Organizing music activities for the elderly is another way that supports their well-being. Angklung, an Indonesian musical instrument, has been used for music activities for the elderly in Thailand with the hand signs...
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Dadahup Swamp Irrigation Area (DIR) in Kapuas Regency, Central Kalimantan is developed for agricultural activities to provide food security after the pandemic. The water system consists of various channels, gates, and...
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Resource management is always an important issue related to good governance decision making. One of the common problem faced in managing IT Infrastructure is about allocating server resources to improve the performanc...
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Resource management is always an important issue related to good governance decision making. One of the common problem faced in managing IT Infrastructure is about allocating server resources to improve the performance. In this study we use a machine learning approach to make predictions about the performance of information technology infrastructure. The experiment took data from several servers in a company to be tested. The performance measure of resources used in this study are CPU Performance, Disk performance, Memory capacity, and Network performance. Several algorithms and machine learning methods are tested, such as Linear Regression, kNN, SVR, Decision Tree and Random Forest, to find the best model fit for the servers. The comparison result shows that Linear regression and kNN perform well in predicting the network performance in those three servers.
This paper reviews the latest trends and challenges in implementing digital twin technology. A digital twin is a tool used in various industries to improve efficiency, optimize processes, and enable advanced analysis....
This paper reviews the latest trends and challenges in implementing digital twin technology. A digital twin is a tool used in various industries to improve efficiency, optimize processes, and enable advanced analysis. The review involved searching major research databases and search engines for articles published between 2018 and 2023. The findings reveal several important trends, including the development of different types of digital twin dimensions, each with its own advantages and limitations. The benefits of digital twin implementation include improved decision-making, increased productivity, and operational efficiency. However, there are challenges, such as data integration, security and privacy concerns, a lack of standardization, and the need for experts to effectively design and operate digital twins. The implications of these trends and challenges are discussed regarding their impact on the successful adoption and implementation of digital twin technology. The review also highlights the need to address these challenges and explore new approaches for maximizing the benefits of digital twin technology. Overall, this comprehensive review is a valuable resource for researchers, practitioners, and organizations seeking to understand the current landscape, identify areas for improvement, and make informed decisions when implementing digital twin technology.
Content-Based Image Retrieval (CBIR) have shown promising results in the field of medical diagnosis, which aims to provide support to medical professionals (doctor or pathologist). However, the ultimate decision regar...
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Scholars have long demonstrated the effectiveness of using visual representations to aid non-native speakers in learning Mandarin tone. Among these methods, presenting pitch contours visually has proven to be the most...
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DRL has emerged as a promising approach for mobile robot navigation in unknown environments without a prior map. However, the performance of DRL methods for this task varies greatly, depending on the choice of algorit...
DRL has emerged as a promising approach for mobile robot navigation in unknown environments without a prior map. However, the performance of DRL methods for this task varies greatly, depending on the choice of algorithm, state representation, and training procedure. In this paper we explore various cutting-edge DRL algorithms, such as policy-, value-, and actor-critic-based approaches. Our results demonstrate the effectiveness of the ranging sensor approach, which achieves robust navigation policies capable of generalizing to unseen virtual environments with a high success rate. We combine Behavior Cloning with Imitation Learning to expedite the training process, leveraging expert demonstrations and reinforcement learning. Our methodology enables faster training while enhancing the learning efficiency and performance of the robot, obtaining better results in terms of crash and success rate, and being able to reach a cumulative reward of approximately 12000.
Air pollution is one of the most important health problems causing various diseases. According to the World Health Organization (WHO), it is estimated that more than 7 million deaths are due to air pollution. For this...
Air pollution is one of the most important health problems causing various diseases. According to the World Health Organization (WHO), it is estimated that more than 7 million deaths are due to air pollution. For this reason, air quality monitoring is an important indicator for guiding public policy. However, government stations are widely scattered and their high cost makes it unprofitable to invest in higher resolution. Low-cost air quality monitoring sensors can overcome this problem, but also bring new challenges. This paper presents the development of a low-cost air quality monitoring device. The low-cost station collects the following measurements: carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), ozone (O 3 ), and particulate matter. The collected data is corrected using a machine learning model and sent to the Internet in real time via the LoRaWAN protocol. Mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), linearity coefficient (R 2 ), and Pearson r were used to compare model performance. Our results show that linear models such as the Alphasense equation and linear model regression cannot accurately describe the sensor response to the reference gas sensor, whereas the RF model performs better in each metric. The performance of the RF model demonstrates the potential to improve air quality monitoring and the decision-making process.
Path planning is a crucial component of autonomous navigation and frequently demands different priorities such as path length, safety, or energy consumption, with the latter being particularly important in the context...
Path planning is a crucial component of autonomous navigation and frequently demands different priorities such as path length, safety, or energy consumption, with the latter being particularly important in the context of unmanned autonomous vehicles. In many applications, the agent may have to react to environment shifting. Algorithms such as geometric and dynamic programming as well as techniques such as artificial potential fields have been employed to tackle this local planning problem. In recent years, machine learning has gained more evidence in many research fields due to its flexibility and generalization capabilities. In this study, we propose a Q-learning-based approach to local planning, which weighs three crucial factors- path length, safety, and energy consumption- that can be freely adjusted by the user to suit its application’s needs. The performance of the proposed method was tested in simulated static and dynamic scenarios as well as benchmarked with a baseline approach. The results show that it can perform well in both kinds of environments without struggling with the commom pitfalls of other local planning algorithms.
This study investigates the application of diffusion models in medical image classification (DiffMIC), focusing on skin and oral lesions. Utilizing the datasets PAD-UFES-20 for skin cancer and P-NDB-UFES for oral canc...
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