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检索条件"任意字段=2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2023"

共 1096 条记录，以下是51-60 订阅

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Deep Reinforcement Learning Aided Device Profiling for Anomaly Detection in IIoT Over Zero Trust Security Network

Deep Reinforcement Learning Aided Device Profiling for Anoma...

引用

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

作者： Singh, Anamika Dhanaraj, Rajesh Kumar Hsu, Ching-Hsien Sharma, Anupam Kumar Kumar, Pardeep School of Computing Science and Engineering Galgotias University U.P Greater Noida India Pune India Asia University Dept. Computer Science and Information Engineering Taiwan Manav Rachna International Institute of Research and Studies India

ISBN: (纸本)9798350341072

The application of internet of things in industries is the fourth revolution of industries and is referred to as industry 4.0 or industrial internet of things. The shifting of manual and conventional approach to a digital platform is witnessed as a key consideration for the emergence of industrial internet of things. Industrial internet of things works around thousands of devices connected remotely and the overall monitoring, functioning and storage is performed virtually by and on devices, hence more prone to cyber threads. With the amplification in the growth and application of more complicated networks, the requirements for the security are also increased significantly. Zero trust security stands as an efficient solution for all the security questions. Zero trust security involves continuous monitoring of devices for verifying the authenticity before permitting the access to network. Continuous monitoring for detecting the honest and malicious device adversely effects the productivity of the IIoT environment. The proposed paper presents a novel approach to generate a unique profile of devices via deep reinforcement learning to avoid irrelevant authentication cycles and produce more optimized results. The proposed work surpasses the existing approaches (CRH-SRP) [1] and Cyber Security via Determinism [2] with enhanced data confidentiality rate by 3.25% and reduced false positive rate by 30.5% respectively. © 2023 ieee.

关键词： Anomaly detection

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Supporting End User Comprehension in Intelligent Decision Support Systems Using data Visualisation Techniques

Supporting End User Comprehension in Intelligent Decision Su...

引用

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

作者： Rumsamrong, Maneerat Chiou, Andrew Mahidol Univ Sch Interdisciplinary Studies Kanchanaburi Campus Kanchanaburi Thailand Cent Queensland Univ Sch Engn & Technol Sydney Campus Sydney NSW Australia

ISBN: (纸本)9781665495523

This project explores the use of data visualisation techniques to render the outcome of an inference process using graphics, symbolic and schemas in stylised pictograms that supports understanding. The example demonstrated in this paper is a continuation of CRISIS-Expert, an intelligent decision support system that accepts multiple input of various data types and then generate an outcome which is further rendered into symbols that within reason is, understandable by the end user.

关键词： data visualization intelligent support systems gamification self explanatory

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Lung Cancer Detection Using Machine Learning Methods

Lung Cancer Detection Using Machine Learning Methods

引用

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

作者： Arka, Dipak Debnath Tafhim, Sad Md. Anan, Rawnak Muntaha Rahat, Nusaibah Ishan, Samiu Mostafa Tanvir, Sifat Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

Cancer-related death is now more common than ever with cancer of the lungs being the leading cause of it. In such regards, the survival percentage for individuals with lung cancer must be increased through early identification. In this paper, we propose a machine learning-based approach for lung cancer detection using data from patients. The proposed method has three stages: preprocessing, feature extraction, and classification. In preprocessing, we extracted the necessary data from the dataset we collected. In the feature extraction stage, we processed the raw data while keeping the original information unchanged, in order for the classifier to work on. Finally, in the classification stage, we use 6 different classifier models to classify the extracted features. We evaluated the proposed method on a publicly available dataset from Kaggle of lung cancer causes and age. The results of the trial confirm the feasibility of the suggested strategy to achieve high accuracy, sensitivity, specificity, and AUC score. In conclusion, using machine learning, The suggested approach offers a powerful and efficient way to detect lung cancer. The proposed method can potentially assist in the detection of lung cancer, thereby improving the survival rate of lung cancer patients. © 2023 ieee.

关键词： Feature extraction

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PAAI: Password Authentication Using AI

PAAI: Password Authentication Using AI

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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...

引用

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 ...

引用

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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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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Stuttering Prediction With Machine Learning

Stuttering Prediction With Machine Learning

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

作者： Scott, Thomas Seliya, Naeem University of Wisconsin - Eau Claire Dept. of Computer Science Eau ClaireWI United States

ISBN: (纸本)9798350341072

Addressing stuttering is an important problem. It affects the quality of an affected person's life. If machine learning algorithms can be designed to detect stuttering in audio files containing speech, there is great potential for helping people with stuttering communicate better. Recently, there have been some labeled (stuttering types) speech datasets made available to the public. The Sep-28k and FluencyBank datasets collectively account for 32,000 three-second audio clips containing labels on whether stuttering is present or not. This paper models this data using a variety of machine learning models, including Decision Trees, Random Forest, Gradient Boosting, XGBoost, and CatBoost. In addition, different empirical case studies are conducted: predicting an individual stuttering type;predicting different types of stuttering with one model and predicting the presence or absence of any stuttering speech at all. The features in the model are a combination of audio features extracted using the Librosa Python package, and a new custom-derived audio feature. Model accuracies computed on the test data are in the 80s, which is competitive to some more advanced models. © 2023 ieee.

关键词： Decision trees

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Breaking the Cyberbullying Cycle With Machine Learning

Breaking the Cyberbullying Cycle With Machine Learning

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

作者： Chan, Johnny Kishore, Shohil Yang, Xin Information Systems and Operations Management University of Auckland Auckland New Zealand

ISBN: (纸本)9798350341072

The proliferation of digital communication platforms has provided adolescents with unparalleled connectivity, yet it has concurrently amplified the menace of cyberbullying. This paper introduces 'Serenity,' a chat application prototype underpinned by advanced machine learning (ML) and natural language processing (NLP) techniques, designed to proactively identify and counteract cyberbullying incidents in real-time. Unlike traditional lexiconbased approaches, Serenity employs dynamic ML models, ensuring adaptability to the evolving linguistic patterns. The application not only detects potential cyberbullying but also incorporates mechanisms to deter such behaviors, thereby safeguarding the mental well-being of its young users. By integrating parental oversight without infringing on adolescents' privacy, Serenity strikes a balance between safety and autonomy. This research underscores the imperative of embedding sophisticated technological interventions in digital platforms catering to vulnerable populations and advocates for the broader adoption of such mechanisms in mainstream social media platforms. The design principles presented herein hold implications for platform developers, policymakers, and educators striving to consider in co-creating a safer digital ecosystem for adolescents. © 2023 ieee.

关键词： Human computer interaction

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