Artificial Intelligence
This work presents a large-scale three-fold annotated, low-cost microscopy image dataset of potato tubers for plant cell analysis in deep learning (DL) framework which has huge potential in the advancement of plant cell biology research. Indeed, low-cost microscopes coupled with new-generation smartphones could open new aspects in DL-based microscopy image analysis, which offers several benefits including portability, ease of use, and maintenance.
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This data can be used for all the experiments related to the Mars rovers, as this data are accurate and used in a new algorithm called Limited Weighted Sum Genetic Algorithm for Multi-Objectives optimisation (LWSGA-MO).
The Mars exploration rover dataset is created in two steps: the data generation, and the data processing.
1) The data generation was done by Unity, and the code was written by C# scripts.
2) The data processing was done by RStudio and R.
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AI-powered binary code similarity detection (BinSD), which transforms intricate binary code comparison to the distance measure of code embedding through neural networks, has been widely applied to program analysis. However, due to the diversity of the adopted embedding strategies, evaluation methodologies, running environments, and/or benchmarks, it is difficult to quantitatively understand to what extent the BinSD problem has been solved, especially in real-world applications.
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As the harmful effects of climate change on human society increase, the analysis of abnormal weather is becoming an important issue. Therefore, this work provides the Korean weather dataset, including the anomaly score measurements by using seven different methods. In this dataset, seven types of weather data for each day in 64 Korean cities from 2010 to 2020 are provided by Weather Radar Center in Korea Meteorological Administration.
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As the harmful effects of climate change on human society increase, the analysis of abnormal weather is becoming an important issue. Therefore, this work provides the Korean weather dataset, including the anomaly score measurements by using seven different methods. In this dataset, seven types of weather data for each day in 64 Korean cities from 2010 to 2020 are provided by Weather Radar Center in Korea Meteorological Administration.
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The data included here within is the associated model training results from the correlated paper "Distribution-Driven Augmentation of Real-World Datasets for Improved Cancer Diagnostics With Machine Learning". This paper focuses on using kernel density estimators to curate datasets by balancing classes and filling missing null values though synthetically generated data. Additionally, this manuscript proposes a technique for joining distinct datasets to train a model with necessary features from multiple different datasets as a type of transfer-learning.
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The data set has been prepared as 2 different versions. The data set was shared in two versions due to the fact that the researchers could easily reproduce the tests and hardware limitations. The first version (small_dataset) was prepared using a 10% sub-sample of all dataset. The other version (big_dataset) contains the entire data. In this study, the scenarios tested were run on the small_dataset. The most successful configuration that was selected as a result of the analysis on small_dataset was applied to big_dataset.
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