Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data

Classification of target from a mixture of multiple target information is quite challenging. In This paper we have used supervised Machine learning algorithm namely Linear Regression to classify the received data which is a mixture of target-return with the noise and clutter. Target state is estimated from the classified data using Kalman filter. Linear Kalman filter with constant velocity model is used in this paper. Minimum Mean Square Error (MMSE) analysis is used to measure the performance of the estimated track at various Signal to Noise Ratio (SNR) levels. The results state that the error is high for Low SNR, for High SNR the error is Low

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Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

JMIR Medical Informatics ◽

10.2196/20995 ◽

2020 ◽

Vol 8 (9) ◽

pp. e20995

Author(s):

Debbie Rankin ◽

Michaela Black ◽

Bronac Flanagan ◽

Catherine F Hughes ◽

Adrian Moore ◽

...

Keyword(s):

Machine Learning ◽

Cognitive Function ◽

Cognitive Performance ◽

Cognitive Dysfunction ◽

Service Providers ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Score Range

Background Machine learning techniques, specifically classification algorithms, may be effective to help understand key health, nutritional, and environmental factors associated with cognitive function in aging populations. Objective This study aims to use classification techniques to identify the key patient predictors that are considered most important in the classification of poorer cognitive performance, which is an early risk factor for dementia. Methods Data were used from the Trinity-Ulster and Department of Agriculture study, which included detailed information on sociodemographic, clinical, biochemical, nutritional, and lifestyle factors in 5186 older adults recruited from the Republic of Ireland and Northern Ireland, a proportion of whom (987/5186, 19.03%) were followed up 5-7 years later for reassessment. Cognitive function at both time points was assessed using a battery of tests, including the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), with a score <70 classed as poorer cognitive performance. This study trained 3 classifiers—decision trees, Naïve Bayes, and random forests—to classify the RBANS score and to identify key health, nutritional, and environmental predictors of cognitive performance and cognitive decline over the follow-up period. It assessed their performance, taking note of the variables that were deemed important for the optimized classifiers for their computational diagnostics. Results In the classification of a low RBANS score (<70), our models performed well (F1 score range 0.73-0.93), all highlighting the individual’s score from the Timed Up and Go (TUG) test, the age at which the participant stopped education, and whether or not the participant’s family reported memory concerns to be of key importance. The classification models performed well in classifying a greater rate of decline in the RBANS score (F1 score range 0.66-0.85), also indicating the TUG score to be of key importance, followed by blood indicators: plasma homocysteine, vitamin B6 biomarker (plasma pyridoxal-5-phosphate), and glycated hemoglobin. Conclusions The results suggest that it may be possible for a health care professional to make an initial evaluation, with a high level of confidence, of the potential for cognitive dysfunction using only a few short, noninvasive questions, thus providing a quick, efficient, and noninvasive way to help them decide whether or not a patient requires a full cognitive evaluation. This approach has the potential benefits of making time and cost savings for health service providers and avoiding stress created through unnecessary cognitive assessments in low-risk patients.

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Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques

APOPTOSIS ◽

10.1007/s10495-018-1454-y ◽

2018 ◽

Vol 23 (5-6) ◽

pp. 290-298 ◽

Cited By ~ 4

Author(s):

Jingwen Feng ◽

Tong Feng ◽

Chengwen Yang ◽

Wei Wang ◽

Yu Sa ◽

...

Keyword(s):

Machine Learning ◽

Flow Cytometry ◽

Cell Apoptosis ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Imaging Flow Cytometry ◽

Learning Techniques ◽

Diffraction Imaging ◽

Free Classification

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Classification of Chaotic Behaviors in Jerky Dynamical Systems

Complex Systems ◽

10.25088/complexsystems.30.1.93 ◽

2021 ◽

Vol 30 (1) ◽

pp. 93-110

Author(s):

Tianyi Wang ◽

Keyword(s):

Machine Learning ◽

Chaotic Systems ◽

Chaotic Behavior ◽

Learning Algorithm ◽

Model Systems ◽

Supervised Machine Learning ◽

Learning Program ◽

New Methods ◽

Over Time

Differential equations are widely used to model systems that change over time, some of which exhibit chaotic behaviors. This paper proposes two new methods to classify these behaviors that are utilized by a supervised machine learning algorithm. Dissipative chaotic systems, in contrast to conservative chaotic systems, seem to follow a certain visual pattern. Also, the machine learning program written in the Wolfram Language is utilized to classify chaotic behavior with an accuracy around 99.1±1.1%.

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