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共 514 条记录，以下是21-30 订阅

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EMP-GAN: Encoder-Decoder Generative Adversarial Network for Mobility Prediction

EMP-GAN: Encoder-Decoder Generative Adversarial Network for ...

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2023 IEEE INFOCOM Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Bang, Sammy Yap Xiang Raza, Syed M. Yang, Huigyu Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665494274

Ultra-dense cell deployments in Beyond 5G and 6G result in extensive overlapping between cells. This makes current reactive handover mechanism inadequate due to availability of multiple strong signals at a position. Moreover, recently proposed predictive mobility management schemes are also not suitable as they may lead to unnecessary handovers. A predictive path-based mobility management scheme can solve these issues, but forecasting User Equipment (UE) paths with high accuracy is a challenging task. This paper proposes Encoder-Decoder Generative Adversarial Network (EMP-GAN) for forecasting multi-step ahead UE path. EMP-GAN architecture consists of generator and discriminator neural networks, where the generator predicts mobility (next multi-step target sequence) and the discriminator classifies between the predicted target sequence and the ground truth in adversarial learning. Besides adversarial learning, feature matching and fact forcing training methods are employed for fast convergence of GAN and performance improvement. EMP-GAN is evaluated on mobility dataset collected from the wireless network of Pangyo ICT Research Center, Korea, and results show that it outperforms state-of-the-art prediction models. In particular, EMP-GAN achieves 95.55%, 94.70%, 93.50%, and 92.39% accuracies for 3, 5, 7, and 9-step predictions, respectively. © 2023 IEEE.

关键词： Generative adversarial networks

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Feature Pool Exploitation for Disease Detection in Fundus Images 17

Feature Pool Exploitation for Disease Detection in Fundus Im...

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Xiang Bang, Sammy Yap Le, Kim-Ngoc Thi Le, Duc-Tai Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Retinal fundus diseases without immediate diagnoses and treatment may lead to serious consequences such as permanent visual impairment. Recently, many machine learning (ML) and deep learning (DL) models have been introduced for fundus image classification. However, those heavy models require high-end graphics processing units for training and testing, thus not suitable for real-case usage in fundus cameras with limited computation power. In this paper, we demonstrate the effectiveness of our proposed two-block model, Feature Exploitation Lightweight network (FEL-net) which consists of a Feature Exploitation Block (FEB) and Lightweight Classification Block (LCB) by comparing it with other DL models. The experiment was carried out on a dataset of 21, 697 fundus images and our model achieves 99% binary classification accuracy. The proposed robust FEB is used to generate a refined feature pool for fundus images to build an efficient ML classifier that can distinguish fundus images with disease from the normal case with high accuracy and low running time. © 2023 IEEE.

关键词： Feature extraction

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Maintenance of Model Resilience in Distributed Edge Learning Environments 19

Maintenance of Model Resilience in Distributed Edge Learning...

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19th International Conference on Intelligent Environments, IE 2023

作者： Wang, Qiyuan Anagnostopoulos, Christos Fornes, Jordi Mateo Kolomvatsos, Kostas Vrachimis, Andreas University of Glasgow School of Computing Science United Kingdom University of Lleida Department of Computer Science Spain University of Thessaly Dept. of Informatics and Telecommunications Greece Glasgow University School of Computing Science United Kingdom

ISBN: (纸本)9798350312225

Distributed Machine Learning (DML) at the edge of the network involves model learning and inference across networking nodes over distributed data. One type of model learning could be the delivery of predictive analytics services to formulate intelligent environments, however, those environments heavily rely on real-time inference and are significantly influenced by changes in the underlying data (concept drifts). Moreover, the quality of service and availability in DML environments are directly tied to each node's reliability, since such environments are highly susceptible to the impact of node failures. Even if such challenges can be tackled with distributed resilience mechanisms, their effectiveness and efficiency, due to concept drifts, should be maintained to ensure continuous and sustained quality of service. DML systems operate in dynamic environments, thus, they require their models to be updated according to the novel trends embedded in the new data they encounter. We, therefore, introduce several model maintenance mechanisms to ensure resilient DML systems in the long term when concept drifts emerge. We provide a comprehensive experimental evaluation of our resilience maintenance mechanisms over synthetic and real data showcasing their importance and applicability in edge learning environments. © 2023 IEEE.

关键词： Quality of service

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Leveraging Deep Learning for Access Control and Data Security in Cloud Environments

Leveraging Deep Learning for Access Control and Data Securit...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Blessing Ifeoluwa Omogbehin Tshiamo Sigwele Thabo Semong Dept. of Computing and Informatics Botswana International University of Science and Technology Palapye Botswana

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Cloud computing emerged as a promising model for delivering services over the internet, offering users a pay-as-you-use structure that grants access to infrastructures (Infrastructure as a service), platforms for running various programs (Platform as a Service) and software (Software as a Service) at minimal cost. Surprisingly, as cloud environments evolve and expand, access control has become a critical concern to ensure that the data stored in these systems maintains confidentiality, integrity, and availability. The increasing complexity of cloud architecture highlights the urgent need for a robust access control model, as the consequences of inadequate protection could be disastrous. This article provides a novel approach to addressing the vulnerabilities in cloud security, emphasizing the need for enhanced access control measures. It investigates how deep learning can provide a defense-in-dept. strategy to detect and prevent unauthorized access and data breaches by analyzing current gaps in cloud security research. Through a systematic analysis, this study aims to bridge these gaps, offering insights into how deep learning models can strengthen cloud environments against potential threats. By combining deep learning with traditional methods, the model dynamically optimizes access control decisions and strengthens data confidentiality. Results show that the proposed outperforms current works with accuracy of 94.39% that demonstrates considerable gains in data security, resilience, and overall performance that effectively addresses anomalies in cloud environments.

关键词： Access control Deep learning Cloud computing Accuracy Systematics Data security Cloud computing security Software as a service Data models Resilience

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Product Recommendation Using Collaborative Filtering and K-Means Clustering 5

Product Recommendation Using Collaborative Filtering and K-M...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, M. Ranjith Vishnu, S. Roshen, G. Kumar, D. Niranjan Revathi, P. Baster, David Ricardo Ledo Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India C. Abdul Hakeem College of Engineering and Technology Dept. of Computer Science & Engineering Vellore Melvisharam India University of Holguin Dept. of Informatics and Mathematics Cuba

ISBN: (纸本)9798350383522

The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today's world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-dept. examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era. © 2024 IEEE.

关键词： clustering collaborative filtering E-commerce systems K-means algorithm recommender system

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Rib Segmentation and Sequence Labeling via Biaxial Slicing and 3D Reconstruction 18

Rib Segmentation and Sequence Labeling via Biaxial Slicing a...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kim, Hyunsung Ko, Seonghyun Bum, Junghyun Le, Duc-Tai Choo, Hyunseung College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Dept. of AI System Engineering Sungkyunkwan University Suwon Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

The process of diagnosing rib lesions involves radiologists interpreting 2D CT images produced by a CT scanner. To identify the location of the lesion and make an accurate diagnosis, hundreds of 2D CT images are meticulously reviewed and ribs are classified. This study proposes Transverse and Frontal Rib Segmentation (TFRS) to address the issues of labor-intensive process, and performs Sequential labeling based on it. TFRS trains 2D images composed of Transverse and Frontal planes from the chest CT volume in the U-Net model. The combination of segmentation masks produced by the model complements spatial information from different planes, reconstructing a 3D rib volume. The performance of TFRS is evaluated using Dice, Recall, and Precision metrics, showing Dice of 90.29, Recall of 89.74, and Precision of 90.72. Sequential labeling is evaluated using the Successful Labeling rate, determining whether the 12 pairs of ribs within the chest volume have been accurately labeled in sequence. The performance of Sequential labeling based on TFRS demonstrated that out of 460 test sets, 448 were correctly labeled. © 2024 IEEE.

关键词： Image reconstruction

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Analysing and Modelling the Accuracy and Latency Trade-offs in Rate Limiting on API-Gateway

Analysing and Modelling the Accuracy and Latency Trade-offs ...

引用

International Research Conference on Smart computing and Systems Engineering (SCSE)

作者： Krishnamoorthy Caucidheesan Guhanathan Poravi Dept. of Computer Science and Engineering University of Westminster London UK Dept. of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331523268

ISBN: (纸本)9798331523275

The rate limiting service in API gateways controls request entry by throttling requests with a boundary. The rate limiting accuracy determines how efficiently it works and whether it allows requests within the throttle count or exceeds the throttle count. Latency, on the other hand, is the round-trip time of a particular API call. The accuracy of rate limiting service is defined using spillover error percentage. It is the error calculation for the requests that rate limiting service allows more than the throttle count. Requests must be sent to the rate limiting service in order to rate limit requests in the API gateway. The rate limiting service decides whether the incoming requests must be throttled. The time taken for the requests to be decided by the rate limiting service adds additional latency to the round-trip time of requests. This additional latency can be controlled by introducing a timeout. However, this could result in a degradation in the accuracy of the rate limiting. This paper investigates the particular problem and models the relationship between accuracy and round-trip latency. The findings of this research address the analysis of the accuracy and latency trade-off with respect to the parameters influencing them, and also address the prediction outcome using random forest regressor and present key findings.

关键词： Hands Degradation Analytical models Limiting Accuracy Logic gates Predictive models Low latency communication

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GAN vs. LSTM: Network State Prediction 17

GAN vs. LSTM: Network State Prediction

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Byun, Gyurin Raza, Syed M. Yang, Huigyu Kim, Moonseong Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Seoul Theological University Dept. of It Convergence Software Bucheon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Network Traffic prediction is the prerequisite for proactive traffic management, where a longer duration and high accuracy of prediction ensures a more effective solution. This paper exploits Generative Adversarial Network (GAN) architecture to propose a model that utilizes spatiotemporal features in network traffic data for extended prediction of future traffic. The generator in GAN consists of a Convolutional Long Short-Term Memory (ConvLSTM) model for predicting network traffic, while the discriminator uses Convolutional Neural Network (CNN) model that provides feedback to the generator for training. The experiments based on network traces show that the proposed model reduces the error by 12% compared to the baseline model while predicting next 48 mins of network traffic. © 2023 IEEE.

关键词： Generative adversarial networks

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Visual Field Prediction for Fundus Image with Generative AI 18

Visual Field Prediction for Fundus Image with Generative AI

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kang, Honggu Ko, Seonghyeon Kim, Juchan Le, Duc-Tai Bum, Junghyun Han, Jongchul Choo, Hyunseung Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of Ophthalmology Samsung Medical Center Seoul Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

Accurate diagnosis of glaucoma is crucial due to its high risk of blindness, but the long examination time often undermines the reliability of results by examinee's subjective factors. We propose a method to reduce examination time by generating static perimetry results with Conventional Fundus Images (CFIs) utilizing the CFI2GM technique, which leverages multimodal data. Based on data from 3,306 glaucoma patients at Samsung Medical Center in Seoul, we conducted ophthalmic image translation utilizing the Pix2Pix model. Our method, combining cGAN, L1, and SSIM loss, achieved MSE 57.9886 and PSNR 30.6057 dB. Furthermore, we received positive feedback from ophthalmologist regarding the high practical applicability of the images generated by our method. This indicates that CFI2GM can enhance the reliability of glaucoma examination results as well as reduce testing time. © 2024 IEEE.

关键词： Diagnosis

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Control of LED using BOLT Wi-Fi Module through Bubble web app over Internet

Control of LED using BOLT Wi-Fi Module through Bubble web ap...

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2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Raj, Abhishek De, Arnab Roy, Bappadittya Bhattacharya, Ankan Bhattacharjee, Koyndrik Bhattacharjee, Anup Kumar National Institute of Technology Dept. of ECE Durgapur India Vignan's Foundation for Science Technology & Research School of Computing and Informatics Dept. of ACSE AP Guntur India VIT-AP University School of Electronics Engg AP Amaravati India Hooghly Engineering & Technology College Dept. of ECE Hooghly India Dr. B.C. Roy Engineering College Dept. of Civil Engg Durgapur India

ISBN: (纸本)9798350381887

This paper presents an economical and reliable solution for LED control by utilizing the Bolt Wi-Fi module through a Bubble web app integrated with the Internet of Things (IoT), eliminating the need for programming skills. Leveraging a User Interface (UI) builder, Application Programming Interface (API), and the Bolt Wi-Fi module ESP 8266MOD (ISM-2.4GHz, PA +25dBm, 802.11b/g/n) operating at 2.4 GHz frequency, LED control is achieved. The paper introduces a block control system for LED ON and LED OFF, allowing users to select appearance styles - a success button for LED ON and a danger button for LED OFF. Digital wire plugins are employed for both LED states, utilizing pin zero with a high status for LED ON and low for LED OFF. © 2024 IEEE.

关键词： Bolts

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