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arXiv 2023年

作者： Yuan, Xinjing Pu, Lingjun Jiao, Lei Wang, Xiaofei Yang, Meijuan Xu, Jingdong Institute of Systems and Networks College of Computer Science Nankai University China Department of Computer Science University of Oregon United States School of Computer Science and Technology Tianjin University China

In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence). Based on this model, we introduce Resource Usage Effectiveness (RUE), a novel performance metric integrating training utility with system cost, and formulate a multivariate scheduling problem that maximizes RUE by comprehensively taking client admission, model partition, server selection, routing and bandwidth allocation into account (i.e., mixed-integer fractional programming). We design Refinery, an efficient approach that first linearizes the fractional objective and non-convex constraints, and then solves the transformed problem via a greedy based rounding algorithm in multiple iterations. Extensive evaluations corroborate that CPN-FedSL is superior to the standard and state-of-the-art learning frameworks (e.g., FedAvg and SplitFed), and besides Refinery is lightweight and significantly outperforms its variants and de facto heuristic methods under a variety of settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Integer programming

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VAE Latent Space Quantization Methods for UAVs Video Stream 8

VAE Latent Space Quantization Methods for UAVs Video Stream

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8th International Conference on Information, Control, and communication Technologies, ICCT 2024

作者： Chenskiy, Alexander Berezkin, Alexander Vivchar, Roman Kirichek, Ruslan Kukunin, Dmitry Bonch-Bruevich Saint-Petersburg University of Telecommunications Advanced Projects and Developments Department Saint-Petersburg Russia Bonch-Bruevich Saint-Petersburg University of Telecommunications Software Engineering and Computer Science Department Saint-Petersburg Russia Bonch-Bruevich Saint-Petersburg University of Telecommunications Communication Networks and Data Transmission Department Saint-Petersburg Russia

ISBN: (纸本)9798331517564

In FPV control of unmanned aerial vehicles, video frames are transmitted to the external pilot's station via a down link. Down links utilize mobile and hybrid orbital-terrestrial communication networks. The efficiency increase in their utilization is achieved by reducing the required bandwidth for each FPV-controlled unmanned aircraft. This requires ensuring a high compression rate of video stream frames, for which methods based on the application of variational autoencoders can be used. The principle of their operation is as follows: encoding frames into latent space, quantization and lossless compression. This paper presents new quantization algorithms and investigates the effect of applying different quantization algorithms to compression rate of latent space of VQ-D6 variational autoencoder from Stable Diffusion during further lossless compression using LZMA algorithm. In the result, the most effective quantization is selected to be used together with lossless compression. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Role of RPA to Seize Opportunities in 5G Edge Computing in Telecommunications

Role of RPA to Seize Opportunities in 5G Edge Computing in T...

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2024 International Conference on Advances in Computing Research on science Engineering and Technology, ACROSET 2024

作者： Yendluri, Dileep Kumar Tatikonda, Reshmi Tatikonda, Ramya Ponnala, Jainath Kempanna, M. Bhuvanesh, A. University of the Cumberlands Department of Computer Science Williamsburg United States University of the Central Missouri Department of Computer Information Systems Warrensburg United States University of the Cumberlands Department of Information Technology Williamsburg United States Jawaharlal Technological University Department of Electronics and Communication Engineering Hyderabad India Bangalore Institute of Technology Department of Ai and Ml Bangalore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350388800

This paper explores the role of Robotic Process Automation (RPA) in leveraging opportunities presented by 5G edge computing in the telecommunications industry. Integrating RPA and 5G edge computing offers numerous benefits, including enhanced operational efficiency, real-time decision-making, improved service delivery, and cost savings. However, it also poses challenges such as complex integration, security concerns, and the need for effective change management. The paper comprehensively analyzes the potential benefits, challenges, and recommendations for implementing RPA in 5G edge computing. It explores use cases, future advancements, emerging technologies, and anticipated opportunities and challenges. The findings highlight the need for clear objectives, process optimization, collaboration between IT and business teams, and continuous monitoring to leverage RPA in 5G edge computing effectively. © 2024 IEEE.

关键词： Telecommunication industry

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Mining the Chatbot Brain to Improve COVID-19 Bot Response Accuracy

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computers, Materials & Continua 2022年第2期70卷 2619-2638页

作者： Mukhtar Ghaleb Yahya Almurtadha Fahad Algarni Monir Abdullah Emad Felemban Ali M.Alsharafi Mohamed Othman Khaled Ghilan Department of Information Systems College of Sciences and ArtsUniversity of BishaAl Namas67392Saudi Arabia Department of Computer Science College of Computing and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia Computer Engineering Department Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Communication Technology and Network Universiti Putra MalaysiaSelangor43400Malaysia Faculty of Public Health and Tropical Medicine Jazan UniversityJazan45142Saudi Arabia

People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased ***,chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their *** employ Natural Language Understanding(NLU)to infer their *** is a need for a chatbot that can learn from inquiries and expand its area of experience with *** chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast *** study proposes a methodology to enhance a chatbot’s brain functionality by clustering available knowledge bases on sets of related themes and building representative *** used a COVID-19 information dataset to evaluate the proposed *** pandemic has been accompanied by an“infodemic”of fake *** chatbot was evaluated by a medical doctor and a public trial of 308 real *** obtained and statistically analyzed tomeasure effectiveness,efficiency,and satisfaction as described by the ISO9214 *** proposed COVID-19 chatbot system relieves doctors from answering *** provide an example of the use of technology to handle an infodemic.

关键词： Machine learning text classification e-health chatbot COVID-19 awareness natural language understanding

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Delay Considerations for Reliable communications in LoRaWAN 19

Delay Considerations for Reliable Communications in LoRaWAN

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19th IEEE Annual Consumer communications and Networking Conference, CCNC 2022

作者： Rydell, Joar Blom Otterlind, Oliver Butun, Ismail Chalmers University of Technology Department of Computer Science and Engineering GothenburgSE-412 96 Sweden Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Division of Networks and Systems StockholmSE-100 44 Sweden

ISBN: (纸本)9781665431620

With the advent of the latest technologies, the Internet of Things (IoT) is the most widely used field in which billions of devices have been connected over the Internet. Long Range Wide Area Network (LoRaWAN) is among one of the most emerging technologies that deliver packets over very long-range with extremely low power consumption. However, it faces the problem of high latency. To cope with this problem, this paper aims to evaluate LoRaWAN in terms of latency by comparing how the default latency method that exists in LoRaWAN fair against an alternative method that has been researched and presented in the literature. Furthermore, having the concrete results of the latency will help companies to determine whether LoRaWAN is a suitable technology to use for Industrial IoT (IIoT) devices. Moreover, due to its backbone TCP/IP communication support, LoRaWAN will also facilitate the future architecture for the Web of Things (WoT). © 2022 IEEE.

关键词： Internet of things

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Enhancing Quality of Service in IoT-WSN through Edge-Enabled Multi-Objective Optimization

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IEEE Transactions on Consumer Electronics 2025年

作者： Singh, Shailendra Pratap Kumar, Naween Kumar, Gyanendra Balusamy, Balamurugan Bashir, Ali Kashif Al Dabel, Maryam M. Department of Computer Science and Engineering MMMUT UP Gorakhpur India SCSET Bennett University Greater Noida India Department of IoT and Intelligent Systems Manipal University Jaipur Jaipur India India Networks and Security Manchester Metropolitan University United Kingdom Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia

The demand for real-time, high-quality services (QoS) is increasing with the proliferation of the resource-constrained nature of edge devices that facilitate the Internet of Things (IoT) and wireless sensor network (WSN) applications. Several existing multi-objective algorithms, such as MOPSO, Elitism MOGA, MODE, and others, are capable of balancing exploration and exploitation;they assist in efficient QoS management for WSN-IoT applications, address resource limitations, and align with the objectives of the applications. However, they suffer from showing robustness in the solution and efficient convergence rates on benchmark functions impacting overall QoS. This paper proposes a multi-objective optimization and edge-intelligent adaptation-based strategy to address QoS management issues, jointly optimize several competing objectives, like energy and latency, and maximize localization and coverage rates while considering the limitations of edge devices. The proposed work uses a novel Grey-wolf optimizer (GWO) Algorithm with an innovative bird-edge-computing adaptation approach to analyze the complex connections between input parameters, edge resources, and QoS indicators to generate Pareto-optimal solutions. The evaluation of the proposed edge intelligence technique with IoT applications demonstrates its effectiveness compared to conventional heuristic-based approaches. This approach enhances the QoS in IoT applications and improves resource utilization and scalability in edge computing environments. © 2025 IEEE.

关键词： Quality of service

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Using Deep Learning Neural networks to Recognize and Authenticate the Identity of the Speaker

Using Deep Learning Neural Networks to Recognize and Authent...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Seddiq Q. Abd Al-Rahman Sameeh Abdulghafour Jassim Department of Computer Networks Systems College of Computer Science and Information Technology University of Anbar Anbar Iraq General Directorate of Education in Anbar Ministry of Education Anbar Iraq

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Nowadays, the advancement of technology is given that permeates all parts of daily life and is dependent on electronic services like banking and financial transfers, project management, health care, and other essential areas. These applications' fundamental components (person recognition and/or authentication procedures) can be viewed as one of their challenging constraints. Therefore, using biometric features in these sectors can result in promising outcomes. A person's voice is a distinctive bio-feature that may be used for identification, verification, and prevents others from mistaking them for someone else without their knowledge or consent. The Speaker Recognition System that was proposed utilized the biometric method to confirm the speaker's identity and then authenticate that identity by using the password to prevent replay attacks. The objective of the system is to distinguish the speaker by utilizing the corpus of the English language dataset of 20 adult speakers (12 male and 8 female) and for each person there 500 utterances samples were utilized for the training and testing purpose equally. Additionally, the input speech signal is processed to get a specific number of variables, named features. Features extraction is utilized to select the most appropriate features to reduce the computational power load and thus reduce the time needed for speaker recognition. MFCC and LPCCs are integrated as speech feature extraction and DFFNNs as speech classifiers. The classification classifies the speaker by utilizing the features defined by the feature extraction block and audio clips in the database. Furthermore, the MATLAB (R2023a) tool was utilized for the entire simulation. The result got from this proposal proved that the integration of MFCC and LPCC techniques has higher accuracy than utilizing every technique individually as well as utilizing the DFFNNs to classify the speakers' got high recognition results among the speakers. As well as the authentication pro

关键词： Training Biometrics (access control) System performance Passwords Feature extraction Speaker recognition Proposals

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Artificial Intelligent based Models for Event Extraction using Customer Support Applications 2

Artificial Intelligent based Models for Event Extraction usi...

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2nd International Conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Mayuranathan, M. Akilandasowmya, G. Jayaram, B. Velrani, K. Sundara Kumar, M Jogendra Vidhya, R.G. Srm Valliammai Engineering College Department of Computer Science Engineering India Sri Sairam Institute of Technology Department of Computer Communication and Engineering India Rmk Engineering College Department: Computer Science and Business Systems Tamilnadu Gummidipoondi Taluk Tiruvallur District Kavaraipettai India School of Computing Sathyabama Institute of Technology Information Technology India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350325799

Recently, AI and NLP have been used to improve customer care. Customer support relies on event extraction and relevant data. In customer support applications, AI-based algorithms extract events. Event extraction identifies and extracts specific events or activities from textual data like customer service chats, emails, and social media interactions. Customer support teams may discover issues, trends, and improve the customer experience by automatically extracting events. Customer support event extraction can be done using AI-based models. Event extraction relies on named entity recognition (NER), sentiment analysis, and text classification. NER classifies customer names, product names, and problem keywords. Text classification classifies client issues into preset categories like billing, technical support, or product feedback, while sentiment analysis assesses the customer's emotional tone. Event extraction AI models use machine learning algorithms. Support vector machines (SVM) and random forests can be trained on annotated customer support data to reliably classify events. Recurrent neural networks (RNNs) and transformers can capture complicated patterns in textual data, making them promising event extraction methods. Customer support applications benefit from AI-based event extraction algorithms. It frees up support teams to focus on client issues by automating evaluation and categorization. It also helps firms identify emerging trends and enhance their products and services to answer client complaints. AI-based customer support event extraction models face hurdles. Labeled training data is costly and time-consuming to obtain. Ensuring model generalizability across multiple domains or languages is ongoing research. In conclusion, AI-based event extraction in customer support systems can improve customer experiences and support team efficiency. © 2023 IEEE.

关键词： Support vector machines

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A novel cooperative collision avoidance system for vehicular communication based on deep learning

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1661-1675页

作者： Farhat, Wajdi Ben Rhaiem, Olfa Faiedh, Hassene Souani, Chokri Laboratory of Microelectronics and Instrumentation University of Monastir Monastir Tunisia Higher Institute of Applied Sciences and Technology Sousse University Sousse Tunisia Computer Science Department College of Science Northern Border University Arar Saudi Arabia Digital Research Center of SFax (CRNS) SM@RTS (Laboratory of Signals systeMs aRtificial Intelligence and neTworkS) Sfax Tunisia

Global road traffic injuries represent a significant safety challenge, with the highest fatality rates worldwide stemming from a combination of drivers' reckless behavior and the ever-expanding number of vehicles on the roads. In light of these factors, Autonomous Vehicles (AVs) have received a lot of attention as a technology that has the potential to revolutionize various industries and solve a lot of problems. The rise of the Internet of Vehicles (IoV) and Connected Vehicles (CVs) enables AVs to perceive and react swiftly in complex road scenarios, prioritizing safety. These vehicles prioritize individual safety in all situations, significantly enhancing road safety. Mobile Edge Computing (MEC) plays a crucial role thanks to its capacity for minimal latency and high bandwidth capabilities, enabling critical vehicular applications. In this paper, we introduce a cooperative collision avoidance system based on MEC, named 2CAS-MEC, that proactively identifies and locates road hazards. Our system is based on MEC servers positioned alongside roads, it consistently analyzes traffic and hazard data, issuing targeted alerts to nearby vehicles regarding potential risks. Particularly, our work introduces an advanced deep learning system that utilizes 5G, mobile edge computing, and cloud intelligence to prevent vehicle collisions. The experimental results show that our system outperforms the existing related works and exhibits the anticipated performance, effectively helping drivers in accident avoidance. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Cloud Connected and autonomous vehicles Deep learning IOV MEC Vehicle to-everything (V2X)

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Analysis and Assessment of Wind Energy Potential of Al-Hodeidah in Yemen

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computers, Materials & Continua 2021年第11期69卷 1995-2011页

作者： Fahd N.Al-Wesabi Murad A.Almekhlafi Mohammed Abdullah Al-Hagery Mohammad Alamgeer Khalid Mahmood Majdy M.Eltahir Ali M.Al-Sharafi Amin M.El-Kustaban Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA Department of Electronic Faculty of EngineeringUSTSana’aYemen

Renewable energy is one of the essential elements of the social and economic development in any civilized *** use of fossil fuels and the non-renewable form of energy has many adverse effects on the most of *** the high potential of renewable energy sources in Yemen and the absence of similar studies in the region,this study aimed to examine the wind energy potential of Hodeidah-Yemen Republic by analyzing wind characteristics and assessment,determining the available power density,and calculate the wind energy extracted at different *** average wind speed of Hodeidah was obtained only for the data currently available for the five years 2005–2009(due to the current economic and the political situation in Yemen).The results show that the average wind speed in the five years is(25.2 W/m2 at 10 m,93.9 W/m2 at 30 m,and 173.5 W/m2 at 50 m).The average yearly wind power density(25.2 W/m2 at 10 m,93.9 W/m2 at 30 m and 173.5 W/m2 at 50m),and the average yearly energy density(220.8 KWh/m2/year at 10 m,822.6 KWh/m2/year at 30 m and 1519.9 KWh/m2/year at 50 m).This research is a preliminary assessment of the potential of wind energy in Hodeidah,which provides useful information for developing wind energy and an efficient wind *** to the International Wind Energy Rating criteria,the region of Hodeidah falls under‘Class 2’and is classified as‘Marginal’for most of the year.

关键词： Energy potential wind speed weibull distribution wind power density

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