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AI and Financial Markets
AI and Financial Markets
Autore Hamori Shigeyuki
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Descrizione fisica 1 electronic resource (230 p.)
Soggetto topico Economics, finance, business & management
Soggetto non controllato algorithmic trading
Stop Loss
Turtle
ATR
community finances
fiscal flexibility
individualized financial arrangements
sustainable financial services
price momentum
hidden markov model
asset allocation
blockchain
BlockCloud
Artificial Intelligence
consensus algorithms
exchange rates
fundamentals
prediction
random forest
support vector machine
neural network
deep reinforcement learning
financial market simulation
agent based simulation
artificial market
simulation
CAR regulation
portfolio
contract for difference
CfD
reinforcement learning
RL
neural networks
long short-term memory
LSTM
Q-learning
deep learning
uncertainty
economic policy
text mining
topic model
yield curve
term structure of interest rates
machine learning
autoencoder
interpretability
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557584903321
Hamori Shigeyuki  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Bibliometric Studies and Worldwide Research Trends on Global Health
Bibliometric Studies and Worldwide Research Trends on Global Health
Autore Manzano Agugliaro Francisco
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Descrizione fisica 1 electronic resource (326 p.)
Soggetto topico Research & information: general
Soggetto non controllato social networks
health
young people
bibliometric study
scientometrics
obesity
interventions
children
youths
pediatrics
reclaimed water
advanced oxidation process
microorganisms
concern emergent contaminant
worldwide
content analysis
text mining
diabetes
QOL
artificial intelligence
machine learning
bibliometric
LDA
HIV/AIDS
quality of life
global health
public health
scientometric study
knowledge map
visualization analysis
CiteSpace
COPD
QoL
medicinal plants
drugs
worldwide research
bibliometrics
traditional medicine
asthma
HRQoL
Scival
patents
Spain
Research and Development (R&
D)
social returns
COVID-19
biomechanics
musculoskeletal disorders
RULA
ergonomics
applications
climate change
infectious diseases
bibliometric analysis
co-word analysis
biclustering
strategic diagram
academic performance
citation network
motivation
microplastics
network analysis
VOSviewer software
research hotspots
pulmonary disease
musculoskeletal risks
wastewater treatment
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557629603321
Manzano Agugliaro Francisco  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Big Data Analytics and Information Science for Business and Biomedical Applications
Big Data Analytics and Information Science for Business and Biomedical Applications
Autore Ahmed S. Ejaz
Pubbl/distr/stampa Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Descrizione fisica 1 electronic resource (246 p.)
Soggetto topico Humanities
Social interaction
Soggetto non controllato high-dimensional
nonlocal prior
strong selection consistency
estimation consistency
generalized linear models
high dimensional predictors
model selection
stepwise regression
deep learning
financial time series
causal and dilated convolutional neural networks
nuisance
post-selection inference
missingness mechanism
regularization
asymptotic theory
unconventional likelihood
high dimensional time-series
segmentation
mixture regression
sparse PCA
entropy-based robust EM
information complexity criteria
high dimension
multicategory classification
DWD
sparse group lasso
L2-consistency
proximal algorithm
abdominal aortic aneurysm
emulation
Medicare data
ensembling
high-dimensional data
Lasso
elastic net
penalty methods
prediction
random subspaces
ant colony system
bayesian spatial mixture model
inverse problem
nonparamteric boostrap
EEG/MEG data
feature representation
feature fusion
trend analysis
text mining
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557614803321
Ahmed S. Ejaz  
Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational Methods for the Analysis of Genomic Data and Biological Processes
Computational Methods for the Analysis of Genomic Data and Biological Processes
Autore Gómez Vela Francisco A
Pubbl/distr/stampa Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Descrizione fisica 1 electronic resource (222 p.)
Soggetto topico Research & information: general
Biology, life sciences
Soggetto non controllato HIGD2A
cancer
DNA methylation
mRNA expression
miRNA
quercetin
hypoxia
eQTL
CRISPR-Cas9
single-cell clone
fine-mapping
power
RNA N6-methyladenosine site
yeast genome
methylation
computational biology
deep learning
bioinformatics
hepatocellular carcinoma
transcriptomics
proteomics
bioinformatics analysis
differentiation
Gene Ontology
Reactome Pathways
gene-set enrichment
meta-analysis
transcription factor
binding sites
genomics
chilling stress
CBF
DREB
CAMTA1
pathway
text mining
infiltration tactics optimization algorithm
classification
clustering
microarray
ensembles
machine learning
infiltration
computational intelligence
gene co-expression network
murine coronavirus
viral infection
immune response
data mining
systems biology
obesity
differential genes expression
exercise
high-fat diet
pathways
potential therapeutic targets
DNA N6-methyladenine
Chou's 5-steps rule
Convolution Neural Network (CNN)
Long Short-Term Memory (LSTM)
machine-learning
chromatin interactions
prediction
genome architecture
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910557129603321
Gómez Vela Francisco A  
Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational Systems Biology of Pathogen-Host Interactions
Computational Systems Biology of Pathogen-Host Interactions
Autore Reinhard Guthke
Pubbl/distr/stampa Frontiers Media SA, 2016
Descrizione fisica 1 electronic resource (198 p.)
Collana Frontiers Research Topics
Soggetto non controllato Image-based Systems Biology
Network Inference
OMICS data
Computational Biology
bioinformatics
protein-protein interaction
text mining
Constraint-based modeling
gene regulatory network
pathogen-host interaction
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910161648403321
Reinhard Guthke  
Frontiers Media SA, 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining for the social sciences : an introduction / / Paul Attewell and David B. Monaghan
Data mining for the social sciences : an introduction / / Paul Attewell and David B. Monaghan
Autore Attewell Paul A. <1949->
Pubbl/distr/stampa Oakland, California : , : University of California Press, , 2015
Descrizione fisica 1 online resource (265 p.)
Disciplina 006.3/12
Soggetto topico Social sciences - Data processing
Social sciences - Statistical methods
Data mining
Soggetto non controllato analyzing data
bayesian networks
big data
bootstrapping
business analytics
chaid
classification and regression trees
classification trees
confusion matrix
data analysis
data mining
data processing
data scholarship
data science
hardware for data mining
heteroscedasticity
naive bayes
partition trees
permutation tests
scholarly data
social science
social scientists
software for data mining
statistical methods
statistical modeling
studying data
text mining
vif regression
weka
ISBN 0-520-28098-9
0-520-96059-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front matter -- CONTENTS -- ACKNOWLEDGMENTS -- 1. WHAT IS DATA MINING? -- 2. CONTRASTS WITH THE CONVENTIONAL STATISTICAL APPROACH -- 3. SOME GENERAL STRATEGIES USED IN DATA MINING -- 4. IMPORTANT STAGES IN A DATA MINING PROJECT -- 5. PREPARING TRAINING AND TEST DATASETS -- 6. VARIABLE SELECTION TOOLS -- 7. CREATING NEW VARIABLES -- 8. EXTRACTING VARIABLES -- 9. CLASSIFIERS -- 10. CLASSIFICATION TREES -- 11. NEURAL NETWORKS -- 12. CLUSTERING -- 13. LATENT CLASS ANALYSIS AND MIXTURE MODELS -- 14. ASSOCIATION RULES -- CONCLUSION. Where Next? -- BIBLIOGRAPHY -- NOTES -- INDEX
Record Nr. UNINA-9910788152303321
Attewell Paul A. <1949->  
Oakland, California : , : University of California Press, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data mining for the social sciences : an introduction / / Paul Attewell and David B. Monaghan
Data mining for the social sciences : an introduction / / Paul Attewell and David B. Monaghan
Autore Attewell Paul A. <1949->
Pubbl/distr/stampa Oakland, California : , : University of California Press, , 2015
Descrizione fisica 1 online resource (265 p.)
Disciplina 006.3/12
Soggetto topico Social sciences - Data processing
Social sciences - Statistical methods
Data mining
Soggetto non controllato analyzing data
bayesian networks
big data
bootstrapping
business analytics
chaid
classification and regression trees
classification trees
confusion matrix
data analysis
data mining
data processing
data scholarship
data science
hardware for data mining
heteroscedasticity
naive bayes
partition trees
permutation tests
scholarly data
social science
social scientists
software for data mining
statistical methods
statistical modeling
studying data
text mining
vif regression
weka
ISBN 0-520-28098-9
0-520-96059-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front matter -- CONTENTS -- ACKNOWLEDGMENTS -- 1. WHAT IS DATA MINING? -- 2. CONTRASTS WITH THE CONVENTIONAL STATISTICAL APPROACH -- 3. SOME GENERAL STRATEGIES USED IN DATA MINING -- 4. IMPORTANT STAGES IN A DATA MINING PROJECT -- 5. PREPARING TRAINING AND TEST DATASETS -- 6. VARIABLE SELECTION TOOLS -- 7. CREATING NEW VARIABLES -- 8. EXTRACTING VARIABLES -- 9. CLASSIFIERS -- 10. CLASSIFICATION TREES -- 11. NEURAL NETWORKS -- 12. CLUSTERING -- 13. LATENT CLASS ANALYSIS AND MIXTURE MODELS -- 14. ASSOCIATION RULES -- CONCLUSION. Where Next? -- BIBLIOGRAPHY -- NOTES -- INDEX
Record Nr. UNINA-9910814373503321
Attewell Paul A. <1949->  
Oakland, California : , : University of California Press, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data Science and Knowledge Discovery
Data Science and Knowledge Discovery
Autore Portela Filipe
Pubbl/distr/stampa Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Descrizione fisica 1 electronic resource (254 p.)
Soggetto topico Information technology industries
Computer science
Soggetto non controllato crisis reporting
chatbots
journalists
news media
COVID-19
textbook research
digital humanities
digital infrastructures
data analysis
content base image retrieval
semantic information retrieval
deep features
multimedia document retrieval
data science
open government data
governance and social institutions
economic determinants of open data
geoinformation technology
fractal dimension
territorial road network
box-counting framework
script Python
ArcGIS
internet of things
LoRaWAN
ICT
The Things Network
ESP32 microcontroller
decision systems
rule based systems
databases
rough sets
prediction by partial matching
spatio-temporal
activity recognition
smart homes
artificial intelligence
automation
e-commerce
machine learning
big data
customer relationship management (CRM)
distracted driving
driving behavior
driving operation area
data augmentation
feature extraction
authorship
text mining
attribution
neural networks
deep learning
forensic intelligence
dashboard
WebGIS
data analytics
SARS-CoV-2
Big Data
Web Intelligence
media analytics
social sciences
humanities
linked open data
adaptation process
interdisciplinary research
media criticism
classification
information systems
public health
data mining
ioCOVID19
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910576878103321
Portela Filipe  
Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Empirical Finance
Empirical Finance
Autore Hamori Shigeyuki
Pubbl/distr/stampa MDPI - Multidisciplinary Digital Publishing Institute, 2019
Descrizione fisica 1 electronic resource (276 p.)
Soggetto non controllato short-term forecasting
wavelet transform
IPO
volatility
US dollar
institutional investors’ shareholdings
neural network
financial market stress
market microstructure
text similarity
TVP-VAR model
Japanese yen
convolutional neural networks
global financial crisis
deep neural network
cross-correlation function
boosting
causality-in-variance
flight to quality
bagging
earnings quality
algorithmic trading
stop loss
statistical arbitrage
ensemble learning
liquidity risk premium
gold return
futures market
take profit
currency crisis
spark spread
city banks
piecewise regression model
financial and non-financial variables
exports
data mining
latency
crude oil futures prices forecasting
random forests
wholesale electricity
SVM
random forest
bank credit
deep learning
Vietnam
inertia
MACD
initial public offering
text mining
bankruptcy prediction
exchange rate
asset pricing model
LSTM
panel data model
structural break
credit risk
housing and stock markets
copula
ARDL
earnings manipulation
machine learning
natural gas
housing price
asymmetric dependence
real estate development loans
earnings management
cointegration
predictive accuracy
robust regression
quantile regression
dependence structure
housing loans
price discovery
utility of international currency
ATR
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910346675203321
Hamori Shigeyuki  
MDPI - Multidisciplinary Digital Publishing Institute, 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Human and Animal Sensitivity: How Stock-People and Consumer Perception Can Affect Animal Welfare
Human and Animal Sensitivity: How Stock-People and Consumer Perception Can Affect Animal Welfare
Autore Napolitano Fabio
Pubbl/distr/stampa MDPI - Multidisciplinary Digital Publishing Institute, 2019
Descrizione fisica 1 electronic resource (234 p.)
Soggetto non controllato education
animal welfare
young adult
welfare
stunning
human health
perception
slaughter
pig
pigs
children
livestock
laying hen
racehorse welfare
qualitative research
free elicitation narrative interviews
knowledge
fear
milk production
ethical concerns
food safety concerns
agreement
stakeholder perception
castration
sheep farmers
farmer perception
animal ethics
goat
benefit
horse
dairy buffalo
animal attitudes
turkey
farm animal welfare (FAW)
animal
survey
Animal welfare
husbandry practices
willingness to pay
donkey
avoidance distance
training
stockperson behaviour
stockpeople attitudes
farm animal welfare
farm animals
veterinary students
broiler
text mining
religious slaughter
profit
consumer
sheep
egg farm
Halal meat
pain
employee relations
standards of care
animal behavior
consumer demand
albumen corticosterone
aggression
technology
transport
test-retest reliability
desensitization
producer perspective
economics
pain perception
Asia
horse–human relationship
lambs
veterinary student
human-animal relationship
information
citizen perception
immunocastration
perceived consumer effectiveness
staff shortages
ISBN 3-03921-262-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti Human and Animal Sensitivity
Record Nr. UNINA-9910367568603321
Napolitano Fabio  
MDPI - Multidisciplinary Digital Publishing Institute, 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
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