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1Development of Power System Stabilizer Using Modern Optimization Approach
M. ul Zaman | Mr. Deepak Joshi | Mr. Yuvraj Ranawat. INTERNATIONAL JOURNAL OF TREND IN SCIENTIFIC RESEARCH AND DEVELOPMENT, 6 (1): 96-99 (December 2021)
2 years ago by @ijtsrd
show all tags
(Artificial
(PSS),
ABC
Bee
Colony),
Transmission
controller,
lines,
optimal
power
stabilizer
system
(Artificial(PSS),ABCBeeColony),Transmissioncontroller,lines,optimalpowerstabilizersystem
(0)
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3A General Two-Step Approach to Learning-Based Hashing
G. Lin, C. Shen, D. Suter, and A. v. d. Hengel. 2013 IEEE International Conference on Computer Vision, page 2552-2559. (December 2013)
3 years ago by @dsuter
show all tags
(artificial
approach;hash
based
bit
codes;Hamming
codes;hashing;image
cryptography;learning
distance;Kernel;Optimization;Support
function
function;hash
hashing;optimization
intelligence);optimisation;binary
learning
learning;hash
learning;hashing
machines;Testing;Training;binary
optimization
problem;learning
problems;complex
problems;general
process;Binary
quadratic
retrieval
step
two
vector
(artificialapproach;hashbasedbitcodes;Hammingcodes;hashing;imagecryptography;learningdistance;Kernel;Optimization;Supportfunctionfunction;hashhashing;optimizationintelligence);optimisation;binarylearninglearning;hashlearning;hashingmachines;Testing;Training;binaryoptimizationproblem;learningproblems;complexproblems;generalprocess;Binaryquadraticretrievalsteptwovector
(0)
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2Fast Point Cloud Registration using Semantic Segmentation
G. Truong, S. Gilani, S. Islam, and D. Suter. 2019 Digital Image Computing: Techniques and Applications (DICTA), page 1-8. (December 2019)DST Best Science Paper Award.
3 years ago by @dsuter
show all tags
(artificial
algorithm;visual
cloud
computer
data
data-sets;point
data;large
data;semantic
graphics;image
information;deep
intelligence);point
learning;point
learning;semantic
matches;semantic
point
registration
registration;deep
registration;image
registration;point-cloud
scale
segmentation
segmentation;large
segmentation;learning
sets;optimal
(artificialalgorithm;visualcloudcomputerdatadata-sets;pointdata;largedata;semanticgraphics;imageinformation;deepintelligence);pointlearning;pointlearning;semanticmatches;semanticpointregistrationregistration;deepregistration;imageregistration;point-cloudscalesegmentationsegmentation;largesegmentation;learningsets;optimal
(0)
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3Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality
C. Sandrock, M. Herde, A. Calma, D. Kottke, and B. Sick. International Joint Conference on Neural Networks (IJCNN), page 1--8. IEEE, (2019)
3 years ago by @ies
show all tags
(artificial
annotators;confidence
annotators;expertise
annotators;multiple
assignment;self-reported
classification;probability;multiple
confidence
confidences;numerical
confidences;{\gamma}-
data
different
estimation;pattern
evaluation;graphical
evaluation;probabilistic
intelligence);maximum
levels;{\alpha}-confidence;benevolent
likelihood
machines
majority
mining;learning
model;nonnormalized
models;{\beta}-confidence
outputs;intelligent
posses
quality;class
uncertain
vote;label
weighted
(artificialannotators;confidenceannotators;expertiseannotators;multipleassignment;self-reportedclassification;probability;multipleconfidenceconfidences;numericalconfidences;{\gamma}-datadifferentestimation;patternevaluation;graphicalevaluation;probabilisticintelligence);maximumlevels;{\alpha}-confidence;benevolentlikelihoodmachinesmajoritymining;learningmodel;nonnormalizedmodels;{\beta}-confidenceoutputs;intelligentpossesquality;classuncertainvote;labelweighted
(0)
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1A Bibliometric Analysis of Different Maximum Power Point Tracking Methods for Photovoltaic Systems
S. Werulkar. International Journal of Trend in Scientific Research and Development, 5 (4): 1353-1357 (June 2021)
3 years ago by @ijtsrd
show all tags
&
(Artificial
(Incremental
(Maximum
(PSO)
(Perturb
(Photo
ANN
Conductance)
InC
MPPT
Network)
Neural
O
Observe)
Optimization
P
PV
Particle
Point
Power
Swarm
Tracking)
Voltaic)
&(Artificial(Incremental(Maximum(PSO)(Perturb(PhotoANNConductance)InCMPPTNetwork)NeuralOObserve)OptimizationPPVParticlePointPowerSwarmTracking)Voltaic)
(0)
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2Robust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis
J. Yamagishi, T. Nose, H. Zen, Z. Ling, T. Toda, K. Tokuda, S. King, and S. Renals. IEEE Transactions on Audio, Speech, and Language Processing, 17 (6): 1208-1230 (August 2009)
4 years ago by @m-toman
show all tags
(artificial
HTS;HMM-based
Markov
Speech
Synthesis
System,
adaptation;speech
analysis;Speech
analysis;full-covariance
approach;text-to-speech
conversion
hidden
intelligence);speech
model;speaker-dependent
modeling;mixed-gender
modeling;robust
models;High
models;learning
reactor;Councils;Hidden
science;Continuous-stirred
science;Nose;Robustness;Speech
speaker-adaptive
speech
superconductors;Information
synthesis;Average
synthesis;Computer
synthesis;speaker
synthesis;text
synthesis;voice
tank
temperature
voice;HMM
(artificialHTS;HMM-basedMarkovSpeechSynthesisSystem,adaptation;speechanalysis;Speechanalysis;full-covarianceapproach;text-to-speechconversionhiddenintelligence);speechmodel;speaker-dependentmodeling;mixed-gendermodeling;robustmodels;Highmodels;learningreactor;Councils;Hiddenscience;Continuous-stirredscience;Nose;Robustness;Speechspeaker-adaptivespeechsuperconductors;Informationsynthesis;Averagesynthesis;Computersynthesis;speakersynthesis;textsynthesis;voicetanktemperaturevoice;HMM
(0)
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1Accurate visible speech synthesis based on concatenating variable length motion capture data
J. Ma, R. Cole, B. Pellom, W. Ward, and B. Wise. IEEE Transactions on Visualization and Computer Graphics, 12 (2): 266-276 (2006)
4 years ago by @m-toman
show all tags
(artificial
Automated;Reproducibility
Biological;Movement;Pattern
Computer-Assisted;Imaging,
Enhancement;Image
Graphics;Computer
Intelligence;Computer
Interface;Video
Interpretation,
Measurement;User-Computer
Production
Recognition,
Recording
Results;Sensitivity
Retrieval;Models,
Simulation;Face;Image
Specificity;Speech;Speech
Storage
Three-Dimensional;Information
algorithm;speech
analysis;Speech
analysis;learning
and
animation
animation;Humans;Large-scale
animation;character
animation;coarticulation
animation;face
capture
codes;Facial
computer
data;optimal
effect;facial
effect;virtual
face
human.;visible
intelligence);search
learning
learning;Prototypes;Speech
model;coarticulation
modelling;speech
motion
motion;lip
motion;machine
of
problems;solid
processing;Speech
prototype;Concatenated
recognition;image
sequence;search
speech
speech;Algorithms;Artificial
speech;visual
synthesis;3D
synthesis;Face
synthesis;visual
system;visible
systems;Lips;Machine
technique;motion
(artificialAutomated;ReproducibilityBiological;Movement;PatternComputer-Assisted;Imaging,Enhancement;ImageGraphics;ComputerIntelligence;ComputerInterface;VideoInterpretation,Measurement;User-ComputerProductionRecognition,RecordingResults;SensitivityRetrieval;Models,Simulation;Face;ImageSpecificity;Speech;SpeechStorageThree-Dimensional;Informationalgorithm;speechanalysis;Speechanalysis;learningandanimationanimation;Humans;Large-scaleanimation;characteranimation;coarticulationanimation;facecapturecodes;Facialcomputerdata;optimaleffect;facialeffect;virtualfacehuman.;visibleintelligence);searchlearninglearning;Prototypes;Speechmodel;coarticulationmodelling;speechmotionmotion;lipmotion;machineofproblems;solidprocessing;Speechprototype;Concatenatedrecognition;imagesequence;searchspeechspeech;Algorithms;Artificialspeech;visualsynthesis;3Dsynthesis;Facesynthesis;visualsystem;visiblesystems;Lips;Machinetechnique;motion
(0)
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2Automatic generation of synthesis units for trainable text-to-speech systems
H. Hon, A. Acero, X. Huang, J. Liu, and M. Plumpe. Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1, page 293-296. Seattle, WA, USA, (May 1998)
4 years ago by @m-toman
show all tags
(artificial
characteristics;allophones;automatic
criteria;phonetic
data
databases;synthesis
engine;acoustic
generation;Databases;Engines;Humans;Learning
generation;automatic
generation;design;optimization
intelligence);optimisation;speech
learning
learning;recording
methods;Speech
segments;probabilistic
sentences;scaleable
speech;trainable
synthesis
synthesis;Synthesizers;Training
synthesis;Whistler
systems;Character
systems;Loudspeakers;Optimization
text-to-speech
unit
unit;segmentation;spectral
units;synthetic
variations;speech
(artificialcharacteristics;allophones;automaticcriteria;phoneticdatadatabases;synthesisengine;acousticgeneration;Databases;Engines;Humans;Learninggeneration;automaticgeneration;design;optimizationintelligence);optimisation;speechlearninglearning;recordingmethods;Speechsegments;probabilisticsentences;scaleablespeech;trainablesynthesissynthesis;Synthesizers;Trainingsynthesis;Whistlersystems;Charactersystems;Loudspeakers;Optimizationtext-to-speechunitunit;segmentation;spectralunits;syntheticvariations;speech
(0)
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2Using change point detection to automate daily activity segmentation
S. Aminikhanghahi, and D. Cook. Proc. IEEE Int. Conf. Pervasive Computing and Communications Workshops (PerCom Workshops), page 262--267. (March 2017)
4 years ago by @annakrause
show all tags
(artificial
Activity
Change
Context,
Feature
Hidden
Markov
Real-time
Recognition,
Segmentation,
Smart
activity
algorithm,
algorithms,
automatic
automation,
aware
boundaries,
change
continuous
daily
datasets,
detection
detection,
extraction,
feature
home
homes,
human
image
intelligence),
learning
learning-based
machine
methods,
models,
object
point
real
recognition
recognition,
segmentation
segmentation,
smart
streaming,
systems,
time
transition
(artificialActivityChangeContext,FeatureHiddenMarkovReal-timeRecognition,Segmentation,Smartactivityalgorithm,algorithms,automaticautomation,awareboundaries,changecontinuousdailydatasets,detectiondetection,extraction,featurehomehomes,humanimageintelligence),learninglearning-basedmachinemethods,models,objectpointrealrecognitionrecognition,segmentationsegmentation,smartstreaming,systems,timetransition
(0)
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2A Flexible Architecture for Cognitive Sensing of Activities in Ambient Assisted Living
R. Zgheib, A. Nicola, M. Villani, E. Conchon, and R. Bastide. 2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), page 284-289. (June 2017)
5 years ago by @wita.rocha
show all tags
(artificial
Detection;Message
Middleware;Sensors
Ontology
Oriented
Sensing;Activity
Things;middleware;ontologies
acquisition;health
acquisition;heterogeneous
activity
annotation;cognitive
application;IoT-based
architecture;Ambient
architecture;human
assisted
care;inference
data
detection
detection;Semantics;Ontologies;Sensors;Method
devices;reasoning
fusion;cognitive
healthcare
intelligence);open
interoperability;data
interoperability;semantic
knowledge;expert
living;Cognitive
living;data
living;flexible
logic;cognitive
mechanism;expert
mechanisms;Internet
message
middleware;SeMoM;IoT
moments;Middleware;Computer
monitoring;semantic
network
of
ontology;CoSSN
ontology;formal
oriented
representation;semantic
semantic
sensing;ambient
sensor
sensors;sensor
systems;sensor
systems;technical
(artificialDetection;MessageMiddleware;SensorsOntologyOrientedSensing;ActivityThings;middleware;ontologiesacquisition;healthacquisition;heterogeneousactivityannotation;cognitiveapplication;IoT-basedarchitecture;Ambientarchitecture;humanassistedcare;inferencedatadetectiondetection;Semantics;Ontologies;Sensors;Methoddevices;reasoningfusion;cognitivehealthcareintelligence);openinteroperability;datainteroperability;semanticknowledge;expertliving;Cognitiveliving;dataliving;flexiblelogic;cognitivemechanism;expertmechanisms;Internetmessagemiddleware;SeMoM;IoTmoments;Middleware;Computermonitoring;semanticnetworkofontology;CoSSNontology;formalorientedrepresentation;semanticsemanticsensing;ambientsensorsensors;sensorsystems;sensorsystems;technical
(0)
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1Automatically Classifying Functional and Non-functional Requirements Using Supervised Machine Learning
Z. Kurtanović, and W. Maalej. 25th IEEE International Requirements Engineering Conference (RE), page 490-495. (2017)
6 years ago by @andiv
show all tags
(artificial
Data
Learning;Imbalanced
Machine
Vector
algorithm;security;meta-data;automatically
analysis;nonfunctional
analysis;text
attributes;lexical
challenge;performance
classification;quality
classification;support
classifier
data
data;pattern
extraction;Vegetation;Support
features;sampling
features;syntactical
intelligence);meta
learning
learning;RE17
machine
machines;Requirements;Classification;Machine
machines;systems
requirements
requirements;classifiers;Support
requirements;supervised
strategies;Training;Usability;Security;Feature
vector
(artificialDataLearning;ImbalancedMachineVectoralgorithm;security;meta-data;automaticallyanalysis;nonfunctionalanalysis;textattributes;lexicalchallenge;performanceclassification;qualityclassification;supportclassifierdatadata;patternextraction;Vegetation;Supportfeatures;samplingfeatures;syntacticalintelligence);metalearninglearning;RE17machinemachines;Requirements;Classification;Machinemachines;systemsrequirementsrequirements;classifiers;Supportrequirements;supervisedstrategies;Training;Usability;Security;Featurevector
(0)
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1The Temple University Hospital EEG corpus
A. Harati, S. Choi, M. Tabrizi, I. Obeid, J. Picone, and M. Jacobson. Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE, page 29-32. (December 2013)
9 years ago by @templehpc
show all tags
(artificial
Consortium;Temple
Data
EEG
Engineering
Hospital
University
algorithms;medical
assessments;Educational
classification
corpus;closed-loop
electroencephalography;learning
engineering;Radio
event
frequency;Testing
histories;open
information;physician
institutions;Electroencephalography;History;Medical
intelligence);medical
learning
occurring
prediction;machine
problem;patient
processing;NEDC;Neural
services;Neural
set
signal
testing;commonly
(artificialConsortium;TempleDataEEGEngineeringHospitalUniversityalgorithms;medicalassessments;Educationalclassificationcorpus;closed-loopelectroencephalography;learningengineering;Radioeventfrequency;Testinghistories;openinformation;physicianinstitutions;Electroencephalography;History;Medicalintelligence);medicallearningoccurringprediction;machineproblem;patientprocessing;NEDC;Neuralservices;Neuralsetsignaltesting;commonly
(0)
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2Learning 3-D Scene Structure from a Single Still Image
A. Saxena, M. Sun, and A. Ng. IEEE IJCV, page 1--8. (2007)
11 years ago by @guillem.palou
show all tags
(artificial
(computer
Markov
field,convex
graphics)3D
image,supervised
intelligence),random
learning
optimization
problem,convex
processes,Markov
processes,rendering
processing,image-based
programming,image
random
rendering,learning
scene
still
structure,single
(artificial(computerMarkovfield,convexgraphics)3Dimage,supervisedintelligence),randomlearningoptimizationproblem,convexprocesses,Markovprocesses,renderingprocessing,image-basedprogramming,imagerandomrendering,learningscenestillstructure,single
(0)
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2A Domain-Independent Ontology for Non-Functional Requirements
G. Dobson, S. Hall, and G. Kotonya. e-Business Engineering, 2007. ICEBE 2007. IEEE International Conference on, page 563 -566. (October 2007)
12 years ago by @fritzsolms
show all tags
(artificial
analysis;
domain-independent
intelligence);quality
of
ontology;nonfunctional
requirement;quality
service
service;systems
specification;formal
specification;ontologies
(artificialanalysis;domain-independentintelligence);qualityofontology;nonfunctionalrequirement;qualityserviceservice;systemsspecification;formalspecification;ontologies
(0)
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1Model Based Requirements Specification and Validation for Component Architectures
I. Cardei, M. Fonoage, and R. Shankar. Systems Conference, 2008 2nd Annual IEEE, page 1--8. (April 2008)
12 years ago by @fritzsolms
show all tags
(artificial
Language
Modeling
Prolog;
Unified
architectures;
automation;
based
component
component-based
cycle;
design
development
development;
diagrams;
driven
formal
functional
intelligence);
knowledge
language;
model
object-oriented
of
ontologies
ontology-based
programming;
quality
representation;
requirements
requirements;
ruled-based
service;
specification;
structure
system
system;
systems
systems;
validation;
{SysML}
{UML/SysML}
{XMI}
{analysisOWL};
(artificialLanguageModelingProlog;Unifiedarchitectures;automation;basedcomponentcomponent-basedcycle;designdevelopmentdevelopment;diagrams;drivenformalfunctionalintelligence);knowledgelanguage;modelobject-orientedofontologiesontology-basedprogramming;qualityrepresentation;requirementsrequirements;ruled-basedservice;specification;structuresystemsystem;systemssystems;validation;{SysML}{UML/SysML}{XMI}{analysisOWL};
(0)
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26Ontology learning for the Semantic Web
A. Maedche, and S. Staab. Intelligent Systems, IEEE, 16 (2): 72 - 79 (2001)
12 years ago by @kkrieger
show all tags
(artificial
Internet;
Semantic
Web;
XML
acquisition;
construction
data;
documents;
engineering
environments;
hypermedia
information
intelligence);
knowledge
languages;
learning
learning;
machine
markup
meta
metadata;
ontology
resources;
semiautomatic
tools;
understanding;
(artificialInternet;SemanticWeb;XMLacquisition;constructiondata;documents;engineeringenvironments;hypermediainformationintelligence);knowledgelanguages;learninglearning;machinemarkupmetametadata;ontologyresources;semiautomatictools;understanding;
(0)
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2Learning through Reinforcement for Repeated Power Control Game in Cognitive Radio Networks
P. Zhou, Y. Chang, and J. Copeland. IEEE Global Telecommunications Conference (GLOBECOM 2010), page 1 -6. (December 2010)
12 years ago by @chsivic
show all tags
(artificial
Bush-Mosteller
automaton;power
constraints;learning
control
control;
control;telecommunication
exchange;repeated
game;cognitive
game;repeated
information
intelligence);power
learning;cognitive
networks;interference
power
radio
radio;game
reinforcement
stochastic
strategy
theory;learning
(artificialBush-Mostellerautomaton;powerconstraints;learningcontrolcontrol;control;telecommunicationexchange;repeatedgame;cognitivegame;repeatedinformationintelligence);powerlearning;cognitivenetworks;interferencepowerradioradio;gamereinforcementstochasticstrategytheory;learning
(0)
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1Optical character recognition without segmentation
M. Ozdil, and F. Vural. Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on, 2, page 483--486. IEEE Computer Society, (1997)
14 years ago by @cocus
show all tags
(artificial
algebra,
archive,
attribute
character
control
cursive
extraction
extraction,
feature
group,
image
intelligence),
learning
matching
matching,
matrix
matrix,
measures,
mode,
off-line
optical
overlap
point
position
probability
probability,
process,
quality,
recognition,
script,
segmentation-free
set,
training
vectors,
vectorsalphabet,
word
(artificialalgebra,archive,attributecharactercontrolcursiveextractionextraction,featuregroup,imageintelligence),learningmatchingmatching,matrixmatrix,measures,mode,off-lineopticaloverlappointpositionprobabilityprobability,process,quality,recognition,script,segmentation-freeset,trainingvectors,vectorsalphabet,word
(0)
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1A method to design a neural pattern recognition system by using a genetic algorithm with partial fitness and a deterministic mutation
M. Fukumi, and N. Akamatsu. Systems, Man, and Cybernetics, 1996. IEEE International Conference on, 3, page 1989--1993. IEEE Computer Society, (1996)
14 years ago by @cocus
show all tags
(artificial
Fourier
algorithm,
algorithms,
coin
convergence,
deterministic
features
fitness,
genetic
intelligence),
invariant
learning
learning,
multilayer
multilayered
mutation,
network
network,
neural
partial
pattern
perceptrons,
problem,
recognition
recognitionFourier
rotated
rotation
spectra,
system,
transform,
transforms,
(artificialFourieralgorithm,algorithms,coinconvergence,deterministicfeaturesfitness,geneticintelligence),invariantlearninglearning,multilayermultilayeredmutation,networknetwork,neuralpartialpatternperceptrons,problem,recognitionrecognitionFourierrotatedrotationspectra,system,transform,transforms,
(0)
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2Designing a neural network by a genetic algorithm with partial fitness
M. Fukumi, S. Omatu, and Y. Nishikawa. Proceedings of the IEEE International joint Conference on Neural Networks 1995, IJCNN'95, 4, page 1834--1838. IEEE Computer Society, (November 1995)
14 years ago by @cocus
show all tags
(artificial
Fourier
algorithm,
algorithms,
chromosome,
coin
convergence
convergence,
crossover
feedforward
fitness,
genetic
intelligence),
learning
learning,
methods,
nets,
network,
neural
numerical
object
of
operation
operation,
partial
recognition
recognitionFourier
selection
spectra,
system,
transform
transform,
(artificialFourieralgorithm,algorithms,chromosome,coinconvergenceconvergence,crossoverfeedforwardfitness,geneticintelligence),learninglearning,methods,nets,network,neuralnumericalobjectofoperationoperation,partialrecognitionrecognitionFourierselectionspectra,system,transformtransform,
(0)
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1Development of Power System Stabilizer Using Modern Optimization Approach

3A General Two-Step Approach to Learning-Based Hashing

2Fast Point Cloud Registration using Semantic Segmentation

3Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality

1A Bibliometric Analysis of Different Maximum Power Point Tracking Methods for Photovoltaic Systems

2Robust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis

1Accurate visible speech synthesis based on concatenating variable length motion capture data

2Automatic generation of synthesis units for trainable text-to-speech systems

2Using change point detection to automate daily activity segmentation

2A Flexible Architecture for Cognitive Sensing of Activities in Ambient Assisted Living

1Automatically Classifying Functional and Non-functional Requirements Using Supervised Machine Learning

1The Temple University Hospital EEG corpus

2Learning 3-D Scene Structure from a Single Still Image

2A Domain-Independent Ontology for Non-Functional Requirements

1Model Based Requirements Specification and Validation for Component Architectures

26Ontology learning for the Semantic Web

2Learning through Reinforcement for Repeated Power Control Game in Cognitive Radio Networks

1Optical character recognition without segmentation

1A method to design a neural pattern recognition system by using a genetic algorithm with partial fitness and a deterministic mutation

2Designing a neural network by a genetic algorithm with partial fitness

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