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Dataset results
6 results for “New Methods of Computation”
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 19. Accuracy of classification using the three methods: KNN, SVM and our method for MCI subjects
<p>Whatever the patient condition, Normal, MCI or AD, our method has provided us with better results. Advocate Example precision for Normal Patients was found 96% as opposed to 88% for the SVM method and 84% for KNN. For MCI patients was found 88% as opposed to 80% for the SVM method and 72% for KNN. Also for AD patients were found 92% as opposed to 88% for the SVM method and 80% for KNN. Our classification method gave us the best results, finding overall accuracy of 92% as opposed to 84% for the SVM method and 78.66% for KNN. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 20. The accuracy of classification using the three methods, KNN, SVM and our method, for AD subjects
<p>Whatever the patient condition, Normal, MCI or AD, our method has provided us with better results. Advocate Example precision for Normal Patients was found 96% as opposed to 88% for the SVM method and 84% for KNN. For MCI patients was found 88% as opposed to 80% for the SVM method and 72% for KNN. Also for AD patients were found 92% as opposed to 88% for the SVM method and 80% for KNN. Our classification method gave us the best results, finding overall accuracy of 92% as opposed to 84% for the SVM method and 78.66% for KNN. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 18. Accuracy of classification using the three methods, KNN, SVM and our method, for normal subjects
<p>We present three figures representing the accuracy of the classification using the three methods, KNN, SVM and our method for normal, MCI and Alzheimer subjects. </p>
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 10. The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods
<p>The figure shows the calculation results of the four distances: Dice, PSNR, Hausdorff and MSS using the four methods (Caselles Chan & Vese, Lankton, our method), compared with the ground truth on three samples.</p> <p>The results of calculating the Hausdorff distances, Dice, PSNR, MSSD between the four methods (Caselles, Chan & Vese, Lanktom, and our method) and the ground truth about a subject Normal following the segmentation of the hippocampus</p> <p> </p>
Figures for "New method for computing post-seismic deformations in a realistic gravitational viscoelastic Earth model"
<p>Here are all the figures used in the paper "New method for computing post-seismic deformations in a realistic gravitational viscoelastic Earth model". </p>
A Computer Graphics Approach to Creating New Method for Generating 3D Gesture Animations in Bangla Sign Language via HamNoSys to SiGML Conversion
<p>To prepare the system, we employed 94 classes of data. In this dataset, there are 13 Bangla numerical data classes, 36 Bangla alphabet data classes, and 41 Bangla word data classes. Every class of data contains different data types like Hand Shape, Hand Orientation, Hand Movement, Notations, etc. These data were created in SiGML tags. Every class of data is unique and different from others. The system was prepared using BdSL. And BdSL is an uncommon and unique sign language, among others. That's why every class of data is unique and created by us. We search HamNoSys notations for Bangla alphabets, words, and numbers in English HamNoSys datasets (almost 6,000 data). However, we find only 20% of the data, which is quite similar to BdSL. We create 80% HamNoSys notation for BdSL and we modify the matching 20% notations. Then, we converted them into SiGML and made the data classes. This was a big challenge in our research. </p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.