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Eng. Proc., 2025, SSIM 2024

2024 4th International Conference on Social Sciences and Intelligence Management (SSIM 2024)
Taichung, Taiwan | 20–22 December 2024

Volume Editors:
Teen-Hang Meen, National Formosa University, Taiwan
Liza Lee, Chaoyang University of Technology, Taiwan
Cheng-Fu Yang, National University of Kaohsiung, Taiwan; Chaoyang University of Technology, Taiwan

Number of Papers: 4
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Cover Story (view full-size image): This volume compiles the works from the 2024 4th International Conference on Social Sciences and Intelligence Management held in Taichung, Taiwan, from December 20 to 22, 2024. By breaking up the [...] Read more.
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7 pages, 557 KiB  
Proceeding Paper
The Application and Performance Comparison of Different Versions of YOLO Image Recognition Systems
by You-Shyang Chen and Yi-Xuan Chen
Eng. Proc. 2025, 98(1), 1; https://doi.org/10.3390/engproc2025098001 - 30 May 2025
Viewed by 185
Abstract
With advancements in computational power and artificial intelligence (AI), image recognition has become more efficient and widely used. You Only Look Once (YOLO) stands out for its fast and accurate object detection, making it popular among researchers. The technology enhances daily life, from [...] Read more.
With advancements in computational power and artificial intelligence (AI), image recognition has become more efficient and widely used. You Only Look Once (YOLO) stands out for its fast and accurate object detection, making it popular among researchers. The technology enhances daily life, from smartphone facial recognition improving security to thermal imaging aiding public health during the pandemic. However, identifying the right image recognition system for varied image types remains challenging. By evaluating the performance of YOLO’s versions (e.g., YOLOv3 and YOLOv4) regarding structure, speed, accuracy, and adaptability, we identified appropriate algorithms for specific tasks, recommending optimal image recognition techniques for different applications. Full article
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6 pages, 362 KiB  
Proceeding Paper
An Integrated Fuzzy Convolutional Neural Network Model for Stock Price Prediction
by Jia-Wen Wang and Jr-Shian Chen
Eng. Proc. 2025, 98(1), 3; https://doi.org/10.3390/engproc2025098003 - 30 May 2025
Viewed by 131
Abstract
Stock market forecasting has always been researched extensively in Social Sciences. In this research, Fuzzy time series with deep learning is widely adopted to create a fuzzy convolutional neural network integration model as this model enhances the fuzzification of values to enhance feature [...] Read more.
Stock market forecasting has always been researched extensively in Social Sciences. In this research, Fuzzy time series with deep learning is widely adopted to create a fuzzy convolutional neural network integration model as this model enhances the fuzzification of values to enhance feature characteristics using two-dimensional input data in convolutional neural networks (CNNs). This allows the model to retain complete feature information. The fuzzy convolutional neural network (FCNN) model autonomously learns and extracts crucial features by integrating stock market data, resulting in improved forecasting accuracy. In this study, the model was tested for forecasting the Taiwan Weighted Stock Index and metrics such as the mean squared error (MSE) and mean absolute error (MAE), and the results were compared with the real data. The results showed that the model provided accurate predictions. Full article
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8 pages, 502 KiB  
Proceeding Paper
Adaptive Frequency and Assignment Algorithm for Context-Based Arithmetic Compression Codes for H.264 Video Intraframe Encoding
by Huang-Chun Hsu and Jian-Jiun Ding
Eng. Proc. 2025, 98(1), 4; https://doi.org/10.3390/engproc2025098004 - 4 Jun 2025
Viewed by 4
Abstract
In modern communication technology, short videos are increasingly used on social media platforms. The advancement of video codecs is pivotal in communication. In this study, we developed a new scheme to encode the residue of intraframes. For the H.264 baseline profile, we used [...] Read more.
In modern communication technology, short videos are increasingly used on social media platforms. The advancement of video codecs is pivotal in communication. In this study, we developed a new scheme to encode the residue of intraframes. For the H.264 baseline profile, we used context-based arithmetic variable-length coding (CAVLC) to encode the residue of integer transforms in a block-wise manner. In the developed method, the DC and AC coefficients are separated. In addition, context assignment, adaptive scanning, range increment, and mutual learning are adopted in a mixture of fixed-length and variable-length schemes, and block-wise compressions of the frequency table are applied to obtain improved compression rates. Compressing the frequency prevents CAVLC from being hindered by horizontally/vertically dominated blocks. The developed method outperforms CAVLC, with average reductions of 7.81, 8.58, and 7.88% in quarter common intermediate format (QCIF), common intermediate format (CIF), and full high-definition (FHD) inputs. Full article
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701 KiB  
Proceeding Paper
Development of Artificial Urine Using Commonly Available Ingredients for Ultraviolet–Visible Spectroscopy
by Patryk Sokołowski and Maria Babińska
Eng. Proc. 2025, 98(1), 5; https://doi.org/10.3390/engproc2025098005 - 5 Jun 2025
Abstract
A simple urine phantom was developed to replicate the ultraviolet–visible (UV–Vis) spectrum of healthy human urine. Made from four safe and widely available ingredients, it addresses the challenges of biological samples, including safety risks and storage issues. The spectroscopic analysis results confirmed strong [...] Read more.
A simple urine phantom was developed to replicate the ultraviolet–visible (UV–Vis) spectrum of healthy human urine. Made from four safe and widely available ingredients, it addresses the challenges of biological samples, including safety risks and storage issues. The spectroscopic analysis results confirmed strong similarity to natural urine, making the phantom appropriate for testing spectroscopic methods, calibrating optical devices, and evaluating diagnostic sensors. It can also serve as a starting point for advanced phantoms tailored to specific patient needs or diseases. This reliable alternative facilitates research in optical diagnostics or biosensor development by simplifying preliminary sensor testing. Full article
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