61 skills found · Page 1 of 3
mathworks / Simscape Battery Electric Vehicle ModelA Battery Electric Vehicle (BEV) model in Simscape for longitudinal powertrain analysis
drizopoulos / JMbayes2Extended Joint Models for Longitudinal and Survival Data
chl8856 / Dynamic DeepHitDynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data
ASKurz / Applied Longitudinal Data Analysis With Brms And The TidyverseTranslating ML into Bayes, one line at a time
ginettelafit / PowerAnalysisILShiny app and R package to perform a power analysis to select the number of participants in intensive longitudinal studies
AliAmini93 / MRI MS Plaques SegmentationA 3D Attention U-Net model is developed, aimed at segmenting and tracking Multiple Sclerosis lesions in MRI images.
ouyangjiahong / Longitudinal Neighbourhood EmbeddingSelf-supervised Longitudinal Neighbourhood Embedding (LNE), MICCAI2021 & Speicial Issue on Medical Image Analysis
sydeaka / Neural Networks LongitudinalDemo use of Neural Networks for Longitudinal Data Analysis
OrysyaStus / UCSD Data Science And EngineeringIn the Data Science and Engineering program, engineering professionals combine the skills of software programmer, database manager, and statistician to create mathematical models of the data, identify trends/deviations, then present them in effective visual ways that can be understood by others. Data scientists unlock new sources of economic value, provide fresh insights into science, and inform decision makers by analyzing large, diverse, complex, longitudinal, and distributed data sets generated from instruments, sensors, internet transactions, email, video, and other digital sources. Students entering the MAS program for a degree in Data Science and Engineering will undertake courses in programming, analysis, and applications management and visualization. This program requires three foundational courses, four core courses, and two electives totaling thirty-four units, plus a capstone team project course of four units, for a total of thirty-eight units.
networkl / NetworklNetworkL is a Python package which extends the scope of the NetworkX package to (L)arge time-varying graphs. It supports the manipulation and efficient longitudinal analysis of complex networks
HMIS / LSASampleCodeLongitudinal System Analysis (LSA) Sample Code and Documentation
bionlplab / Longitudinal Transformer For Survival Analysis[npj Digital Medicine] "Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling" by Gregory Holste, Mingquan Lin, Ruiwen Zhou, Fei Wang, Lei Liu, Qi Yan, Sarah H Van Tassel, Kyle Kovacs, Emily Y Chew, Zhiyong Lu, Zhangyang Wang, & Yifan Peng
deroux / Longitudinal Analysis CowrieLongitudinal Analysis of SSH Cowrie Honeypot Logs
jlugomar / Longitudinal Microbiome Analysis PublicNo description available
RRShieldsCutler / SplinectomeRR package version of the splinectomy longitudinal statistical analysis tools
kuncorotriandonomukti / Lane Detection Using Edge DetectionSelf-driving or Autonomous driving, Advanced Driving Assistance System (ADAS) is one of the most popular topics in research related to vehicle safety. One of the most useful technologies in autonomous driving is lane detection that uses longitudinal marks (e.g. straight and dashed lines) as a reference to keep the vehicle running on lane. Various operators on edge detection are proposed to obtain the best accuracy of lane detection. However, the movement of the line marks between frames will vary depending on the speed of the vehicle. If the system fails to detect the line marker at high speed, will cause the autonomous driving system make a wrong decision. In this final task, we will perform a comparison analysis of Canny, Laplacian of Gaussian (Marr-Hildreth) and Kirsch's ability on edge detection methods to detect dashed line marks at varying speeds. The results showed that all operators succeeded in achieving the minimum detection target of 80% and obtained the best operators for line marker detection is Kirsch with the highest percentage at all speeds 30, 50 and 80 km / h.
raphael-group / CalderCALDER (Cancer Analysis of Longitudinal Data through Evolutionary Reconstruction) reconstructs evolutionary trees from longitudinal bulk DNA sequencing data
infomindgithub / Machine Learning Engineer Nanodegree Capstone PROJECT ANALYSIS*****PROJECT SPECIFICATION: Machine Learning Capstone Analysis Project***** This capstone project involves machine learning modeling and analysis of clinical, demographic, and brain related derived anatomic measures from human MRI (magnetic resonance imaging) tests (http://www.oasis-brains.org/). The objectives of these measurements are to diagnose the level of Dementia in the individuals and the probability that these individuals may have Alzheimer's Disease (AD). In published studies, Machine Learning has been applied to Alzheimer’s/Dementia identification from MRI scans and related data in the academic papers/theses in References 10 and 11 listed in the References Section below. Recently, a close relative of mine had to undergo a sequence of MRI tests for cognition difficulties.The motivation for choosing this topic for the Capstone project arose from the desire to understand and analyze potential for Dementia and AD from MRI related data. Cognitive testing, clinical assessments and demographic data related to these MRI tests are used in this project. This Capstone project does not use the MRI "imaging" data and does not focus on AD, focusses only on Dementia. *****Conclusions, Justification, and Reflections***** [Student adequately summarizes the end-to-end problem solution and discusses one or two particular aspects of the project they found interesting or difficult.] The formulation of OASIS data (Ref 1 and 2) in terms of a dementia classification problem based on demographic and clinical data only (and without directly using the MRI image data), is a simplification that has major advantages and appeal. This means the trained model can classify whether an individual has dementia or not with about 87% accuracy, without having to wait for radiological interpretation of MRI scans. This can provide an early alert for intervention and initiation of treatment for those with onset of dementia. The assumption that the combined cross-sectional and longitudinal datasets would lead to dementia label classification of acceptable accuracy came out to be true. The method required careful data cleaning and data preparation work, converting it to a binary classification problem, as outlined in this notebook. At the outset it was not clear which algorithm(s) would be more appropriate for the binary and multi-label classification problem. The approach of spot checking the algorithms early for accuracy led to the determination of a smaller set of algorithms with higher accuracy (e.g. Gadient Boosting and Random Forest) for a deeper dive examination, e.g. use of a k-fold cross-validation approach in classifying the CDR label. The neural network benchmark model accuracy of 78% for binary classification was exceeded by the classification accuracy of the main output of this study, the trained Gradient Boosting and Random Forest classification models. This builds confidence in the latter model for further training with new data and further classification use for new patients.
philips-labs / Demo Clustering Longitudinal DataSupplementary materials for the manuscript "Clustering of longitudinal data: A tutorial on a variety of approaches" by N. G. P. Den Teuling, S.C. Pauws, and E.R. van den Heuvel (2026)
isaacgerg / Ubiome Longitudinal AnalysisGenerates a set of plots showing ubiome data over time.
