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

作者： Wijaya, Santo Gaol, Ford Lumban Wibowo, Antoni Zarlis, Muhammad Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Information System Management Information System Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

Real-world practical systems inherently exhibit non-linearities in their dynamics. Also, it is known that a time-varying delay exists in the system state or input-output. Combined, it affects the stability of the closed-loop system. It also increases the complexity of the controller design. In-depth controller design research on the class of Nonlinear Systems with Time-Varying Delay (NSTVD) has been the focus of the control community for many years. However, there is a lack of Systematic Literature Review (SLR) and classifications of the papers on this topic. This paper aims to review controller design utilizing a neural network model for the class of NSTVD systems. The study employs Kitchenham's SLR method to gather, analyze and synthesize published papers from reliable databases between 2017 and 2021. The bibliometric analysis for the selected 38 papers reveals the prolific authors, countries, affiliations, publishers, co-authorship network, co-occurrences of keywords, and ten most-cited papers. Finally, this paper developed a conceptual map outlining six multi-layered findings: the addressed problem, control design method, nonlinear system properties, time-varying delay properties, system constraint properties, and actuator limit properties. A brief qualitative analysis of the ten most-cited papers is performed based on the map. The findings highlighted that the proposed methods have shown encouraging results in the simulation domain and can be used as a source of inspiration for future studies and implementation of the neural controller design of the NSTVD system. © 2023 IEEE.

关键词： Nonlinear systems

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Drug Demand Prediction Based on Epidemiology Factors Using Random Forest 15

Drug Demand Prediction Based on Epidemiology Factors Using R...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Emmanuel, Gunadi Arifin, Yulyani Sonata, Ilvico Zarlis, Muhammad Bina Nusantara University Doctor of Computer Science Program Jakarta Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia Binus Graduate Program Bina Nusantara University Information System Management Department Jakarta Indonesia

ISBN: (纸本)9798350383829

The characteristics of the disease that spreads quickly, the number of sufferers, and the severity of sufferers of Coronavirus Disease 2019 are components of uncertainty during the pandemic. In an uncertain situation, prediction models for the need for drugs and medical devices are of great concern to policymakers in government, drug manufacturers, distributors, and pharmaceutical installation managers to maintain drug availability. Drug need prediction models that rely on historical data components on drug use are no longer reliable. Learning from the COVID-19 case, epidemiological variables correlate with predicting drug demand. This research includes data on ten major diseases in private hospital units for 2017-2022 to complete historical data on drug use. This study implements the Random Forest algorithm. The research method uses literature studies and processing field data from pharmaceutical installations. The analysis process uses KNIME software. The level of accuracy in predicting drug demand from historical drug use data was 77.272%, increasing to 81.818% with a model for predicting drug demand based on consumption cycles and classification of drug therapy groups. Furthermore, predictions of drug demand can consider variables recorded in medical records related to the seasonal frequency of diseases. © 2023 IEEE.

关键词： Forecasting

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Diabetes Classification Using Support Vector Machine: Binary Classification Model 4

Diabetes Classification Using Support Vector Machine: Binary...

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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Rabiha, Suciana Ghadati Wibowo, Antoni Lukas Heryadi, Yaya Computer Science Department BINUS Graduate Program-Doctor of Computer Science. Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University 11480 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University 11480 Indonesia

ISBN: (纸本)9781665433945

One of the health problems that require special attention is diabetes, besides the growth of this disease infection is increasing in various circles ranging from children, adults, men, women and the elderly. So to detect diabetes at an early stage, machine learning algorithms are the right choice to help detect indications of diabetes. This paper presents experimental results related to the exploration of the use of a Super Vector Machine (SVM) for data classification of Diabetes. The model used in this experiment is the Binary Classification Model. Based on the test results, the highest accuracy is obtained by using a total of 700 lines of data, which is 75,7143%. The Best estimated feasible point in Box Constraint is 272.29 with a KernelScale area of 72.627. © 2021 IEEE

关键词： Face recognition

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Digital Nudge Evaluation on COVID-19 tracing Application 9

Digital Nudge Evaluation on COVID-19 tracing Application

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9th International Conference on Information and Communication Technology, ICoICT 2021

作者： Sukmaningsih, Dyah Wahyu Abdurachman, Edi Purwandari, Betty Trisetyarso, Agung Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (纸本)9781665404471

Nudge is considered as an intervention to change user behavior and influence decision-making. Mobile apps have become a part of our everyday life. In this pandemic era, governments use mobile apps' technology to control the spread of COVID-19 infection. Many governments implemented COVID-19 tracing apps, and citizens are forced to use the apps to record their contact tracing and alert them if they have contact with an infected person. The key to control the increasing spread of the virus also depends on citizens adhere to health protocol. Government has experience difficulty in enforcing the protocol without strict surveillance. Therefore, building awareness of risk is essential-this study attempt to design nudge intervention into COVID-19 tracing apps. From the evaluation of the original apps, four nudge interventions can be applied in the application. © 2021 IEEE.

关键词： COVID-19

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Handling Problems of Credit Data for Imbalanced Classes using SMOTEXGBoost 3

Handling Problems of Credit Data for Imbalanced Classes usin...

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3rd International Conference on Computational science and Information Management, ICoCSIM 2019

作者： Mardiansyah, Heru Widia Sembiring, Rahmat Efendi, Syahril Graduate Program of Computer Science Department of Information Technology Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medam Indonesia

Some researchers find data with imbalanced class conditions, where there are data with a number of minorities and a majority. SMOTE is a data approach for an imbalanced classes and XGBoost is one algorithm for an imbalanced data problems. This research uses SMOTE and XGBoost or abbreviated as SMOTEXGBoost for handling data with an imbalanced classes. The results showed almost the same accuracy value between SMOTE and SMOTEXGBoost at 99%. While the value of AUC SMOTEXBoost has a more stable value than SMOTE that is equal to 99.89% for training and 98.51% for testing. © Published under licence by IOP Publishing Ltd.

关键词： Data handling

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Identification of Features in ANN's Black Box using Dimensional Reduction and Correlation Analysis: A Case Study of Cardiovascular Disease Dataset

Identification of Features in ANN's Black Box using Dimensio...

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2021 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2021

作者： Siswanto, Boby Heryadi, Yaya Suparta, Wayan Wibowo, Antoni Soeparno, Haryono Lukas Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia Jakarta Indonesia

ISBN: (纸本)9781665499699

Artificial Neural Network (ANN) is a machine learning algorithm that can perform classification. ANN has limitations;namely, it has a black box working principle, which is unsure which feature is the most influential. This study is to identify the most influential features inside the ANN's black box using a classification model by applying Principal Component Analysis (PCA) dimension reduction combined with Pearson correlation analysis. The result of the proposed model can identify the name of the main features of the data inside the ANN's black box. This study uses two public Kaggle cardiovascular datasets. The first dataset consists of 13 features, and the second dataset consists of 12 features. The result is height and gender are the most influential features in the first dataset with the correlation value of 0.734;sex and smoking are the most influential features in the second dataset with the correlation value of 0.728. Black box model result with 2 PCA's features against a model with height and gender features in the first dataset resulting from the same accuracy on the test dataset of the classification prediction results with the value of 49.90%, while on the second dataset 58.30%. © 2021 IEEE.

关键词： Principal component analysis

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Performance Improvement of Clustering Affinity Propagation Method using Principal Component Analysis 4

Performance Improvement of Clustering Affinity Propagation M...

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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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