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Networking, Electrical Engineering, computer science, and Technology (IConNECT), International Conference on

作者： Tamsir Hasudungan Sirait Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Computer Science Department Universitas Indonesia Jakarta Indonesia

Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology.

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Dynamic Healthcare Embeddings for Improving Patient Care 14

Dynamic Healthcare Embeddings for Improving Patient Care

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14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

作者： Jang, Hankyu Lee, Sulyun Hasibul Hasan, D.M. Polgreen, Philip M. Pemmaraju, Sriram V. Adhikari, Bijaya University of Iowa Department of Computer Science Iowa CityIA United States University of Iowa Interdisciplinary Graduate Program in Informatics Iowa CityIA United States University of Iowa Department of Internal Medicine Iowa CityIA United States

ISBN: (纸本)9781665456616

As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare professionals, prescription data, procedures data, and data on patient admission, discharge, and transfers. This has opened up many fascinating avenues for healthcare-related prediction tasks for improving patient care. However, in order to leverage off-the-shelf machine learning software for these tasks, one needs to learn structured representations of entities involved from heterogeneous, dynamic data streams. Here, we propose DECENT, an auto-encoding heterogeneous co-evolving dynamic neural network, for learning heterogeneous dynamic embeddings of patients, doctors, rooms, and medications from diverse data streams. These embeddings capture similarities among doctors, rooms, patients, and medications based on static attributes and dynamic interactions. DECENT enables several applications in healthcare prediction, such as predicting mortality risk and case severity of patients, adverse events (e.g., transfer back into an intensive care unit), and future healthcare-associated infections. The results of using the learned patient embeddings in predictive modeling show that DECENT has a gain of up to 48.1% on the mortality risk prediction task, 12.6% on the case severity prediction task, 6.4% on the medical intensive care unit transfer task, and 3.8% on the Clostridioides difficile (***) Infection (CDI) prediction task over the state-of-the-art baselines. In addition, case studies on the learned doctor, medication, and room embeddings show that our approach learns meaningful and interpretable embeddings. © 2022 IEEE.

关键词： Heterogeneous networks

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Classification of Highly Similar Fish Species Using Machine Learning

Classification of Highly Similar Fish Species Using Machine ...

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Leandro Magno C. Silva Franklin Cesar Flores Rosilene Luciana Delariva Gustavo Z. Felipe Yandre M. G. Da Costa Graduate Program in Computer Science State University of Maringá Federal Institute of Paraná Maringá Brazil Department of Informatics State University of Maringá Maringá Brazil Center for Biological and Health Sciences State University of Western Paraná Cascavel Brazil Graduate Program in Computer Science State University of Maringá Maringá Brazil

ISBN: (数字)9798350391886

ISBN: (纸本)9798350391893

Fish species inventory and monitoring is a timely concern due to the increasing environmental imbalance. In this sense, the development of machine learning strategies aiming to support automatic fish species identification is an important contribution, both in environmental and scientific terms. This study explores handcrafted features aiming to perform fish species identification in a quite challenging scenario, in which the inter-classes differences are minimal, as the species belong to the same family considering the hierarchy of biological classification (i.e. Characidae). For this purpose, it is proposed a set of features designed properly, aiming to capture visual properties closely related to each species that are difficult to be noted by experts in this domain of investigation. An additional contribution of this work is a dataset of 4870 images taken from 416 individuals from nine fish species. The Region of Interest (RoI) corresponding to the fish body is segmented, and horizontal and vertical histograms are collected from each sample, aiming to capture information related to the body shape, and other characteristics particular to each species. Experiments are performed using the Support Vector Machine (SVM), K-Nearest Neighbours (K-NN), Random Forest and Naive-Bayes classifiers and, in the best scenario, it is obtained an accuracy rate of 99%. This result corroborates the hypothesis that the use of handcrafted features specifically developed for this type of application can be quite effective in addressing this classification problem.

关键词： Support vector machines Image segmentation Visualization Histograms Image recognition Shape Fish

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Twitter Data Sentiment Analysis of COVID-19 Vaccination using Machine Learning 5

Twitter Data Sentiment Analysis of COVID-19 Vaccination usin...

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5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022

作者： Maulana, Fairuz Iqbal Adi, Puput Dani Prasetyo Lestari, Dian Purnomo, Agung Prihatin, Sukeipah Yuli School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia BINUS Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia Informatics Study Program Politeknik Kota Malang Malang Indonesia

ISBN: (纸本)9781665455121

The expansion of the web is accelerating, which helps encourage the creation of fresh ideas. In today's internet era, we must suggest techniques to filter out various information. Social media sentiment analysis based on Twitter data can monitor the real-Time monitoring of the COVID-19 vaccine. In this way, relevant organizations or governments can take proactive steps to address misinformation and inappropriate behaviour around the COVID-19 vaccine, which threatens the success of the national vaccination campaign. The purpose of this research is to determine if there is a link between how people feel about the COVID-19 vaccine on Twitter and how many people actually get vaccinated against it. This study uses the COVID-19 All Vaccines Tweet dataset sourced from Kaggle. This research Identifies public sentiment, emotion, word usage, and trend of all filtered tweets. The results show that there are 31% positive tweets, 10% negative tweets, and 58% neutral tweets. Tweets with neutral subjective valence tend to cluster in the middle of the polarity scale (between-1 and +1), whereas tweets with strong subjective valence are spread across the scale. © 2022 IEEE.

关键词： COVID-19

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ζ-QVAE: A Quantum Variational Autoencoder utilizing Regularized Mixed-state Latent Representations

arXiv

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arXiv 2024年

作者： Wang, Gaoyuan Warrell, Jonathan Emani, Prashant S. Gerstein, Mark Program in Computational Biology and Bioinformatics Department of Molecular Biophysics and Biochemistry Department of Computer Science Department of Statistics & Data Science Department of Biomedical Informatics & Data Science Yale University New HavenCT06520 United States NEC Laboratories America PrincetonNJ08540 United States

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves finding low-dimensional representations that preserve essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no quantum model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To address this, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied to map both classical and quantum data to a lower-dimensional space, while effectively reconstructing much of the original state from it. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full-data density matrix and allow for a training objective defined on probabilistic mixtures of input data. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results indicate that ζ-QVAE consistently learns representations that better utilize the capacity of the latent space and exhibits similar or better performance compared to ma

关键词： Qubits

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Feature Selection and Classification Algorithm Approaches for Parkinson's Disease Detection 5

Feature Selection and Classification Algorithm Approaches fo...

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5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Marcos, Hendra Warsito, Budi Wibowo, Adi Diponegoro University Doctoral Program Information System Department Informatics Universitas Amikom Purwokerto Purwokerto Indonesia Diponegoro University Faculty Of Science And Mathematics Department Of Statistics Semarang Indonesia Department Of Computer Science Informatics Diponegoro University Semarang Indonesia

ISBN: (纸本)9781665401968

Millions of people worldwide have developed Parkinson's disease (PD), which is more common by 50. Advances in information technology, especially in the field of machine learning, are being challenged for the development of early detection algorithms. This helps doctors to detect disease early and more accurately based on data. The available data has large dimensions and features so that the feature selection method can be used. This research paper aims to provide insight by comparing several algorithms for feature selection and PD classification. PD classification can help improve treatment efficiency and save time. The problem that arises is how the classification algorithm can show better and more efficient accuracy results. A large number of datasets is one of the challenges in the development of classification algorithms. The voice input dataset was obtained from the UCI machine learning repository. Bagging's algorithm out performed on average 100%, and MLP 96.58%, and RF 92.52%. © 2021 IEEE.

关键词： Classification (of information)

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Smart Home Gas Sensor Optimization Using Kalman Filter for Data Processing 9

Smart Home Gas Sensor Optimization Using Kalman Filter for D...

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9th International Conference on informatics and Computing, ICIC 2024

作者： Somantri Thohir, Muhamad Ikhsan Sujjada, Alun Setiawan, Deni Komara, Ilham Efendi, Fikri Ardiansyah Muslih, Muhamad Abdullah, Zubaile Bin Saringat, Mohd Zainuri Department of Software Engineering University Tun Hussein Onn Malaysia Malaysia Nusa Putra University Informatics Engineering Study Program Sukabumi Indonesia Department of Information System Nusa Putra University Sukabumi Indonesia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Department of Software Engineering Johor Bahru Malaysia

ISBN: (纸本)9798331517601

The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system's reliability and functionality. © 2024 IEEE.

关键词： Data reduction

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), Engineering (3.9%), computer science (2.6%), Environmental science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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Vehicle Occurrence-Based Parking Space Detection

Vehicle Occurrence-Based Parking Space Detection

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IEEE International Conference on Systems, Man and Cybernetics

作者： Paulo R. Lisboa de Almeida Jeovane Honório Alves Luiz S. Oliveira Andre Gustavo Hochuli João V. Fröhlich Rodrigo A. Krauel Department of Informatics Federal University of Paraná (UFPR) Curitiba Brazil Graduate Program in Informatics Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil Department of Computer Science Santa Catarina State University (UDESC) Joinville Brazil

Smart-parking solutions use sensors, cameras, and data analysis to improve parking efficiency and reduce traffic congestion. computer vision-based methods have been used extensively in recent years to tackle the problem of parking lot management, but most of the works assume that the parking spots are manually labeled, impacting the cost and feasibility of deployment. To fill this gap, this work presents an automatic parking space detection method, which receives a sequence of images of a parking lot and returns a list of coordinates identifying the detected parking spaces. The proposed method employs instance segmentation to identify cars and, using vehicle occurrence, generate a heat map of parking spaces. The results using twelve different subsets from the PKLot and CNRPark-EXT parking lot datasets show that the method achieved an AP25 score up to 95.60% and AP50 score up to 79.90%.

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E-Government Interoperability: Provincial-Level Architecture Model to Enable Fast Healthcare Interoperability Resources (FHIR)

E-Government Interoperability: Provincial-Level Architecture...

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International Conference on Information Management and Technology (ICIMTech)

作者： Diana Wijayanti Satriawansyah Urbaya Taufiq Sitompul Verry Adrian Department of Information Systems BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Country Health Information Systems and Data Use (CHISU) Program USAID Indonesia Jakarta Indonesia Department of Informatics University of Oslo Oslo Norway Computer Science Department Binus Graduate Program – Doctor Computer Science Bina Nusantara University Jakarta Indonesia Center of Data and Information Technology Jakarta Provincial Health Office Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

The process of using ICT to provide services to the public is known as the Indonesian e-Government system, or Sistem Pemerintahan Berbasis Elektronik (SPBE). The e-Government initiative in Jakarta Provincial Health Office involves enhancing collaboration among public health entities for efficient data exchange and streamlined processes, especially between the Provincial and District Health Offices, public hospitals, government clinics, and primary health care centers (Puskesmas). Achieving interoperability requires standardized protocols and a well-defined architectural model to integrate data seamlessly. This study presents a provincial-level architectural model focused on improving electronic health records interoperability, aiming to promote the adoption of the national Fast Healthcare Interoperability Resources (FHIR) health information exchange platform and enhance the integrity of health data in Jakarta. The study methodology involves conducting literature reviews, observations, and discussions with representatives from healthcare facilities to develop the e-Government architecture model and prototype of the infrastructure layer aiming to facilitate the interoperability of Electronic Health Records (EHRs) across 93 healthcare facilities, all of which are part of the SPBE users.

关键词： Prototypes Medical services Data models Security Public healthcare Electronic medical records Interoperability Information exchange Standards Electronic government

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