainable use and protection of biological resources. Finally, it emphasizes the importance of policy formulation, public participation, and interdisciplinary collaboration in promoting biological resource management and sustainable development. therefore, the effective utilization of big data technology in the biological field not only enhances resource management efficiency but also provides new ideas for achieving global sustainable development goals. © 2024 ieee.

关键词： Biotic

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A Toolkit for Text Extraction and Analysis for Natural Language Processing Tasks 5

A Toolkit for Text Extraction and Analysis for Natural Langu...

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5th international conference on Artificial intelligence, big data, computing and data Communication Systems (ICABCD)

作者： Sefara, Tshephisho Joseph Mbooi, Mahlatse Mashile, Katlego Rambuda, thompho Rangata, Mapitsi CSIR Data Sci Dept Pretoria South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Text extraction is an important part of natural language processing (NLP) tasks. Most NLP tasks like text classification, machine translation, text-to-speech, text-based language identification, text summarization, and named-entity recognition involve the use of textual data. Such data is limited for low-resourced languages making it difficult to experiment advanced NLP techniques on these languages. this paper presents a Python-based toolkit for text analysis and text extraction from different types of images, documents, and audio files. the toolkit is built as a library that has functions that can be imported and utilized for text extraction.

关键词： natural language processing text extraction text analysis

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Temporal Shifts in Customer Sentiment: Analysing Airasia Pre- And Post-Pandemic Reviews 5

Temporal Shifts in Customer Sentiment: Analysing Airasia Pre...

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5th international conference on Artificial intelligence and data Sciences, AiDAS 2024

作者： Idris, Sheema Liza Mohamad, Masurah Academy of Language Studies UiTM Perak Branch Perak Seri Iskandar Malaysia College of Computing Informatics & Mathematics UiTM Perak Branch Tapah Campus Perak Malaysia

ISBN: (纸本)9798331528553

this study thoroughly analyses customer sentiment within AirAsia reviews sourced from Skytrax during two distinct periods: 2017-2019 (pre-pandemic) and 2021-2023 (post-pandemic). AirAsia, a prominent low-cost carrier in Southeast Asia, experienced significant operational changes and customer interactions in response to the COVID-19 pandemic. the research aims to assess how these changes influenced customer sentiments and perceptions using Python language and Valence Aware Dictionary and Sentiment Reasoner (VADER) model. Results showed a trend from predominantly positive pre-pandemic sentiments to more varied and negative post-pandemic sentiments, reflecting concerns about safety, flight disruptions, and service quality. Limitations include potential biases in online reviews and data availability constraints. Future research directions involve exploring deeper contextual factors influencing sentiment shifts and integrating additional data sources for a more comprehensive analysis, offering actionable insights for airlines to enhance service quality and customer experiences amidst crisis events like the COVID-19 pandemic. © 2024 ieee.

关键词： Sales

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Novel data-Based Model for Future Epidemiology 5

Novel Data-Based Model for Future Epidemiology

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5th international conference on Artificial intelligence, big data, computing and data Communication Systems (ICABCD)

作者： Mathebula, Dephney Univ South Africa Decis Sci Pretoria South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Epidemiological studies aim at predicting the outbreak of diseases as epidemics and pandemics. this goal is often realized using closed form expressions that significantly utilize systems of differential equations. these systems of equations are often derived by groups of researchers working in global collaborative efforts. However, groups of researchers can experience a high workload in scenarios when there is large number of non - active participating researchers in a collaborative study. In addition, data explosion and increased availability can also undermine the efforts of hardworking researchers. this is because of the high velocity and variety associated with big data availability. Hence, an approach that helps researchers to address these challenges is required the discussion in this research proposes a suitable solution in this regard the proposed solution introduces the notion of cognitive epidemiology and epidemiological crawlers in a novel computing framework In the proposed computing framework, crawlers and existing closed form expressions used in epidemiological studies interact Prior to this interaction, epidemiological closed form expressions are formatted in a manner to enable the incorporation of intelligence capability. the research presents execution paths and discusses the incorporation alongside the integration of the proposed mechanism in a manner suitable for integration with the internet.

关键词： Artificial intelligence Goals Epidemiology Internet Crawlers Machine learning computing Framework Conventional Epidemiological Cycle COVID-19 bi-directional communications

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