In recent years, natural language processing has gained significant popularity in various sectors, including the legal domain. This paper presents NeCo Team's solutions to the Vietnamese text processing tasks prov...
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Tuberculosis (TB) is an infectious disease that ranks 13th as the deadliest disease in the world. Without treatment, pulmonary TB has a 50% chance of causing death. The presence of Covid-19 has further hindered the ma...
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Road deaths are still alarmingly high. It also ranks first regarding causes of death unrelated to pre-existing health status. More than half of all road traffic deaths occur among vulnerable road users like pedestrian...
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One of the most essential technologies for removing harmful particles from many industries is electrostatic precipitators (ESPs). Many current studies on this subject are constantly improving as a result of the inhere...
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In the mature stage of IoT, the horizontal approach is focused. For this purpose, the IoT platform with the commonality of functions should be deployed. However, the IoT platform has various options. This paper survey...
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The global energy landscape is undergoing a significant transformation, emphasizing the need for sustainable and renewable energy sources. Solar energy, with its abundant availability, stands out as a potential soluti...
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Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread ap...
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Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions adequately. To address these challenges, this study introduces a novel hybrid model called Transfer LSTM to GRU (TLTG), which combines the strengths of deep and shallow networks using transfer learning. The TLTG model integrates Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU) to enhance predictive accuracy while maintaining computational efficiency. Gaussian transformation is applied to the input data to reduce outliers and skewness, creating a more normal-like distribution. The proposed approach is validated on datasets from various wells in the Tahe oil field, China. Experimental results highlight the superior performance of the TLTG model, achieving 100% accuracy and faster prediction times (200 s) compared to eight other approaches, demonstrating its effectiveness and efficiency.
The cocoa industry serves as a vital economic pillar, holding a pivotal role in the daily lives of farmers in Central Sulawesi. However, a prevailing challenge for cocoa cultivators in this region resides in the inher...
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We develop a novel approach to construct a knowledge graph encompassing legal case documents and relevant legislation to improve legal information organization and retrieval. Our method involves data collection, entit...
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The performance and reliability of production systems is greatly affected by sudden breakdowns. In order to avoid these unforeseen interruptions, predictive maintenance (PdM) systems are being widely used to predict f...
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