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A review of emotion recognition methods based on keystroke dynamics and mouse movements
2013
2013 6th International Conference on Human System Interactions (HSI)
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The paper describes the approach based on using standard input devices, such as keyboard and mouse, as sources of data for the recognition of users' emotional states. ...
A number of systems applying this idea have been presented focusing on three categories of research problems, i.e. collecting and labeling training data, extracting features and training classifiers of ...
An application of mouse movements analysis in an intelligent tutoring system was presented in [23] , where only two mouse parameters were analyzed: control selection rate and mouse movement rate. ...
doi:10.1109/hsi.2013.6577879
dblp:conf/hsi/Kolakowska13
fatcat:xwcnwevb45dill473wfpkuicie
Confident AI
[article]
2022
arXiv
pre-print
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Each of the tenets is used to explore fundamental issues in current AI/ML systems and together provide an overall approach to Confident AI. ...
In this paper, we propose "Confident AI" as a means to designing Artificial Intelligence (AI) and Machine Learning (ML) systems with both algorithm and user confidence in model predictions and reported ...
This method is based on the need for a given error rate of the classifier on the accepted examples. ...
arXiv:2202.05957v1
fatcat:pvdrdcm7t5c7fdfs6dukb4evky
Edge-centric Optimization of Multi-modal ML-driven eHealth Applications
[article]
2022
arXiv
pre-print
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We demonstrate the practical use cases of smart eHealth applications in everyday settings, through a sensor-edge-cloud framework for an objective pain assessment case study. ...
Smart eHealth applications sense input data from multiple modalities, transmit the data to edge and/or cloud nodes, and process the data with compute intensive machine learning (ML) algorithms. ...
Considering the run-time variation of system dynamics, and making an optimal orchestration choice requires intelligent monitoring, analysis, and decision making. ...
arXiv:2208.02597v1
fatcat:5gb4hnmmofakrad4hsdgv2nsye
Development and Maintenance of Fuzzy Models in Financial Applications
[chapter]
2004
Soft Methodology and Random Information Systems
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Then we focus on the design and implementation of a fuzzy rule-based classifier (FRC). ...
The SRD is also used as a reference for testing and performing a five-fold cross-validation of the classifiers. Finally, we focus on the monitoring and maintenance of the FRC. ...
; Global Accuracy: Percentage of correct decisions, including making correct rate class decisions and making a correct decision to refer cases to human underwriters as a fraction of total input cases Using ...
doi:10.1007/978-3-540-44465-7_6
fatcat:lpwiw5qb3vcglexuavdywojgve
A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application
2016
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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The proposed system combining SBCSP, fuzzy integral, and PSO exhibits robust performance for off-line single-trial classification of MI and real-time control of a robotic arm using MI. ...
To aggregate outputs of ensemble members, this study uses fuzzy integral with particle swarm optimization (PSO), which can regulate subject-specific parameters for the assignment of optimal confidence ...
Jyh-Yeong Chang and all of the members of the Brain Research Center, National Chiao Tung University, Taiwan. ...
doi:10.1109/fuzz-ieee.2016.7738007
dblp:conf/fuzzIEEE/WuLCLLZCLL16
fatcat:licixaqzqbcrflrjklkfmr5s2e
Intelligent Stress Monitoring Assistant for First Responders
2021
IEEE Access
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for Smart Cities", and by the Department of National Defence's Innovation for Defence Excellence and Security (IDEaS) program, Canada. ...
ACKNOWLEDGMENTS This Project was partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) through grant "Biometric-enabled Identity management and Risk Assessment ...
Using the RESP sensor only for decision produces an accuracy of 84.84% which is higher than the original 82.85% when using data from multiple sensors. − The SMA is an integral part of the intelligent decision ...
doi:10.1109/access.2021.3057578
fatcat:43odlfwaerd5lnvpjrs5gt6gtu
Vision-based precise cash counting in ATM machines
2014
2014 IEEE International Conference on Mechatronics and Automation
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Traditional cash counting in ATM machines is based on the mechanical mechanism that is in touch with cash and may cause some damages from time to time. ...
To achieve precise counting, intelligent decision making algorithms are deployed to evaluate the counting performance so that the machine will not produce incorrect counts. ...
An intelligent decision making system should be used to decide which sensors have the most reliable count and whether there is an agreement on count number within all analysis methods. ...
doi:10.1109/icma.2014.6885752
fatcat:vy7rrt5m7rcpzcyk3j74dq3rmi
Advancements in Agricultural Technology: A Comprehensive Review of Machine Learning and Deep Learning Approaches for Crop Management and Disease Detection
2024
International Journal of Advanced Research in Science, Communication and Technology
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We look at techniques like ensemble modelling, which optimises crop selection and fertiliser consumption depending on soil properties, and advanced image processing methods, which use leaf images to diagnose ...
Our goal in conducting this study is to provide light on how agricultural technology is developing and how it affects contemporary farming methods. ...
Multiple regression algorithms are used to forecast crop yields based on several parameters that impact agricultural productivity. ...
doi:10.48175/ijarsct-15415
fatcat:b36betmzpfcp3i3lyyybaxmkvi
Integrated Learning-Based Algorithm for Predicting Graduates' Employment Mental Health
2022
Mathematical Problems in Engineering
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The Adaboost algorithm is then used to train the decision tree classifier for multiple iterations in order to improve its classification efficiency, and a mental health prognosis model is constructed. ...
The method first extracts the features of mental health test data, and after data cleaning and normalization, the data are mined and analyzed using a decision tree classifier. ...
C4.5 is an enhanced decision tree method proposed by J Ross Quinlan on the basis of ID3, which is based on the theory of information and classifies data according to information entropy and information ...
doi:10.1155/2022/5761815
fatcat:wxyvzxi475hadnmj7yp2pd3dfu
Fuzzy Integral With Particle Swarm Optimization for a Motor-Imagery-Based Brain–Computer Interface
2017
IEEE transactions on fuzzy systems
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One robust algorithm that can successfully 10 cope with the individual differences in MI-related rhythmic pat-11 terns is to create diverse ensemble classifiers using the subband 12 common spatial pattern ...
To aggregate outputs 13 of ensemble members, this study uses fuzzy integral with parti-14 cle swarm optimization (PSO), which can regulate subject-specific 15 parameters for the assignment of optimal confidence ...
Fur-387 thermore, PSO is used to update the confidence of the employed 388 classifiers. ...
doi:10.1109/tfuzz.2016.2598362
fatcat:2732zulievgjlavnsswhlqe2uy
Human–Machine Interaction Issues in Quality Control Based on Online Image Classification
2009
IEEE transactions on systems, man and cybernetics. Part A. Systems and humans
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The first one is a clustering-based classifier based on vector quantization [19] and exploits the idea of a vigilance parameter motivated in [8] for forming an incremental and evolving variant. ...
. 4) In this case, the system is updated using its own decision output and taking into account the effect on class imbalance-for example, a maximal imbalance of onethird and two-third is allowed in 0/1 ...
His current research interests include the theory and application of intelligent systems that adapt their learning strategies in response to experience and the interface between humans and adaptive intelligent ...
doi:10.1109/tsmca.2009.2025025
fatcat:ntxfhv74ovc37orwqmyxohn7rm
Confidence-based cue integration for visual place recognition
2007
2007 IEEE/RSJ International Conference on Intelligent Robots and Systems
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We propose a new method for measuring the confidence level of the classification output, based on the distance of a test image and the average distance of training vectors. ...
We focus on the visual place recognition problem for topological localization, and we take an SVM approach. ...
While using multiple visual cues improves both classification accuracy and relative confidence, it is computationally expensive (more features to compute and classify), which is undesirable for an autonomous ...
doi:10.1109/iros.2007.4399493
dblp:conf/iros/PronobisC07
fatcat:yffa7iv3wvcqjkcwvjxepcsb5e
Secure Decision-Making Approach to Improve Knowledge Management Based on Online Samples
2018
International Journal of Intelligent Engineering and Systems
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In this paper, we propose a decision process that has decision-making process and game theory along with a coding technique to acquire confident knowledge from the available data set in a secure way. ...
Our mechanism is simulated and the results show a considerable improvement in the decision-making process. ...
The data with the least uncertainty value is taken for decision-making. For decision-making, a Naïve Bayes classifier is used which is used to classify data sets that have huge variation [38] . ...
doi:10.22266/ijies2018.0228.06
fatcat:m4ejb7p5hjga3cwj2xfv2tf2lm
Architecture of Generalized Network Service Anomaly and Fault Thresholds
[chapter]
2001
Lecture Notes in Computer Science
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GAFT monitors many network parameters simultaneously, analyzes statistically their performance, combines intelligently the individual decisions and derives an integrated result of compliance for each service ...
neural network classifier, 12 monitored network performance parameter decisions into a unified result. ...
Architecture of Generalized Network Service Anomaly and Fault Thresholds ...
doi:10.1007/3-540-45508-6_21
fatcat:hiokbacg7jeu3lggjxkmuzqve4
BLOOD CELL IDENTIFICATION USING A SIMPLE NEURAL NETWORK
2008
International Journal of Neural Systems
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The EmNN has emotional weights and two emotional parameters; anxiety and confidence, which are updated during learning. ...
This paper presents an emotional neural network (EmNN) that is based on the emotional back propagation (EmBP) learning algorithm. ...
The emotional factors are updated during learning, and the final emotional weights are used together with the network's conventional weights to make decisions. ...
doi:10.1142/s0129065708001713
pmid:18991367
fatcat:j25j5xqrjnc6taqbfsngukzlvm
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