Hong Jing (Jingles) jinglescode

Name	Latitude	Longitude
Athens International Airport	37.9356467	23.9484156
Ancient Agora of Athens	37.9746507	23.7219716
Tzistarakis Mosque	37.9759204	23
Roman Forum	37.9743749	23.7255435
Theatre of Dionysus	37.9703658	23.7278553
Parthenon	37.9715285	23.7267166
Acropolis Museum	37.9684499	23.7285227
Temple of Olympian Zeus	37.9693	23

Name	Latitude	Longitude	Cluster	Day
Athens International Airport	37.9356467	23.9484156	2	1
Acropolis of Athens	37.9715323	23.7257492	2	1
Pl. Agias Irinis 2	37.977418	23.7280221	2	1
Diporto - Secret underground restaurant	37.9806622	23.7257688	2	1
Temple of Zeus	37.9692838	23.7331012	2	1
Plaka	37.9725529	23.7303363999999	2	1
Temple of Olympian Zeus	37.9693	23.7331	2	1
Lake Vouliagmeni	37.8078002	23.7855018	2	1

Authors	Paper	Method	Region of Interest	Application
Zheng et al.	Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label	U-Net	Lung	Diagnosis
Cao et al.	Longitudinal Assessment of COVID-19 Using a Deep Learning–based Quantitative CT Pipeline: Illustration of Two Cases	U-Net	Lung, Lesion	Quantification
Huang et al.	Serial Quantitative Chest CT Assessment of COVID-19: Deep-Learning Approach	U-Net	Lung, Lung lobes, Lesion	Quantification
Qi et al.	Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study	U-Net	Lung lobes, Lesion	Quantification
Gozes et al.	[Rapid ai development cycle for the c

	from scipy.spatial.distance import cdist

	distance_matrix = cdist(
	mean_lat_long_by_group.values,
	mean_lat_long_by_group.values
	)

	df_distance_matrix = pd.DataFrame(distance_matrix)
	df_distance_matrix

	starting_point = 2 #@param {type:"integer"}
	cur_index = starting_point

	seq = [cur_index]
	while len(seq) < len(list(df_distance_matrix.keys())):
	nearest_clusters = list(df_distance_matrix[cur_index].sort_values().index)
	for cluster_id in nearest_clusters:
	if cluster_id != cur_index and cluster_id not in seq:
	seq.append(cluster_id)
	cur_index = cluster_id

	places = []

	with open(kml_filename, "r") as file:
	content = file.readlines()
	content = "".join(content)
	bs_content = BeautifulSoup(content, "xml")

	placemarks = bs_content.findAll('Placemark')
	for placemark in placemarks:

	# KMeans
	from sklearn.cluster import KMeans
	kmeans = KMeans(n_clusters=int(num_days), random_state=0).fit(places_lat_long)
	group = list(kmeans.labels_)

	# MeanShift
	from sklearn.cluster import MeanShift, estimate_bandwidth
	bandwidth = estimate_bandwidth(places_lat_long, quantile=0.2)
	clustering = MeanShift(bandwidth=bandwidth).fit(places_lat_long)
	group = clustering.labels_