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A Game Theoretic Approach to Enhance DCF Performance in Full Duplex Ad-hoc WLANs 12

A Game Theoretic Approach to Enhance DCF Performance in Full...

引用

12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Politis, Anastasios C. Anastassiu, Hristos T. Hilas, Constantinos S. International Hellenic University Informatics and Telecommunications Engineering Dept. of Computer Serres Greece

ISBN: (纸本)9798350321074

Physical Full Duplex (FD) operation in WLANs is a promising technology aiming at substituting the traditional Half Duplex (HD) functionality. An increased performance is expected which, theoretically, can be expressed as a double increase in system throughput. However, due to the initial design of the basic access method in WLANs, known as Distributed Coordination Function (DCF), this performance boost is poorly exhibited. Game theory is an invaluable tool to model the performance of Medium Access Control (MAC) protocols used in WLANs. In this paper, we exploit game theory to propose a simple enhancement to the DCF access method, in order to increase the benefit received when the FD capability is enabled in ad-hoc WLANs. © 2023 IEEE.

关键词： Game theory

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A Framework Leveraging Robotics and Machine Learning Technologies for Early Disease and Pest Detection in Greenhouse Tomato Crops 4

A Framework Leveraging Robotics and Machine Learning Technol...

引用

4th International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2023

作者： Kounalakis, Nikolaos Kalykakis, Emmanouil Kosmopoulos, Dimitrios Fasoulas, John Sfakiotakis, Michael Heraklion Greece University of Patras Dept. of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350300918

We present an integrated framework for intelligent automated plant monitoring, towards early disease and pest detection in greenhouse tomato crops. The framework combines the use of a robotic mobile platform to autonomously collect multi-spectral images of the plants, with a tool that utilizes Faster R-CNN to detect regions that signify the presence of a disease or pest. The robot is based on a modified mobile vertical mast lift platform, and integrates a 6-dof robotic arm that is used to position the plant imaging multi-spectral camera. The robot can navigate autonomously inside the greenhouse via a magnetic guidance sensor. Results from a series of experiments demonstrate the validity and effectiveness of the implemented framework. © 2023 IEEE.

关键词： Machine learning

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The Impact of Weather Conditions on Renewable Energy Production: A Machine Learning Approach

The Impact of Weather Conditions on Renewable Energy Product...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Özbay, Recep Çelebi, Merve Yavanoǧlu, Uraz Gazi University Graduate School of Informatics Dept. Of Computer Forensics Ankara Turkey Hatay Mustafa Kemal University Faculty of Education Dept. Of Computer Education and Instructional Technologies Hatay Turkey Gazi University Faculty of Engineering Dept. of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350362480

In recent decades, obtaining energy from wind, solar, and hydro energy has brought hope for a sustainable future. Machine learning (ML) significantly impacts the field of energy systems. ML techniques have emerged as a powerful tool for analyzing and optimizing energy systems. This approach can potentially reduce the cost of analyzing a device by focusing solely on the data generated during its operation. This paper investigates the application of regression models to predict renewable energy production based on weather conditions, aiming to enhance the reliability and efficiency of renewable energy systems. Experiments are performed using Decision Tree (DT), k-nearest Neighbors (KNN), Multiple Linear (MLR), Polynomial (PR), Random Forest (RF), and Support Vector (SVR) Regression algorithms. The performances of ML models are compared based on the evaluations obtained through the Coefficient of Determination (R2), and error measures namely Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). RF is the most successful algorithm in predicting renewable energy production depending on weather conditions as it has the highest R2 value and the lowest error measures. © 2024 IEEE.

关键词： Clean energy

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To Split or not to Split? A Simulation Study on the Network Convergence Duration of Multi-Area OSPF 46

To Split or not to Split? A Simulation Study on the Network ...

引用

46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Sachinidis, Theofilos Politis, Anastasios C. Hilas, Constantinos S. International Hellenic University Dept. of Computer Informatics and Telecommunications Engineering Serres Greece

ISBN: (纸本)9798350303964

Internetworks with a large number of routing devices that utilise the Open Shortest Path First (OSPF) routing protocol, benefit greatly if they are designed in a hierarchical manner. The multi-area feature of OSPF leads to reduced network overhead, low processing and memory requirements and smaller routing tables. This, however, comes at the cost of increased configuration complexity, especially at the Area Border Routers. The threshold after which splitting the OSPF domain into multiple areas, is an ongoing debate among network administrators. A well known rule-of-thumb is recommended by large vendors of networking devices setting that threshold to 50 routing devices. The objective of this preliminary work is to investigate that threshold by conducting a simulation-based study using the OPNET network simulation tool. To assess the network performance, we select the network convergence duration as the quantitative Key Performance Indicator (KPI) of our study. Results indicate that the above mentioned threshold does not appear to be appropriate and a revision of that empirical rule should be considered. © 2023 IEEE.

关键词： Routing protocols

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Transformer-Based Embeddings for Greek Language Categorization 24

Transformer-Based Embeddings for Greek Language Categorizati...

引用

24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Liapis, Charalampos M. Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Patras Computer Technology Institute & Press 'Diophantus' Mathematics Dept. Patras26504 Greece University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350376647

The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems. © 2024 IEEE.

关键词： Contrastive Learning

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A Comparative Analysis of Transformer Models in Zero-Shot Text Classification 15

A Comparative Analysis of Transformer Models in Zero-Shot Te...

引用

15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Kyritsis, Konstantinos Liapis, Charalampos M. Spatiotis, Nikolaos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical & Computer Engineering Dept. Patras26334 Greece University of Patras Mathematics Dept. Patras26504 Greece Computer Technology Institute & Press 'Diophantus' Patras26504 Greece University of Patras Computer Engineering & Informatics Dept. Patras26504 Greece

ISBN: (纸本)9798350368833

Ahstract-Zero-shot text classification leverages pre-trained transformer models to categorize texts without the need for task-specific training. This paper analyzes a variety of transformer-based pre-trained methods on the task of zero-shot text classi-fication. Specifically, we present a deep comparative analysis of various transformer models, such as BART, DeBerta, DistiIBART, RoBERTa, and their variants, and evaluate their performance in various zero-shot text classification schemes. Furthermore, we examine the models' generalization capabilities. The findings highlight key strengths and weaknesses of each model, providing insights into their suitability for different text categorization tasks. Our research contributes to the broader understanding of transformer-based approaches and offers guidance for selecting models for zero-shot text classification. © 2024 IEEE.

关键词： Zero-shot learning

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A Highly Interpretable Deep Equilibrium Network for Hyperspectral Image Deconvolution 48

A Highly Interpretable Deep Equilibrium Network for Hyperspe...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gkillas, Alexandros Ampeliotis, Dimitris Berberidis, Kostas University of Patras Dept. of Computer Engineering & Informatics Greece Ionian University Dept. of Digital Media and Communication Greece

ISBN: (纸本)9781728163277

In this paper, a novel technique for the hyperspectral image deconvolution problem is developed. First, considering the highly ill-posed nature of the examined problem, it is imperative to incorporate proper priors (regularizers) to capture the strong spectral and spatial dependencies of the hyperspectral images. Then, in light of this, a novel optimization problem is proposed by employing a convolutional neural network to act as a regularizer, which is learnt to reflect the properties of the signals of interest. To solve the proposed optimization problem, we use the half quadratic splitting methodology, thus designing an efficient iterative solver (iteration map). Based on the Deep Equilibrium (DEQ) modeling, which aims to express the proposed iterative solver as an equilibrium (fixed-point) computation, a highly interpretable deep learning-based network is derived, which can be trained endto-end. Extensive numerical results using two publicly available datasets illustrate that the proposed method markedly outperforms other state-of-the-art approaches. © 2023 IEEE.

关键词： deconvolution deep equilibrium models deep unrolling Hyperspectral images learnable regularizers

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI 13

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

引用

Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Achmad Sulistyo Putro, Nur Widyawan Prakosa, Setya Avian, Cries National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Universitas Negeri Yogyakarta Dept. of Electronics and Informatics Engineering Education Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350373332

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems. © 2024 IEEE.

关键词： Fuzzy clustering

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Deep Equilibrium Models Meet Federated Learning 31

Deep Equilibrium Models Meet Federated Learning

引用

31st European Signal Processing Conference, EUSIPCO 2023

作者： Gkillas, Alexandros Ampeliotis, Dimitris Berberidis, Kostas Dept. of Computer Engineering and Informatics University of Patras Greece Dept. of Digital Media and Communication Ionian University Greece

ISBN: (纸本)9789464593600

In this study the problem of Federated Learning (FL) is explored under a new perspective by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL, such as the communication overhead due to the sharing large models and the ability to incorporate heterogeneous edge devices with significantly different computation capabilities. Additionally, a weighted average fusion rule is proposed at the server-side of the FL framework to account for the different qualities of models from heterogeneous edge devices. To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning, contributing to the development of an efficient and effective FL framework. Finally, promising initial experimental results are presented, demonstrating the potential of this approach in addressing challenges of FL. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Deep learning

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Selection of Outsource Security Guard using Multi-Objective Optimization Ratio Analysis Method 7

Selection of Outsource Security Guard using Multi-Objective ...

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Aditya Firmansyah, Mochamad Rafly Ismail, Iklima Ermis Hidayati, Anita Politeknik Negeri Jakarta Dept. of Computer and Informatics Engineering Jawa Barat Depok Indonesia

ISBN: (纸本)9798350342154

The security guard profession is no longer a profession that only relies on the physical but also other aspects, such as attitude, leadership, language, and others. There are several problems faced by companies providing security guards, namely the demand for security personnel is quite high, the number of applicants is also large, but the selection process has to go through an administrative and interview process. With a limited number of interviewers, it takes a longer time to get the selection results. Interview results also tend to be more subjective, making it difficult to determine which candidates are under the company's criteria and specifications. This study aims to develop a decision support system for selecting the acceptance of new outsourced security guards using the MOORA method for a security guard service provider company, namely PT. Purba Alter Service. There are 19 criteria used in the selection of security guards. This research developed the system on a web-based using the MOORA method for calculating the selection of alternative security guards. By comparing the results of the selection system processing using the MOORA method with the results of the previous three years without a system, of the 14 alternative names tested, 12 names appeared in both results, and obtained 85.7% accuracy. The result shows that the MOORA method is successfully implemented in selecting security personnel. © 2023 IEEE.

关键词： Decision support systems

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