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IEEE Region 10 International Conference TENCON

作者： Toshiro Nunome Yoshiyuki Takada Department of En-gineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technol-ogy Nagoya Japan

In this paper, we exploit caches on intermediate nodes for QoE enhancement of multi-view video and audio transmission over ICN/CCN by controlling the content request start timing of consumers. We assume the selected single viewpoint transmission method; a consumer receives video and audio streams of a requested viewpoint. We perform a simple experiment with two consumers. When the consumers play video and audio with the time difference, we assess the effect of cached content by the former consumer's request on the output quality of the latter consumer. We deal with two types of viewpoint change strategies for the former consumer, which affect the efficiency of cache utilization. From the assessment results, we see that cache utilization has an important factor in enhancing QoE.

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An Efficient Model for Optimal Allocation of Renewable Energy Sources in Distribution Networks with Variable Loads

An Efficient Model for Optimal Allocation of Renewable Energ...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Mahdavi, Meisam Schmitt, Konrad Bayne, Stephen Chamana, Manohar University of Jaén Department of Electrical Engineering Jaén Linares Spain Texas Tech University Electrical and Computer Engineering Department LubbockTX United States Texas Tech University Renewable Energy Program LubbockTX United States

ISBN: (纸本)9781665490719

Operational costs are affected by energy losses of distribution systems, in which these losses are higher than those of the transmission system. The placement of distributed generation (DG) units in power distribution networks is an efficient way of energy loss reduction. Today, DG penetration in distribution systems is increasing because of the great effect of renewable energy resources on environmental sustainability. Power demand variations have an essential role in the determination of energy losses amount and optimal generation of DGs. Besides, considering the variability of load level in the DG allocation problem raises the burden and computational time substantially. Therefore, this paper proposes an effective DG allocation strategy for energy loss minimization in the presence of variable power demand. The evaluation of numerical results indicates the desired performance of the presented framework for the optimal placement of DG units in distribution systems with time-varying loads. © 2023 IEEE.

关键词： Energy dissipation

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Study on Optimal Machine Learning Approaches for Kidney Failure Detection System Based on Ammonia Level in the Mount 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Phandinata, Nicholas Puji, Muhammad Nurul Astuti, Winda Andriatin, Yuli Astuti Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Nursery Department Cilacap Regional General Hospital Gatot Subroto No.28 Central Jawa Cilacap53223 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Patients with kidney failure can emit bad breath with a certain level of ammonia content. The interpretation of ammonia levels in patients with renal failure was identified in this work. This research used the Chronic Kidney Disease dataset from UCI Machine Learning Repository. The dataset was processed first to get the value of eGFR (estimated Glomerular Filtration Rate) and ppb (parts per billion) of ammonia. Based on the eGFR value, the severity of kidney failure was divided into 5 categories, namely normal (Stage 1), mild (stage 2), moderate (stage 3), severe (stage 4) and failure (stage 5). The values of eGFR features are used as input for the machine learning technique in order to predict the level of kidney failure. Four different types of machine learning techniques, namely Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and K-nearest neighbors (KNN), are applied and compared. The last process was to identify kidney failure using the K-Nearest Neighbors (KNN) method based on eGFR and ppb of ammonia dataset. AI-based kidney failure severity identification system with KNN algorithm had an average accuracy of 89.9% and 95.65% for training and testing accuracy, respectfully. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Support vector machines

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Reconfiguration of Power Distribution Systems in the Presence of Voltage-Dependent Loads

Reconfiguration of Power Distribution Systems in the Presenc...

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2023 IEEE Texas Power and Energy Conference, TPEC 2023

ISBN: (纸本)9781665490719

Load power can be changed by voltage fluctuation of network buses, in which reconfiguring the topology of distribution systems impacts the magnitude of bus voltages. Only a few papers have considered voltage-dependent loads in their reconfiguration models but introduced nonlinear formulations or highly approximated linear approaches. While nonlinear models can be solved by metaheuristic algorithms without guaranteeing the optimality of solutions or may be implemented by commercial nonlinear solvers, they require intensive and time-consuming computations. Moreover, linearized models are highly approximated by piecewise linear functions with many unknown parameters. Thus, this paper presents the effective design of the reconfiguration problems, including voltage dependency of loads, which can be solved using commercial linear solvers. The proposed models are precise enough to find accurate results for the reconfiguration problem and are adequately fast to converge to optimal solutions. The results show that the proposed solutions not only reduce active power losses significantly but increase the minimum voltage of the system effectively. © 2023 IEEE.

关键词： Power quality

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A Discontinuous Galerkin Time-Domain Scheme to Simulate Semiconductor Lasers

A Discontinuous Galerkin Time-Domain Scheme to Simulate Semi...

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European Conference on Antennas and Propagation, EuCAP

作者： Ming Dong Liang Chen Ran Zhao Hakan Bagci Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

A computational framework, which relies on the discontinuous Galerkin time-domain scheme, is proposed to simulate transient lasing generated by interactions of light with an active medium. The proposed scheme solves a coupled system of the Maxwell and the rate equations. The active medium inside the laser region is described quantum-mechanically by the rate equations to account for the atomic transitions of a multi-level system, while electromagnetic field interactions are described classically by the Maxwell equations. Numerical examples are provided to demonstrate the accuracy and the applicability of the proposed framework.

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Towards Explainability of Dimension Reduction Plots of Unsupervised Learning Model Outcomes 21

Towards Explainability of Dimension Reduction Plots of Unsup...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Davila, Tony E. Astuhuaman D.B., Hier T., Obafemi-Ajayi Missouri State University Computer Science Dept. SpringfieldMO United States Missouri University of Science & Technology Kummer Institute RollaMO United States Missouri State University Engineering Program SpringfieldMO United States

ISBN: (纸本)9798350356632

Dimension reduction methods are used to visualize the output of unsupervised learning models when applied to complex data. These techniques improve interpretability by transforming a high-dimension space to a lower-dimension space (usually 2D or 3D). The results are typically viewed as 2D scatter plots, and class centroids may be added to increase interpretability. Although useful, the relationship of these class centroids to the underlying feature space remains opaque. The innovative aspect of this work is to create a strong link between the dimension-reduced space and the underlying high-dimension feature space by adding selected feature centroids to the 2D scatter plots. This approach simultaneously visualizes the centers for the classes and the features on the same 2D scatter plot. Since classes are often imbalanced, we provide a method to balance class sizes. We present an automated framework that performs a grid search to find the optimal dimension reduction parameters, balances the class sizes, uses an ensemble approach to find the most important features, and adds class centroids and selected feature centroids to 2D dimension-reduced plots. This is especially useful when applied to complex, feature-rich biomedical data, as addition of feature centroids to 2D scatter plots serve as landmarks for the previously featureless dimension-reduced space. The utility of this approach is demonstrated by its application to seven classes of neurogenetic diseases with 31 defining phenotypic features. © 2024 IEEE.

关键词： Unsupervised learning

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Automated Image Captioning with Multi-layer Gated Recurrent Unit 30

Automated Image Captioning with Multi-layer Gated Recurrent ...

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30th European Signal Processing Conference, EUSIPCO 2022

作者： Moral, Özge Taylan Kiliç, Volkan Onan, Aytug Wang, Wenwu Electrical and Electronics Engineering Graduate Program Izmir Katip Celebi University Turkey Department of Computer Engineering Izmir Katip Celebi University Turkey University of Surrey United Kingdom

ISBN: (纸本)9789082797091

Describing the semantic content of an image via natural language, known as image captioning, has recently attracted substantial interest in computer vision and language processing communities. Current image captioning approaches are mainly based on an encoder-decoder framework in which visual information is extracted by an image encoder and captions are generated by a text decoder, using convolution neural networks (CNN) and recurrent neural networks (RNN), respectively. Although this framework is promising for image captioning, it has limitations in utilizing the encoded visual information for generating grammatically and semantically correct captions in the RNN decoder. More specifically, the RNN decoder is ineffective in using the contextual information from the encoded data due to its limited ability in capturing long-term complex dependencies. Inspired by the advantage of gated recurrent unit (GRU), in this paper, we propose an extension of conventional RNN by introducing a multi-layer GRU that modulates the most relevant information inside the unit to enhance the semantic coherence of captions. Experimental results on the MSCOCO dataset show the superiority of our proposed approach over the state-of-the-art approaches in several performance metrics. © 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Recurrent neural networks

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Optimization of Node Reduction Using BRKGA for GNN-Based Traffic Speed Forecasting 13

Optimization of Node Reduction Using BRKGA for GNN-Based Tra...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Inokuchi, Yuto González, Pedro Henrique Mendonça, Israel Aritsugi, Masayoshi Graduate School of Science and Technology Kumamoto University Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350308365

While Spatio-Temporal Graph Convolutional Networks (STGCNs) are an effective method for traffic speed fore-casting, their training and inference tend to be time-consuming. In this paper, we aim to refine these networks by strategically reducing their number of nodes, thereby boosting computational efficiency. The nodes in these graphs represent data observed for road segments, and by analyzing the interconnections and layout of the graph, we can identify nodes with minimal contribution to overall performance. Removing these nodes can potentially decrease computation time while maintaining the prediction accuracy. We employ the Biased Random-Key Genetic Algorithm (BRKGA) to identify a good set of nodes for removal, based on a predefined percentage reduction of the original graph size (e.g., retaining 95 % of the original graph). We evaluate different graph size configurations, ranging from 95 % to 70 % node retention, to determine the least impactful node set performance. Our experiments on three real-world datasets reveal that reducing nodes can decrease computation time by up to 29%, and as a byproduct of removing noise, even improve the prediction accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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Improved Metaheuristics with Machine Learning Enabled Medical Decision Support System

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computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

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Auxiliary Classifier based Residual RNN for Image Captioning 30

Auxiliary Classifier based Residual RNN for Image Captioning

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30th European Signal Processing Conference, EUSIPCO 2022

作者： Çayli, Özkan Kiliç, Volkan Onan, Aytug Wang, Wenwu Electrical and Electronics Engineering Graduate Program Izmir Katip Çelebi University Turkey Department of Computer Engineering Izmir Katip Çelebi University Turkey University of Surrey United Kingdom

ISBN: (纸本)9789082797091

Image captioning aims to generate a description of visual contents with natural language automatically. This is useful in several potential applications, such as image understanding and virtual assistants. With recent advances in deep neural networks, natural and semantic text generation has been improved in image captioning. However, maintaining the gradient flow between neurons in consecutive layers becomes challenging as the network gets deeper. In this paper, we propose to integrate an auxiliary classifier in the residual recurrent neural network, which enables the gradient flow to reach the bottom layers for enhanced caption generation. Experiments on the MSCOCO and VizWiz datasets demonstrate the advantage of our proposed approach over the state-of-the-art approaches in several performance metrics. © 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Recurrent neural networks

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