Education is always the most important part of our lives. To make education make a significant impact on today's learners, teaching and learning must evolve to match with today's learner's profile. Any edu...
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LoRa's biggest advantage is its flexibility, which is the ability to increase or decrease data rate and range while decreasing or increasing sensitivity. Whenever propagation conditions change frequently, this fun...
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programming can help K-12 students to develop their 21st-century core skills. Despite the benefits, programming is not common to be delivered in Indonesian K-12 education. There is a need to understand potential chall...
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According to technical reports prepared by United Nations International Children's Emergency Fund and World Health Organization, there is a high percentage of child violence occurring around the world. This fact i...
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Bipolar disorder is a mental health condition characterized by extreme mental states ranging from manic highs to depressive lows. Early intervention is crucial to prevent progression and complications of bipolar disor...
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The expansion of deep learning techniques, as well as the availability of large audio/sound datasets, have fueled tremendous breakthroughs in audio/sound classification during the last several years. The transfer lear...
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ISBN:
(数字)9798350364101
ISBN:
(纸本)9798350364118
The expansion of deep learning techniques, as well as the availability of large audio/sound datasets, have fueled tremendous breakthroughs in audio/sound classification during the last several years. The transfer learning approach has emerged as one of the primary approaches for improving the accuracy and durability of classification systems. This study conducts a comprehensive comparative analysis to determine the effectiveness and performance of this method in environment sound classification. This current investigation focuses on environmental sound classification using VGGish and YAMNet pre-trained models with the ESC-50 and BDLib2 datasets. In the ESC-50 dataset, VGGish improves accuracy to 372.22%, while YAMNet improves accuracy to 383.33% when compared to baseline models. Similarly, in the BDLib2 dataset, accuracy increases significantly to 221.43% with VGGish and 246.43% with YAMNet. Transfer learning exhibits remarkable effectiveness in enhancing model performance, with significant accuracy boosts observed in both datasets. YAMNet, designed specifically for sound classification tasks, surpasses VGGish in improving environmental sound classification performance, potentially due to its architecture’s adaptability and diverse training on environmental sounds.
Preventing agricultural resource loss caused by pests remains a crucial issue. While technological advancements are being achieved, the current agricultural management methods and equipment have yet to meet the requir...
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In the digital era, digital talent development has become increasingly vital for organizations and economies to thrive. While much focus has been placed on technical skills, this report emphasizes the often-overlooked...
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In this work we present the results of the creation and evaluation of a tool prototype that automatically calculates the size of the non-functional requirements (NFR) of the User Interface 2.1 subcategory of the SNAP ...
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Solar energy is one of the most abundant sources of renewable energy in Indonesia. Solar energy is now typically harnessed using solar panels, but the low efficiency of photovoltaic cells requires the development of o...
Solar energy is one of the most abundant sources of renewable energy in Indonesia. Solar energy is now typically harnessed using solar panels, but the low efficiency of photovoltaic cells requires the development of other alternatives. The heliostat is a sunlight directing device with mirrors that can be used in a concentrated solar power system. Current heliostats require high capital investment due to their large frames and expensive components. This research was undertaken to develop a lower cost heliostat using a smaller frame, ESP32 microcontroller, servo motor and low-cost components. The position of the sun can be determined using an algorithm based on the National Oceanic and Atmospheric Administration (NOAA) solar calculator, and the mirror is moved to maintain the sun's reflection on a target. The result of this research is a set of heliostat prototypes consisting of the frame and control system. Tests were carried out to test the performance of the designed heliostat and it was found that the heliostat has an accuracy of about 60 cm and can raise temperatures up to 3.41°C. The conclusion is that the heliostat can be used in a concentrated solar power system to heat boilers in solar power towers.
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