Logistic Regression for Employability Prediction

Celine*, S.; Devi, M. Savitha

doi:10.35940/ijitee.c8170.019320

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…A binary LR is a machine learning approach for dataset classification into only two classes. The best fitting model uses the maximum likelihood method to design the best fitting function to maximize the probability of classes, which helps identify the data into correct vision (Celine et al, 2020). The KNN is a non‐parametric algorithm that is used for classification and as well as for regression purposes.…”

Section: Methodsmentioning

confidence: 99%

Machine learning for post‐traumatic stress disorder identification utilizing resting‐state functional magnetic resonance imaging

Saba

Rehman

Shahzad

et al. 2022

Microscopy Res & Technique

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Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains.The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms. HighlightsMachine learning techniques are applied to identify cancerous patients utilizing resting-state fMRI data. Among several MLAs, the SVM-RBF performed a better prediction rate at higher reliability than other competitive models.

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Section: Methodsmentioning

confidence: 99%

Machine learning for post‐traumatic stress disorder identification utilizing resting‐state functional magnetic resonance imaging

Saba

Rehman

Shahzad

et al. 2022

Microscopy Res & Technique

View full text Add to dashboard Cite

show abstract

“…Either Yes or No, 0 or 1, True or False, etc. but instead of giving a direct value such as 0 and 1, it provides possible values between 0 and 1 [27].…”

Section: Logistic Regressionmentioning

confidence: 99%

Improvements in Weather Forecasting Technique Using Cognitive Internet of Things

Yadav¹,

Singh²,

Tiwari³

2023

Computer Systems Science and Engineering

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Forecasting the weather is a challenging task for human beings because of the unpredictable nature of the climate. However, effective forecasting is vital for the general growth of a country due to the significance of weather forecasting in science and technology. The primary motivation behind this work is to achieve a higher level of forecasting accuracy to avoid any damage. Currently, most weather forecasting work is based on initially observed numerical weather data that cannot fully cover the changing essence of the atmosphere. In this work, sensors are used to collect real-time data for a particular location to capture the varying nature of the atmosphere. Our solution can give the anticipated results with the least amount of human engagement by combining human intelligence and machine learning with the help of the cognitive Internet of Things. The Authors identified weatherrelated parameters such as temperature, humidity, wind speed, and rainfall and then applied cognitive data collection methods to train and validate their findings. In addition, the Authors have examined the efficacy of various machine learning algorithms by using them on both data sets i.e., pre-recorded metrological data sets and live sensor data sets collected from multiple locations. The Authors noticed that the results were superior on the sensor data. The Authors developed ensemble learning model using stacked method that achieved 99.25% accuracy, 99% recall, 99% precision, and 99% F1-score for Sensor data. It also achieved 85% accuracy, 86% recall, 85% precision, and 86% F1 score for Australian rainfall data.

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Determination of Competitive Management Perception in Family Business Leaders Using Data Mining

Bedoya

Mendoza-Saboyá²,

Luna-Eraso

2021

Studies in Computational Intelligence

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Logistic Regression for Employability Prediction

Cited by 6 publications

References 6 publications

Machine learning for post‐traumatic stress disorder identification utilizing resting‐state functional magnetic resonance imaging

Machine learning for post‐traumatic stress disorder identification utilizing resting‐state functional magnetic resonance imaging

Improvements in Weather Forecasting Technique Using Cognitive Internet of Things

Determination of Competitive Management Perception in Family Business Leaders Using Data Mining

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