Artificial Intelligence
The Landsat 8 imagery, sourced from USGS Earth Explorer, covers diverse regions like the northeastern USA snow region, Brazilian forests, UAE deserts, and Indian zones (northern, central, and southern) from 2018 to 2023, capturing long-term trends and seasonal changes. The dataset, including bands B4, B5, and B10 with 30-meter resolution from LANDSAT/LC08/C02/T1\_TOA imagery, is crucial for accurate LST and emissivity prediction models. These bands capture vital land surface properties like vegetation health, moisture, and thermal characteristics, enhancing model reliability.
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While picking robots aim to address this, the complex growth environment poses challenges in identifying and locating fruits due to factors like light and leaf occlusion. This study focuses on designing a recognition and localization method tailored to the natural growth conditions of melons and fruits, aiming to provide precise positional information for effective harvesting. Leveraging GTR-Net and binocular stereo vision, the proposed technology integrates a lightweight backbone network with Ghost bottleneck and TCSPG modules.
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In this work, we download the circRNA-drug sensitivity associations from the circRic database, in which the drug sensitivity data comes from the GDSC database, containing 80076 associations that involve 404 circRNAs and 250 drugs.
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The following are three publicly available datasets for experiments related to federated learning or machine learning.
Availability of Data and Materials: The datasets used to support the findings of this study are publicly available on Internet as follow:
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The python code of Graph Neural Network (GRN). Recent studies have shown that the predictive performance of graph neural networks (GNNs) is inconsistent and varies across different experimental runs, even with identical parameters. The prediction variability limits GNNs' applicability, and the underlying reasons remain unclear. We have identified a key factor contributing to this issue: the oscillation of some nodes' predicted classes during GNN training.
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Popularity of smartphones also popularized, reading content using smartphones. Reading using smartphones quite differs from reading using desktop system. Mouse and Keyboard are the peripherals associated with the reading in desktop systems. Study of the handling of such devices has led to provide implicit feedback of the content read. Similar study in smartphones to get implicit feedback remains to be a huge gap. Reading using smartphones involves screen gestures like pinch to zoom, tap, scroll, orientation change and screen capture.
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Existing datasets of infrared and visible images only contain few extreme scenes, we construct a dataset of images with haze based on the M3FD dataset. We pick 450 aligned image pairs from M3FD dataset and synthesize hazy visible images using the ASM. Due to the unique imaging principle of infrared images, rarely affected by haze, there is no need to do additional process for infrared images. Finally, a dataset named MHS has been released, which contains 450 pairs of images in hazy conditions.
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data have 16 features with 1 target value
Scope: Primarily focused on diabetes-related information.
Data Size: Contains a substantial volume of records.
Variables: Likely includes patient demographics, medical history, lab results, medications, treatments, and outcomes.
Temporal Range: Time span covered by the dataset may vary.
Privacy Measures: Anonymized to protect patient identities.
Ethical Considerations: Collected and shared adhering to ethical guidelines.
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This dataset contains RGB+D videos and skeleton data for human behavior. The behavior data is captured by 3 Microsoft Kinect V2 cameras from 40 human subjects, with a total of 56,880 samples containing 60 categories totaling 4 million frames, where the maximum frame for all samples is 300. 25 joints are recorded for each body skeleton. The dataset provides two original settings, namely two evaluation protocols, Cross-Subject (Xsub) and Cross-View (Xview). In Xsub protocol, the training set contains 40,320 samples from 20 subjects, and the remaining 16,560 samples are used for testing.
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The research team conducted logistic and Cox regression according to the behavioral data of gastroesophageal reflux disease patients who had long been drinking caffeinated coffee drinks, and determined the sensitivity and mathematical rationality of AI prediction model in behavioral science, which can support the research team to build a deep learning neural network and complete the prediction of gastrointestinal tract involvement.
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