scholarly journals CLASSIFICATION OF PEOPLE BY PSYCHOLOGICAL PERSONALITY TYPES BASED ON THE HISTORY OF CORRESPONDENCE

2021 ◽  
Vol 335 (1) ◽  
pp. 45-53
Author(s):  
S. Ryskulbek ◽  
O. Mamyrbayev ◽  
A. Turganbayeva
Keyword(s):  
Decision Making ◽  
Random Forest ◽  
Supervised Learning ◽  
Personality Types ◽  
Binary Classifier ◽  
Average Accuracy ◽  
History Of ◽  
The Mind ◽  
Perception Analysis

Temperament is a set of innate tendencies of the mind associated with the processes of perception, analysis and decision-making. The purpose of this article is to predict the psychotype of individuals based on chat stories and follow the Keirsi model, according to which the psycho type is classified as a craftsman, guardian, idealist and mind. The proposed methodology uses a version of LIWC, a dictionary of words, to analyze the context of words and uses supervised learning using KNN, SVM, and Random Forest algorithms to train the classifier. The average accuracy obtained was 88.37% for artisan temperament, 86.92% for caregivers, 55.61% for idealists, and 69.09% for rationality. When using the binary classifier, the average accuracy was 90.93% for artisan temperament, 88.98% for caregivers, 51.98% for idealism, and 71.42% for rationality.

CLASSIFICATION OF PEOPLE BY PSYCHOLOGICAL PERSONALITY TYPES BASED ON THE HISTORY OF CORRESPONDENCE

2021 ◽  
Vol 335 (1) ◽  
pp. 45-53
Author(s):  
S. Ryskulbek ◽  
O. Mamyrbayev ◽  
A. Turganbayeva
Keyword(s):  
Decision Making ◽  
Random Forest ◽  
Supervised Learning ◽  
Personality Types ◽  
Binary Classifier ◽  
Average Accuracy ◽  
History Of ◽  
The Mind ◽  
Perception Analysis

Temperament is a set of innate tendencies of the mind associated with the processes of perception, analysis and decision-making. The purpose of this article is to predict the psychotype of individuals based on chat stories and follow the Keirsi model, according to which the psycho type is classified as a craftsman, guardian, idealist and mind. The proposed methodology uses a version of LIWC, a dictionary of words, to analyze the context of words and uses supervised learning using KNN, SVM, and Random Forest algorithms to train the classifier. The average accuracy obtained was 88.37% for artisan temperament, 86.92% for caregivers, 55.61% for idealists, and 69.09% for rationality. When using the binary classifier, the average accuracy was 90.93% for artisan temperament, 88.98% for caregivers, 51.98% for idealism, and 71.42% for rationality.

scholarly journals A DECISION TREE-BASED CLASSIFICATION FRAMEWORK FOR USED OIL ANALYSIS APPLYING RANDOM FOREST FEATURE SELECTION

2018 ◽  
Keyword(s):  
Decision Making ◽  
Feature Selection ◽  
Random Forest ◽  
Decision Tree ◽  
Condition Monitoring ◽  
Critical Parameters ◽  
Oil Analysis ◽  
Used Oil ◽  
Maintenance Decision

Lubricant condition monitoring (LCM), part of condition monitoring techniques under Condition Based Maintenance, monitors the condition and state of the lubricant which reveal the condition and state of the equipment. LCM has proved and evidenced to represent a key concept driving maintenance decision making involving sizeable number of parameter (variables) tests requiring classification and interpretation based on the lubricant’s condition. Reduction of the variables to a manageable and admissible level and utilization for prediction is key to ensuring optimization of equipment performance and lubricant condition. This study advances a methodology on feature selection and predictive modelling of in-service oil analysis data to assist in maintenance decision making of critical equipment. Proposed methodology includes data pre-processing involving cleaning, expert assessment and standardization due to the different measurement scales. Limits provided by the Original Equipment Manufacturers (OEM) are used by the analysts to manually classify and indicate samples with significant lubricant deterioration. In the last part of the methodology, Random Forest (RF) is used as a feature selection tool and a Decision Tree-based (DT) classification of the in-service oil samples. A case study of a thermal power plant is advanced, to which the framework is applied. The selection of admissible variables using Random Forest exposes critical used oil analysis (UOA) variables indicative of lubricant/machine degradation, while DT model, besides predicting the classification of samples, offers visual interpretability of parametric impact to the classification outcome. The model evaluation returned acceptable predictive, while the framework renders speedy classification with insights for maintenance decision making, thus ensuring timely interventions. Moreover, the framework highlights critical and relevant oil analysis parameters that are indicative of lubricant degradation; hence, by addressing such critical parameters, organizations can better enhance the reliability of their critical operable equipment.

scholarly journals Severity Classification of Multiple Sclerosis Disease: A Rough Set-Based Method

2019 ◽  
Vol 8 (9S) ◽  
pp. 307-314
Keyword(s):  
Multiple Sclerosis ◽  
Random Forest ◽  
Rough Set ◽  
Weighted Average ◽  
Support Vector ◽  
Neurologic Disorder ◽  
Severity Classification ◽  
Average Accuracy ◽  
Multiple Sclerosis Disease

Multiple sclerosis (MS) is among the world’s most common neurologic disorder. Severity classification of MS disease is necessary for treatment and medication dosage decisions and to understand the disease progression. To the best of authors’ knowledge, this is the first study for the severity classification of MS disease. In this study, Rough set (RS) approach is applied to discern the three classes (mild, moderate, and severe) of the severity of MS disease. Furthermore, the performance of the RS approach is compared with Machine learning (ML) classifiers namely, random forest, K-nearest neighbour, and support vector machine. The performance is evaluated on the dataset acquired from Multiple sclerosis outcome assessments consortium (MSOAC), Arizona, US. The weighted average accuracy, precision, recall, and specificity values for the RS approach are found to be 84.04%, 76.99%, 76.75%, and 83.84% respectively. However, among the ML classifiers, the performance of random forest classifier is found best for which the weighted average accuracy, precision, recall, and specificity values are 62.19 %, 52.65 %, 56.84 %, and 59.87 % respectively. The RS approach is found much superior to ML classifiers and may be used for MS disease severity classification. This study may be helpful for the clinicians to assess the severity of the MS patients and to take medication and dosage decisions.

An efficient approach to detect IoT botnet attacks using machine learning

2020 ◽  
Vol 26 (3) ◽  
pp. 241-254
Author(s):  
Zainab Alothman ◽  
Mouhammd Alkasassbeh ◽  
Sherenaz Al-Haj Baddar
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
State Of The Art ◽  
False Negative ◽  
Sampling Technique ◽  
Binary Classifier ◽  
Design And Implementation ◽  
Multiple Classifiers ◽  
Iot Devices

The numerous security loopholes in the design and implementation of many IoT devices have rendered them an easy target for botnet attacks. Several approaches to implement behavioral IoT botnet attacks detection have been explored, including machine learning. The main goal of previous studies was to achieve the highest possible accuracy in distinguishing normal from malicious IoT traffic, with minimal regard to the identification of the particular type of attack that is being launched. In this study, we present a machine learning based approach for detecting IoT botnet attacks that not only helps distinguish normal from malicious traffic, but also detects the type of the IoT botnet attack. To achieve this goal, the Bot-IoT dataset, in which instances have main attack and sub-attack categories, was utilized after performing the Synthetic Minority Over-sampling Technique (SMOTE), among other preprocessing techniques. Moreover, multiple classifiers were tested and the results from the best three, namely: J48, Random Forest (RF), and Multilayer Perceptron (MLP) networks were reported. The results showed the superiority of the RF and J48 classifiers compared to the MLP networks and other state-of-the-art solutions. The accuracy of the best binary classifier reported in this study reached 0.999, whereas the best accuracies of main attack and subcategories classifications reached 0.96 and 0.93, respectively. Only few studies address the classification errors in this domain, yet, it was assessed in this study in terms of False Negative (FN) rates. J48 and RF classifiers, here also, outperformed the MLP network classifier, and achieved a maximum micro FN rate for subcategories classification of 0.076.

Classification of Informative Reviews Based on Personal Values

2014 ◽  
Vol 18 (3) ◽  
pp. 331-339
Author(s):  
Yasufumi Takama ◽  
◽  
Zhongjie Mao ◽  
Shunichi Hattori
Keyword(s):  
Decision Making ◽  
Personal Values ◽  
The Other ◽  
Independent Factor ◽  
Vast Amount ◽  
Personal Value ◽  
Other Hand ◽  
Average Accuracy ◽  
Copy And Paste

This paper proposes a method for classifying informative reviews based on personal values. Reviews of an item are useful for a user who is considering purchasing it. However, it is difficult for readers to find informative reviews from vast amount of reviews because of existence of too many uninformative reviews. This paper supposes that the value of a review is affected by reader-dependent and independent factors. Typical uninformative reviews in terms of reader-independent factor are copy-and-paste reviews, which do not provide any readers with useful information for their decision-making. On the other hand, it is supposed different readers regard different reviews as informative, which is affected by their personal values. This paper focuses on such a readerdependent factor, and proposes a methods for classifying informative reviews based on reader’s personal value. Experiments are conducted using actual review data provided by Rakuten Inc., of which the results show about 0.7 of average accuracy is achieved. Furthermore, it is also shown proposed method can model judging criteria common to those who have similar personal values.

Massive Modularity

2012 ◽  
Author(s):  
Richard Samuels
Keyword(s):  
Decision Making ◽  
Cognitive Processes ◽  
Human Mind ◽  
Domain Specificity ◽  
Functional Modules ◽  
Cognitive Mechanisms ◽  
Central Regions ◽  
Massive Modularity ◽  
History Of ◽  
The Mind

The objective of the article is to discuss the evolution, hypothesis, and some the more prominent arguments for massive modularity (MM). MM is the hypothesis that the human mind is largely or entirely composed from a great many modules. Modules are functionally characterizable cognitive mechanisms that tend to possess several features, which include domain-specificity, informationally encapsulation, innateness, inaccessibility, shallow outputs, and mandatory operation. The final thesis that comprises MM mentions that modules are found not merely at the periphery of the mind but also in the central regions responsible for such higher cognitive capacities as reasoning and decision-making. The central cognition depends on a great many functional modules that are not themselves composable into larger more inclusive systems. One of the families of arguments for MM focuses on a range of problems that are familiar from the history of cognitive science such as problems that concern the computational tractability of cognitive processes. The arguments may vary considerably in detail but they share a common format. First, they proceed from the assumption that cognitive processes are classical computational ones. Second, given the assumption that cognitive processes are computational ones, intractability arguments seek to undermine non-modular accounts of cognition by establishing the intractability thesis.

scholarly journals Classification of Unifloral Honeys from SARDINIA (Italy) by ATR-FTIR Spectroscopy and Random Forest

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 88
Author(s):  
Marco Ciulu ◽  
Elisa Oertel ◽  
Rosanna Serra ◽  
Roberta Farre ◽  
Nadia Spano ◽  
...  
Keyword(s):  
Random Forest ◽  
Ftir Spectroscopy ◽  
Infrared Spectra ◽  
Scientific Community ◽  
Cross Validation ◽  
Phenolic Fraction ◽  
Arbutus Unedo ◽  
Average Accuracy ◽  
Asphodelus Microcarpus

Nowadays, the mislabeling of honey floral origin is a very common fraudulent practice. The scientific community is intensifying its efforts to provide the bodies responsible for controlling the authenticity of honey with fast and reliable analytical protocols. In this study, the classification of various monofloral honeys from Sardinia, Italy, was attempted by means of ATR-FTIR spectroscopy and random forest. Four different floral origins were considered: strawberry-tree (Arbutus Unedo L.), asphodel (Asphodelus microcarpus), thistle (Galactites tormentosa), and eucalyptus (Eucalyptus calmadulensis). Training a random forest on the infrared spectra allowed achieving an average accuracy of 87% in a cross-validation setting. The identification of the significant wavenumbers revealed the important role played by the region 1540–1175 cm−1 and, to a lesser extent, the region 1700–1600 cm−1. The contribution of the phenolic fraction was identified as the main responsible for this observation.

scholarly journals Discussion on the Feasibility of Nudge in Education

2021 ◽  
Vol 5 (1) ◽  
pp. 68
Author(s):  
Liu Hongyang ◽  
Kremenkova Lucie
Keyword(s):  
Decision Making ◽  
Behavioral Science ◽  
Specific Behavior ◽  
Self Interest ◽  
Decision Making System ◽  
History Of ◽  
Policy Shift ◽  
Definition Of ◽  
Public Decision

This paper first introduces the definition of nudge, the history of nudge and the advantages of nudge. This concept means a relatively subtle policy shift that encourages people to make decisions that are in their broad self-interest. It relies on insights from behavioral science, and when used ethically, it can be very helpful. Subsequently, the research results of the nudge method in public decision-making fields such as health and environmental protection are listed, inferring the feasibility of nudge method in the field of education, especially learners’ specific behavior decision. Then according to the classification of the nudge method by the decision-making system, the research on nudge used in the education field is enumerated. It demonstrates that nudge still has a lot of room for expansion in the field of education.

How Homo economicus lost her mind and how we can revive her

2018 ◽  
Vol 41 ◽  
Author(s):  
Peter DeScioli
Keyword(s):  
Economic Thought ◽  
History Of Economic Thought ◽  
Homo Economicus ◽  
Economic Theories ◽  
Target Article ◽  
History Of ◽  
The Mind ◽  
And Behavior

AbstractThe target article by Boyer & Petersen (B&P) contributes a vital message: that people have folk economic theories that shape their thoughts and behavior in the marketplace. This message is all the more important because, in the history of economic thought, Homo economicus was increasingly stripped of mental capacities. Intuitive theories can help restore the mind of Homo economicus.

Elaborating the role of reflection and individual differences in the study of folk-economic beliefs

2018 ◽  
Vol 41 ◽  
Author(s):  
Kevin Arceneaux
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Cognitive Processes ◽  
Evolutionary History ◽  
Behavioral Responses ◽  
History Of ◽  
Economic Beliefs

AbstractIntuitions guide decision-making, and looking to the evolutionary history of humans illuminates why some behavioral responses are more intuitive than others. Yet a place remains for cognitive processes to second-guess intuitive responses – that is, to be reflective – and individual differences abound in automatic, intuitive processing as well.

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