Showing the design and layout of the building using a model, making this model requires time and accuracy because the model is built on a scale that has been adjusted to the building to be made later, a problem that o...
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Showing the design and layout of the building using a model, making this model requires time and accuracy because the model is built on a scale that has been adjusted to the building to be made later, a problem that o...
Showing the design and layout of the building using a model, making this model requires time and accuracy because the model is built on a scale that has been adjusted to the building to be made later, a problem that often occurs in modeling is the suitability of the scale and detail of the model. Augmented Reality as a variation of the virtual environment. Users of AR technology can see the real world, with virtual objects added to the real world. So, users see virtual objects and real objects in the same place. Submission of information by utilizing the three-dimensional visualization technique is very interesting because it can display the 3D model of an object before it is built in real terms. The faculty of computerscience and informationtechnology Mulawarman University Samarinda has an exterior building design of a new building, but there are no interactive media that can display the exterior of the building as a whole and in detail. This application is able to be used to apply the 3D model of the building as a whole and can be seen from various viewpoints.
As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention...
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As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.
The k-Nearest Neighbors (kNN) algorithm is one of the most widely used techniques for data classification. However, the imbalanced class is a key problem for its declining performance. Therefore, the kNN algorithm is ...
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The coronavirus disease 2019 (COVID-19) has posed significant challenges globally, with image classification becoming a critical tool for detecting COVID-19 from chest X-ray and CT images. Convolutional neural network...
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With the rise of Arabic digital content, effective summarization methods are essential. Current Arabic text summarization systems face challenges such as language complexity and vocabulary limitations. We introduce an...
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Classifying textual data is crucial in the expanding digital landscape, especially for underrepresented cursive languages like Urdu, which pose unique challenges due to their intricate linguistic features and vast dig...
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Predicting the accurate future price of the agricultural crops is important to avoid overproduction or shortages in the food supply chain. To obtain accurate predictions, the process usually involves large and complex...
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Predicting the accurate future price of the agricultural crops is important to avoid overproduction or shortages in the food supply chain. To obtain accurate predictions, the process usually involves large and complex datasets, which would add to computational costs for developing a model with good performance. Therefore, this study introduces the single-layer Transformer Convolutional Encoder algorithm (STCE), an improved version of the traditional transformer encoder. STCE is computationally efficient and does not compromise the accuracy of the prediction. In STCE, the fully connected Convolutional Neural Network (CNN) layer is used in the transformer to get the first temporal features and record long-range dependencies with Multi-Head Attention. To minimize complexity while maintaining performance, a single dense layer is used for the output instead of the Multi-Layer Perceptron (MLP) and omit positional encoding, which leverages the natural sequence order of the time series data. Additionally, since time-series price data normally comes with missing values, this study introduce a sequence nearest neighbor imputation algorithm for anchoring that data to complement the STCE method. This study focuses on various vegetable prices, such as tomatoes, long beans, and cucumbers, with empirical validation across various prediction prices, specifically 30-day, 60-day, and 90-day predictions. Predictions made with Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) show that the STCE algorithm is better than other deep learning algorithms, even the traditional transformer encoder. STCE algorithm not only has better performance, but it also reduces the computational time in the training with 12% fewer seconds compared to the transformer encoder and 22% fewer seconds for LSTM. This study not only provides valuable insights for farmers and planners in the agriculture market but also highlights the robust potential of transforme
Vehicle location prediction and the use of vehicle location tracking are increasingly important topics of discussion among connected vehicle researchers. Location tracking for mobile users is essential due to the corr...
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This project develops an AI-based anomaly detection system. In the field of autonomous driving, abnormal data will directly affect the safety of autonomous driving systems, especially in terms of abnormal camera senso...
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