scholarly journals IMPROVING STUDENTS PERFORMANCE PREDICTION USING MACHINE LEARNING AND SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE

2021 ◽  
Vol 25 (6) ◽  
pp. 56-64
Author(s):  
Nibras Z. Salih ◽  
◽  
Walaa Khalaf ◽  
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Performance Prediction ◽  
Supervised Classification ◽  
Learning Algorithm ◽  
University Education ◽  
Machine Learning Algorithm ◽  
And Control ◽  
The Relationship ◽  
Academic Failures

Classification under supervision is the most common job that performed by machine learning. However, most Educators were worried about the rising evidence of student academic failures in university education. So, this study presents a supervised classification strategy of machine learning algorithm using an actual dataset contains 44 students, fourteen attributes for three previous academic years. We have proposed features that show the relationship among three main subjects which are, calculus, mathematical analysis, and control system in the education course. The objective of this study is to identify the student’s failure in the control system subject and to enhance his performance by Multilayer Perceptron (MLP) algorithm. The dataset is unbalanced, which causes overfitting of the results. Synthetic Minority Oversampling Technique has applied to a dataset for obtaining balance dataset using Weka tool. Several standard metrics used to evaluate the classifier results. Therefore, the suitable results occurred after applying SMOTE with an accuracy of 76.9%.

scholarly journals CAN A MACHINE LEARNING ALGORITHM IDENTIFY SARS-COV-2 VARIANTS BASED ON CONVENTIONAL rRT-PCR? PROOF OF CONCEPT

2021 ◽  
Author(s):  
jorge cabrera Alvargonzalez ◽  
Ana Larranaga Janeiro ◽  
Sonia Perez ◽  
Javier Martinez Torres ◽  
Lucia martinez lamas ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Supervised Classification ◽  
Learning Algorithm ◽  
Support Vector ◽  
Classification Algorithms ◽  
Machine Learning Algorithm ◽  
Proof Of Concept ◽  
The Past ◽  
Number Of Cycles

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges humanity has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Finally, the results obtained from the classification show how the appearance of each wave is coincident with the surge of each of the variants present in the region of Galicia (Spain) during the development of the SARS-CoV-2 pandemic and clearly identified with the classification algorithm.

scholarly journals I'm Simply the Best, Better than All the Rest - Narcissistic Leaders and Corporate Fundraising Success

2020 ◽  
Author(s):  
Dritjon Gruda ◽  
Jim McCleskey ◽  
Dimitra Karanatsiou ◽  
Athena Vakali
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Organizational Leaders ◽  
Grandiose Narcissism ◽  
Large Sample ◽  
The Relationship ◽  
Better Than

We examine the relationship between leader grandiose narcissism, composed of admiration and rivalry, and corporate fundraising success in a sample of 2377 organizational leaders. To examine a large sample of leaders, we applied a machine-learning algorithm to predict leaders' personality scores based on leaders' Twitter profiles. We found that admiration was positively related to - while rivalry was negatively related to corporate fundraising success (in '000s). Analyses also showed that leader gender does not moderate this relationship, unlike initially expected. We discuss and compare our findings to previous work on narcissism and crowdfunding.

scholarly journals Performance Prediction Of Production Lines Using Machine Learning Algorithm

2021 ◽  
Author(s):  
Ayomide Emmanuel Adesiyan
Keyword(s):  
Machine Learning ◽  
Performance Prediction ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Smart Manufacturing ◽  
Machine Learning Algorithm ◽  
Failure Rates ◽  
Production Lines

Manufacturing today considers data-drive business operations at different levels leading to the growth of various paradigms in manufacturing, of which emerged smart manufacturing. However data can be used to predict equipment failure rates, streamline and optimize inventory management and prioritize processes. The use of parameter tuning and optimization, grid-search, cross-validation, to predict the best performing machine learning algorithm. This research work evaluates the time potential failure-rates, against the lines which peaks and drops depending on its components RUL(Remaining Useful Life). The accuracy of the machine learning algorithms that are employed in this studies, are hence subjected to some metrics for evaluation, these are : MCC and AUC-ROC. This study has analyzed and evaluated some annoymized dataset from a manufacturing company, using some metrics and machine learning algorithms for performance prediction of their production lines using unsupervised learning. This study would served as a good reference for anyone wanting to use the best performance model, for further research work.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

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

Feature extraction and prediction of Dengue Outbreaks

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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