A Differential Geometry-based Machine Learning Algorithm for the Brain Age Problem

IntroductionRecent studies have shown that it is important to understand the brain mechanism specifically by focusing on the common and unique functional connectivity in each disorder including depression.ObjectivesTo specify the biomarker of major depressive disorder (MDD), we applied the sparse machine learning algorithm to classify several types of affective disorders using the resting state fMRI data collected in multiple sites, and this study shows the results of depression as a part of those results.AimsThe aim of this study is to understand some specific pattern of functional connectivity in MDD, which would support diagnosis of depression and development of focused and personalized treatments in the future.MethodsThe neuroimaging data from patients with major depressive disorder (MDD, n = 100) and healthy control adults (HC: n = 100) from multiple sites were used for the training dataset. A completely separate dataset (n = 16) was kept aside for testing. After all preprocessing of fMRI data, based on one hundred and forty anatomical region of interests (ROIs), 9730 functional connectivities during resting states were prepared as the input of the sparse machine-learning algorithm.ResultsAs results, 20 functional connectivities were selected with the classification performance of Accuracy: 83.0% (Sensitivity: 81.0%, Specificity: 85.0%). The test data, which was completely separate from the training data, showed the performance accuracy of 83.3%.ConclusionsThe selected functional connectivities based on the sparse machine learning algorithm included the brain regions which have been associated with depression.Disclosure of interestThe authors have not supplied their declaration of competing interest.

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What’s in the brain that ink may character ….

Scientific Study of Literature ◽

10.1075/ssol.7.1.02jac ◽

2017 ◽

Vol 7 (1) ◽

pp. 4-51 ◽

Cited By ~ 11

Author(s):

Arthur M. Jacobs ◽

Sarah Schuster ◽

Shuwei Xue ◽

Jana Lüdtke

Keyword(s):

Machine Learning ◽

Narrative Analysis ◽

Learning Algorithm ◽

Empirical Studies ◽

Analysis Data ◽

Machine Learning Algorithm ◽

Semantic Association ◽

Theoretical Paper ◽

The Many ◽

The Brain

Abstract In this theoretical paper, we would like to pave the ground for future empirical studies in Neurocognitive Poetics by describing relevant properties of Shakespeare’s 154 sonnets extracted via Quantitative Narrative Analysis. In the first two parts, we quantify aspects of the sonnets’ cognitive and affective-aesthetic features, as well as indices of their thematic richness, symbolic imagery, and semantic association potential. In the final part, we first demonstrate how the results of these quantitative narrative analyses can be used for generating testable predictions for empirical studies of literature. Second, we feed the quantitative narrative analysis data into a machine learning algorithm which successfully classifies the 154 sonnets into two main categories, i.e. the young man and dark lady poems. This shows how quantitative narrative analysis data can be combined with computational modeling for identifying those of the many quantifiable sonnet features that may play a key role in their reception.

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Analysis of Brain Tumor Disease Detection Using Convolutional Neural Network

10.3233/apc210246 ◽

2021 ◽

Author(s):

Yogesh Deshmukh ◽

Samiksha Dahe ◽

Tanmayeeta Belote ◽

Aishwarya Gawali ◽

Sunnykumar Choudhary

Keyword(s):

Neural Network ◽

Machine Learning ◽

Brain Tumor ◽

Human Brain ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Vital Role ◽

Machine Learning Algorithm ◽

Different Types ◽

The Brain

Brain Tumor detection using Convolutional Neural Network (CNN) is used to discover and classify the types of Tumor. Over a amount of years, many researchers are researched and planned ways throughout this area. We’ve proposed a technique that’s capable of detecting and classifying different types of tumor. For detecting and classifying tumor we have used MRI because MRI images gives the complete structure of the human brain, without any operation it scans the human brain and this helps in processing of image for the detection of the Tumor. The prediction of tumor by human from the MRI images leads to misclassification. This motivates us to construct the algorithm for detection of the brain tumor. Machine learning helps and plays a vital role in detecting tumor. In this paper, we tend to use one among the machine learning algorithm i.e. Convolutional neural network (CNN), as CNNs are powerful in image processing and with the help of CNN and MRI images we designed a framework for detection of the brain tumor and classifying its Different types.

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Multi-Class Brain Age Discrimination Using Machine Learning Algorithm

2019 International Conference on Machine Learning and Cybernetics (ICMLC) ◽

10.1109/icmlc48188.2019.8949317 ◽

2019 ◽

Author(s):

Hsiao-Chi Li ◽

Chang-Yu Cheng ◽

Chia-Hui Chou ◽

Chien-Chang Hsu ◽

Meng-Lin Chang ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Age Discrimination ◽

Machine Learning Algorithm ◽

Brain Age

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Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

10.1055/s-0038-1660728 ◽

2018 ◽

Author(s):

C.H.B. van Niftrik ◽

F. van der Wouden ◽

V. Staartjes ◽

J. Fierstra ◽

M. Stienen ◽

...

Keyword(s):

Machine Learning ◽

Brain Tumor ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Tumor Surgery ◽

Early Complications ◽

Brain Tumor Surgery

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Feature extraction and prediction of Dengue Outbreaks

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206544 ◽

2020 ◽

pp. 216-222

Author(s):

Kunal Parikh ◽

Tanvi Makadia ◽

Harshil Patel

Keyword(s):

Public Health ◽

Machine Learning ◽

Developing Countries ◽

Feature Extraction ◽

Predictive Analytics ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Health Concerns ◽

The World ◽

Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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