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Simulation Winter Conference

作者： Almir Antonio Monteiro Junior Rafael Schneider Vanessa De Almeida Guimarães Pedro Henrique González System Engineering and Computer Science Program Federal University of Rio de Janeiro Brazil COPPEAD Graduate School of Business Federal University of Rio de Janeiro Brazil

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

This study addresses the Testing Facility Location with Constrained Queue Time Problem. This optimization problem focuses on determining the best places to deploy testing sites and their available testers for infectious diseases, while constraining the maximum time in the queue with a given probability. An integer programming model is introduced and applied to the three biggest counties, in terms of population, of Florida, United States. Moreover, the Monte Carlo method is used to evaluate the model's output, aiming to check if the queueing time constraint is being satisfied. Through the experiments, a testing facility deployment plan can be determined for each county and further validated by the simulation. The results show that the solutions returned by the model behaved successfully when submitted to the Monte Carlo method, not exceeding the time in the queue in more than the predefined probability.

关键词： Integer programming Monte Carlo methods Infectious diseases Time factors Optimization Testing

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review 2

Trends, Opportunities, and Challenges in Detecting Depressiv...

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2nd International Conference on computer System, Information Technology, and Electrical engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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Improving Land Cover Segmentation Using Multispectral Dataset 8

Improving Land Cover Segmentation Using Multispectral Datase...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Herlawati, Herlawati Handayanto, Rahmadya Trias Heryadi, Yaya Abdurachman, Edi Soeparno, Haryono Irwansyah, Edy Miranda, Eka Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Universitas Islam 45 Computer Engineering Bekasi Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Trisakti Institute of Transportation and Logistic Jakarta13410 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350342604

computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images but also multispectral dataset with some channels including RGB, infrared, short-wave, and thermal wave. Most of the dataset is panchromatic (black and white) and RGB, for example Google Map and other satellite-based map applications. This study examines the effects of multispectral dataset for semantic segmentation of land cover. The comparison between RGB with band 2 to band 7 of Landsat 8 Satellite shows an improvement of accuracy from 90.283 to 94.473 for U-Net and from 91.76 to 95.183 for DeepLabV3+. In addition, this research also compares two well-known semantic segmentation methods, namely U-Net and DeepLabV3+, that shown that DeepLabV3+ outperformed U-Net regarding to speed and accuracy. Testing was conducted in the Karawang Regency area, West Java, Indonesia. © 2023 IEEE.

关键词： Landsat

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Uncertainty Quantification Using Subsampling for Assembly-Free Estimates of Genomic Distance and Phylogenetic Relationships 26th

Uncertainty Quantification Using Subsampling for Assembly-F...

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26th International Conference on Research in Computational Molecular Biology, RECOMB 2022

作者： Rachtman, Eleonora Sarmashghi, Shahab Bafna, Vineet Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego La JollaCA United States Department of Electrical and Computer Engineering UC San Diego La JollaCA United States Department of Computer Science and Engineering UC San Diego La JollaCA United States

Computing the distance between two genomes, often without using alignments or even access to assembled sequences, is fundamental to many downstream analyses. © 2022, The Author(s), under exclusive license to Spri... 详细信息

ISBN: (纸本)9783031047480

关键词： Uncertainty analysis

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Thy-Sys: A Preliminary Thyroid Wellness Assessment Through Machine Learning Using Pathological Factors 1

Thy-Sys: A Preliminary Thyroid Wellness Assessment Through M...

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1st International Conference in Information and Computing Research, iCORE 2021

作者： Francisco, Ismael R. Ferolin, Mary Bernadette J. Pena, Christine F. Ferolin, Rosana J. University of San Carlos Department of Computer Information Sciences and Mathematics Cebu City Philippines University of San Carlos Graduate Engineering Program School of Engineering Department of Computer Engineering Cebu City Philippines

ISBN: (纸本)9781665402101

The thyroid gland is a butterfly-shaped organ located lower front of the neck that plays a critical role in one's overall well-being. According to survey, thyroid dysfunction is observed in 8.53% of Filipino adults aged 42 to 62 years old, implying that 1 out of 12 Filipino adults has some form of thyroid function abnormality. To promote awareness of thyroid health, the researchers developed the Thyroid System for Wellness Assessment (Thy-Sys) which aims to determine the wellness of a person's thyroid through machine learning. The system assesses the user's thyroid wellness based on their responses to questions related to observable characteristics of the more common thyroid diseases like hypothyroidism and hyperthyroidism. Prior to the development, four machine learning models, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and SVM-KNN were evaluated in classifying the state of the thyroid through pathological factors. Training and testing of the machine were done over a dataset with 1,464 entries and a total of 18 pathological attributes. 10-fold cross-validation was performed to verify the models' accuracy while observing other metric scores. The SVM-KNN model garnered the highest accuracy of 99.55% with 98.75% precision, and 98.61 % recall and F1 scores, and was integrated into the system. © 2021 IEEE.

关键词： Decision trees

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The Impact of ICN/CCN Cache Decision Policies on Video and Audio Transmission QoE

The Impact of ICN/CCN Cache Decision Policies on Video and A...

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Australasian Telecommunication Networks and Applications Conference, ATNAC

作者： Keisuke Kobayashi Toshiro Nunome Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9781665471039

ISBN: (纸本)9781665471046

This paper considers video and audio transmission in ICN (Information-Centric Networking) CCN (Content- Centric Networking), in which each intermediate node can cache content. LCE (Leave Copy Everywhere) has been known as a generic cache decision policy. However, because LCE caches at all the intermediate nodes, the cache of intermediate nodes can be duplicated. Therefore, various cache decision policies that eliminate redundancy have been proposed. In this paper, we evaluate the effect of the cache decision policies on QoE of video and audio transmission in ICN/CCN. We assess application-level QoS using a computer simulation with a tree network and QoE by means of subjective experiment.

关键词： computer simulation Redundancy Information-centric networking Quality of service Liquid crystal displays Communications technology Quality of experience

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Non-Hermitian selective thermal emitter for thermophotovoltaics

Non-Hermitian selective thermal emitter for thermophotovolta...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Prasad, Ciril S. Raphaelson, Samuel C. Naik, Gururaj V. Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Material Science and NanoEngineering Rice University HoustonTX United States

ISBN: (纸本)9781957171258

Selective thermal emitters can boost the efficiency of heat-to-electricity conversion in thermophotovoltaic systems only if their spectral selectivity is high. We demonstrate a non-Hermitian metasurface-based selective emitter operating at 1200 K in a GaSb-thermophotovoltaic cell. © Optica Publishing Group 2023 © 2023 The Author(s)

关键词： III-V semiconductors

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Learning-Based Tone Mapping to Improve 3D SAS ATR

Learning-Based Tone Mapping to Improve 3D SAS ATR

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Gregory D. Vetaw Benjamin Cowen Daniel C. Brown David P. Williams Suren Jayasuriya School of Electrical Computer and Energy Engineering Arizona State University The Applied Research Laboratory Graduate Program in Acoustics Pennsylvania State University

Automatic target recognition (ATR) for 3D synthetic aperture sonar (SAS) imagery is an intrinsic challenge in highly cluttered ocean environments, especially for objects partially or completely buried in the sediment. Conventional dynamic range compression (DRC) techniques such as log-compression, which is a type of tone mapping intended to appeal to the human visual system, can further obscure the sonar signatures of these already physically occluded objects and lead to suboptimal downstream ATR performance, particularly for convolutional neural networks (CNNs). In this paper, we present a novel machine learning-based approach for tone mapping sub-bottom SAS imagery as a pre-processing stage in the 3D SAS ATR pipeline. This learned tone mapping function can be jointly optimized with a CNN-based ATR algorithm. We train and validate our method on measured volumetric SAS data captured by the Sediment Volume Search Sonar (SVSS) system.

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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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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space

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Procedia computer Science 2023年 227卷 1046-1053页

作者： Fergianto E. Gunawan Arief S. Budiman Bens Pardamean Endang Juana Sugiarto Romeli Tjeng W. Cenggoro Kartika Purwandari Alam A. Hidayat Anak. A.N.P. Redi Muhammad Asrol Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia PT Impack Pratama Industri Jakarta 14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99.

关键词： closed space deep learning humity prediction indoor temperature prediction

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