Machine Learning

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.

Low-light images and video footage often exhibit issues due to the interplay of various parameters such as aperture, shutter speed, and ISO settings. These interactions can lead to distortions, especially in extreme lighting conditions. This distortion is primarily caused by the inverse relationship between decreasing light intensity and increasing photon noise, which gets amplified with higher sensor gain. Additionally, secondary characteristics like white balance and color effects can also be adversely affected and may require post-processing correction.

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.

The growing antenna array scale, the uncorrelated fadings between downlink and uplink of frequency division duplex (FDD) or analog beamforming design increases the difficulty of channel sounding or estimation. Non-wireless channel detection or beam weight prediction method is a promising solution to help obtain timely and accurate wireless channel state. Furthermore, beamforming can be enhanced by the powerful sensing capability of cameras.