Knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover f...
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This study explores and systematically reviews advancements in applying artificial intelligence (AI) and Internet of Things (IoT) technologies to the high-pressure die casting (HPDC) process. Widely utilized in alumin...
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ISBN:
(数字)9798331515478
ISBN:
(纸本)9798331515485
This study explores and systematically reviews advancements in applying artificial intelligence (AI) and Internet of Things (IoT) technologies to the high-pressure die casting (HPDC) process. Widely utilized in aluminum automotive part production, HPDC presents significant challenges, including defect reduction, process optimization, and predictive maintenance. The primary objective of this research is to examine how AI and IoT can address these challenges by enhancing decision-making, improving quality control, and increasing operational efficiency. The systematic review, based on PRISMA guidelines, analyzed 16 peer-reviewed studies that focus on the integration of machine learning (ML) and deep learning (DL) techniques in HPDC. The findings reveal critical applications, such as defect detection, predictive modeling for optimizing the solidification cycle, and fault diagnosis. Despite the potential benefits, challenges remain, including high implementation costs, data quality issues, and the limited interpretability of AI models. Furthermore, the analysis underscores the predominance of private datasets and highlights the need for public, anonymized repositories to foster collaborative research. This study contributes by providing a comprehensive overview of AI applications in HPDC, identifying existing barriers, and proposing directions for future research. These findings reinforce the transformative potential of AI in advancing Industry 4.0 and offer valuable insights for academia and industry, paving the way for more sustainable and efficient HPDC operations.
Background: Electronic health records (EHRs) play a crucial role in healthcare decision-making by giving physicians insights into disease progression and suitable treatment options. Within EHRs, laboratory test result...
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Background: Electronic health records (EHRs) play a crucial role in healthcare decision-making by giving physicians insights into disease progression and suitable treatment options. Within EHRs, laboratory test results are frequently utilized for predicting disease progression. However, processing laboratory test results often poses challenges due to variations in units and formats. In addition, leveraging the temporal information in EHRs can improve outcomes, prognoses, and diagnosis predication. Nevertheless, the irregular frequency of the data in these records necessitates data preprocessing, which can add complexity to time-series analyses. Methods: To address these challenges, we developed an open-source R package that facilitates the extraction of temporal information from laboratory records. The proposed lab package generates analysis-ready time series data by segmenting the data into time-series windows and imputing missing values. Moreover, users can map local laboratory codes to the Logical Observation Identifier Names and Codes (LOINC), an international standard. This mapping allows users to incorporate additional information, such as reference ranges and related diseases. Moreover, the reference ranges provided by LOINC enable us to categorize results into normal or abnormal. Finally, the analysis-ready time series data can be further summarized using descriptive statistics and utilized to develop models using machine learning technologies. Results: Using the lab package, we analyzed data from MIMIC-III, focusing on newborns with patent ductus arteriosus (PDA). We extracted time-series laboratory records and compared the differences in test results between patients with and without 30-day in-hospital mortality. We then identified significant variations in several laboratory test results 7 days after PDA diagnosis. Leveraging the time series– analysis-ready data, we trained a prediction model with the long short-term memory algorithm, achieving an area un
Using the Scopus database, this study aims to investigate the use of artificial intelligence for cancer detection in the last ten years from 2013 to 2022. The researchers used bibliometric analysis combined with VosVi...
Using the Scopus database, this study aims to investigate the use of artificial intelligence for cancer detection in the last ten years from 2013 to 2022. The researchers used bibliometric analysis combined with VosViewer and Rstudio software quantification method for literature analysis. The results of the flushed articles show data such as publication year, journal, country, keywords, and authors, to the highest number of citations. Common keywords used by researchers are artificial intelligence, medical, and human. Researchers limited the findings through keywords such as in the last ten years, documents are journals, and English only so that 1868 articles were obtained. The results found that Harvard Medical School affiliation had the highest number of articles, with 101 articles, by subject area, Medicine had a proportion of 41.9% (n=1320). This bibliometric study will be useful for other researchers to examine the development of research on Artificial Intelligence for Cancer Detection in the last ten years.
Data mining is a technique of extracting information that has not been known before in a collection of data in the database. Data mining has been applied in various fields that require extracting information, some of ...
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The success of machine learning models relies heavily on effectively representing high-dimensional data. However, ensuring data representations capture human-understandable concepts remains difficult, often requiring ...
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The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Ma...
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ISBN:
(数字)9798331519643
ISBN:
(纸本)9798331519650
The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to detect fraud in digital payment systems. One of the main challenges addressed in this study is the severe class imbalance in the dataset, where fraudulent transactions account for only 0.17% of total transactions. To overcome this, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to balance the dataset, allowing the model to better recognize fraudulent patterns. The results indicate that the SVM model achieved an accuracy of 99.93%, with a precision of 86.23% and a recall of 75.51%. These results demonstrate that SVM, combined with SMOTE and RBF kernel, is highly effective in detecting fraudulent transactions while minimizing false positives. This research provides a strong foundation for improving fraud detection models in the context of digital payment systems, offering enhanced security and trust for users. Further research could explore hybrid models and real-time data analysis to improve performance.
Fetal cardiac anatomical structure interpretation by ultrasound (US) is a key part of prenatal assessment. Unfortunately, the numerous speckles in US video, the small size of fetal cardiac structures, and unfixed feta...
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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 cof...
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The Massification of remote work, in response to the COVID-19 pandemic, has been causing significant changes in productive and working arrangements, both for individuals, organizations, and society. At the level of pe...
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