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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Simanullang, Jasael Zarlis, Muhammad Muisa Zamzami, Elviawaty Graduate Program of Computer Science Indonesia Faculty of Computer Science and Information Technology Indonesia Universitas Sumatera Utara Medan Indonesia

Affinity Propagation Method it is necessary to modify the algorithm by using Principal Component Analysis (PCA). PCA method is used to reduce the attributes or characteristics that are less influential on the data so that the most influential attributes are obtained to then be carried out the clustering process with Affinity Propagation. The comparison results of the PCA + AP grouping model have better performance than the conventional AP grouping model. This is justified because the number of iterations and clusters produced by the PCA + AP clustering model does not change and converges when there are 8 optimal cluster clusters. While the performance of conventional clustering models produces an optimal number of clusters from 14 clusters with a significant number of iterations. So it can be concluded that the PCA + AP grouping model is suitable for the Air Quality dataset because it produces an optimal number of clusters and iterations of 8 clusters. The comparison results of the PCA + AP grouping model have better performance than the conventional AP grouping model. This is justified because the number of iterations and clusters produced by the PCA + AP clustering model does not change and converges when the optimal number of clusters is 5 clusters. While the performance of conventional clustering models produces a suboptimal number of 10 clusters with a significant number of iterations. So it can be concluded that the PCA + AP grouping model is suitable for the Water Quality Status dataset because it produces an optimal number of clusters and 5 cluster repetitions. © Published under licence by IOP Publishing Ltd.

关键词： Principal component analysis

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All-in-One Machine Learning Framework for Early Detection and Characterization of Sugar Beet Diseases Using Hyperspectral Imaging

SSRN

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SSRN 2025年

作者： Abdalla, Alwaseela Todd, Olivia E. Pennam, Sai V.P.K. Hoopes, Emma Nguyen, Nga T. Dorn, Kevin M. Dao, Phuong D. Department of Agricultural Biology Colorado State University Fort CollinsCO80523 United States Graduate Degree Program in Ecology Colorado State University Fort CollinsCO80523 United States School of Global Environmental Sustainability Colorado State University Fort CollinsCO80523 United States USDA-ARS Soil Management and Sugarbeet Research Unit Fort CollinsCO80626 United States Department of Computer Science Colorado State University Fort CollinsCO80523 United States

Soilborne diseases like Fusarium oxysporum and Rhizoctonia solani significantly impact sugar beet production, causing major yield losses. Accurate disease rating and characterization enhance disease management and breeding by tracking progression, assessing resistance, and guiding control strategies. Existing diagnostic approaches often focus on limited aspects of disease assessment, addressing only one or two ICQP objectives—identification, classification, quantification, and prediction—leaving gaps in comprehensive disease management. This study proposes an all-in-one framework that integrates hyperspectral imaging and machine learning to address all ICQP objectives. Hyperspectral data were collected from 122 plants inoculated with F. oxysporum and R. solani over 30 days using a Specim IQ hyperspectral sensor (400–1000 nm, 204 bands). To ensure accurate spectral data extraction, image segmentation was performed using a trained Deeplabv3+ model. Optimal wavelengths for each ICQP task were identified using the ANOVA algorithm and fed into three machine learning classifiers, including random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM). The study revealed that no single spectral region or machine learning model was universally optimal across all ICQP objectives. Chlorophyll-sensitive wavelengths (670–700 nm) were optimal for both F. oxysporum and R. solani disease identification, while the near-infrared range (830–1000 nm) provided critical insights for disease type classification. RF achieved the highest accuracy (96%) in identifying healthy and infected plants and demonstrated strong performance in disease type classification. For disease quantification, MLP achieved superior results with 94% accuracy and an IoU of 88%, enabling detailed pixel-level mapping of disease severity with high confidence. This study demonstrates the importance of task-specific optimization in spectral analysis and machine learning, linking spectral features t

关键词： Image segmentation

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Towards an optimal driving trains in single line using crossing loops

Towards an optimal driving trains in single line using cross...

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International Conference on computer Supported Cooperative Work in Design

作者： Dordal, Osmar Betazzi Borges, Andre Pinz Ribeiro, Richardson Enembreck, Fabricio Scalabrin, Edson Emilio Avila, Braulio Coelho Graduate Program in Computer Science Pontifical Catholic University of Paraná Curitiba Paraná Brazil

ISBN: (纸本)9781457703874

This paper describes an intelligent approach based on agents that are able to drive and coordinate trains on stretches of railway line containing a crossing loop. Halts close to or even in crossing loops lead to increased consumption of fossil fuels, longer journey times and exhaustion of track capacity. In this paper the agents make use of a set of resources railway line characteristics, train characteristics, driving rules and information about other trains to generate their action policy. The agents perception is guaranteed by a set of sensors that provide data such as speed, position and information about the line. The tasks that the agent performs include carrying out actions such as increasing or reducing the speed of the train. The main objective of this study was to avoid unnecessary halts, which are the main cause of increased fuel consumption and journey time. Our results show that strong reductions can be made in terms of fuel consumption (average reduction of 25.5%), journey time (average reduction of 22.5%) and exhaustion of track capacity. Simulations were performed in which traditional driving techniques, with halts at several points along the stretch of track, were compared with driving performed by the multi-agent system, without any halts. © 2011 IEEE.

关键词： Multi agent systems

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A power-efficient hierarchical network-on-chip topology for stacked 3D ICs

A power-efficient hierarchical network-on-chip topology for ...

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2013 IFIP/IEEE 21st International Conference on Very Large Scale Integration, VLSI-SoC 2013

作者： Matos, Debora Reinbrecht, Cezar Motta, Tiago Susin, Altamiro PPGC -Graduate Program in Computer Science -UFRGS Institute of Informatics Porto Alegre Brazil

ISBN: (纸本)9781479905249

Multi-Processors Systems-on-Chip (MPSoCs) are demanding for high performance, low power and high density, and therefore, three-dimensional integrated circuits (3DIC) emerge as a solution to integrate these systems. In order to appropriately interconnect the layers of these systems in terms of flexibility and scalability, a Network-on-Chip (NoC) is typically employed. In this paper, we argue about the scenario of 3D designs, covering all important issues about this new concept. In agreement with all features discussed in this paper, we have proposed a hierarchical 3D topology that meets well the reality of these designs. Experimental results analyze different topologies and show the large benefits in area and power of our proposal. © 2013 IEEE.

关键词： Network-on-chip

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Simulation of a teleautonomous multiple robot system with a single human operator

Simulation of a teleautonomous multiple robot system with a ...

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2009 Spring Simulation Multiconference, SpringSim 2009

作者： Adler, Rachel F. Program in Computer Science Graduate Center City University of New York United States

This paper reports on a simulation conducted to determine the number of semiautonomous robots that one operator can handle. Robots can be teleoperated, meaning a human controls the robot and tells it what to do;autonomous, where the robot makes its own decisions;or teleautonomous, a combination of the two. The use of autonomous robots may reduce operator errors due to fatigue. However, at times, these robots may require human intervention, e.g., if they get stuck or in Urban Search and Rescue if a victim is found. A simulation of multiple autonomous robots and one human operator was conducted. The results indicate that while 7-10 robots give the operator barely any idle time, 1-4 robots allow too much. Based on these results, a single human operator handling somewhere between 5-6 robots might be ideal.

关键词： Discrete event simulation

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Implementation of Dynamic Image for Facial Expression Recognition on Indonesian Facial Expression Dataset 6

Implementation of Dynamic Image for Facial Expression Recogn...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Iswanto, Irene Anindaputri Chowanda, Andry Soeparno, Haryono Budiharto, Widodo Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350389692

In recent times, there has been considerable attention directed towards Facial Expression Recognition (FER) due to its extensive utility across diverse domains. However, the universality of facial expressions has been challenged by studies suggesting that cultural backgrounds significantly influence the perception and recognition of emotions. This paper addresses the need for culturally specific datasets in FER tasks, particularly in underrepresented regions like Indonesia. The study introduces dynamic images as an alternative input representation for facial expression recognition tasks, aiming to assess their efficacy using the Indonesian Mixed Emotions Dataset (IMED). Through experimentation using EfficientNet model, the performance of dynamic images is compared with static image and video inputs. Results indicate that dynamic images exhibit promising performance, with an accuracy of 94.28%. These results outperform static image datasets and nearly match the performance of video-based models, which achieved an accuracy of 97.93 %, despite using fewer data. Nonetheless, challenges such as data imbalance and the quality of generated dynamic images persist, suggesting avenues for further research and model refinement. This study provides valuable insights into methodological advancements in FER, particularly in limited dataset conditions, laying the groundwork for future developments in dynamic image-based facial expression recognition algorithms. © 2024 IEEE.

关键词： Dynamic Image EfficientNet Facial Expression Recognition IMED

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Energy Sector Stock Price Prediction Using the CNN, GRU & LSTM Hybrid Algorithm

Energy Sector Stock Price Prediction Using the CNN, GRU & LS...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Sulistio, Bambang Warnars, Harco Leslie Hendric Spits Gaol, Ford Lumban Soewito, Benfano Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Nowadays, many people are starting to care about early investment. One of the most popular investments lately, especially for millennials, is a stock investment. In investing, there are advantages and risks of loss. One way to reduce the risk of loss is by using price predictions before investing in stocks. This paper proposes the use of deep learning in making stock predictions. We conducted research by calculating the performance of six deep-learning algorithms to predict stock closing prices. The application of the CNN-LSTM-GRU hybrid algorithm combination produces the best performance compared to other methods, based on the value: Root Mean Squared Error (RMSE) decreased by 1.100 by 14%, Mean Absolute Error (MAE) was successfully reduced by 0.798 by 13.4%, and R Square increased by 0.957 by 3.9%. In predicting stock prices on the Indonesian Stock Exchange, especially in the energy sector, CNN-LSTM-GRU is more appropriate for investors than using a single algorithm to make decisions in investing in stocks.. © 2023 IEEE.

关键词： Long short-term memory

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Distributed usage control architecture for business coalitions

Distributed usage control architecture for business coalitio...

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2009 IEEE International Conference on Communications, ICC 2009

作者： Stihler, Maicon Santin, Altair Olivo Calsavara, Alcides Marcon Jr., Arlindo L. Pontifical Catholic University of Paraná Graduate Program on Computer Science Curitiba PR Brazil

ISBN: (纸本)9781424434350

The dynamic environment of Business Coalition (BC) requires a flexible access control approach to deal with user management and policy writing. However, the traditional approach applied to BC assigns to access control a burden, mainly to the service provider, thus requiring ad hoc schemes to mitigate the lack of controls developed to BC needs. We present a brokered access control architecture, based on UCONABC, to obtain an integrated usage control management for BC. The broker intermediates contract establishment between service provider and consumer, and derives from it the policies to regulate the usage at service-level. The consumer defines user-level policies to control the usage of the contracted services. We developed a web services based prototype to evaluate the feasibility of our proposal. The proposed architecture enables distribution of duties and integration of usage control management in a loosely coupled fashion, providing the flexibility desired in BC environments. ©2009 IEEE.

关键词： Access control

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Detection of dos attacks using naive bayes method based on internet of things (iot) 3

Detection of dos attacks using naive bayes method based on i...

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3rd International Conference on Vocational Education and Technology, IConVET 2020

作者： Setiadi, F.F. Kesiman, M.W.A. Aryanto, K.Y.E. Department of Computer Science Graduate Program Universitas Pendidikan Ganesha Singaraja Bali Indonesia

Internet of Things (IoT) is a technology that is currently on a trend. Interconnecting networks on IoT are useful in the automation process, but it has vulnerabilities to network-based disruptions and attacks;such as Denial of Service (DoS). This study aimed at implementing the Naive Bayes algorithm to predict attribute classes using training data-sets from NSLKDD with the KDD99 format and obtained testing-data from the logging process of DoS attacks on IoT-based devices. The advantage of using Naive Bayes is that this method only requires a small amount of training data to determine the estimated parameters needed in the classification process. The results of the conducted research could detect attacks on IoT devices by using the help of snort tools to capture traffic logs. The results from the log were then converted into KDD99 format and processed by the Naive Bayes method. This research uses a training dataset from NSLKDD with KDD99 format which is widely used in various studies and testing data obtained from the IDS log process on the Raspberry Pi 3. The attributes used are 9 attributes namely;service, flag, src_bytes, dst_bytes, srv_serror_rate, same_srv_rate, diff_srv_rate, dst_host_srv_diff_host_rate and dst_host_srv_serror_rate. The results of the research analysis showed an accuracy of 64.02%. These results were smaller than previous results, but some aspects were still different from the actual results because the testing data and training data were taken from two different data-sets, thereby they had different characteristics. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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Augmented Reality in Chemistry Learning: A Systematic Literature Review 7

Augmented Reality in Chemistry Learning: A Systematic Litera...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Ariessanti, Hani Dewi Prabowo, Harjanto Gaol, Ford Lumban Meyliana Bina Nusantara University Binus Graduate Program - Information System Jakarta11480 Indonesia Management Bina Nusantara University Binus Graduate Program - Computer Science Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Jakarta11480 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

This study reviews factors affecting AR-based chemistry learning with the BIM model. Although there have been many kinds of research explaining the application of augmented reality (AR) and building information modeling (BIM) in learning, no research has been found that discusses the factors affecting the integration of BIM in AR-based learning. This study adopted the SLR method with the PRISMA protocol by scanning articles written in English or articles written in Indonesian from accredited online databases, and fully accessible by the authors. The articles reviewed were related to AR, BIM, Virtual Reality (VR), and chemistry learning. The predefined strings were used to extract the 156 articles. Only 41 articles met the inclusion criteria that were used as the reference for this study. Six factors were found that affect the AR-based chemistry learning with the BIM model. They are AR work performance, accessibility, user interaction with the system, presentation of material, understanding, and models. This study implies that the factors extracted can be considered as a model in developing learning media to improve chemistry learning. The limitation of this study is the absence of further analysis of which critical factors should be addressed. Researchers recommend further research by reviewing more literature. © 2022 IEEE.

关键词： Augmented reality

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