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SSRN 2023年

作者： do Nascimento, Amanda Roseane Farias Greati, Victor Rodrigues Leopoldino, Ramon Weyler Duarte Martins, Rand Randall Barbosa, Euzebio Guimarães Graduate Program in Pharmaceutical Sciences Health Sciences Center Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil Graduate Program in Systems and Computing Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil

Introduction: The intensive care of neonates is associated with the entry of a large volume of data in their medical records. The treatment of this data can be done through Machine Learning: a tool capable of assisting in the detection and decision-making of a wide range of medical conditions, including adverse drug reactions (ADR). Objective: To train a prediction model to assist in the detection of adverse drug reactions in neonates admitted to an ICU. Methodology: observational study developed in the Neonatal Intensive Care Unit of a teaching hospital in Brazil. Clinical data were collected from daily pharmacotherapeutic follow-up, processed and analyzed by machine learning through libraries written in Python language. Results: Eight hundred and three neonates were included in the study, with a mean gestational age of 32.2 ± 4.2 weeks and mean birth weight of 1807.2 ± 936.6g. The incidence of ADR was 10.8%. Antimicrobials, especially aminoglycosides, were the most prescribed drugs for this population. An algorithm was trained and tested to predict ADR in the NICU, in which the metrics were precision (0.35) and recall (0.823), with specificity (80%) and sensitivity (67%). Conclusion: There is a high potential in the machine learning method to predict ADR in neonates admitted to an ICU. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Develop learning methods imr (integrated material realistic) alternative mathematics learning during a pandemic 7

Develop learning methods imr (integrated material realistic)...

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7th International Conference on mathematics, Science, and Education 2020, ICMSE 2020

作者： Rusmana, I.M. Zainuri Wardono Nulhakim, A.L. Doctoral Program Universitas Negeri Semarang Indonesia Department of Mathematic Education Faculty of Mathematics and Natural Science Universitas Indraprasta Pgri Jakarta Indonesia Department of Mathematic Education Faculty of Mathematics and Natural Science Universitas Negeri Semarang Indonesia Department of Informatics Engineering Faculty of Engineering and Computer Science Universitas Indraprasta Pgri Jakarta Indonesia

The learning methods used by teachers in schools in Serang City are very ineffective, especially during the pandemic. This is because not integrated content of material in one subject with other subjects. Never mind between subjects, sometimes the material is not well integrated. The purpose of this study is to provide an alternative learning method during a pandemic, namely a method that integrates material with the realities of life which is abbreviated as IMR (Integrated Realistic Material). The method in this research is Research and Development (RnD) with the ADDIE model. In making this learning method according to the stages from the ADDIE study. The results of this study are in the form of learning methods that are integrated with the realities of life during the pandemic. © Published under licence by IOP Publishing Ltd.

关键词： Learning systems

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Sentiment analysis of Aceh tourism and its culture from Twitter data using support vector machine (SVM), naive Bayesian and k-nearest neighbour (KNN)

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AIP Conference Proceedings 2024年第1期3082卷

作者： Reza Irwanda Muslim Amiren Zulfan Viska Mutiawani Subianto Rasuddin Rini Deviani Informatics Study Program Faculty of Mathematics and Natural SciencesUniversity of Syiah Kuala Darussalam - Banda Aceh Indonesia

Social media shares many opinions. Users can disseminate information, search for messages, announce activities, or communicate with others. A social media often used is Twitter. It includes opinion of the Indonesian people towards Aceh, especially regarding tourism and culture. To know that, the researchers conducted a sentiment analysis on Twitter using Support Vector Machine (SVM), Naive Bayessian and K-Nearest Neighbor (KNN). The steps taken are data collection, data preprocessing and analysis of classification results. The total data collected is 10,320 tweet data. Sentiment classification is divided into three categories, namely positive, negative and neutral. The accuracy of the model built with SVM is divided into three categories, namely positive-negative with an accuracy of 80.40%, neutral-negative with an accuracy of 81.11%, positive-negative with an accuracy of 76.50%. Using Naive Bayessian, model accuracy built is only 65.445% with a percentage split of 80 and K-Folds 10. The best model accuracy 71.3787% is built using K-Nearest Neighbor and K-value of 17, a percentage split of 80 and K-Folds 10. It results that related to Aceh’s tourism and culture, negative sentiment is more than positive sentiment, 81.11% compared to 80.40%. Therefore, more efforts are needed to make Aceh’s image more positive.

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Bhandarkar, Vishakha D. Rongali, Aruna Sri Shamout, Mohamed Dawood Pund, Sachin Nama, Gigih Forda Patil, Shrishailappa Yeshwantrao Chavan College of Engineering Applied Mathematics and Humanities India University of the Cumberlands Graduate Doctoral Information Technology Information Technology Emphasis/Information Technology United States College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Doctoral Program of Environtmental Science University of Lampung Department of Informatics Bandar Lampung Indonesia Vishwakarma Institute of Technology Computer Engineering Pune India

ISBN: (纸本)9798350357769

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the 'performing Scalable Inference' technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way.. © 2023 IEEE.

关键词： adjustments an a lytics automated preserves statistics

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Local super antimagic total vertex coloring of some wheel related graphs 3

Local super antimagic total vertex coloring of some wheel re...

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3rd International Conference on Combinatorics, Graph Theory, and Network Topology, ICCGANT 2019

作者： Pratama, S.A. Setiawani, S. Slamin Cgant Research Group University of Jember Indonesia Department of Mathematics Education University of Jember Indonesia Study Program of Informatics University of Jember Indonesia

Let G be a simple and finite graph with vertex set V and edge set E. The local antimagic total labeling of G is a map from V ∪ E to the set of positive integers from 1 to |V| + |E| such that the weight of two adjacent vertices are distinct. The weight of a vertex v is calculated by the sum of the label of the vertex v and the labels of all edges incident to it. If the vertices of G is labelled by the smallest labels, that is, {1, 2,.., |V|}, then the such labeling is called local super antimagic total labeling. Thus, the local super antimagic total labeling induces a proper vertex coloring of G where the vertex v is colored by the weight of vertex v. The minimum number of colors taken over all colorings induced by super local antimagic total labeling of G, is called local super antimagic total chromatic number of graph G, and denoted by χlsat (G). In this paper, we consider the local super antimagic total chromatic number of some wheel related graphs such as fans, even gear graphs, and sun flower graphs. © Published under licence by IOP Publishing Ltd.

关键词： Wheels

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification 4

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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4th International Conference on informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Fitrianah, Devi Suryaningrum, Kristien Margi Sagala, Noviyanti Tri Maretta Ayumi, Vina Lim, Siew Mooi School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Mathematics Statistics Department Jakarta Indonesia Universitas Mercu Buana Informatics Engineering Program Faculty of Computer Science Jakarta Indonesia Tunku Abdul Rahman University College Faculty of Computing and Information Technology Computer Science Department Kuala Lumpur Malaysia

ISBN: (纸本)9781665473279

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%. © 2022 IEEE.

关键词： Fish

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Application of factor analysis method to support the users acceptance model of ERP systems implementation 4

Application of factor analysis method to support the users a...

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4th International Conference on Combinatorics, Graph Theory, and Network Topology, ICCGANT 2020

作者： Sardjono, W. Sudirwan, J. Priatna, W. Putra, G.R. Information Systems Management Department BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Department BINUS Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Informatics Engineering Engineering Bhayangkara University Jakarta Raya Indonesia Computer Science Study Program Faculty of Mathematics and Natural Sciences Pakuan University Bogor Indonesia

An e-Commerce company has been using an Enterprise Resource Planning (ERP) system for several years, but is still constrained in its implementation, this is reflected in the number of issue/change request tickets submitted by users who have not been able to reach ideal conditions. To overcome this problem, we need an appropriate reference model, the ERP system user acceptance model in companies. The study conducted factors that influence the level of acceptance of SAP ERP users in the XYZ group to then determine a model that is able to explain practically the level of ERP system user acceptance, which in turn can produce concrete proposals to increase ERP system user acceptance. Research is quantitative descriptive using factor analysis and regression analysis methods. The research method uses a questionnaire with a total of 107 valid respondents coming from 8 company names under the XYZ group in 6 Southeast Asian countries. The conclusion of the study is that there are 24 indicators and 4 factors that influence user acceptance of the SAP ERP system. These four factors are the low contribution of the ERP system, the quality of ERP systems and data, the development of ERP systems, and also the low quality of ERP system supporters. It is hoped that by optimizing the value of the 4 factors, the XYZ group can increase user acceptance of the SAP ERP system so that the system can provide benefits expected by the company to be competitive advantage. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Factor analysis

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A New Hybrid Algorithm of Bisection and Modified Newton's Method for the nth root-finding of a Real Number 10

A New Hybrid Algorithm of Bisection and Modified Newton's Me...

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2020 10th International Conference on Applied Physics and mathematics, ICAPM 2020

作者： Pinkham, S. Sansiribhan, S. Informatics Mathematics Program Faculty of Science and Technology Suan Sunandha Rajabhat University 10300 Thailand Applied Physic Program Faculty of Science and Technology Suan Sunandha Rajabhat University 10300 Thailand

This paper presents a new algorithm to approximate the nth root of a given real number z. The proposed algorithm is a hybrid algorithm between the Bisection method and the combination of the inverse of sine series and Newton's method. The proposed algorithm will be tested to find the numerical results on Matlab programming. The results showed that if z > 1, the proposed method converges with the initial interval [1, z] and if 0 © Published under licence by IOP Publishing Ltd.

关键词： Newton-Raphson method

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Monitoring System for Elderly Health Care Using Smart Band, Raspberry Pi, and Node-Red 2

Monitoring System for Elderly Health Care Using Smart Band, ...

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2nd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2022

作者： Ardiyanto, Yudhi Adi, Kusworo Kurnianingsih Putra, Karisma Trinanda Utomo, Pradityo System School of Postgraduate Studies Universitas Diponegoro Doctoral Program of Information Semarang Indonesia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia Universitas Diponegoro Faculty of Science and Mathematics Department of Physics Semarang Indonesia Politeknik Negeri Semarang Faculty of Engineering Department of Electrical Engineering Semarang Indonesia Universitas Merdeka Madiun Faculty of Engineering Department of Informatics Management Madiun Indonesia

ISBN: (纸本)9781665465410

The decline in physical, cognitive, and sensory functions for most elderly people leads to the development of several health issues such as memory loss, a tendency to fall, dizziness, and reduced ability to care for oneself independently. These can be mitigated using devices designed to monitor vital signs and human activity such as a smart band. However, the application of a smart band is characterized by a disadvantage which involves storing user data in the vendor's cloud and only one device can access the client applications. Therefore, nurses, doctors, and family members cannot simultaneously analyze data and receive alerts when abnormal conditions occur in elderly patients. This study aims to develop a system capable of monitoring the health conditions of multiple elderly individuals and sending alerts during abnormal conditions based on the heart rate value read by the smart band sensor. The process involved installing a database on the Raspberry Pi to store data while a monitoring system was built on the Node-RED platform to display elderly health data and made it accessible locally. The detection of an abnormal heart rate condition in the elderly patient leads the system to send notifications via telegram to multiple receivers registered in the system using their telegram identity. © 2022 IEEE.

关键词： Heart

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Discoffery: a Coffee Types Detection Apps using an EfficienNet-Lite Architecture 22

Discoffery: a Coffee Types Detection Apps using an EfficienN...

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Proceedings of the 7th International Conference on Sustainable Information Engineering and Technology

作者： Satrio Arif Budiman Andi Pramono Bayu Priyambadha Annisa Maharani Arya Putri Agung Purnomo Ilham Setyo Nugroho Information System Computer Science Faculty University of Brawijaya Indonesia Interior Design Department School of Design Bina Nusantara University Indonesia Department of Informatics Computer Science Faculty University of Miyazaki Japan & Brawijaya University Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Brawijaya University Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Indonesia

ISBN: (纸本)9781450397117

According to data from the World Food and Agriculture Organization (FAO), Indonesia produced the fourth-most coffee in the world in 2017 and 2018. Gayo, Robusta Dampit, and Toraja coffees are only a few well-known coffee varieties Indonesian growers produce. This research aims to create an app that can identify the type of coffee and serve as a coffee-related educational tool. A single case study was the research methodology used. By employing the EfficienNet-Lite architecture for transfer learning, a model for categorizing coffee beans is created. Users can get information through the photographs they submit with the help of the type of application development that uses deep learning to do categorization based on image data of coffee bean types. The coffee bean classification feature was built using transfer learning with the EfficientNet architecture. A training accuracy of 87% and a validation accuracy of 81% were achieved using the EfficientNet-Lite 0 architecture.

关键词： Smartphone EfficienNet-Lite Transfer Learning Coffee Beans Classification Deep Learning

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