scholarly journals EEG-Based Personality Prediction Using Fast Fourier Transform and DeepLSTM Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Harshit Bhardwaj ◽  
Pradeep Tomar ◽  
Aditi Sakalle ◽  
Wubshet Ibrahim
Keyword(s):  
Data Collection ◽  
Short Term Memory ◽  
State Of The Art ◽  
Myers Briggs Type Indicator ◽  
K Nearest Neighbors ◽  
Dry Electrode ◽  
Type Indicator ◽  
Eeg Data ◽  
Personality Prediction ◽  
Artificial Neural Network Ann

In this paper, a deep long short term memory (DeepLSTM) network to classify personality traits using the electroencephalogram (EEG) signals is implemented. For this research, the Myers–Briggs Type Indicator (MBTI) model for predicting personality is used. There are four groups in MBTI, and each group consists of two traits versus each other; i.e., out of these two traits, every individual will have one personality trait in them. We have collected EEG data using a single NeuroSky MindWave Mobile 2 dry electrode unit. For data collection, 40 Hindi and English video clips were included in a standard database. All clips provoke various emotions, and data collection is focused on these emotions, as the clips include targeted, inductive scenes of personality. Fifty participants engaged in this research and willingly agreed to provide brain signals. We compared the performance of our deep learning DeepLSTM model with other state-of-the-art-based machine learning classifiers such as artificial neural network (ANN), K-nearest neighbors (KNN), LibSVM, and hybrid genetic programming (HGP). The analysis shows that, for the 10-fold partitioning method, the DeepLSTM model surpasses the other state-of-the-art models and offers a maximum classification accuracy of 96.94%. The proposed DeepLSTM model was also applied to the publicly available ASCERTAIN EEG dataset and showed an improvement over the state-of-the-art methods.

Personality Based Lipstick Color Recommender System using K-Nearest Neighbors Algorithm

2019 ◽  
Vol 6 (1) ◽  
pp. 55-59
Author(s):  
Ryan Adiputra ◽  
Ni Made Satvika Iswari ◽  
Wella Wella
Keyword(s):  
Recommender System ◽  
Euclidean Distance ◽  
Nearest Neighbors ◽  
Training Data ◽  
Personality Test ◽  
Myers Briggs Type Indicator ◽  
K Nearest Neighbors ◽  
Type Indicator ◽  
Myers Briggs ◽  
The Right

Lipstick is a lip color which available in many colors. A research said instant valuation of woman personality can be figured by their lipstick color choice. Therefore there is a necessity to use the right lipstick color to obtain a harmony between personality and appearance. This experiment was conducted to give lipstick color recommendation by using K-Nearest Neighbors algorithm, and Myers-Briggs Type Indicator (MBTI) personality test instrument. The system was built on Android application. Euclidean distance value is affected by 5 factors which are age, introvert, sensing, thinking, and judging. Lipstick color recommendation is obtained by fetching 7 training data with nearest Euclidean distance when compared to personality test result. The colors used in this experiment are nude, pink, red, orange, and purple. After evaluation, it is obtained the application’s accuracy of 87.38% which considered as good classification, both precision and recall with 75.68% which considered as fair classification. The score for software quality is 79.13% which considered as good quality. Keywords—K-Nearest Neighbors, Data Mining, Myers-Briggs Type Indicator,Recommender System, Lipstick.  

scholarly journals Prediction Of Myers-Briggs Type Indicator Personality Using Long Short-Term Memory

2021 ◽  
Vol 21 (2) ◽  
pp. 104
Author(s):  
Mawadatul Maulidah ◽  
Hilman Ferdinandus Pardede
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Personality Type ◽  
Myers Briggs Type Indicator ◽  
Short Term ◽  
Term Memory ◽  
Learning Speed ◽  
Type Indicator ◽  
Myers Briggs ◽  
Long Short Term Memory

Personality is defined as the mix of features and qualities that make up an individual's particular character, including thoughts, feelings, and behaviors. With the rapid development of technology, personality computing is becoming a popular research field by providing users with personalization. Many researchers have used social media data to automatically predict personality. This research uses a public dataset from Kaggle, namely the Myers-Briggs Personality Type Dataset. The purpose of this study is to predict the accuracy and F1-score values so that the performance for predicting and classifying Myers–Briggs Type Indicator (MBTI) personality can work optimally by using attributes from the MBTI dataset, namely posts and types. Predictive accuracy analysis was carried out using the Long Short-Term Memory (LSTM) algorithm with random oversampling technique with the Imblearn library for MBTI personality type prediction and comparing the performance of the method proposed in this study with other popular machine learning algorithms. Experiments show that the LSTM model using the RMSprop optimizer and learning speed of 10-3 provides higher performance in terms of accuracy while for the F1-score the LSTM model using the RMSprop Optimizer and learning speed of 10-2 gives a higher value than the proposed machine learning algorithm so that the model MBTI dataset using LSTM with random oversampling can help in identifying the MBTI personality type.

scholarly journals MBTI Based Personality Prediction of a User Based on Their Writing on Social Media

2019 ◽  
Vol 9 (1) ◽  
pp. 3372-3376
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Language Processing ◽  
Personality Types ◽  
Support Vector ◽  
Myers Briggs Type Indicator ◽  
Accuracy Rate ◽  
Original Dataset ◽  
Type Indicator ◽  
Personality Prediction

The undeniable power that various e-commerce and streaming websites exert on their users’ in terms of what they buy and what they watch is unquestionable, so the creation of better targeted advertisements and recommender systems is the need of the hour. Prediction of a person’s personality can be the key for the achievement of these goals. A novel way to understand the various facets of a person’s personality is by analyzing their MBTI (Myers–Briggs Type Indicator). This paper aims at classifying a user into any one of the sixteen personality types, defined by MBTI, through the use of natural language processing (NLP) and support vector machine (SVM) which was implemented on the MBTI dataset. Since the original dataset is unevenly distributed, SVM has been applied to the original dataset and an under sampled version of the MBTI dataset. The highest accuracy rate of 78.52% for the traits (thinking/feeling) was achieved in the original dataset whereas for the under sampled dataset it was 60.2% for the traits (judging/perceiving).

scholarly journals Peningkatan Akurasi pada Prediksi Kepribadian Mbti Pengguna Twitter Menggunakan Augmentasi Data

2020 ◽  
Vol 7 (4) ◽  
pp. 815
Author(s):  
Rizki Nurhaliza Harahap ◽  
Kemas Muslim
Keyword(s):  
Random Forest ◽  
Text Mining ◽  
Data Augmentation ◽  
Myers Briggs Type Indicator ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency ◽  
Type Indicator ◽  
Augmentation Techniques ◽  
Personality Prediction

<p class="Abstrak">Kepribadian suatu individu perlu diketahui untuk membantu seseorang dalam mempertimbangkan beberapa hal, salah satunya perekrutan karier. Pada umumnya, kepribadian dapat diketahui melalui metode wawancara, observasi, maupun survei kuesioner. Akan tetapi, metode konvensional tersebut dinilai kurang praktis dari segi waktu dan materi karena dibutuhkan waktu yang lama dan biaya yang cukup besar untuk mengolah data. Selain itu, penggunaan metode konvensional juga dapat menimbulkan bias karena melibatkan orang ketiga dalam pengolahan data. Penelitian ini mencoba memberikan solusi dengan membangun model yang dapat melakukan prediksi terhadap kepribadian seseorang berdasarkan analisis data dan informasi dari media sosial Twitter. Data dan informasi tersebut akan diproses sehingga didapatkan prediksi kepribadian orang tersebut. Teori klasifikasi kepribadian yang digunakan adalah teori Myers-Briggs Type Indicator (MBTI). Penelitian ini juga mencoba menerapkan teknik augmentasi data untuk meningkatkan performa dari text mining task yang memiliki dataset sedikit. Hasil terbaik didapatkan dengan metode Random Forest menggunakan pembobotan Term Frequency-Inverse Document Frequency (TF-IDF) dan fitur yang tersedia pada Twitter. Penggunaan teknik augmentasi dapat meningkatkan akurasi hingga 30% dari akurasi awal sehingga hasil penelitian menunjukkan bahwa penggunaan teknik augmentasi data dapat meningkatkan performa pada model prediksi kepribadian MBTI.</p><p class="Abstrak"><em>Abstract</em></p><p><em>The personality of an individual needs to be known to help people in considering things, one of them is career recruitment. In general, personality can be known through interviews, observations, and questionnaire surveys. However, the conventional method is judged to be impractical in terms of time and material because it takes a long time and has considerable costs to process data. After all, the use of conventional methods can also cause bias because it involves a third person in data processing. The research tries to provide a solution by building a system that can predict the personality of a person based on the analysis of data and information from social media Twitter. The data and information will be processed so that the personality prediction is obtained. The personality classification theory used is the Myers-Briggs Type Indicator (MBTI) theory. The research also tries to implement data augmentation techniques to improve the performance of text mining tasks that have a slight dataset. The best results are obtained by the Random Forest method using the Term Frequency-Inverse Document Frequency (TF-IDF) weighted and the features available on Twitter. The use of augmentation techniques can increase accuracy by up to 30% from initial accuracy. So, the use of data augmentation techniques can be used to improve the performance of MBTI personality prediction models.</em></p>

scholarly journals High Accuracy and Short Delay 1ch-SSVEP Quadcopter-BMI Using Deep Learning

2020 ◽  
Vol 32 (4) ◽  
pp. 738-744
Author(s):  
Kazumi Ishizuka ◽  
◽  
Nobuaki Kobayashi ◽  
Ken Saito
Keyword(s):  
Short Term Memory ◽  
High Accuracy ◽  
Eeg Signals ◽  
Short Delay ◽  
Research Attention ◽  
Dry Electrode ◽  
Eeg Data ◽  
Significant Research ◽  
Machine Interface ◽  
Long Short Term Memory

This study considers a brain-machine interface (BMI) system based on the steady state visually evoked potential (SSVEP) for controlling quadcopters using electroencephalography (EEG) signals. An EEG channel with a single dry electrode, i.e., without conductive gel or paste, was utilized to minimize the load on users. Convolutional neural network (CNN) and long short-term memory (LSTM) models, both of which have received significant research attention, were used to classify the EEG data obtained for flickers from multi-flicker screens at five different frequencies, with each flicker corresponding to a drone movement, viz., takeoff, forward and sideways movements, and landing. The subjects of the experiment were seven healthy men. Results indicate a high accuracy of 97% with the LSTM model for a 2 s segment used as the unit of processing. High accuracy of 93% for 0.5 s segment as a unit of processing can remain in the LSTM classification, consequently decreasing the delay of the system that may be required for safety reasons in real-time applications. A system demonstration was undertaken with 2 out of 7 subjects controlling the quadcopter and monitoring movements such as takeoff, forward motion, and landing, which showed a success rate of 90% on average.

Assessing the dream-lag effect among participants with different intuition score measured by the Myers–Briggs Type Indicator.

Dreaming ◽  
2020 ◽  
Vol 30 (3) ◽  
pp. 267-277
Author(s):  
Jiaxi Wang ◽  
Xiaoling Feng ◽  
Ting Bin ◽  
Huiying Ma ◽  
Heyong Shen
Keyword(s):  
Myers Briggs Type Indicator ◽  
Type Indicator ◽  
Lag Effect ◽  
Myers Briggs

A Systematic Literature Review of the EFQM Excellence Model from 1991 to 2019

1970 ◽  
Vol 2 (2) ◽  
pp. 11-15
Author(s):  
Muhammad Yousaf ◽  
Petr Bris
Keyword(s):  
Quality Management ◽  
Data Collection ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Quantitative Research ◽  
Web Of Science ◽  
State Of The Art ◽  
Future Research ◽  
European Foundation ◽  
The Subject

A systematic literature review (SLR) from 1991 to 2019 is carried out about EFQM (European Foundation for Quality Management) excellence model in this paper. The aim of the paper is to present state of the art in quantitative research on the EFQM excellence model that will guide future research lines in this field. The articles were searched with the help of six strings and these six strings were executed in three popular databases i.e. Scopus, Web of Science, and Science Direct. Around 584 peer-reviewed articles examined, which are directly linked with the subject of quantitative research on the EFQM excellence model. About 108 papers were chosen finally, then the purpose, data collection, conclusion, contributions, and type of quantitative of the selected papers are discussed and analyzed briefly in this study. Thus, this study identifies the focus areas of the researchers and knowledge gaps in empirical quantitative literature on the EFQM excellence model. This article also presents the lines of future research.

Campus Social Climate Correlates of Environmental Type Dimensions

NASPA Journal ◽  
2004 ◽  
Vol 41 (4) ◽  
Author(s):  
Daniel W. Salter ◽  
Reynol Junco ◽  
Summer D. Irvin
Keyword(s):  
Work Environment ◽  
Personal Growth ◽  
Type Theory ◽  
General Purpose ◽  
Myers Briggs Type Indicator ◽  
Environment Scale ◽  
Type Indicator ◽  
Group Environment ◽  
System Maintenance ◽  
Environment Type

To address the ability of the Salter Environment Type Assessment (SETA) to measure different kinds of campus environments, data from three studies of the SETA with the Work Environment Scale, Group Environment Scale, and University Residence Environment Scale were reexamined (n = 534). Relationship dimension scales were very consistent with extraversion and feeling from environmental type theory. System maintenance and systems change scales were associated with judging and perception on the SETA, respectively. Results from the SETA and personal growth dimension scales were mixed. Based on this analysis, the SETA may serve as a general purpose environmental assessment for use with the Myers-Briggs Type Indicator.

Sign in / Sign up

Export Citation Format

Share Document