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IEEE/ACM 15th international conference on Utility and Cloud computing (UCC) / 9th international conference on big data computing, Applications and Technologies (BDCAT)

作者： Tanim, Mehedi Hasan Vicenciodelmoral, Abdi Yamil Templin, Zoe Zhao, Xinghui Zhao, Feng Washington State Univ Sch Engn & Comp Sci Vancouver WA 98686 USA

ISBN: (纸本)9781665460903

Modern computing applications increasingly rely on technologies in artificial intelligence, machine learning, and big data analytics. These applications often demand more powerful and energy-efficient hardware. Resistive switching random access memory (ReRAM) has emerged as a promising solution to satisfy both the storage and computing needs. In this paper, a natural organic honey film embedded with carbon nanotube (CNT) was fabricated into a resistive switching device, and the resistive switching behaviors were investigated. Endurance test results show the cycle-to-cycle variation of set and reset voltages. On/Off ratio in retention test was found to be in the order of similar to 10(5) which proves its potential as a non-volatile memory device to support neuromorphic computing applications. This research opens up opportunities to execute big data and machine learning applications with modest energy consumption and minimal electronic waste.

关键词： Neuromorphic computing machine learning nonvolatile memory resistive switching energy-efficient computing

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Hardware and Software Infrastructure Driven by big data: Challenges and Prospects 3

Hardware and Software Infrastructure Driven by Big Data: Cha...

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2023 3rd international conference on big data, Artificial intelligence and Risk Management, ICBAR 2023

作者： Chen, Kangyelin Wang, Fang Wei, Zhe School of Science Civil Aviation Flight University of China Sichuan Deyang618000 China School of Computer Science Civil Aviation Flight University of China Sichuan Deyang618000 China

ISBN: (纸本)9798400716478

This article provides a comprehensive definition of big data and focuses on the current status and development trends of big data hardware and software infrastructure by comprehensively summarizing relevant research at home and abroad. Explored potential challenges in practice and proposed corresponding solutions. In terms of hardware, the impact of storage technology, computing resources, and networks on big data processing performance and scalability was discussed. In terms of software, the focus was on the functions and characteristics of big data processing frameworks, as well as comparing their advantages and limitations in practical applications. By comprehensively summarizing the hardware and software infrastructure of big data and exploring solutions to challenges, a solid foundation can be laid for the development of the big data field, achieving more efficient, secure, and intelligent big data processing and applications. © 2023 ACM.

关键词： big data

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Development and Verification of Multi-class data in Artificial intelligence Model Group Based on Cloud computing 5

Development and Verification of Multi-class Data in Artifici...

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5th IEEE international conference on Artificial intelligence and big data, ICAIBD 2022

作者： Xiang, Qian Sichuan Sino-Tech College of Information Technology Chengdu China

ISBN: (纸本)9781665499132

Artificial intelligence (AI) has been widely studied. Due to the increasingly wide application of multi-class data, with the characteristics of rapid arrival, rapid change, huge quantity, and potential infinity, the original classification algorithm for the static database cannot adapt to the reality of multi-class data. In order to achieve reliable and accurate data dependency analysis by loop-level parallelism detection in large programs, this paper firstly designs and implements a parallel Naive Bayes (NB) multiclass data analysis algorithm. Then, we adjust the number and weight of classifiers as needed to maintain high classification accuracy and reduce the cost. Meanwhile, this paper establishes an optimal complexity model by realizing the self-organizing control of the data mining process. The results show that the parallel NB classification algorithm can bring excellent performance improvement and good scalability and can meet individuals' performance requirements for massive data classification to a certain extent. © 2022 IEEE.

关键词： Cloud computing

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Research on learning vocabulary test system under computer big data technology

Research on learning vocabulary test system under computer b...

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2022 international conference on data Analytics, computing and Artificial intelligence, ICDACAI 2022

作者： Wei, Siyu Xi'an Eurasia University Shaanxi Xi'an710065 China

ISBN: (数字)9781665454704

ISBN: (纸本)9781665454704

The application of computer big data technology in education is an inevitable technology trend leading to educational reform. The wide application of big data technology has brought new development opportunities to English learning, and big data technology has enriched the ways and means of English professional vocabulary learning. Therefore, the English vocabulary test system driven by big data has become one of the essential topics in the current education reform and the cultivation of high-quality professionals. English learning and vocabulary testing have always been the bottleneck in English learning. We must use the big data platform to provide timely and accurate data and an accurate teaching system to break this bottleneck. This paper has some enlightenment effects on English Vocabulary learning from the background of the times, vocabulary learning mode and practice, application conditions of a test system, theory, and test items. © 2022 IEEE.

关键词： big data

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Wearable Sensors Based Human Core Body Temperature computing Method

Wearable Sensors Based Human Core Body Temperature Computing...

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2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive intelligence and computing, 2023 international conference on Cloud and big data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Jiang, Linbo Wang, Yinuo Li, Ye Tao, Bo Sun, Fangmin Speakman, John Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan430081 China Hong Kong Polytechnic University Kowloon Hong Kong Shenzhen Institute of Advanced Technology Chinese Academy Sciences 518005 China University of Aberdeen King's College AberdeenAB24 3FX United Kingdom

ISBN: (纸本)9798350304602

Measuring core body temperature has significant implications in various fields, including healthcare, sports medicine, and military operations. Existing core body temperature measurement, such as rectal and esophageal measurements, are invasive, costly, and may not provide continuous monitoring, highlighting the need for non-invasive and reliable alternatives. With the development of wearable and computer technology, predicting core body temperature based on continuous measurement of skin temperature, ambient temperature, and other physiological parameters has become a trend for future development. In this study, we proposed a novel non-invasive method for predicting core body temperature using wearable sensors. A stacking fusion model was proposed to improve the core temperature prediction accuracy. The skin temperature and ambient temperature were collected by a wearable device and processed to remove outliers. Simultaneously, the core body temperature collected by a capsule temperature sensor was used as the gold standard. Then, we used the preprocessed data to train a machine learning algorithm, which was validated on collecting human data. The results showed that the algorithm based on stacking model fusion performed best, with an RMSE of 0.0448, MAE of 0.0214, and MAPE of 0.0582%, indicating high precision in predicting core body temperature, which can be used for continuous monitoring and accurate diagnosis, effective medical treatment. © 2023 IEEE.

关键词： Forecasting

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Guest Editorial for Special Section on the 16th international conference on Intelligent computing (ICIC)

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第6期19卷 3124-3125页

作者： Huang, De-Shuang Han, Kyungsook Akutsu, Tatsuya Guangxi Acad Sci Big Data & Intelligent Comp Res Ctr Nanning Peoples R China Inha Univ Dept Comp Engn Incheon 22212 South Korea Kyoto Univ Inst Chem Res Bioinformat Ctr Kyoto 6068501 Japan

The eight papers in this special section were presented at the Sixteenth international conference on Intelligent computing (ICIC) that was held in Bari, Italy, on October 2-5, 2020. ICIC was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring together researchers and practitioners from both academia and industry to share ideas, problems and solutions related to the multifaceted aspects of intelligent computing.

关键词： Special issues and sections Meetings Machine learning Bioinformatics Computational biology

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INTERPRETABILITY AND COMPLEXITY REDUCTION IN IOT NETWORK ANOMALY DETECTION VIA XAI 49

INTERPRETABILITY AND COMPLEXITY REDUCTION IN IOT NETWORK ANO...

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49th IEEE international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Nascita, Alfredo Carillo, Raffaele Giampetraglia, Federica Iacono, Antonio Persico, Valerio Pescape, Antonio Univ Napoli Federico II Naples Italy

ISBN: (纸本)9798350374520;9798350374513

Due to their versatility and effectiveness, Deep Learning (DL) approaches are increasingly used in designing Network Intrusion Detection Systems (NIDSs). Specifically, Anomaly Detection (AD) approaches such as AutoEncoders proved suitable when malicious traffic for training is not available. However, understanding how and why DL models provide a certain decision is often challenging, since they are often considered black boxes. In this paper, we develop a methodology based on SHAP-a well-known eXplainable Artificial intelligence (XAI) technique-to elucidate the contribution of traffic features to the decisions of anomaly detectors. The interpretability gained through our methodology facilitates a deeper understanding of the characteristics of network traffic that drive the detection process. We evaluate our methodology on two recent IoT datasets including attack traffic (Kitsune and IoT-23). Leveraging the interpretability results, our investigation yields substantial enhancements in model complexity (up to -98%) without compromising its detection capabilities. The experimental results underscore the potential of XAI in refining and advancing the landscape of NIDSs.

关键词： Internet of Things eXplainable Artificial intelligence Intrusion Detection System Anomaly Detection

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Technology and HealthCare in the Coming Era 7

Technology and HealthCare in the Coming Era

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7th international conference on computing, Communication and Security, ICCCS 2022 and 2022 4th international conference on big data and Computational intelligence, ICBDCI 2022

作者： Lhamo, Kuenzang Gupta, Deepa Chaudhary, Alka Kumar, Anil Amity University Amity Institute of Information Technology Uttar Pradesh Noida India DIT University School of Computing Dehradun India

ISBN: (纸本)9798350309904

The fast-emerging technologies in this century fueled by covid19 crises has taught every stakeholder (technology giants, pharmacists, hospitals, patients etc.) a great lesson. Hospital with good healthcare system will have some of these technologies while there are least developed countries with no infrastructure and no technology. Using technology in healthcare will change the working of existing healthcare system to brighter one. This paper will portray different technology trends like Artificial intelligence, Machine Learning, Block chain, Robotic Surgery, Smart devices, telemedicine and so on. The promises and the obstacles with impact of the technology are discussed too. © 2022 IEEE.

关键词： Hospitals

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Analysis of Medical data Processing Technologies 63

Analysis of Medical Data Processing Technologies

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63rd international Scientific conference on Information-Technology-and-Management-Science of Riga-Technical-University (ITMS)

作者： Narigina, Marta Osadcijs, Eduards Romanovs, Andrejs Riga Tech Univ Dept Modelling & Simulat Riga Latvia

ISBN: (数字)9798350399851

ISBN: (纸本)9798350399851

This paper considers the technologies to analyze and process medical data. Such technologies as big data, blockchain, cloud computing, IoT, machine learning, and artificial intelligence are discussed in the paper. The advantages and disadvantages of these technologies for use in healthcare are considered. A comparative analysis of technologies was carried out in order to identify the optimal solution for implementation to the current healthcare system, that meets the main requirements for healthcare systems: data confidentiality, fault-tolerant system and fast access to data.

关键词： Artificial intelligence Blockchain big data Cloud computing Healthcare IoT Machine Learning Medical data analysis

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Technical Scheme for Optimizing Urban and Rural Logistics Operations and Improving the Informatization of Rural Logistics 7

Technical Scheme for Optimizing Urban and Rural Logistics Op...

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7th international conference of Artificial intelligence, Medical Engineering, Education, AIMEE 2023

作者： Zheng, Zhong He, Wanxian Wei, Ganglan College of Business Nanning University Nanning China King Mongkut's Institute of Technology Ladkrabang Bangkok10520 Thailand Artificial Intelligence Research Institute Guangxi Academy of Sciences Nanning China Administration Section Guangxi Mingyang Farms Nanning China

ISBN: (纸本)9781643684901

In order to optimize the core problems encountered in the operation of urban and rural logistics and improve the informatization degree of rural logistics, the authors of this paper conduct research on the application of big data, cloud computing, and other technologies in urban and rural logistics and informatization. Based on the current problems, the authors adopt the hierarchical design, C/S architecture, and cloud API technology to improve the low level of informatization in urban and rural logistics, as well as optimize the dispersion and repetitive construction of logistics resources for logistics enterprises in urban and rural areas. Overall, a feasible sharing technology solution for urban-rural logistics informatization has been provided, providing a certain reference for optimizing urban-rural logistics operations and improving rural informatization issues. © 2024 The Authors.

关键词： Informatization

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