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SUCP Analysis for Region-Centric UAV-Assisted MEC Networks 20th

SUCP Analysis for Region-Centric UAV-Assisted MEC Networks

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20th IFIP WG 10.3 international conference on Network and Parallel computing, NPC 2024

作者： Li, Yan Yi, Zhaozhi Long, Qingmin Luo, Lailong Guo, Deke School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410004 China National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819628636

With the increase of computing-intensive and delay-sensitive applications, mobile edge computing (MEC) technology has sprung up. It effectively satisfies the needs of user equipment (UE) for real-time computing resources by placing servers at the edge of the network. However, traditional MEC infrastructures are constrained by their fixed locations and emergency mobility needs. Unmanned aerial vehicles (UAVs) offer an effective solution with low cost, high mobility, and flexible deployment capabilities. In this paper, we propose a region-centric UAV-assisted MEC model, where the whole network space is divided into a set of hexagonal cells of equal area, and all UAVs in the same cell jointly process UE offloading data. Assume all UAVs are assumed to be equipped with independent MEC servers, and both UAVs and terrestrial UE follow independent homogeneous Poisson point process (PPP) distributions. Using the stochastic geometry analysis framework, we derive the successful uplink communication probability (SUCP) of uplink transmission for users. Finally, we compare the simulation results with the theoretical values to verify the model’s accuracy and evaluate the influence of key performance parameters on the network performance. © IFIP international Federation for information Processing 2025.

关键词： Stochastic systems

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Predictive Maintenance for Hard Disk Drives Using Random Forest 8th

Predictive Maintenance for Hard Disk Drives Using Random For...

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8th IFIP TC 12 international conference on Computer, communication and Signal Processing, ICCCSP 2024

作者： Baniya, Achal Vinod, Abhay Chinta, Hitesh Surya Manathanathu Pushakara, Bhavith Pati, Peeta Basa Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031736162

As digital data continues to grow exponentially, the reliability of Hard Disk Drives (HDDs) becomes a critical concern for ensuring data integrity and preventing unexpected system downtimes. The research dataset contains historical SMART attribute data collected from HDDs, encompassing parameters such as spin-up time, temperature reallocated sector count and other relevant metrics. The study explored usage of various exploratory data analysis (EDA) & machine learning (ML) techniques for forecasting HDD failures. EDA techniques include Synthetic Minority Over-sampling Technique (SMOTE) algorithm for balancing class representation in training set and Principal Component Analysis (PCA) for dimensionality reduction. Diverse machine learning techniques, such as Random Forest (RF), Decision Tree (DT), K-nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Adaptive Boosting (AdaBoost), XGBoost, Gradient Boosting (GB), Linear Discriminant Analysis (LDA), Logistic Regression (LR), are employed to predict the hard drive failure. Local Interpretable Model-agnostic Explanations (LIME) is employed to identify which features have the greatest influence on a specific prediction, providing insights into the factors driving the model’s decisions. Random Forest exhibited superior performance of accuracy 99.9 and F1 score 0.98 compared to other models. © IFIP international Federation for information Processing 2025.

关键词： Dimensionality reduction

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Design of Wide Neural Network Model for Controlling Tunable LED Luminaire: (Neural Network based Luminaire Control) 5

Design of Wide Neural Network Model for Controlling Tunable ...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Aranha, Ancy Princia George, Anna Merine Kamath, Vedavyasa Kurian, Ciji Pearl Padmashree, K.S. Manipal Institute of Technology Manipal Academy of Higher Education Department of Electrical and Electronics Engineering Manipal India Bms Institute of Technology & Management Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350356236

This paper explores machine learning models to design adjustable light spectra for supporting circadian entrainment using LED luminaires. The approach combines practical experimental work with sophisticated machine learning methods to enhance lighting solutions. There is a growing demand for adjustable sources to improve efficiency and reduce costs, leading to the development of multichannel LED drivers capable of operating at different brightness levels and spectra for each LED channel. The multichannel LED driving circuitry is a critical component of human-centric lighting, which aims to enhance the well-being and health of occupants. The experiment utilized a four-channel warm white and cool white combination LED luminaire under dimming and Correlated Colour Temperature (CCT) variation. Current-controllable LEDs for different CCTs and intensities were tested with the help of DMX 512 Decoder and master control. The Wide Neural Network model designed outperforms other models in performance, helping to determine the current requirement of the lighting control system to set the desired circadian entrainment conditions. © 2025 IEEE.

关键词： Controllability

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Performance Measurement of LoRa Modules for Extending Smart Applications to The Things Network 5

Performance Measurement of LoRa Modules for Extending Smart ...

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5th international conference on advances in Electrical, Electronics and computing technology, EECT 2025

作者： Khoury, Afifi Al-Zoubi, Abdallah National Electric Power Company Amman Jordan Princess Sumaua University for Technology Amman Jordan

ISBN: (纸本)9798331541545

The performance of three LoRa modules with similar characteristics and designs was tested to select suitable options for smart applications. The modules were tested in a specific location in Jordan, which has unique terrains and weather conditions. A GPS module was added to simplify measurements of location and elevation. The data collected included the signal-to-noise ratio (SNR), the received signal strength indicator (RSSI), and benchmark sensor values for temperature and humidity. A Blynk server and Google Sheets scripts facilitated the real-time tracking process and automation of data collection. The results on signal behavior with distance should provide a guide in LoRa module design specification. A low-cost LoRa development board, which achieved over 7.5 Km in range and provided adequate signal stability, was utilized with a gateway to create a LoRaWAN-based system for smart applications. Distribution system. Further integration to The Things Network (TTN) produced promising outcomes for the LoRaWAN-based system, highlighting its potential for sustainable water management solutions. © 2025 IEEE.

关键词： Machine-to-machine communication

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Inference method for power large-scale model operation based on distributed computing resource scheduling

Inference method for power large-scale model operation based...

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2024 international conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Huang, Xiaoguang Qin, Yu Liu, Yaqing Cui, Yingbao Guo, Ting Gu, Qingtong State Grid Information and Communication Industry Group Co. Ltd Beijing102211 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

Electric power computing resources are distributed in the respective regions of provincial data centers and transmission and transformation equipment, and the scheduling efficiency of computing resources is limited by conditions such as long distribution locations. Composite data model inference has been widely used in power equipment maintenance analysis, and large-scale model inference requires the use of distributed computing resources. In order to improve the scheduling and fusion efficiency of distributed computing power resources, this paper adopts a lightweight language model to infer and calculate the scheduling parameters of computing power resources in different regions. At the same time, in order to make more rational use of computing power resources, the intelligent models and algorithms used for power equipment maintenance are optimized. Configure the adaptation parameters of the model according to different application scenarios, and adjust the fusion method of input data for the fault diagnosis model and the classification strategy for the fault prediction model separately. Using a fine-tuning language model to map the predicted results to the semantics of power business, and then enhancing the rationality and interoperability of the answer results based on historical knowledge memory, ultimately pushing the reasoning results of the reasons and the classification results of the equipment defect diagnosis classifier to the equipment diagnosis designer. © 2025 SPIE.

关键词： Data modeling Distributed computing Education and training Video Machine learning Semantics Video coding Instrument modeling Power grids Target detection

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DyPSS: Dynamic Priority Signalling System for Emergency Vehicles using Fog and YOLOv8 17

DyPSS: Dynamic Priority Signalling System for Emergency Vehi...

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17th international conference on communication Systems and NETworkS, COMSNETS 2025

作者： Kakde, Anuj Nath, Shubha Brata Ghosh, Soumya K. Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331531195

Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dynamic Priority Signalling System (DyPSS), an intelligent, multi-layered traffic management solution designed to minimize ERV delays by creating "green corridors"at traffic signals. Using technologies like fog computing, cloud computing, global positioning system and deep learning, DyPSS adjusts traffic lights instantly to clear congestion as an ERV approaches. Simulation testing was conducted using SUMO and it demonstrated DyPSS's potential to significantly reduce ERV travel time in dense urban settings with minimal disruption to general traffic flow. This system could drastically improve emergency response by reducing delays and helping responders reach their destinations faster, potentially saving more lives. © 2025 IEEE.

关键词： Emergency vehicles

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Joint Resource and Delay Optimization for Computation Offloading in Hierarchical MEC Networks Using Stackelberg Game 3rd

Joint Resource and Delay Optimization for Computation Offl...

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3rd international conference on advances in information and communication technology, ICTA 2024

作者： Nguyen, Thanh-Dao Nguyen, Ngoc-Tan Hoang, Trong-Minh Nguyen, Hoai-Son Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi Viet Nam

ISBN: (纸本)9783031809422

Multi-access edge computing (MEC) is a cutting-edge technology integral to 5G and future mobile networks. However, the computational capabilities of edge servers may sometimes be insufficient to meet service delay requirements. As a result, computation tasks need to be offloaded to other MEC servers for processing. In this paper, we introduce a novel offloading solution based on Stackelberg game, i.e., Game-based Offloading (GBO), to maximize the processed computation tasks while ensuring the service delay requirements. To tackle the problem, we use the block coordinate descent method to find the optimal solution within given delay constraints. Simulation results demonstrate that under various experimental circumstances, the proposed GBO method significantly outperforms baseline methods, such as the non-offloading and the ODO [9] methods in terms of the probability of successfully processed tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computation offloading

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Optimizing English Lexical Databases with BERT and Reinforcement Learning 5

Optimizing English Lexical Databases with BERT and Reinforce...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Saranya, V. Murthy, G.R.K. Alapati, Purnachandra Rao Mythili, M. Swarnamughi, K. Bala, B. Kiran Panimalar Engineering college Department of English Chennai India Narasaraopet India Prasad V Potluri Siddhartha Institute of Technology Department of English Andhra Pradesh India Department of English Erode India Sathyabama Institute of Science And Technology Department of English Chennai India K.Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350356236

The evolution of natural language processing (NLP) has significantly transformed the way this study analyzes and understand language. This study presents a novel approach to generating lexical databases for English language sources using AI-powered methods, specifically leveraging BERT and reinforcement learning. Traditional methods for lexical database creation often rely on manual compilation or semi-automated techniques, which can be time-consuming and may lack comprehensive coverage of linguistic nuances. These existing approaches struggle with scalability and adaptability, particularly when addressing evolving language use and the inclusion of rare or domain-specific terms. The Open English WordNet dataset serves as a foundational resource, providing rich lexical relationships and contextual information necessary for effective database generation. Our proposed method integrates BERT for context-aware embedding generation and reinforcement learning for continuous optimization of lexical extraction and enrichment processes. This innovative combination not only enhances the accuracy of detecting synonyms, antonyms, hypernyms, and partof-speech tags but also ensures that the system learns from user feedback, leading to ongoing refinement. The advantages of this method include improved precision and recall, rapid processing times, and the ability to adapt to new linguistic data dynamically. Ultimately, this study demonstrates that AI-driven approaches can significantly enhance the quality and usability of lexical databases, paving the way for more sophisticated applications in natural language processing and linguistics. © 2025 IEEE.

关键词： Natural language processing systems

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Integrating Blockchain and Artificial Intelligence for Securing Payment processes Against Fraudulent Transactions 5

Integrating Blockchain and Artificial Intelligence for Secur...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Ramesh, J. V. Naga Shah, Nusrat Hamid Alazzam, Malik Bader Nimma, Divya Selvi, V. Balakumar, A. Graphic Era Hill University Department of Cse Dehradun India Graphic Era Deemed To Be University Department of Cse Uttarakhand Dehradun India College of Engineering and Computer Science Jazan University Saudi Arabia Jadara University Faculty of Information Technology Irbid Jordan University of Southern Mississippi Ummc United States Mother Teresa Women's University Department of Computer Science Kodaikanal India K. Ramakrishnan College of Engineering Department of Ece Trichy India

ISBN: (纸本)9798350356236

In recent years, the fast growth of digital payment processes has raised large safety concerns, which include fraud and data breaches, prompting the need for robust safety mechanisms. Existing techniques, which include conventional rule-based systems, regularly fall quick in adaptability and accuracy, leading to multiplied vulnerabilities within the economic zone. This study proposes a unique integration of Blockchain technology with Artificial Intelligence (AI), specially making use of Generative Adversarial Networks (GANs), to enhance the security of monetary transactions. The technique includes growing a decentralized ledger device to make sure transparency and immutability, coupled with a GAN framework for real-time anomaly detection. Then behavior experiments the use of a complete dataset of monetary transactions to validate the effectiveness of the included technique. The consequences imply a considerable improvement in detecting fraudulent activities, accomplishing higher accuracy and decrease false positive rates in comparison to conventional methods. This research no longer most effective addresses the crucial safety gaps found in contemporary monetary transaction systems however additionally demonstrates the potential of integrating Blockchain and AI to create an extra stable monetary environment. The findings recommend that this integrated technique can serve as a scalable solution for enhancing transaction safety, providing valuable insights for both academia and industry practitioners. © 2025 IEEE.

关键词： Intelligent systems

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LLM-Based Extraction of Contradictions from Patents 24th

LLM-Based Extraction of Contradictions from Patents

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24th international TRIZ Future conference

作者： Trapp, Stefan Warschat, Joachim Univ Hagen Univ Str 47 D-58097 Hagen Germany

ISBN: (纸本)9783031759215;9783031759192;9783031759185

Already since the 1950s TRIZ shows that patents and the technical contradictions they solve are an important source of inspiration for the development of innovative products. However, TRIZ is a heuristic based on a historic patent analysis and does not make use of the ever-increasing number of latest technological solutions in current patents. Because of the huge number of patents, their length, and, last but not least, their complexity there is a need for modern patent retrieval and patent analysis to go beyond keyword-oriented methods. Recent advances in patent retrieval and analysis mainly focus on dense vectors based on neural AI Transformer language models like Google BERT. They are, for example, used for dense retrieval, question answering or summarization and key concept extraction. A research focus within the methods for patent summarization and key concept extraction are generic inventive concepts respectively TRIZ concepts like problems, solutions, advantage of invention, parameters, and contradictions. Succeeding rule-based approaches, finetuned BERT-like language models for sentence-wise classification represent the state-of-the-art of inventive concept extraction. While they work comparatively well for basic concepts like problems or solutions, contradictions - as a more complex abstraction - remain a challenge for these models. Even PaTRIZ, the latest and complicated multi-stage approach to extract contradictions, delivers only mixed results. This paper goes one step further, as it presents a method to extract TRIZ contradictions from patent texts based on Prompt Engineering using a generative Large Language Model (LLM), namely OpenAI's GPT-4. The existing annotated patent dataset "PaGAN" is used to demonstrate the LLM-capabilities for extracting TRIZ contradictions from the section "State-of-the-Art" of USPTO patents. Contradiction detection, sentence extraction, contradiction summarization, parameter extraction and assignment to the 39 abstract TRIZ

关键词： AI BERT Classification Contradiction Finetuning GPT-4 information Extraction information Retrieval Inventive Concept LangChain Large Language Model (LLM) NLP OpenAI Prompt Engineering Summarization Transformer TRIZ

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