Artificial Intelligence (AI) seems to be a disruptive technology that defines and reshapes the economy, more efficient industrial processes, new business models, and the service sector, becoming the development of dif...
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ISBN:
(纸本)9798350399080
Artificial Intelligence (AI) seems to be a disruptive technology that defines and reshapes the economy, more efficient industrial processes, new business models, and the service sector, becoming the development of different practices than before. The literature shows that it will be included in all fields and people's lives, as is the case in the digital economy. Artificial Intelligence for Digital Economy has not been studied significantly. This study uses bibliometric analysis to graphically map scientific publications and research trends in the Artificial Intelligence for Digital Economy sector around the world in the last ten years. The Scopus database was used to collect metadata information for this study, and VOSViewer was used to demonstrate bibliometric network mapping. We use an article selection procedure starting with the searched keywords and year constraints and then exporting the database to a RIS file format. Over the last ten years, we retrieved 540 scientific publications published between 2012 and 2021 from the Scopus database. From the data obtained, researchers at the Russian Federation have the most published papers indexed by Scopus among the most productive countries (N=131), the most productive authors are Petrenko, S.A. (N=4), and the most subject area is computerscience (N=246). We also use VOSViewer to map the Network Theme. This study recommends incorporating research subjects Artificial Intelligence for Digital Economy: Thing, Education, Platform, Global Economy, abbreviated as TEPGE research theme.
This paper presents an approach to analyze clusters as a means to determine the characteristics of strength training motion patterns. The proposed method emphasizes the observation of dominance sequences within cluste...
This paper presents an approach to analyze clusters as a means to determine the characteristics of strength training motion patterns. The proposed method emphasizes the observation of dominance sequences within clusters and is reinforced by the formation of specific characteristics within each cluster. Data collection is performed using video-guided strength training exercises equipped with 1 kg dumbbells and recorded by a sensor embedded in smartwatches. The analysis method involves applying the concept of density affinity, which calculates the density ratio of clusters to the recognized motions. Subsequently, the dominance sequence is observed to identify which clusters exhibit distinct characteristics, ultimately determining the intended motions. The research findings demonstrate the potential for further investigation into a more comprehensive understanding of motion patterns, leading to the development of models that can be integrated into mobile devices or smartwatches.
Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher educ...
Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education.
Cell-free massive MIMO systems are currently being considered as potential enablers of future (6G) technologies for wireless communications. By combining distributed processing and massive MIMO, they are expected to d...
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Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which ...
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ISBN:
(数字)9798350364637
ISBN:
(纸本)9798350364644
Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers.
University Course Timetabling (UCT) is a common problem in educational institutions. The preparation of the class schedule must pay attention to available resources without violating set constraints. This research app...
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The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be...
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This paper presents the development of an object detection system based on the deep learning approach of computer vision to support the laparoscopic surgical robotic position control system. The system comprises two m...
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Motivation: Bulk RNA-Seq is a widely used method for studying gene expression across a variety of contexts. The significance of RNA-Seq studies has grown with the advent of high-throughput sequencing technologies. Com...
Predicting financial distress can avoid firm bankruptcy. That is an important issue in matters of company sustainability and the economic growth in general. Indonesia as a developing country needs a reliable system th...
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