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| 1 | Computational Decision Support System for ADHD Identification显示文摘Attention deficit/hyperactivity disorder(ADHD)is a common disorder among children.ADHD often prevails into adulthood,unless proper treatments are facilitated to engage self-regulatory systems.Thus,there is a need for effective and reliable mechanisms for the early identification of ADHD.This paper presents a decision support system for the ADHD identification process.The proposed system uses both functional magnetic resonance imaging(fMRI)data and eye movement data.The classification processes contain enhanced pipelines,and consist of pre-processing,feature extraction,and feature selection mechanisms.fMRI data are processed by extracting seed-based correlation features in default mode network(DMN)and eye movement data using aggregated features of fixations and saccades.For the classification using eye movement data,an ensemble model is obtained with 81%overall accuracy.For the fMRI classification,a convolutional neural network(CNN)is used with 82%accuracy for the ADHD identification.Both ensemble models are proved for overfitting avoidance. | Senuri De Silva Sanuwani Dayarathna Gangani Ariyarathne Dulani Meedeniya Sampath Jayarathna Anne M.P.Michalek | 2021 | International Journal of Automation and computing2021，18，2： | 2 |
| 2 | Identification of inflicted traumatic brain injury in well-appearing infants using serum and cerebrospinalmarkers:A possible screening tool显示文摘 | Berger R P Dulani T Adelson P D | 2006 | Pediatrics2006，117，2： | 1 |
| 3 | Identification of inflicted traumatic brain injury in well-appearing infants using serum and cerebrospinal markers:a possible screening tool显示文摘 | Berger RP Dulani T Adelson PD | 2006 | Pediatrics2006，117，2： | 1 |
| 4 | Identification of inflicted traumatic brain injury in well - appearing infants using serum and cerebrospinal markers: A possible screening tool显示文摘 | Berger RP Dulani T Adelson PD | 2006 | Pediatrics2006，117，2： | 1 |
| 5 | A case of syntactical learning and judgement:how conscious and how abstract?显示文摘 | Dulany D E R A Carlson and G I Dewey | 1984 | Journal of Experimental Psychology:General1984，，54： | 1 |
| 6 | Identification of inflicted traumatic brain injury in well-appearing infants using serum and cerebrospinal markers:a possible screen- ing tool显示文摘 | Berger R P Dulani T Adelson P D | 2006 | Pediatrics2006，117，2： | 1 |
| 7 | Identification of inflicted traumatic brain injury in well-appearing infants using serum and ccrebrospinal markers: a possible screening tool显示文摘 | BERGER R P DULANI T ADELSON P D | 2006 | Pediatrics2006，117，2： | 1 |
| 8 | Identification ofinflicted traumatic brain injury in well-appearing infants usingserum and cerebrospinal markers: a possible screening tool显示文摘 | BERGER RP DULANI T ADELSON PD | 2006 | Pediatrics2006，117，： | 1 |
| 9 | Glaucoma Detection with Retinal Fundus Images Using Segmentation and Classification显示文摘Glaucoma is a prevalent cause of blindness worldwide.If not treated promptly,it can cause vision and quality of life to deteriorate.According to statistics,glaucoma affects approximately 65 million individuals globally.Fundus image segmentation depends on the optic disc(OD)and optic cup(OC).This paper proposes a computational model to segment and classify retinal fundus images for glaucoma detection.Different data augmentation techniques were applied to prevent overfitting while employing several data pre-processing approaches to improve the image quality and achieve high accuracy.The segmentation models are based on an attention U-Net with three separate convolutional neural networks(CNNs)backbones:Inception-v3,visual geometry group 19(VGG19),and residual neural network 50(ResNet50).The classification models also employ a modified version of the above three CNN architectures.Using the RIM-ONE dataset,the attention U-Net with the ResNet50 model as the encoder backbone,achieved the best accuracy of 99.58%in segmenting OD.The Inception-v3 model had the highest accuracy of 98.79%for glaucoma classification among the evaluated segmentation,followed by the modified classification architectures. | Thisara Shyamalee Dulani Meedeniya | 2022 | Machine Intelligence Research2022，19，6： | 1 |
| 10 | Identification of inflicted traumatic brain injury in well - appearing infants using serum and cerebrospinal markers : A possible screening tool 显示文摘 | Berger RP Dulani T Adelson D | 2006 | Pediatrics2006，117，2： | 1 |
| 11 | Identification of inflicted traumatic brain injury in well-appearing infants using serum and cerebrospinal markers:a possible screening tool显示文摘 | BERGER R P DULANI T ADELSON P D | 2006 | Pediatrics2006，117，2： | 1 |
| 12 | Identification of inflicted traumatic brain injury in well-appearing infants using serum and cerebrospinal markers a possible screening tool显示文摘 | Berger RP Dulani T Adelson PD | 2006 | Pediatrics2006，117，20： | 1 |
| 13 | Identification of inflicted traumatic brain injury in well-appearing infants using serum and cerebrospinal markers:a possible screening tool显示文摘 | Berger RP Dulani T Adelson PD | | 0，，02： | 1 |
| 14 | Identification of inflicted traumatic brain injury in well - appearing infants using serum and cerebrospinal markers : A possible screening tool 显示文摘 | Berger RP Dulani T Adelson PD | 2006 | Pediatrics2006，117，2： | 1 |
| 15 | Identification of inflicted traumatic brain injury in weU-appearlng infants using serum and cerebrospinal markers: a possible screening tool 显示文摘 | Berger RP Dulani T Adelson PD | 2006 | Pediatrics2006，117，2： | 1 |
| 16 | Identification of in- flicted traumatic brain injury in well-appearing infants using serum and eerebrospinalmarkers: a possible screening tool 显示文摘 | Berger RP Dulani T Adelson PD | 2006 | Pediatrics2006，117，2： | 1 |
| 17 | Identification of inflicted trau-matic brain injury in well - appearing infants using serum and cerebro- spinal markers: A possible screening tool 显示文摘 | Berger RP Dulani T Adelson PD | 2006 | Pediatrics2006，117，2： | 1 |
| 18 | On Consciousness in Syntactic Learning and Judgment: A Reply to Reber, Allen, and Regan显示文摘 | Dulany D E Carlson R A Dewey G I | 1985 | J Exp Psyehol:General1985，114，1： | 1 |
| 19 | Identification of inflicted traumatic brain injury in well-appearing infants using serum and cerebrospinal markers: a possible screening tool 显示文摘 | Berger RP Dulani T Adelson PD | 2006 | Pediatrics2006，117，2： | 1 |
| 20 | Integration of Facial Thermography in EEG-based Classification of ASD显示文摘Autism spectrum disorder(ASD)is a neurodevelopmental disorder affecting social,communicative,and repetitive behavior.The phenotypic heterogeneity of ASD makes timely and accurate diagnosis challenging,requiring highly trained clinical practitioners.The development of automated approaches to ASD classification,based on integrated psychophysiological measures,may one day help expedite the diagnostic process.This paper provides a novel contribution for classifing ASD using both thermographic and EEG data.The methodology used in this study extracts a variety of feature sets and evaluates the possibility of using several learning models.Mean,standard deviation,and entropy values of the EEG signals and mean temperature values of regions of interest(ROIs)in facial thermographic images were extracted as features.Feature selection is performed to filter less informative features based on correlation.The classification process utilizes Naive Bayes,random forest,logistic regression,and multi-layer perceptron algorithms.The integration of EEG and thermographic features have achieved an accuracy of 94%with both logistic regression and multi-layer perceptron classifiers.The results have shown that the classification accuracies of most of the learning models have increased after integrating facial thermographic data with EEG. | Dilantha Haputhanthri Gunavaran Brihadiswaran Sahan Gunathilaka Dulani Meedeniya Sampath Jayarathna Mark Jaime Christopher Harshaw | 2020 | International Journal of Automation and computing2020，17，6： | 1 |
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