Sunitha Basodi
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View article: Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility
Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility Open
Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC (The Collaborative Informatics and Neuroimaging Suite Toolkit fo…
View article: Intelligent gradient amplification for deep neural networks
Intelligent gradient amplification for deep neural networks Open
Deep learning models offer superior performance compared to other machine learning techniques for a variety of tasks and domains, but pose their own challenges. In particular, deep learning models require larger training times as the depth…
View article: Decentralized Mixed Effects Modeling in COINSTAC
Decentralized Mixed Effects Modeling in COINSTAC Open
Performing group analysis on magnetic resonance imaging (MRI) data with linear mixed-effects (LME) models is challenging due to its large dimensionality and inherent multi-level covariance structure. In addition, as large-scale collaborati…
View article: Enhancing Collaborative Neuroimaging Research: Introducing COINSTAC Vaults for Federated Analysis and Reproducibility
Enhancing Collaborative Neuroimaging Research: Introducing COINSTAC Vaults for Federated Analysis and Reproducibility Open
Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC is a platform that successfully tackles these challenges through…
View article: Advances in Deep Learning through Gradient Amplification and Applications
Advances in Deep Learning through Gradient Amplification and Applications Open
Deep neural networks currently play a prominent role in solving problems across a wide variety of disciplines. Improving performance of deep learning models and reducing their training times are some of the ongoing challenges. Increasing t…
View article: Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains
Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains Open
With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatial…
View article: ENIGMA + COINSTAC: Improving Findability, Accessibility, Interoperability, and Re-usability
ENIGMA + COINSTAC: Improving Findability, Accessibility, Interoperability, and Re-usability Open
The FAIR principles, as applied to clinical and neuroimaging data, reflect the goal of making research products F indable, A ccessible, I nteroperable, and R eusable. The use of the Collaborative Informatics and Neuroimaging Suite Toolkit …
View article: A survey on algorithms for intelligent computing and smart city applications
A survey on algorithms for intelligent computing and smart city applications Open
With the rapid development of human society, the urbanization of the world’s population is also progressing rapidly. Urbanization has brought many challenges and problems to the development of cities. For example, the urban population is u…
View article: Decentralized Brain Age Estimation using MRI Data
Decentralized Brain Age Estimation using MRI Data Open
Recent studies have demonstrated that neuroimaging data can be used to predict brain age, as it captures information about the neuroanatomical and functional changes the brain undergoes during development and the aging process. However, re…
View article: Analysis of heterogeneous genomic samples using image normalization and machine learning
Analysis of heterogeneous genomic samples using image normalization and machine learning Open
Background Analysis of heterogeneous populations such as viral quasispecies is one of the most challenging bioinformatics problems. Although machine learning models are becoming to be widely employed for analysis of sequence data from such…
View article: Gradient amplification: An efficient way to train deep neural networks
Gradient amplification: An efficient way to train deep neural networks Open
Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural networks. There are several approaches proposed to address these challenges, one of which is to increase the depth of the…
View article: Multivariate time series dataset for space weather data analytics
Multivariate time series dataset for space weather data analytics Open
We introduce and make openly accessible a comprehensive, multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in Spaceweather HMI Active Region Patch (SHARP) series. Our dataset also includes a cros…
View article: Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic Algorithm
Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic Algorithm Open
Convolutional Neural Networks (CNN) have gained great success in many artificial intelligence tasks. However, finding a good set of hyperparameters for a CNN remains a challenging task. It usually takes an expert with deep knowledge, and t…
View article: Gradient Amplification: An efficient way to train deep neural networks
Gradient Amplification: An efficient way to train deep neural networks Open
Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural networks. There are several approaches proposed to address these challenges one of which is to increase the depth of the …
View article: Data integrity attack detection in smart grid: a deep learning approach
Data integrity attack detection in smart grid: a deep learning approach Open
Cybersecurity in smart grids plays a crucial role in determining its reliable functioning and availability. Data integrity attacks at the physical layer of smart grids are mainly addressed in this paper. State vector estimation (SVE) metho…
View article: partition1_instances.tar.gz
partition1_instances.tar.gz Open
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View article: partition4_instances.tar.gz
partition4_instances.tar.gz Open
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View article: partition5_instances.tar.gz
partition5_instances.tar.gz Open
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View article: addenda.tar.gz
addenda.tar.gz Open
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View article: partition3_instances.tar.gz
partition3_instances.tar.gz Open
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View article: partition2_instances.tar.gz
partition2_instances.tar.gz Open
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View article: Analysis of Heterogeneous Genomic Samples Using Image Normalization and Machine Learning
Analysis of Heterogeneous Genomic Samples Using Image Normalization and Machine Learning Open
Background Analysis of heterogeneous populations such as viral quasispecies is one of the most challenging bioinformatics problems. Although machine learning models are becoming to be widely employed for the analysis of sequencing data ass…
View article: SWAN-SF
SWAN-SF Open
We introduce and make openly accessible a comprehensive, multivariate time series (MVTS) data extracted from solar photospheric vector magnetograms in Spaceweather HMI Active Region Patch (SHARP) series.