Fall risk assessment is an effective and simple measure to evaluate the balance of people especially the elderly who are likely to have balance disorder. Lose of body balance increases a fall risk which could lead to ...
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The role of influencers, especially on social media platforms, has grown significantly. A commonly used feature among business professionals today is follower grouping. However, this feature is limited to identifying ...
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ISBN:
(数字)9798331508616
ISBN:
(纸本)9798331508623
The role of influencers, especially on social media platforms, has grown significantly. A commonly used feature among business professionals today is follower grouping. However, this feature is limited to identifying influencers based solely on mutual followership, underscoring the need for a more sophisticated approach to influencer *** study proposes a new method for influencer detection that integrates the Leiden Coloring Algorithm and Matrix Centrality. This approach leverages network analysis to identify patterns and relationships in large-scale datasets. First, the Leiden Coloring Algorithm partitions the network into various communities, which are considered potential influencer groups. Furthermore, Eigenvector and Degree Centrality augment this process by identifying nodes with high connectivity, representing potential *** proposed method is validated using crawled data from the Twitter (X) social media platform with the keyword "GarudaIndonesia." The results of the Leiden Coloring Algorithm recommend 10 accounts as influencers. Based on Eigenvector Centrality and Degree Centrality for a dataset of 1,000 rows, it is observed that the first and second ranks consistently identify the same influencers, namely IndonesiaGaruda and GarudaCares. However, the third to tenth ranks suggest different influencers. For a dataset of 5,000 rows, both methods again identify IndonesiaGaruda as the top-ranked influencer, while the second to tenth ranks differ between the two *** modularity value for the first test scenario is 0.9396, while for the second test scenario, it is 0.9381. The processing time for the first test scenario is 29.5493 seconds, compared to 434.1838 seconds for the second test scenario. Additionally, the number of communities identified by the Leiden Coloring Algorithm increases with dataset size, with 505 communities for the first test scenario and 1,969 communities for the second. This demonstrates that larger datasets res
In today's digital era, the influence of social media influencers has grown significantly. A commonly used feature by business professionals today is follower grouping. However, this feature is limited to identify...
详细信息
ISBN:
(数字)9798331508616
ISBN:
(纸本)9798331508623
In today's digital era, the influence of social media influencers has grown significantly. A commonly used feature by business professionals today is follower grouping. However, this feature is limited to identifying influencers based solely on mutual followership, highlighting the need for a more sophisticated approach to influencer detection. This study proposes a novel method for influencer detection that integrates the Leiden coloring algorithm and Degree centrality. This approach leverages network analysis to identify patterns and relationships within large-scale datasets. First, the Leiden coloring algorithm is employed to partition the network into distinct communities, identifying potential influencer clusters. Subsequently, Degree centrality is utilized to identify nodes within these communities exhibiting high connectivity, indicating individuals with significant influence. The proposed method was validated using data crawled from Twitter (X) social media, employing the keyword "GarudaIndonesia." The data was collected using Tweet-Harvest between January 1, 2020, and October 16, 2024, resulting in a dataset of 22,623 rows. The proposed method was compared to the Louvain coloring method, demonstrating an increase in the modularity value of the Leiden coloring algorithm by 0.0195, a reduction in processing time by 13.93 seconds, and a decrease in the number of communities by 649.
Morphing attacks are based on the technique of digitally fusing two (or more) face images into one, with the final visage resembling the contributing faces. Morphed images not only pose a challenge to Face-Recognition...
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Morphing attacks are based on the technique of digitally fusing two (or more) face images into one, with the final visage resembling the contributing faces. Morphed images not only pose a challenge to Face-Recognition Systems (FRS) but also challenge experienced human observers due to high quality, postprocessing eliminating any visible artifacts, and further the printing and scanning process. Few studies have concentrated on examining how human observers can recognize morphing attacks, even as several publications have examined the susceptibility of automated FRS to morphing attacks and offered morphing attack detection (MAD) approaches. MAD approaches base their decisions either on a single image with no reference to compare against (Single-Image MAD (S-MAD)) or using a reference image (Differential MAD (D-MAD)). One prevalent misconception is that an examiner's or observer's capacity for facial morph detection depends on their subject expertise, experience, and familiarity with the issue. No works have reported the specific results of observers who regularly verify identity (ID) documents for their jobs. As human observers are involved in checking ID documents having facial images, a lapse in their competence can result in significant societal challenges. To assess the observers' proficiency, this research first builds a new benchmark database of realistic morphing attacks from 48 different subjects, resulting in 400 morphed images. Unlike the previous works, we also capture images from Automated Border Control (ABC) gates to mimic realistic border-crossing scenarios in the D-MAD setting with 400 probe images, to study the ability of human observers to detect morphed images. A new dataset of 180 morphing images is also produced to research human capacity in the S-MAD environment. In addition to creating a new evaluation platform to conduct S-MAD and D-MAD analysis, the study employs 469 observers for D-MAD and 410 observers for S-MAD who are primarily governmenta
To address the limitations of typical coil detection systems and enhance the performance of traditional magnetic field imaging (MFI) systems, we propose a MFI system that uses a 4×4 array of anisotropic magnetore...
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Query language execution is widely used in big data. The SQL standard is the major query language. Big data has a lot of SQL-like tools, for example: Spark-SQL, Hive, Drill, and Presto. This paper focused on Hive with...
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Based on WHO's data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (ca...
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Wireless sensors and actor networks (WSANs) have been widely used in various fields, from basic data collection to precise real-time control and monitoring, including battlefield monitoring, rescue, and exploration. T...
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In this study, we use the CIMulator platform to evaluate the performance of neuromorphic accelerators with novel Hf0.5Zr0.5O2 (HZO) ferroelectric fin field-effect transistor (FefinFET) as synaptic device. The MNIST ha...
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The purpose of this research was to design, produce and evaluate a stray child warning system. The device has been designed by using 2 ESP8266 boards to receive signals as specified. The system will notify the informa...
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