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 Inversion of Chlorophyll-a Concentration in Donghu Lake Based on Machine Learning Algorithm

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1179
Author(s):  
Xiaodong Tang ◽  
Mutao Huang
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Learning Algorithm ◽  
Factor Models ◽  
Machine Learning Algorithm ◽  
Single Factor ◽  
Chlorophyll A Concentration ◽  
Donghu Lake ◽  
Svm Model ◽  
Better Than

Machine learning algorithm, as an important method for numerical modeling, has been widely used for chlorophyll-a concentration inversion modeling. In this work, a variety of models were built by applying five kinds of datasets and adopting back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM). The results revealed that modeling with multi-factor datasets has the possibility to improve the accuracy of inversion model, and seven band combinations are better than seven single bands when modeling, Besides, SVM is more suitable than BPNN and ELM for chlorophyll-a concentration inversion modeling of Donghu Lake. The SVM model based on seven three-band combination dataset (SVM3) is the best inversion one among all multi-factor models that the mean relative error (MRE), mean absolute error (MAE), root mean square error (RMSE) of the SVM model based on single-factor dataset (SF-SVM) are 30.82%, 9.44 μg/L and 12.66 μg/L, respectively. SF-SVM performs best in single-factor models, MRE, MAE, RMSE of SF-SVM are 28.63%, 13.69 μg/L and 16.49 μg/L, respectively. In addition, the simulation effect of SVM3 is better than that of SF-SVM. On the whole, an effective model for retrieving chlorophyll-a concentration has been built based on machine learning algorithm, and our work provides a reliable basis and promotion for exploring accurate and applicable chlorophyll-a inversion model.

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 Inversion of chlorophyll-a concentration in Donghu Lake based on machine learning algorithm

2021 ◽  
Author(s):  
Xiaodong Tang ◽  
Mutao Huang
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Chlorophyll A Concentration ◽  
Inversion Model ◽  
Donghu Lake ◽  
Better Than

Machine learning algorithm, as an important method for numerical modeling, has been widely used for chlorophyll-a concentration inversion modeling. This work aims to build an effective inversion model of chlorophyll-a concentration in Lake Donghu based on machine learning algorithm. Toward this aim, a variety of models were built by applying five kinds of dataset and adopting back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM). The model accuracy analysis results revealed that multi-factor dataset for modeling has the possibility to improve the accuracy of the single-factor model, and seven band combinations are better than seven single bands when modeling, Besides, SVM is more suitable than BPNN and ELM for chlorophyll-a concentration inversion modeling of Donghu Lake. SVM3 is the best inversion one among all multi-factor models that the MRE, MAE, RMSE of SF-SVM are 30.82%, 9.44 μg/L and 12.66 μg/L, respectively. SF-SVM performs a better inversion effect than SF-BPNN and SF-ELM, the MRE, MAE, RMSE of SF-SVM are 28.63%, 13.69μg/L and 16.49μg/L, respectively. In addition, the simulation effect of SVM3 is better than that of SF-SVM. On the whole, an effective model for retrieving chlorophyll-a concentration has been built based on machine learning algorithm, and our work provides a reliable basis and promotion for exploring accurate and applicable chlorophyll-a inversion model.

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.

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

Sign in / Sign up

Export Citation Format

Share Document