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International Conference on Computing, Engineering, and Design (ICCED)

作者： Galuh Saputri Joko Suwamo Achmad Lutfi Fuadi Agus Heri Yunial Program Study Informatics EngineeringFaculty of Engineering Universitas Pamulang Tangerang Selatan Banten Indonesia

ISBN: (纸本)9781665453905

Artificial intelligence is one of the technological breakthroughs in science, especially in the field of computers. Object detection and recognition presents a new challenge on how to make machines detect objects automatically. To detect an object, the previous system will take the classifier of an object and evaluate it at various locations and various scales in the image. Smartphones currently play an important role in everyday life, but the existence of smartphones can be one of the causes of the user's lack of interest in reading books. With this Augmented Reality-based object detector, it is hoped that users can experiment on smartphones with the Snapchat application installed to detect objects in books that are around them and the number of objects will be counted automatically, it is also expected to attract users' desire to read books. Image recognition identification is taken through the phone's camera which is recognized as a lens. Lens AR in the Lens Studio application is supported by the SnapML machine learning algorithm, this machine learning function is to display book information and the number of books detected. This research was conducted using Cloud Computing such as Google Collaboratory which uses the python programming language, the book object dataset is selected from the COCO class, the model is exported to Open Neural Network Exchange (.ONNX) and then imported into the Lens Studio application. Publish lenses in the Snapchat app. Finally, a model test was carried out to detect objects and the number of objects in books that had been made using a Smartphone. Open the Snapchat application, scan the Snapcode generated by Lens Studio, AR lenses can be used in real time. AR Book Detection has been tested by other users on Snapchat with 18,845 Lens Views, 18,737 Lens Plays and 16 Lens Shares.

关键词： Image recognition Machine learning algorithms Computational modeling Multimedia Web sites Neural networks Object detection Real-time systems

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Exploring the Impact of Word Embedding Dimensions on Depression Data Classification Using BiLSTM Model

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Procedia Computer Science 2023年 227卷 298-306页

作者： Merinda Lestandy Abdurrahim Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Malang Malang 65144 Indonesia Informatics Master Program Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta 55584 Indonesia

Depression is a mental health issue that impacts over 300 million individuals worldwide. Daily anxiety impacts the relationships of depressed individuals with their family and friends, affects their health, and, in the worst-case scenario, leads to suicide. Due to the an increase of social networks, a vast majority of people now express their thoughts, feelings, and emotions via social media. The researchers mainly focused on features and building predictions, making it difficult for them to detect depression via social media. Word embedding offers a potential solution for processing and transforming unstructured data into meaningful representations. Embedded words represent textual data as numeric numerals with equivalent meaning. Using the Bidirectional Long Short-Term Memory (Bi-LSTM) paradigm, this investigation attempts to experiment with various weighting dimensions using the Word2Vec and GloVe methods. The data used is Depression: Reddit Dataset labelled: depressed and non-depressed. The experimental results of word weight dimensions using Word2Vec with a dimension of 500 are better than Glove. The results of accuracy, precision, recall and f1-score for the Word2Vec method were 96.22%, 97.02%, 95.30% and 96.15%, while the Glove method was 95.91%, 96.40%, 95.30% and 95.85% with a dimension of 200. Overall, Word Embedding Dimensions influences the results of this study. Based on these findings, it is concluded that the proposed Word2Vec method is a significant approach.

关键词： Social Media Depression Word Embedding BiLSTM

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Finding the Best Techniques for Predicting Term Deposit Subscriptions (Case Study UCI Machine Learning Dataset)

Finding the Best Techniques for Predicting Term Deposit Subs...

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International Conference on Sustainable Engineering and Creative Computing (ICSECC)

作者： Lila Setiyani Ayu Indahsari Rosalina Tjong Wansen Master of Science Information Technology study program President University Bekasi Indonesia Informatics Study Program President University Bekasi Indonesia

Knowing a lot about attributes, such as bank client data, marketing campaigns, social and economic context, and so on, is necessary to predict which bank customers are likely to sign up for time deposits. The direct marketing campaigns dataset for the bank institution was made available by the UCI machine learning repository. This dataset had previously been used in studies to forecast customers who would sign up for time deposits, but the outcomes of the two studies were different. The purpose of this study is to determine which machine learning methods are most effective for predicting customers who will sign up for time deposits. In this study, researchers used machine learning methods like random forests, K-nearest neighbors, decision trees, naive bayes, logistic regression, SVM, and XGBoost to uncover the truth. Results showed that XGBoost had the highest accuracy (91.73%), followed by the DT classifier (88.79%), LR classifier (91.2%), NB classifier (84.78%), KNN classifier (90.6%), SVM (89.6%), and XGBoost (91.44%).

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Method to Reduce Lead-time of Business Process discovered 13

Method to Reduce Lead-time of Business Process discovered

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13th International Congress on Image and Signal Processing, BioMedical Engineering and informatics, CISP-BMEI 2020

作者： Dos Santos, Gilberto Aleces Southier, Luiz Fernando Puttow Scalabrin, Edson E. Pucpr Informatics Graduate Program Curitiba Brazil

ISBN: (纸本)9780738105451

The process mining allows to automatically discover process models from event logs. Those models provide information that allow to make many types of analysis based on models, such as: compliance verification, performance analysis and bottlenecks identification on the business process. The bottlenecks identification is highly relevant for process managers to suggest improvements. Here, the improvement which is interesting is the lead-time reduction. A double hypothesis was made. The first is that lead-time reduction goes through the identification and removal of bottlenecks at the business process. The second is that the focused intervention at the critical path allows to reduce lead-time causing small changes on the current process. The process mining allows, by using model process discovery, to obtain basic statistic information of a business process, as: minimal, maximum and average duration of each activity. The technique PERT/CPM allows to calculate for each activity the weighted average of the expected execution and the critical path. This helps to identify the relevant activity focusing on the bottlenecks of a business process. The experiments show that the method allows to reduce the lead-time of a process and indicate the degree of the resource's commitment in each of the method iterations, in terms of similarity among optimized model and original model. The experiments were conducted over real word "Hospital Billing"and "Sepsis cases"databases. © 2020 IEEE.

关键词： Compliance control

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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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SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Medical Image Segmentation Model for Highly Constrained Environments 1

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International Challenge on Segment Anything in Medical Images on Laptop held in conjunction with the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024

作者： Kong, Youngbin Kim, Kwangtai Jeong, Seoi Lee, Kyu Eun Kong, Hyoun-Joong Interdisciplinary Program in Bioengineering Graduate School Seoul National University Seoul Korea Republic of Department of Transdisciplinary Medicine Seoul National University Hospital Seoul Korea Republic of Department of Surgery Seoul National University Hospital and College of Medicine Seoul Korea Republic of Department of Medicine Seoul National University College of Medicine Seoul Korea Republic of Innovative Medical Technology Research Institute Seoul National University Hospital Seoul Korea Republic of Interdisciplinary Program of Medical Informatics Seoul National University College of Medicine Seoul Korea Republic of

ISBN: (数字)9783031818547

ISBN: (纸本)9783031818530

Medical image segmentation is a crucial step for accurate diagnosis and treatment planning, as it provides quantitative information about anatomical structures and pathological lesions in various clinical scenarios. However, the existing methodologies have limitations in terms of their generalizability and computational efficiency. In this study, we propose SwiftMedSAM, an ultra-lightweight prompt-based general model, to enable efficient medical image segmentation even in resource-constrained environments. Based on LiteMedSAM, we significantly reduced the model size and computational complexity through the hyperparameter optimization of the image encoder and mask decoder components. SwiftMedSAM showed remarkable performance across various imaging modalities enabling real-time inference in resource-limited computing environments. It achieved a validation score of 0.75 demonstrating that SwiftMedSAM outperformed the existing methodologies in terms of the trade-off between accuracy and efficiency. Owing to its unprecedented generalizability and low computational cost, SwiftMedSAM is expected to enable high-quality medical image analysis in resource-constrained settings, thereby contributing to advancements in precision medicine and telemedicine. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Medical imaging

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Topic Relation Graph Construction to Identify Challenges in Introducing programming in Indonesian K-12 Education 1

Topic Relation Graph Construction to Identify Challenges in ...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Ayub, Mewati Karnalim, Oscar Toba, Hapnes Wijanto, Maresha Caroline Dulimarta, Hans Saputra, Williyandie Kotambunan, Tabitha Emanuella Master Program of Computer Science Maranatha Christian University Bandung Indonesia Ewha Womans University Dept. of Computer Science and Engineering Seoul Korea Republic of School of Computing Grand Valley State University MI United States Informatics Engineering Maranatha Christian University Bandung Indonesia

ISBN: (纸本)9798350306156

programming can help K-12 students to develop their 21st-century core skills. Despite the benefits, programming is not common to be delivered in Indonesian K-12 education. There is a need to understand potential challenges in introducing programming to K-12 students. We developed a questionnaire survey covering four identified dimensions of challenges: administrative, facilities, teachers, and students. We also asked about common programming assessments and their preferred software features for teaching programming. Forty K-12 teachers were invited to complete the survey. The responses were analyzed with thematic analysis using a bigram-based Latent Dirichlet Allocation topic modeling and descriptive statistics. Our study shows that the challenges include limited learning modules, an insufficient number of computers, limited programming skills, and limited computational thinking skills. Scratch was the most common programming language used and many programming assessments were about debugging a program or writing a small program. Visualization and animation can be helpful in teaching programming. © 2023 IEEE.

关键词： Students

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Design of an Infusion Capacity Monitoring System for Patients Based on the Internet of Things in Hospitals

Design of an Infusion Capacity Monitoring System for Patient...

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International Conference on Business and Industrial Research (ICBIR)

作者： Vina Septiana Windyasari Silvia Ayunda Murad Syahriani Syam Maulia Mahardika Johan Budiman Diah Rahmawati Information Systems Study Program Islamic Syekh-Yusuf University Tangerang Indonesia Information System Department BINUS Online Learning Binus University Jakarta Indonesia Informatics Engineering Study Program Islamic Syekh-Yusuf University Tangerang Indonesia Civil Engineering Study Program Islamic Syekh-Yusuf University Tangerang Indonesia

ISBN: (数字)9798350383027

ISBN: (纸本)9798350383034

The patient's infusion fluid will only be changed if the nurse changes it or based on information from the patient's family. The research method used in terms of fluid volume utilizes the function of the Arduino microcontroller as the brain, the load cell sensor as an infusion fluid weight detector connected to the IoT, fuzzy logic calculations on the infusion volume weight measuring device in line with real-time decision-making when reading gram weight changes with sensors, and the system development method using a prototype model. The test results of three infusion bottles containing mineral water with a liquid volume of 500 ml and a load cell sensor as an infusion weight detector can be accepted by the Arduino Wemos D1 as a load cell sensor processing component. The infusion liquid weight data is uploaded to the MySQL database, and a notification is sent and displayed on the smartphone when the infusion volume reaches 12% of its weight. The results of this study indicate that the design of tools connected to applications on smartphones can work well and facilitate the process of monitoring infusions for patients in real-time

关键词： Weight measurement Fuzzy logic Solid modeling Liquids Databases Volume measurement Prototypes Detectors Real-time systems Monitoring

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TinyPPE: Ensuring Workplace Safety Helmet Compliance with Tiny Machine Learning

TinyPPE: Ensuring Workplace Safety Helmet Compliance with Ti...

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Brazilian Symposium on Computing System Engineering (SBESC)

作者： Derek N. A. Alves Erick De A. Barboza Tiago F. Vieira Edge Innovation Center Maceió Brazil Vertex Institute of Innovation and Technology Maceió Brazil Postgraduate Program in Informatics (PPGI-UFAL) UFAL - Federal University of Alagoas Maceió Brazil

ISBN: (数字)9798331509002

ISBN: (纸本)9798331532857

Ensuring safety in workplaces such as industry and civil construction requires the use of personal protective equipment (PPE). Enhancing compliance with PPE regulations or employing educational measures through a deployable device, which can also restrict access to hazardous areas without proper safety equipment, is crucial to mitigating workplace accidents and reducing the risk of fatalities or disability. This paper proposes the development of a device equipped with embedded machine learning models that use a camera to monitor workers and enforce the usage of PPE. These models are deployed on an Arduino Nano 33 BLE Sense, integrated with an OV7675 camera shield, to capture images and employ our trained computer vision model to classify the usage of safety helmets. The compact size of the trained and quantized model, weighing only 59 KB, taking about 595 milliseconds total time, 92.5% accuracy, demonstrates its efficiency. This innovation not only promotes the adhesion to safety protocols, but also presents significant benefits in scientific and economic contexts.

关键词： Computer vision Accuracy Computational modeling Biological system modeling Tiny machine learning Employment Cameras Safety Monitoring Accidents

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informatics Workforce to Support the Data Modernization Initiative: Assessment and Capacity Building at a State Public Health Agency 11

Informatics Workforce to Support the Data Modernization Init...

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11th IEEE International Conference on Healthcare informatics, ICHI 2023

作者： Rajamani, Sripriya Solarz, Sarah Koskey, Amber Dawson, Taylor Kayser, Ann Woods, Hannah Brueske, Chris Schmit, Aasa Dahlberg University of Minnesota Informatics Program School of Nursing MinneapolisMN United States Infectious Disease Surveillance Division Minnesota Dept of Health St. PaulMN United States Office of Data Strategy and Interoperability Minnesota Dept of Health St. PaulMN United States

ISBN: (纸本)9798350302639

A workforce that is skilled in informatics and data science is critical to achieving the objectives of the Data Modernization Initiative (DMI) in public health. The Minnesota Department of Health conducted an informatics workforce assessment in December 2022. The survey captured information in 5 domain areas and across 6 proficiency levels (no knowledge to expert) with an estimated 15-20 minutes to complete. In all of the 5 domains (performing assessments and leading advancements of health information systems;data exchanges and systems interoperability;data and IT governance;data analytics, visualization and reporting;implementation, development and management of systems), the staff who identified themselves at expert proficiency was © 2023 IEEE.

关键词： Interoperability

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