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检索条件"任意字段=2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022"

共 960 条记录，以下是91-100 订阅

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Intelligent Control System for Efficient Energy Management in the Oil and Gas Industry

Intelligent Control System for Efficient Energy Management i...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Tasmurzayev, Nurdaulet Amangeldy, Bibars Nurakhov, Yedil Shinassylov, Shona Bekele, Samson Dawit Al-Farabi Kazakh National University Department of Artifical Intelligence and Big Data Almaty Kazakhstan Astana It University Department of Information Technology Astana Kazakhstan Al-Farabi Kazakh National University Department of Computer Science Almaty Kazakhstan

ISBN: (纸本)9798350341072

This article focuses on developing a smart Heating, Ventilation, and Air Conditioning (HVAC) control system tailored for the oil, gas, and energy industry. We discuss methods for developing an automated sensor monitoring system and regulating sensor parameters for air conditioning and temperature control. The proposed HVAC intelligent system receives real-time data from temperature sensors, leak sensors, air conditioners, and switches. The article presents the system's architecture, its operational principles, structural diagrams of the control unit, as well as the monitoring and control system. The central part of the real-time monitoring and control system was implemented using the SCADA program for regulating and monitoring sensor parameters. This work underscores the potential of intelligent HVAC systems in promoting energy conservation, enhancing operational safety, and catering to the specific needs of the oil and gas sectors. © 2023 ieee.

关键词： Energy efficiency

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MSBDA-Net: Multi-scale Siamese Building Damage Assessment Network

MSBDA-Net: Multi-scale Siamese Building Damage Assessment Ne...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Zaryabi, Erfan Hasanpour Kalantar, Bahareh Moradi, Loghman Halin, Alfian Abdul Ueda, Naonori College of Engineering University of Tehran Tehran Iran Disaster Resilience Science Team Riken Aip Tokyo Japan Universiti Putra Malaysia Department of Multimedia Serdang Malaysia

ISBN: (纸本)9781665453059

During or after natural disasters, information about location, cause, and severity, is crucial for early responders to act accordingly. Building damage is one of the major disaster types that occurred repeatedly. Being able to estimate the extent and location of damaged buildings are important so that emergency personnel and rescue teams can expedite efforts to the right building in affected location. Satellite imagery is a powerful visual resource that can be used to assess the extent of damages within a wide geographical area. However, current post-disaster practice requires manual annotation of damaged buildings, which is labor intensive and time consuming. Resultantly, traditional damage detection methods have been outperformed in terms of accuracy by Deep Learning (DL) architectures such as the Convolutional Neural Networks (CNN). Therefore, we developed a novel framework named Multi-scale Siamese Building Damage Assessment Network (MSBDA-Net). The proposed framework includes a two-step approach. The first stage is building localization, which a mask of all buildings before disaster will be generated. The second stage is a multi-scale Siamese damage assessment model, where the network takes the image pairs contained pre-and post-disaster as input and classify building on different damage levels. The evaluation results of proposed method indicate the applicability of the proposed method in both building segmentation (Fl-score=86.3%) and damage assessment (Fl-score=78.44 %) © 2022 ieee.

关键词： Buildings

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Adversarial Neuro Encoding with Binary Neural Networks

Adversarial Neuro Encoding with Binary Neural Networks

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2020 ieee asia-pacific conference on computer science and data engineering, csde 2020

作者： Valencia, Raul Sham, Chiu Wing University of Auckland School of Computer Science Auckland New Zealand

ISBN: (纸本)9781665419741

Adversarial neuro encoding could provide new insights for ciphering information with different perspectives. Nevertheless, it is still underexplored with a handful of publications on the subject. This work proposes the implementation of neuroevolved binary neural networks based on boolean logic functions only (BiSUNA) that apply payload ciphering between two agents to disperse information from an observer. The BiSUNA framework provides three distinctive attributions: It uses an adversarial neural encoding environment to improve the system data transmission;one execution yields a diversity of results given its population heuristics;lastly, it is an unconventional proposal to employ binary neural networks for the solution of symmetric ciphered problems. © 2020 ieee.

关键词： Network coding

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A Digital Monitoring System for Parents

A Digital Monitoring System for Parents

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Sharma, Neeraj Anand Kumar, Kunal Ali, A B M Shawkat University of Fiji Saweni Department of Computer Science and Mathematics Lautoka City Fiji CQUniversity Melbourne Campus School of Engineering and Technology VIC300 Australia

ISBN: (纸本)9781728163031

Since the introduction of computers, the number of users has gradually increased. With this there are a lot of tasks one can do such as browse the internet, send and receive emails, downloading contents from the internet and play games. When someone is using a computer, it becomes very difficult for others to know what exactly the other person is doing on the computer. This can be easily applied to parents because they are not aware of what their children are doing while using computers either in their rooms or even at school. This paper will introduce an effective way on how parents can monitor their children digitally with the use of software engineering. © 2019 ieee.

关键词： Monitoring

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data analysis of the National database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) Open data Japan with an adjustment of confounding factors

Data analysis of the National Database of Health Insurance C...

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8th ieee asia-pacific conference on computer science and data engineering (ieee csde)

作者： Ishii, Kazuo Nishio, Takuya Itoigawa, Yuta Sakaki, Seigo Sakagami, Ryuji Shoji, Yoshihisa Fukumoto, Yoshihiro Suwa Univ Sci Fac Engn Dept OfAppl Informat Engn Chino Nagano Japan Fukuoka Dent Coll Dept Odontol Sect Periodontol Fukuoka Japan Kurume Univ Sch Med Dept Neuropsychiat Cognit & Mol Res Inst Brain Dis Kurume Fukuoka Japan Kurume Univ Sch Med Dept Internal Med Div Cardiovasc Med Kurume Fukuoka Japan

ISBN: (纸本)9781665495523

National database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) Open data Japan publishes summary tables of fundamental data from NDB. In this report, we performed a pilot study on the mental health and dental care of patients with noncommunicable diseases (NCDs) using NDB Open data Japan. We performed data analysis using the Sex- and Age-Standardized Regional Difference Index (SA-RDI) to preliminarily show the relationship between diabetes mellitus and periodontal disease. Consequently, we could visualize the relationship between them at the prefectural (community) level.

关键词： NDB NDB Open data Japan Sex- and Age-Standardized Regional Difference Index (SA-RDI) relationship visualization

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An Novel Deep Learning Model For Detection Of Agricultural Pests And Plant Leaf Diseases

An Novel Deep Learning Model For Detection Of Agricultural P...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Uttarwar, Monica Chetty, Girija Yamin, Mohammad White, Matthew University of Canberra Faculty of Science and Technology Australia University of Canberra Faculty of Science and Technology BruceACT2617 Australia King Abdul Aziz University Management Information Systems Faculty of Economics and Administration Saudi Arabia Iig R& D Pty. Ltd. Melbourne Australia

ISBN: (纸本)9798350341072

In this paper a novel deep learning model for detection of plant leaf disease and agricultural pests. The proposed integreated hybrid model development based on EfficientNetB0 deep learning architecture allows model deployment in low resource settings with limited training data, allowing new and rare plant diseases and agricultural pests to be detected accurately. © 2023 ieee.

关键词： Deep learning

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A New Resource Configuring Scheme for Variable Workload in IoT Systems

A New Resource Configuring Scheme for Variable Workload in I...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Nam, Sunhwa A. Cho, Kyungwoon Bahn, Hyokyung Ewha University Dept. of Computer Engineering Seoul Korea Republic of Embedded Software Research Center Ewha University Seoul Korea Republic of

ISBN: (纸本)9781665453059

With the growing data size and the increased amount of computational load in machine learning workloads, configuring the resources of IoT (Internet-of-Things) systems in an energy-saving way is becoming important. For example, the workload of an unmanned aerial vehicle (UAV) depends on weather and obstacle conditions, and reconfiguring the processor voltage and memory state for these conditions is critical to battery life. However, resource configuring in traditional IoT systems is fixed in advance to meet real-time constraints. In this paper, we suggest a new resource configuring scheme for real-time jobs that can handle workload variations in emerging IoT systems. The goal of our scheme is to minimize the energy consumed by proper resource configurations in response to workload fluctuations and eliminate the tardiness of real-time jobs. To handle workload fluctuations, we categorize real-time jobs into primary jobs and additional jobs, and pre-plan the resource configuring for various workload situations. Based on this, we start the IoT system with the configuration for the primary jobs, and when additional jobs are activated, we update the resource configurations promptly for the new situation. In particular, our resource configuring scheme optimizes the supplied voltage of the processor and memory configuration for all real-time job combinations, and reflects to the system instantly as additional jobs are activated. Based on simulation experiments under various workload conditions, we show that the suggested scheme saves the battery power by 32.1% without the tardiness of real-time jobs. © 2022 ieee.

关键词： Machine learning

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Exploring Information Security Processes Effectiveness in Educational Institutions: Impacts of Organizational Factors)

Exploring Information Security Processes Effectiveness in Ed...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Daneshmandnia, Ali New York City College of Technology Computer System Technology Department City University of New York BrooklynNY United States

ISBN: (纸本)9798350341072

Information security effectiveness has become the focus of organizations whether they manage their own information, or rely on providers. The research literature suggests that organizational characteristics have a significant, positive effect on information security [1]-[4]. This paper examines the impact of organizational characteristics, specifically, the size of the organization, culture, IT budget, frequency of reported IT audits, and executive support, on the effectiveness of information security processes at higher educational institutions (HEI.) Nine information governance processes used in this study include communication of information security roles, the protection of sensitive data, and business agreement audits. Findings suggest that organizational culture, frequency of reported audits, and executive-level support positively influence information effectiveness, but the size of institutions and level of IT budget did not show any significant effect. © 2023 ieee.

关键词： Sensitive data

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Novel Metrics for Evaluation and Validation of Regression-based Supervised Learning

Novel Metrics for Evaluation and Validation of Regression-ba...

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2022 ieee asia-pacific conference on computer science and data engineering, csde 2022

作者： Rahman, Md Mostafizur Berger, Derek Levman, Jacob St. Francis Xavier University Computer Science AntigonishNS Canada

ISBN: (纸本)9781665453059

Error consistency is a validation metric for evaluating the sample-based error variability across machine learning models trained as part of in-lab validation. Many machine learning (ML) based regression algorithms are likely to be inconsistent with each other when trained repeatedly on the same task as part of standard cross validation, in part due to sampling, but also, potentially associated with the inclusion of randomness in their training paradigms, which is common in many learning techniques. In this work, we propose a novel approach to validation and evaluation of regression-based learning algorithms, called regression 'error consistency' (EC), to assist in assessing sample-wise consistency of errors as part of in-lab validation. We have applied novel EC metrics on six real-world datasets with six different regressors, evaluated the model performance with well-known metrics and compared the results with previously developed classification EC. The results demonstrate that, out of six models, the random forest achieved high accuracy but exhibited less consistency in its error profiles. This finding matches with classification based EC results. In addition, we applied the EC metrics on the MNIST digits dataset using a convolutional neural network (CNN) as part of a preliminary deep learning experiment. Though MNIST is typically treated as a classification dataset, we considered this dataset as a regression problem and the CNN model developed demonstrated good performance. We believe that the proposed EC metrics will be useful in evaluating and validating regression algorithm error consistency, including in deep learning, and will hopefully guide the machine learning research community to develop more reproducible and predictable (in terms of the errors they will make) regression algorithms. Public domain code is provided. © 2022 ieee.

关键词： Errors

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Telemedicine System Design using Blockchain in Bangladesh

Telemedicine System Design using Blockchain in Bangladesh

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Nusrat, Syeda Ajbina Ferdous, Jannatul Ajmat, Sara Binty Ali, Ameer Sorwar, Golam Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9781728163031

data security is an important issue in healthcare system in Bangladesh, especially in a rural and remote area where proper medical care doesn39;t reach in time, and the infrastructures of the medical system in those areas are impracticable. Providing proper medical care to the citizens of rural and remote regions with secure information is a challenge in Bangladesh. Telemedicine systems have been introduced for serving rural people with proper medical care, but in those systems, patients39;data is insecure. To address this issue, this paper proposed a model of telemedicine system using blockchain technology for medical care of rural people and the security of personal data of patients. Implementation of this model will help those people with proper and reliable medical care with the security of private information and may also bring more medical benefits for the citizens of Bangladesh economically, socially, and technologically. © 2019 ieee.

关键词： Security of data

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