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User Plane Management Function: A Solution for Automatic Deployment of UPF on cloud-native 5G Core Network Architecture 14

User Plane Management Function: A Solution for Automatic Dep...

引用

14th international conference on Information and Communication technology convergence, ICTC 2023

作者： Phung, Kieu-Ha Dung, Luong Vu Tung Thai, Quang Ko, Namseok Hanoi University of Science and Technology Ha Noi Viet Nam Etri Network Research Division Daejeon Korea Republic of

ISBN: (纸本)9798350313277

5G and beyond mobile networks have emerged to provide QoS stringent services for the huge number of user devices with several technological enablers including edge computing, network slicing, SDF, NFV. The Service Based Architecture 5G facilitates the separation of control plane from data plane. The service establishments are directed by network functions in the control plane, while data session establishment are served by User Plane Functions located at the edge. Various UPFs combining to working network slices, serving as the anchor points for user data traffic plays a pivotal role in ensuring seamless data delivery, quality of service (QoS), edge computing, and security. This article proposes a solution to effectively manage UPF, a new network function called User Plane Management Function integrates between SMF and UPF. The point-to-point packet forwarding control protocol is replaced by the Service Based Interface. By implementing our solution, UPF deployment on each network slice can be automatically configured, easily scalable, and efficiently managed;opening the way to deploy UPFs in the cloud-native infrastructure. © 2023 IEEE.

关键词： 5G and beyond Cloud computing Service Based Architecture

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A novel genetic algorithm for resource allocation and task scheduling in MEC scenario 5

A novel genetic algorithm for resource allocation and task s...

引用

5th international conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： He, Ying Zheng, Jianping Department of Service Technology China Mobile Research Institute Beijing100053 China

ISBN: (纸本)9781510662520

Mobile edge computing (MEC) is one of the key technologies of 5G. Due to the limited computing resources of MEC server, it’s a quite hard problem for task offloading decision and resource allocation when multiple users share the resources in MEC. To solve this problem, an improved genetic algorithm is proposed, and a multi-segment chromosome structure and a mixed fitness scaling method are designed with the target of maximizing overall system benefit in terms of time delay utility. Simulation experiments show that this algorithm has good convergence and high computational efficiency. © 2023 SPIE.

关键词： Time delay

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Prediction of Shot Selection of a Batsman in the death over of a T20 Cricket Match using Machine Learning 9

Prediction of Shot Selection of a Batsman in the death over ...

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Sahoo, Deepak Kumar Deyar, Deheem U. Gupta, Deepa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Cricket has seen remarkable changes since the beginning of the 21st century. T20 cricket has gained immense popularity, captivating a global fan base with its fast-paced action and continuous excitement. This study delves into the intriguing dynamics of the final over in T20 cricket, aiming to predict a batsman's shot selection during this crucial phasethat can drastically influence the match's outcome through the implementation of machine learning algorithms. Leveraging a comprehensive dataset obtained from sources like ESPN Cricinfo and ***, which includes ball-by-ball commentary, delivery outcomes, ball types, shot types, required run rates, and more. The proposed model used Ensemble techniques to show noteworthy improvement in the performance metrics. SHAP, an explainable AI tool, was employed to study the impact of different features on the prediction. This research seeks to provide valuable insights for cricket teams, both established and emerging, to enhance their game analysis and strategic planning during the last over, as strategic planning and execution are paramount, particularly in the second innings of a match. © 2024 IEEE.

关键词： Strategic planning

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An Explainable AI Regression Model For Gold Price Prediction 9

An Explainable AI Regression Model For Gold Price Prediction

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Bhadula, Shivam Kartik, Devashish Gupta, Deepa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Gold is an important commodity that has a significant influence on important business sectors and the world economy. Its impact on the dynamics of the market reverberates through multiple channels, making it a highly desirable asset. Central banks in many nations utilize gold as a reserve asset, which they then exchange for national currencies to inject into markets. Its inherent value is recognized by both companies and gold traders, and it is especially evident during times of inflation and market turbulence. The development of a machine learning model to anticipate the price of gold has many potential applications for traders, businesses, investors, and even laypeople who want to make money on gold investments. By utilizing a variety of significant factors that affect gold prices throughout the world, a strong prediction model based on a ten-year market trend can be developed. This research paper aims to democratize gold investing opportunities by providing end users with feasible options so they may maximize profits while minimizing risks and losses. Positive results were obtained from our investigation into a variety of machine learning models. When Explainable AI approaches are applied, it becomes possible to fully comprehend how various algorithms rank different features as key contributors during model training. © 2024 IEEE.

关键词： Investments

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A Hybrid Particle Swarm Optimization with Binary Ali Baba and the Forty Thieves Algorithm for Feature Selection 3

A Hybrid Particle Swarm Optimization with Binary Ali Baba an...

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3rd Odisha international conference on Electrical Power Engineering, Communication and computing technology, ODICON 2024

作者： Sahu, Bibhuprasad Pani, Sasmita Panigrahi, Amrutanshu Pati, Abhilash Patro, Rashmi Rani Ravindra, J.V.R. Department of It Hyderabad India Balasore College of Engineering and Technology Department of Cse Odisha Balasore India Department of Cse Odisha Bhubaneswar India Gita Autonomous College Department of Computer Science & It Odisha Bhubaneswar India Department of Ece Hyderabad India

ISBN: (纸本)9798350354379

The recent progress in technology enables a massive collection of databases. Noisy, redundant features significantly affect the machine-learning model's performance. The researchers adopted feature selection methods to solve this unavoidable scenario. It not only helps to deplete the dimension of the dataset but also plays a pivotal role in improving classification accuracy. This study presents a novel hybrid particle swarm optimization with Binary Ali Baba and the forty thieves' algorithm (BAFT) to select the significant features. The proposed model hybridizes the binary AFT, which provides a suitable search strategy with classical particle swarm optimization, as it is known for its convergence capability toward the best global solutions. KNN is used as a classifier to evaluate the efficacy of the proposed model. Using 07 different datasets, the performance of the proposed model is evaluated and compared with the traditional PSO, BAFT algorithm. To avoid the issue of overfitting, the dataset is subdivided into training and testing datasets with a 10-fold CV. The computational results demonstrate that the proposed method is more efficient than current state-of-the-art techniques. © 2024 IEEE.

关键词： ABFT Feature Selection K-Nearest Neighbor Opposition based learning PSO

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Cell Partitioning Scheme for UAV Communications to Maximize Throughput

Cell Partitioning Scheme for UAV Communications to Maximize ...

引用

2023 IEEE international conference on Metaverse computing, Networking and Applications, MetaCom 2023

作者： Hwang, Su Bin Seo, Bong-Seok Kim, Dong Ho Seoul National University of Science and Technology It Media Engineering Seoul Korea Republic of Seoul National University of Science and Technology Information Technology and Media Engineering Seoul Korea Republic of Seoul National University of Science and Technology Department of Smart Ict Convergence Engineering Seoul Korea Republic of

ISBN: (纸本)9798350333336

In a mobile communication system using UAV as movable base station, it is necessary to design cell partitioning appropriately according to the user's bandwidth demand. In this paper, we propose a partitioning method that considers UAV capacity, minimizing the overall system outage and maximizing throughput. Simulation results show that that our proposed method can improve total throughput without outage compared to the conventional Voronoi cell partitioning. © 2023 IEEE.

关键词： Cells

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Interpretable Anomaly Detection: A Hybrid Approach Using Rule-Based and Machine Learning Techniques 9

Interpretable Anomaly Detection: A Hybrid Approach Using Rul...

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Mohite, Rahul Ouarbya, Lahcen Goldsmiths University of London Department of Computing United Kingdom

ISBN: (纸本)9798350394474

Anomaly detection is a critical aspect of ensuring the security and reliability of various systems in diverse domains, including cybersecurity, finance, and industrial processes. Traditional black-box anomaly detection methods often lack interpretability, making it challenging for users to understand the reasoning behind the detection of anomalies. In this research, we propose a novel hybrid approach that combines rule-based and machine learning techniques to enhance the interpretability of anomaly detection systems. Our method integrates a rule-based system that generates interpretable anomaly detection rules with machine learning components that leverage complex pattern recognition and classification capabilities. We evaluate the proposed approach on a diverse set of real-world datasets, demonstrating its effectiveness in identifying anomalies while providing transparent explanations of the detection process. Through comprehensive experimentation and comparative analysis with existing state-of-the-art methods, we showcase the superior interpretability and performance of our hybrid approach. Our findings highlight the significance of interpretability in anomaly detection systems and underscore the potential of the proposed approach for enhancing transparency and trust in critical decision-making processes. This research contributes to the advancement of interpretable anomaly detection techniques and opens avenues for future research in the domain of transparent and reliable anomaly detection systems. © 2024 IEEE.

关键词： Transparency

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A Cost Effective IoT-Assisted Framework Coupled with Fog computing for Smart Agriculture 8

A Cost Effective IoT-Assisted Framework Coupled with Fog Com...

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8th IEEE international conference for convergence in technology, I2CT 2023

作者： Singh, Gurpreet Singh, Jaspreet Chandigarh University Department of CSE Mohali India

ISBN: (纸本)9798350334012

Organisations must efficiently handle as well as interpret a massive influx of data because of the emergence of technologies like the Internet of Things (IoT) including fog computing. A production strategy known as "smart agriculture"uses technology-enabled knowledge and interaction to monitor fields. Almost 60% of India's companies, to use an analogy, are agriculturally dependent. Improving existing agronomic systems and transforming them into smart agriculture represents the most efficient approach for dealing with agriculture's difficulties. The smart agriculture approach provides useful data collecting, incredibly accurate controls, and automated monitoring tech-niques. This study put out a fog-based model with IoT additional support which thus is promising to provide a smart agriculture infrastructure that is widely available. Fog platform includes proper data processing of information produced by IoT gadgets used in connected agriculture by bringing computing and data storage close as possible to the network's outermost boundaries. The findings suggests the use of the Web of Science database's literature search for the advancement of the IoT-Fog training sample in the creation of smart agricultural production. When properly implemented and compared to current IoT-based farm systems, the suggested method is more efficient. The outcome indicated that the recommended system enhances the precision of that same existing systems geometrically while reducing system performance, computational efficiency, and statistical significance to substantial levels. © 2023 IEEE.

关键词： Internet of things

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Seamless Attendance Logging Application: A Technological Revolution in Logbook Capture 9

Seamless Attendance Logging Application: A Technological Rev...

引用

9th IEEE international conference for convergence in technology, I2CT 2024

作者： Raj, Bhupathi Shwejan Sujith, Bolisetty Krishna, Janagama Vamshi Anjali Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science And Engineering Amritapuri India

ISBN: (纸本)9798350394474

In this age of swift technological progress, Most educational establishments seek novel approaches that may further boost productivity. One such challenge has been the manual attendance tracking system in colleges. This research paper presents the development and implementation of an automated college attendance system powered by deep learning-based text extraction models. The primary objective of this system is to remove the labor-intensive process of taking attendance, improve accuracy, and provide valuable insights into student attendance patterns. The research begins by utilizing state-of-the-art deep learning techniques to extract roll numbers from images of attendance sheets. A Convolutional neural network is utilized for situating and identifying text within images. The system then processes this data and marks students as present or absent based on predefined criteria. The findings demonstrate that the automated attendance system, with its high degree of precision, makes a major dent in the time and energy formerly needed for keeping attendance logs, streamlining the whole process significantly. Furthermore, by enabling the ongoing monitoring and analysis of data in real time, educators and administrators are empowered to make evidence-informed decisions about student attendance through insights continuously gained from observant assessment and evaluation. © 2024 IEEE.

关键词： Students

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Ontology-based Conversational Recommender System for Tablet Products 9

Ontology-based Conversational Recommender System for Tablet ...

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9th IEEE international conference for convergence in technology, I2CT 2024

作者： Dwipayana, Gisella Vania Baizal, Z.K.A. Telkom University Informatics School of Computing Bandung Indonesia

ISBN: (纸本)9798350394474

Technological developments have caused many electronic products to circulate in various circles of society, one of which is tablets. Tablets have various technical specifications which are often difficult to understand for the public, making it challenging for them to determine what they need based on those technical specifications. Therefore, a tablet conversational recommender system (CRS) based on product functional requirements is needed to educate users through conversational interaction. Some previous works have developed CRS framework based on functional requirements. In this paper, we utilize that framework and propose an ontology to build a CRS in tablet domain. The CRS interacts with users to gather their preferences regarding product functional requirements (e.g. a user needs a tablet to edit photos), then it recommends tablet products that meet those functional requirements. On the other hand, ontology is used to map functional requirements to the technical specifications of tablet products. This system is evaluated using accuracy metrics (performance-based evaluation) and user satisfaction evaluation. The evaluation results showed that the system achieves a recommendation accuracy of 95.36% with positive user satisfaction feedback. These results show that the system is effective in providing tablet product recommendations based on user functional requirements. © 2024 IEEE.

关键词： Ontology

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