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2019 International Conference of Science and Information Technology in Smart Administration, ICSINTeSA 2019

作者： Rusdi, Jack Febrian Salam, Sazilah Abu, Nur Azman Sunaryo, Budi Agustina, Nova Gusdevi, Harya Hermansah Hadhiwibowo, Ari Heryati, Tuty Hardi, Richki Taufiq, Rohmat Jufri, Muhammad Taher Sudarsono, Ignatius Utomo, Eko Wahyu Kodong, Frans Richard Fannya, Puteri Informatics Engineering Sekolah Tinggi Teknologi Bandung Bandung Indonesia Information and Communication Technology Faculty Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Electrical Engineering Andalas University Padang Indonesia Rumah Sakit Umum Daerah Majalaya Kabupaten Bandung Majalaya Indonesia Informatics Engineering Computer Science Faculty Universitas Mulya Balikpapan Indonesia Department of Informatics Engineering Engineering Faculty University Muhammadiyah of Tangerang Tangerang15117 Indonesia Program Studi Sistem Informasi Fakultas Teknik Dan Sistem Informasi Universitas Yapis Papua Indonesia Civil Engineering Department Engineering Faculty Universitas Langlangbuana Bandung Bandung Indonesia Department of Informatics Engineering UPN Veteran Yogyakarta Yogyakarta Indonesia Department of Medical Record and Health Information Faculty of Health Universitas Esa Unggul Jakarta Indonesia

In the context of achieving Good Corporate Governance (GCG) in hospitals, among others, it is carried out with control and supervision, including in the case of hospital facility maintenance installations. Besides that, the hospital needs to improve its services. The asset reporting system is one of the high needs in this field, so it requires a solution that can deal with existing problems. Unfortunately, the current reporting system so far is more likely to be conventional, such as by telephone. This study proposes a new solution to the hospital facility maintenance reporting process, namely by utilising smartphone-based applications that directly connected to the server. So, the information distributed to the competent parties. This solution can speed up the reporting process and actions. Also, save the historical equipment that is available from each service room - the results of this study used as a hospital facility maintenance reporting system. © Published under licence by IOP Publishing Ltd.

关键词： Hospitals

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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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Impact of Image Enhancement for Osteoporosis Detection Based on Deep Learning Algorithm

Impact of Image Enhancement for Osteoporosis Detection Based...

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computer System, Information Technology, and electrical engineering (COSITE), International Conference on

作者： Riza Dianil Iman Roslidar Roslidar Kahlil Muchtar Amalia Amalia Mohammad Syaryadhi Alfatirta Mufti Novi Maulina Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Computer Science Universitas Sumatera Utara Medan Indonesia Graduate School of System Designs Tokyo Metropolitan University Tokyo Japan Doctorate Student of Doctoral Program in Medical Sciences Faculty of Medicine Universitas Syiah Kuala Banda Aceh Indonesia

Osteoporosis can be defined as a degenerative disease with reduced bone mass and changes in bone architecture that can lead to bone fragility and the risk of fractures. This abnormality can be indicated by the bone density which in visual can be determined using X-Ray images. However, X-Ray images are susceptible to noise, while in image analysis image contrast affects deep learning abilities. Hence, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm is used as a contrast enhancement technique in X-Ray images. This study aims to build a deep learning model using the CLAHE-enhanced image dataset with the ResNet-50 and ResNet-101 architectures. The model was built using two different datasets, namely the original image dataset and the CLAHE-enhanced image dataset. The result shows that the highest performance is given by the ResNet-101 model using the CLAHE-enhanced image dataset with an accuracy rate of 96%, precision of 95%, specificity of 95%, recall of 97% and an Fl-score of 96%, respectively. By using the CLAHE algorithm, the resulting image has high contrast and looks better at displaying features in the image so as to produce better model performance.

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Advanced Under-Rails Fiber-Optic Sensor for Detection of Rolling Stock and Axles in Tram Transport

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Fajkus, Marcel Jargus, Jan Nedoma, Jan Kepak, Stanislav Krizan, Daniel Cubik, Jakub Krenzelok, Tomas Leal, Arnaldo Marques, Carlos VSB—Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Telecommunications Ostrava708 00 Czech Republic Prodin a.s. Pardubice530 02 Czech Republic Graduate Program in Electrical Engineering Federal University of Espirito Santo Vitoria29075-910 Brazil University of Aveiro CICECO—Aveiro Institute of Materials Physics Department Aveiro3810-193 Portugal VSB—Technical University of Ostrava Department of Physics Ostrava708 00 Czech Republic

This article describes the implementation of an advanced fiber-optic intensity sensor for the comprehensive detection of axles and bogies of rail vehicles in tram transport. The sensor uses the principle of optical fiber bending, where the intensity of transmitted light is modulated due to mechanical stress on the fiber caused by the passage of a tram and the subsequent reversible deformation of the rail. The innovative design of the sensor allows for its easy installation under the rail foot, between the rail and the sleeper, replacing conventional anti-vibration pads, and includes specially designed deformable elements which the optical fiber bends and modulates the transmitted light intensity. During long-term testing (11 months in various weather conditions) conducted in the real tram depot (Ostrava, Czech Republic), 20602 tram passages were secured and analyzed. The sensor demonstrated success in detecting bogies, achieving 100% accuracy with no false positives (FPs) or false negatives (FNs). Axle detection showed an average success rate of 86.1%, with variability among different tram types up to 97.8%. The signal modulation was also subjected to detailed analysis, revealing differences in responses between various tram types, which can be attributed to differences in bogie construction and vehicle weight. Due to its electromagnetic immunity, the implemented fiber-optic sensor offers the potential for more reliable detection and broad application possibilities, including track occupancy monitoring, speed measurement, or determining the direction of travel on single-track lines. With slight optimization, the sensor could also be used to weigh rail vehicles or as an axle counter in railway or light rail transport. © 1963-2012 IEEE.

关键词： Bogies (railroad rolling stock)

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis

Digital Technology in Health Education Trend Mapping through...

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International Conference on informatics, Multimedia, Cyber and Information System (ICIMCIS)

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

ISBN: (纸本)9781665473286

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.

关键词： Training Pediatrics Instruments Education Bibliometrics Transportation Psychology

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Identification of D- and L-Phenylalanine Enantiomeric Mixtures by Employing Deep Neural Network Models

SSRN

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SSRN 2023年

作者： Nigdelioglu, Ebru Toprak, Elif Guney Akkurt, Melike Erol Barkana, Duygun Kazanci, Murat Uyaver, Sahin Calik, Nurullah Istanbul Medeniyet University Nanoscience and Nanoengineering Program Graduate School Istanbul Medeniyet University Istanbul34700 Turkey Istanbul Medeniyet University School of Engineering and Natural Sciences Department of Biomedical Engineering Istanbul Turkey Istanbul34700 Turkey Yeditepe University Faculty of Engineering Department of Electrical and Electronics Engineering Kayışdagı Istanbul34755 Turkey Marmara University Faculty of Technology Department of Computer Engineering Maltepe Istanbul34854 Turkey

Phenylalanine is an aromatic essential amino acid that exhibits the tendency to self-aggregate into fibrillar structures in its enantiomerically pure form. This observation was indicated as the underlying mechanism of phenylketonuria, which is a genetic condition associated with various neurological, physical, and developmental issues, characterized with phenylalanine buildup in the brain. The presence of D-phenylalanine was demonstrated previously to inhibit the formation of fibrils by L-phenlyalanine, indicating its potential use in phenylketonuria treatment. In this study, several combinations of D and L-phenylalanine were examined with the help of state-of-the-art deep learning methods for their fibril forming capacity, demonstrating the usefulness and accuracy of deep learning methods in distinguishing between different self-assembled structures. © 2023, The Authors. All rights reserved.

关键词： Self assembly

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Shun-Cheng Chang Hsin-Pei Yu Yi-Hsien Hsieh Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan Institute of Applied Mathematical Sciences NTU Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.

关键词： Visualization Accuracy Three-dimensional displays Liver Transformers Planning Convolutional neural networks Context modeling Biomedical imaging Tumors

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Shun-Cheng Chang Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan NTU Institute of Applied Mathematical Sciences Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan NTU Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging

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Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Im...

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International Conference on engineering Technology and their Applications (IICETA)

作者： Mohammad Alslatie Hiam Alquran Wan Azani Mustafa Isam Abu-Qasmieh Ali Mohammad Alqudah Ahmed Alkhayyat King Hussein Medical Center Royal Jordanian Medical Service The Institute of Biomedical Technology Amman Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Department of Electrical and Computer Engineering Biomedical Engineering Program University of Manitoba Canada Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images.

关键词： Image segmentation Visualization Transfer learning Feature extraction Convolutional neural networks Artificial intelligence X-ray imaging

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Social Media Analysis using Sentiment Analysis on COVID-19 from Twitter

Social Media Analysis using Sentiment Analysis on COVID-19 f...

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International Conference on Information Technology, Information Systems and electrical engineering (ICITISEE)

作者： Fairuz Iqbal Maulana Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perform well for users in today's internet era. A well-performing recommendation system in question is a reliable recommendation algorithm. This algorithm is fundamental to analyzing various information, such as responses on social media based on user behavior data related to the topic of COVID. This data is crawled from tweets on social media Twitter. The data analysis algorithm obtained uses Python, which is then visualized in the form of a diagram. The processed data is user comments on Twitter, and the text data is analyzed using Python, using more than 60000 data sets taken to form visualizations and conclusions. From sentiment analysis, polarity and subjectivity data are obtained to be analyzed, which are negative, neutral, or positive. The result is show positive tweets with 29.2%, negative tweets is 13%, and 57.8% neutral tweets. Lastly, sentiment analysis can help people effectively infer vast and complex data from social media like Twitter.

关键词： COVID-19 Sentiment analysis Analytical models Data analysis Social networking (online) Blogs Data visualization

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