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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71-80 订阅

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data vault modelling as alternative to dimensional modelling to embrace complexity in data driven decision systems: a critical systems perspective

Data vault modelling as alternative to dimensional modelling...

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

作者： Goede, Roelien North-West University Unit for Data Science and Computing Potchefstroom South Africa

ISBN: (纸本)9781665453059

We live in a complex world where uncertainty is the only certainty. Today's compelling business problem is replaced tomorrow with a problem which was not even imagined yesterday. data driven decision systems are used by managers in support of their strategic decisions and should be agile and flexibility to survive in our ever change world of complex decisions. The argument presented in this conceptual paper is that data vault modeling is inherently better equipped than dimensional modelling to handle the turbulations of our complex world. The paper investigates the underlying assumptions of dimensional modelling and data vault modelling in terms of requirements collection and the resulting data modelling techniques. It uses critical systems thinking as guiding philosophy to reflect on the benefits of understanding and modelling a variety of perspectives in a problem situation. Critical systems thinking also promotes equal opportunity and accountability. It is argued that both these aspirations can better be achieved by using dimensional modeling as alternative to dimensional modelling. We hope to promote the development of sustainable data driven decision systems which can stand the test of our turbulent times. © 2022 ieee.

关键词： Systems thinking

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PAAI: Password Authentication Using AI

PAAI: Password Authentication Using AI

引用

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

作者： Huang, David Pal, Doyel Engineering and Computer Science LaGuardia Community College Cuny Department of Mathematics United States

ISBN: (纸本)9798350341072

Online services have become an integral part of our daily life. With the increasing reliance on online services, the significance of securing the authentication of such services is growing. Most of the authentication nowadays typically uses the traditional textual passwords and provides a second layer of authentication such as SMS-based or app-based. However, it usually suffers from several limitations such as low memora-bility, network availability, susceptibility to brute-force attacks, dictionary attacks, and password reuse. In contrast, graphical passwords offer an alternative form of authentication that is both intuitive and easy to remember. Instead of relying on characters, graphical passwords use images or symbols to authenticate users. In this project, we propose a novel graphical password authen-tication mechanism using text-to-image and artificial intelligence (AI). Our proposed technique, Password Authentication using AI (PAAI), generates images from textual passwords, which makes them easy to memorize and unique while retaining the basis of graphical password authentication. The graphical password mechanism using AI has great potential to revolutionize graphical password authentication, while offering a novel way to improve security and usability in a single system. © 2023 ieee.

关键词： Artificial intelligence

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A New Integrated Fuzzy Optimization Approach for Sustainable Supply Chain Planning Subjected to Sustainability and Uncertain Environments

A New Integrated Fuzzy Optimization Approach for Sustainable...

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

作者： Sutthibutr, Noppasorn Hiraishi, Kunihiko Chiadamrong, Navee Thajchayapong, Suttipong Thammasat University Pathum Thani Thailand Ishikawa Japan National Electronics and Computer Technology Center Data Science and Analytics Research Pathum Thani Thailand

ISBN: (纸本)9798350341072

This study proposes an integrated fuzzy optimization approach for sustainable supply chain planning subjected to sustainability and uncertain environments. The proposed approach combines the concept of Chance-Constrained Programming (CCP) with Intuitionistic Fuzzy Linear Programming (IFLP) that decision makers can control risk violation of fuzzy constraints as well as consider satisfaction and non-satisfaction levels simultaneously. The effectiveness of the proposed approach is demonstrated through a case study by minimizing total costs and maximizing supplier's social and environmental scores where costs and customer demands are imprecise. Our comparative result clearly shows that the obtained solution outperforms the result from the traditional integrated fuzzy optimization approach. The advantages of the proposed approach can assist decision-makers to prepare and take necessary actions for future uncertainty, leading to sustainability. © 2023 ieee.

关键词： Supply chains

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Risk Prediction of Opioid Dependency Using Machine Learning Based on Electronic Health Record

Risk Prediction of Opioid Dependency Using Machine Learning ...

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

作者： Madanian, Samaneh Dai, Sherry Natarajan, Priyadarshini Rasoulipanah, Hamidreza Auckland University of Technology Dept. of Computer Science and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350341072

Opioid abuse and dependence have emerged as a pressing global concern, posing significant challenges to public health and society. Early identification and prediction of opioid dependency represent crucial steps in mitigating its abuse impact on individuals and communities. The application of machine learning techniques to analyze medical data has opened new avenues for achieving this goal. While this field and the prediction is still in its infancy, our research explores the potential of several machine learning algorithms including LightGBM for this risk prediction. To tackle the inherent class imbalance in the MIMICIII dataset, we implemented the Synthetic Minority Oversampling Technique (SMOTE). We developed predictive models using four distinct algorithms: decision trees, random forests, support vector machines, and LightGBM. These models were meticulously evaluated to assess their performance. Ultimately, our findings revealed that the LightGBM model outperformed the other algorithms, demonstrating superior accuracy and achieving a higher Area Under the Curve value. This outcome underscores the potential of LightGBM as a valuable algorithm in the early prediction of the risk of opioid dependence, thereby offering substantial benefits to both patients and society at large. © 2023 ieee.

关键词： Risk assessment

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Orchestrating the Resilience of Cloud Microservices Using Task-Based Reliability and Dynamic Costing

Orchestrating the Resilience of Cloud Microservices Using Ta...

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

作者： O'Neill, Vyas Soh, Ben La Trobe University Department of Computer Science and Information Technology Melbourne Australia

ISBN: (纸本)9781665453059

In recent years, cloud computing has grown in popularity as enterprises realize the technical and economic benefits of managing resources using an on-demand model. With this growth come user expectations of quality of service, availability and reliability. As the trend in cloud systems architecture moves from monolithic systems to microservices, opportunities exist to develop granular solutions for improving the reliability of the overall system. In this paper we propose an approach to the resilience of microservices-based cloud systems using task-based reliability, quantifying of the reliability requirement with respect to the marginal cost of additional redundancy and the cost of failure. Our approach enables the cloud orchestrator to manage replication of microservice containers to reach an optimal, cost-effective level of redundancy. Empirical testing showed our approach achieved a lower overall cost in comparison with a fixed-reliability approach under an exponential failure function. © 2022 ieee.

关键词： Cloud computing

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An Efficient Deep Learning Approach to Detect Neurodegenerative Diseases Using Retinal Images

An Efficient Deep Learning Approach to Detect Neurodegenerat...

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

作者： Irfanuddin, Chowdhury Mohammad Shafin, Wasique Islam Ahmed, Koushik Khan, Md. Hasib Ashraful Alam, Md. Rahman, Rafeed Dipto, Shakib Mahmud Brac University Department of Computer Science and Engineering Dhaka Bangladesh Research Lab Dept. of Cse Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

Neurodegenerative disorders like Multiple Sclerosis, Parkinson's disease, and Alzheimer's disease typically get diagnosed through brain MRI, CT scans, genetic testing, and various laboratory screening tests that are often tedious, time-consuming, and beyond the reach of the financial capability of many people especially, those living in the developing world. Moreover, they often pose potential hazards for patients with certain pre-existing conditions. To remedy this, we propose an efficient deep-learning approach that enables the detection of such neurodegenerative diseases through retinal images rather than brain images. Our proposed system can proactively detect such disorders simply through retinal scans, which are fast and cost-effective compared to traditional diagnostic approaches, which require expensive and sophisticated equipment. We used a dataset containing retinal cross-sectional images of 21 Multiple Sclerosis patients and 14 healthy individuals for our research, and our model achieved 98.85% accuracy while classifying healthy and diseased individuals from retinal scans. © 2023 ieee.

关键词： Neurodegenerative diseases

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Explainable Artificial Intelligence for Maize Disease Diagnostics

Explainable Artificial Intelligence for Maize Disease Diagno...

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

作者： Banadda, Mubaraka Aloysius, Ntanda Kimuli Nakazzi, Solome Ernest, Othieno Benedict Owekitiibwa, Mukisa Samuel Marvin, Ggaliwango Makerere University Department of Computer Science Kampala Uganda Makerere University Department of Computer Kampala Uganda

ISBN: (纸本)9798350341072

This study focuses on the use of machine learning techniques, specifically Explainable AI and Deep Learning, to diagnose two prevalent diseases in maize crops: maize streak and maize leaf blight. These diseases have been major obstacles for farmers in effectively managing and identifying infections, leading to decreased harvest quantity and quality. By developing and training a customized model using supervised machine learning, combined with Transfer Learning using pretrained models like VGG19, MobileNet, we aim to improve maize disease diagnostics. The accuracy of the generated models was assessed, and the best performing model is selected for further analysis utilizing Explainable AI methods. The final step involves deploying the model on a mobile application, with the web application serving as a platform for visualizing the stored data collected through the mobile app in an external database. The ultimate objective of this research is to equip farmers with the necessary tool to enhance agricultural productivity by enabling precise and efficient diagnosis of maize diseases. © 2023 ieee.

关键词： Diagnosis

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Interpretable machine learning-based terrorist attack success rate prediction

Interpretable machine learning-based terrorist attack succes...

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

作者： Luo, Lanjun Li, Boxiao Wang, Xueyan Cui, Lei Liu, Gang School of Management North Sichuan Medical College Nanehong China School of Management Huazhong University of Science and Technology Wuhan China Information Centre Affiliated Hospital of North Sichuan Medical College Nanchong China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665453059

The success of terrorist attacks reflects the capability of terrorists and the vulnerability of the security defense, explainable prediction of the average attack success rate at the country-annual level is crucial for governments. In this study, terrorist attack data from 146 countries between 2002 to 2020 was obtained from the global terrorism database (GTD), and a two-stage prediction task was conducted. First, multiple machine learning models, including XGBoost and Random Forest (RF), are used to predict the average success rate of terrorist attacks in the next year, considering terrorism root factors and statistical results from the previous year. The results show that the RF model performs the best. Second, the prediction outputs of the RF model are explained using interpretable methods, including Accumulate Local Effect (ALE) and SHapley Additive exPlanation (SHAP), to provide counterterrorism insights applicable to countries around the world. © 2022 ieee.

关键词： Terrorism

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Enhancing Customer Experience, and Brand Awareness through Familiar Gameplay in Advergames

Enhancing Customer Experience, and Brand Awareness through F...

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

作者： Khezrian, Navid Farzinnejad, Forouzan Baghaei, Nilufar Coburg University of Applied Sciences Dept. Electrical Engineering and Computer Science Coburg Germany University of Queensland Dept. Information Technology and Electrical Engineering Brisbane Australia

ISBN: (纸本)9781665453059

The marketing field has been looking for more creative and effective ways to attract customers. Advergames have recently attracted a lot of attention in increasing brand awareness and attracting wider groups of audiences. Research studies have indicated that customers have a more positive attitude toward a brand and stronger purchase intentions when playing advergames with brand interactivity rather than without brand interactivity. Despite the increased use of advergames as a brand promotion strategy by a range of well-known brands, there is still a limited understanding of several issues related to the effective use of such games. In this paper, we conducted one study (n= 242) held in one exhibition to examine familiar gameplay in advergames and its effectiveness in attracting and engaging more people. Our results showed that the presence of familiar gameplay in advergames is an essential feature in attracting more people, enhancing their user experience, and increasing brand awareness. © 2022 ieee.

关键词： Sales

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beta Link: A Pattern Recognition Approach for Identifying Superior Event-to-Sink Links in a Spatiotemporal Correlated Sensor Network

beta Link: A Pattern Recognition Approach for Identifying Su...

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

作者： Muriira, Lawrence Mwenda School of Science and Technology Kenya Methodist University Nairobi Kenya

ISBN: (纸本)9781665453059

In large-scale wireless sensor networks (WSNs) deployment, sensed data for dissemination are highly spatial-temporal correlated within close geographical proximity. The sink is not interested in individual's sensor data but accurate information of the physical phenomenon. Pattern recognition has been proven to be a successful technique in identifying WSN links with superior performance for data transmission among spatial-temporal correlated links. This paper proposes βLink a data-driven design framework, which exploits a pattern recognition technique to identify this superior performance route for data transmission (Beta link). The technique provides an insight into;a metric that measures the degree of temporal link correlation using a Two State Markov Chain approach, and pattern classification using Support Vector Machine that also models spatiotemporal link correlation. The scheme could reduce the number of links to be utilized for data dissemination to 21 %, a significant reduction to the overall network's transmission cost. Therefore, the results to be presented are encouraging as it clearly indicates the potential of Pattern Recognition approach in identifying Beta links. © 2022 ieee.

关键词： Wireless sensor networks

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