Recently, the number of AR-based practice cases has been increasing. In this paper, the effect of AR-based circuit practice was examined through an experiment with 60 subjects (control group: 30, experimental group: 30). The report score, completion time, question count, and USE questionnaire were used in the analysis. As a result of the experiment, the report score was significantly increased by 15.48% in the experimental group (△report: +18 points) than in the control group (△report: +7 points). Question count decreased twice as much in the experimental group (△question: -18 times) as in the control group (△question: -9 times). The completion time of the experimental group (△time: -16 min) was reduced by approximately 4 min more than that of the control group (△time: -12 min), however, the difference was not statistically significant. The USE questionnaire received evaluations of 6.0 or higher (on a 7-point scale) in all categories (usefulness, ease of use, ease of learning, satisfaction). Therefore, From the experimental results, the proposed AR-based circuit practice is confirmed to be more effective than traditional circuit practice.

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In the field of Electronic Health (e-Health), Electronic Health Records (EHR) are transmitted between health professionals using e-Health systems for cooperative medical practice, medical monitoring, telemedical expertise, and telemedical imaging. Medical images are a crucial component of EHR and are used in various aspects of telemedicine systems such as expertise, consultation, teaching, and research. However, protecting the authenticity and copyrights of medical images is essential to prevent duplication, modification, or unauthorized distribution. This paper proposes a robust medical image copyright protection method that uses patient palm-print template as watermark and Lorenz chaotic map for template concealing and selecting the appropriate embedding positions in medical images. The novelty of the method lies in optimizing the expected number of modifications per pixel of the medical images after being watermarked. Experimental results indicate that this approach has a high performance with a genuine accept rate of 99.86% and can withstand various image processing attacks, including Gaussian noise, compression, and image rotations, while ensuring personal data security during telemedicine data exchange.

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In the field of Electronic Health (e-Health), Electronic Health Records (EHR) are transmitted between health professionals using e-Health systems for cooperative medical practice, medical monitoring, telemedical expertise, and telemedical imaging. Medical images are a crucial component of EHR and are used in various aspects of telemedicine systems such as expertise, consultation, teaching, and research. However, protecting the authenticity and copyrights of medical images is essential to prevent duplication, modification, or unauthorized distribution. This paper proposes a robust medical image copyright protection method that uses patient palm-print template as watermark and Lorenz chaotic map for template concealing and selecting the appropriate embedding positions in medical images. The novelty of the method lies in optimizing the expected number of modifications per pixel of the medical images after being watermarked. Experimental results indicate that this approach has a high performance with a genuine accept rate of 99.86% and can withstand various image processing attacks, including Gaussian noise, compression, and image rotations, while ensuring personal data security during telemedicine data exchange.

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Multi-object tracking is a crucial aspect of perception in the area of computer vision, widely used in autonomous driving, behavior recognition, and other areas. The complex and dynamic nature of environments, the ever-changing visual features of people, and the frequent appearance of occlusion interactions all impose limitations on the efficacy of existing pedestrian tracking algorithms. This results in suboptimal tracking precision and stability. As a solution, this article proposes an integrated detector-tracker framework for pedestrian tracking. The framework includes a pedestrian object detector that utilizes the YOLOv8 network, which is regarded as the latest state-of-the-art detector, that has been established. This detector provides an ideal detection base to address limitations. Through the combination of YOLOv8 and the DeepSort tracking algorithm, we have improved the ability to track pedestrians in dynamic scenarios. After conducting experiments on publicly available datasets such as MOT17 and MOT20, a clear improvement in accuracy and consistency was demonstrated, with MOTA scores of 63.82 and 58.95, and HOTA scores of 43.15 and 41.36, respectively. Our research highlights the significance of optimizing object detection to unleash the potential of tracking for critical applications like autonomous driving.

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In recent times, there has been a notable increase in interest surrounding the integration of Un-manned Aerial Vehicle (UAV) technology and vehicle routing problems (VRP) for package delivery purposes. While existing studies have explored various types of package deliveries utilizing VRP, limited attention has been given to on-demand food delivery. This study aims to develop a VRP model that incorporates practical constraints such as payload capacity and maximum flying range, with the primary objective of minimizing travel distance in food delivery operations. A comparative analysis is conducted among three delivery methods, including UAV delivery, to determine the most effective approach and assess the feasibility of each method. Through a case study analysis focused on a pizza delivery service in Sri Lanka, it was observed that implementing VRP in a motorbike delivery system resulted in reduced travel distance, time, cost, and CO2 emissions compared to the existing delivery system. Furthermore, the utilization of UAVs in conjunction with VRP yielded even greater improvements across all parameters. Based on a comprehensive cost analysis considering long-term operations, the UAV-based delivery system was identified as the most cost-effective method, followed by the VRP-incorporated motorbike delivery method. Although the VRP-incorporated motorbike delivery system exhibited a slightly higher average time per route compared to the existing method, the total travel time required to complete all routes remained lower. Consequently, the study concludes that the VRP-incorporated motorbike delivery system outperforms the existing delivery method for food delivery, with the use of UAVs incorporating VRP identified as the optimal delivery method among the three alternatives. The findings contribute valuable insights to the optimization of food delivery logistics, emphasizing the potential of VRP and exploring the feasibility of UAVs for sustainable and efficient long-term delivery solutions.

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The best emotion recognition system based on physiological signals with a simple operatory should have higher accuracy and fast response speed. This paper aims to develop an emotion recognition system using a novel hybrid system based on Hidden Markov Model and Poincare plot. For this purpose, an electrocardiogram from the MAHNOB-HCI database was used. A novel feature extraction from a hybrid system combining Hidden Markov Model and Poincare plot was presented. The authors extracted time and frequency domain features from heart rate variability, and used two central hybrid systems, the Support Vector Machine/ Hidden Markov Model and the Hidden Markov Model/ Poincare Plot. Finally, the support vector machine was used as a classifier to classify emotions into positive and negative. The proposed method showed a classification accuracy of 95.02 ± 1.97 % overall. Also, the computing time of the method is around 163 milliseconds. The key of this paper is in the use of hybrid machines to improve accuracy without high computation time. This method can be used as a real-time system due to the low computation time and can be developed in many fields, such as medical examination and security systems.

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Thermography as an innovative diagnostic technique with non-contact temperature measurement is used in many industries – science, industry, medicine, and security. When using thermography in the field of health, images and images sequences obtained from thermal imaging cameras allow to record the temperature distribution in order to further recognize whether the state of the body is consistent with the defined parameters or whether there are deviations. However, it is worth paying attention to the measurement accuracy of thermal imaging cameras, their specification, and image quality of thermograms. In the case of recording stress states, measurement discrepancies between thermal imaging cameras for smartphone may affect the final results. Therefore, this article focuses on the comparison of the possibility of recording and detecting stress using two smartphone thermal imaging cameras: SEEK THERMAL Compact Pro and FLIR ONE Pro. The specifications of both cameras were compared. At the same time, the possibility of recording stress using smartphone thermal imaging cameras was confirmed on the basis of an exemplary study. The results of the comparison and analysis show that smartphone thermography can be a quick registration and diagnostic method in behavioral-biomedical issues.

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In today's digitalized and technology-driven society, where the number of IoT devices and the volume of collected data is exponentially increasing, the use of sensor data has become a necessity in certain fields of activity. This paper presents a concise history of sensor evolution and specialization, with a focus on the sensors used for localization, particularly those present in microelectromechanical systems (MEMS) that make up inertial measurement units. The study starts with a general overview and progresses towards a more specific analysis of data sets collected from an accelerometer. In the materials and methods section, it emphasizes the imperfect nature of sensor measurements and the need to filter digital signals. Three different digital signal filtering techniques belonging to distinct filter categories are comparatively analyzed, with each technique having its own particularities, advantages and disadvantages. The analysis considers the effectiveness in reducing measurement errors, the impact of the filtering process on the original signal, the ability to highlight the underlying phenomenon, as well as the performance of the analyzed algorithms. The ultimate purpose of this article is to determine which filtration method is best suited for the signal at hand in the context of an indoor localization application.

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The acquisition of ECG signals offers physicians and specialists a very important tool in the diagnosis of cardiovascular diseases. However, very often these signals are affected by noise from various sources, including noise generated by movement during physical activity. This type of noise is known as Motion Artifact (MA) which changes the waveform of the signal, leading to erroneous readings. The elimination of this noise is performed by different filtering techniques, where the adaptive filtering using the LMS (least mean squares) algorithm stands out. The objective of this article is to determine which algorithms best deal with motion artifacts, taking into account the use of instruments or wearable equipment, in different conditions of physical activity. A comparison between different algorithms derived from LMS (NLMS, PNLMS and IPNLM) used in adaptive filtering is carried out using indicators such as: Pearson's Correlation Coefficient, Signal to Noise Ratio (SNR) and Mean Squared Error (MSE) as metrics to evaluate them. For this purpose, the mHealth database was used, which contains ECG signals taken during moderate to medium intensity physical activities. The results show that filtering by IPNLMS as well as PNLMS offers an improvement both visually and in terms of SNR, Pearson, and MSE indicators.

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This study utilizes knowledge management (KM) to highlight a documentation-centric approach that is enhanced through artificial intelligence. Knowledge management can improve the decision-making process for predicting models that involved datasets, such as air pollution. Currently, air pollution has become a serious global issue, impacting almost every major city worldwide. As the capital and a central hub for various activities, Jakarta experiences heightened levels of activity, resulting in increased vehicular traffic and elevated air pollution levels. The comparative study aims to measure the accuracy levels of the naïve bayes, decision trees, and random forest prediction models. Additionally, the study uses evaluation measurements to assess how well the machine learning performs, utilizing a confusion matrix. The dataset’s duration is three years, from 2019 until 2021, obtained through Jakarta Open Data. The study found that the random forest achieved the best results with an accuracy rate of 94%, followed by the decision tree at 93%, and the naïve bayes had the lowest at 81%. Hence, the random forest emerges as a reliable predictive model for prediction of air pollution.

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The application of computer science in management and economics is a rapidly growing field that combines the analytical and technological capabilities of computer science with the strategic and operational needs of management and economics. The main aim of this research paper is to analyze the main academic contributors, sources, and international collaborations from 2014 to 2022 in computer science in the areas of management and economics, as well as to analyze the main subtopics developed over time. Bibliometric techniques were used to carry out the literature review, which allows an objective analysis of the academic literature through quantitative indicators. The results reveal a significant shift towards data-driven decision making in management, with artificial intelligence and machine learning improving predictive analytics, operational efficiency, and economic forecasting and modeling, highlighting the essential role of digital transformation in these disciplines, with significant implications for researchers, practitioners and decision-makers. It concludes that all stakeholders should work to develop a more informed and innovative approach to maximize the exploitation of the potential offered by computational sciences in different contexts. This includes the integration of advanced computational tools to improve decision making and operational efficiency, or the exploitation of computational models for more effective forecasting and policy decision making, as well as the continuous analysis of emerging areas in this field, being aware of the ethical, privacy and security challenges presented by these technologies, in order to ensure a responsible and equitable application

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