不定自然変換理論に基づく比喩理解モデルの計算論的実装の試み

2020 ◽  
Author(s):  
Shunsuke Ikeda ◽  
Miho Fuyama ◽  
Hayato Saigo ◽  
Tatsuji Takahashi
Keyword(s):  
Category Theory ◽  
Analogical Reasoning ◽  
Machine Learning Techniques ◽  
Huge Amount ◽  
Metaphor Comprehension ◽  
Weighted Directed Graph ◽  
Learning Techniques ◽  
Descriptive Validity ◽  
Association Data ◽  
Human Participants

Machine learning techniques have realized some principal cognitive functionalities such as nonlinear generalization and causal model construction, as far as huge amount of data are available. A next frontier for cognitive modelling would be the ability of humans to transfer past knowledge to novel, ongoing experience, making analogies from the known to the unknown. Novel metaphor comprehension may be considered as an example of such transfer learning and analogical reasoning that can be empirically tested in a relatively straightforward way. Based on some concepts inherent in category theory, we implement a model of metaphor comprehension called the theory of indeterminate natural transformation (TINT), and test its descriptive validity of humans' metaphor comprehension. We simulate metaphor comprehension with two models: one being structure-ignoring, and the other being structure-respecting. The former is a sub-TINT model, while the latter is the minimal-TINT model. As the required input to the TINT models, we gathered the association data from human participants to construct the ``latent category'' for TINT, which is a complete weighted directed graph. To test the validity of metaphor comprehension by the TINT models, we conducted an experiment that examines how humans comprehend a metaphor. While the sub-TINT does not show any significant correlation, the minimal-TINT shows significant correlations with the human data. It suggests that we can capture metaphor comprehension processes in a quite bottom-up manner realized by TINT.

scholarly journals Heart Disease Prediction using Machine Learning Techniques

2021 ◽  
pp. 42-47
Author(s):  
Ramesh Ponnala ◽  
K. Sai Sowjanya
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Linear Model ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Huge Amount ◽  
Healthcare Enterprise ◽  
Learning Techniques ◽  
Accuracy Level

Prediction of Cardiovascular ailment is an important task inside the vicinity of clinical facts evaluation. Machine learning knowledge of has been proven to be effective in helping in making selections and predicting from the huge amount of facts produced by using the healthcare enterprise. on this paper, we advocate a unique technique that pursuits via finding good sized functions by means of applying ML strategies ensuing in improving the accuracy inside the prediction of heart ailment. The severity of the heart disease is classified primarily based on diverse methods like KNN, choice timber and so on. The prediction version is added with special combos of capabilities and several known classification techniques. We produce a stronger performance level with an accuracy level of a 100% through the prediction version for heart ailment with the Hybrid Random forest area with a linear model (HRFLM).

scholarly journals Morphology and Interaction of Galaxies using Deep Learning

2016 ◽  
Vol 12 (S325) ◽  
pp. 205-208
Author(s):  
Fernando Caro ◽  
Marc Huertas-Company ◽  
Guillermo Cabrera
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Visual Inspection ◽  
Interaction Analysis ◽  
Machine Learning Techniques ◽  
Huge Amount ◽  
New Approaches ◽  
Learning Techniques ◽  
First Galaxies ◽  
The Way

AbstractIn order to understand how galaxies form and evolve, the measurement of the parameters related to their morphologies and also to the way they interact is one of the most relevant requirements. Due to the huge amount of data that is generated by surveys, the morphological and interaction analysis of galaxies can no longer rely on visual inspection. For dealing with such issue, new approaches based on machine learning techniques have been proposed in the last years with the aim of automating the classification process. We tested Deep Learning using images of galaxies obtained from CANDELS to study the accuracy achieved by this tool considering two different frameworks. In the first, galaxies were classified in terms of their shapes considering five morphological categories, while in the second, the way in which galaxies interact was employed for defining other five categories. The results achieved in both cases are compared and discussed.

scholarly journals Heart Disease Prediction Using Machine Learning Algorithms

2020 ◽  
pp. 137-149
Author(s):  
Abhay Agrahary
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Accurate Diagnosis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Disease Prediction ◽  
Huge Amount ◽  
Learning Techniques ◽  
Comparative Results

Heart disease is one of the most fatal problems in the whole world, which cannot be seen with a naked eye and comes instantly when its limitations are reached. Therefore, it needs accurate diagnosis at accurate time. Health care industry produced huge amount of data every day related to patients and diseases. However, this data is not used efficiently by the researchers and practitioners. Today healthcare industry is rich in data however poor in knowledge. There are various data mining and machine learning techniques and tools available to extract effective knowledge from databases and to use this knowledge for more accurate diagnosis and decision making. Increasing research on heart disease predicting systems, it become significant to summarize the completely incomplete research on it. The main objective of this research paper is to summarize the recent research with comparative results that has been done on heart disease prediction and also make analytical conclusions. From the study, it is observed Naive Bayes with Genetic algorithm; Decision Trees and Artificial Neural Networks techniques improve the accuracy of the heart disease prediction system in different scenarios. In this paper commonly used data mining and machine learning techniques and their complexities are summarized.

Quantifying Emotional States Based on Body Language Data Using Non Invasive Sensors

2014 ◽  
Author(s):  
Ishan Behoora ◽  
Conrad S. Tucker
Keyword(s):  
Real Time ◽  
Body Language ◽  
Machine Learning Techniques ◽  
Emotional States ◽  
Participant Engagement ◽  
Non Invasive ◽  
Learning Techniques ◽  
Language Data ◽  
Position Data ◽  
Human Participants

Determining participant engagement is an important issue across a large number of fields, ranging from entertainment to education. Traditionally, feedback from participants is taken after the activity has been completed. Alternately, continuous observation by trained humans is needed. Thus, there is a need for an automated real time solution. In this paper, the authors propose a data mining driven approach that models a participant’s engagement, based on body language data acquired in real time using non-invasive sensors. Skeletal position data, that approximates human body motions, is acquired from participants using off the shelf, non-invasive sensors. Thereafter, machine learning techniques are employed to detect body language patterns representing emotions such as delight, interest, boredom, frustration, and confusion. The methodology proposed in this paper enables researchers to predict the participants’ engagement levels in real time with high accuracy above 98%. A case study involving human participants enacting eight body language poses, is used to illustrate the effectiveness of the methodology. Finally, this methodology highlights the potential of a real time, automated engagement detection using non-invasive sensors which can ultimately have applications in a large variety of areas such as lectures, gaming and classroom learning.

scholarly journals An Insight of Script Text Extraction Performance using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 2581-2588
Keyword(s):  
Text Mining ◽  
Machine Learning Techniques ◽  
Huge Amount ◽  
Text Extraction ◽  
Digital Format ◽  
Learning Techniques ◽  
The World ◽  
Accuracy Level ◽  
Hybrid Machine ◽  
Extraction Performance

With the evolution of huge amount of ancient and modern text available in digital format, it is ascertain to mine for researchers, government, tourist and travelers visiting all over the world. However, it is very challenging and costly. Further, it takes a lot of effort and time for script text mining. Therefore, the study investigates various techniques for script text mining viz supervised and unsupervised techniques. Firstly, the study presents a survey for various kinds of techniques adopted by the users for extraction of text from image. It also delivers information about gaps involved in the various approaches. Furthermore, it incorporate the quantitative comparisons based among the study of various approaches and techniques for text extraction as well as script level comparison. The result inferred on the basis of the script comparison indicates that, the accuracy level of ancient script was found to be 5% lesser than modern script. Again, furthermore comparison has been done on standalone and hybrid machine (Combination of CNN and KNN) / deep learning techniques. The accuracy has been found to be lower(4%) in case of standalone techniques.

scholarly journals Machine Learning for Accurate Prediction of Cardiac Arrhythmia

2020 ◽  
Vol 9 (1) ◽  
pp. 271-274
Keyword(s):  
Machine Learning ◽  
Cardiac Arrhythmia ◽  
Learning Algorithm ◽  
Numerical Data ◽  
Life Forms ◽  
Machine Learning Techniques ◽  
World Health ◽  
Huge Amount ◽  
Learning Techniques ◽  
Short Period

Cardiac Arrhythmia is a state within the heart that is caused due to irregular waveforms generated from sinoatrial node. Around 17.3 million people die due o cardiac arrhythmia as indicated by World Health Organization (WHO), the kind of disruptions that is caused by sinoatrial is easily captured in Electrocardiography (ECG) readings; it records in all the disruptions and makes a record in form of images, waveforms, numerical data and categorical data. The noisy data’s collected during a patient examination is recorded in form of a special character to prompt the missing data. With different set of distinct patients having different classes of arrhythmia the ECG easily records in all the arrhythmia class as Y dependent variable’s that is used to pass the collected data from the ECG to the proposed system in the research study, which give’s in an architectural model for detecting arrhythmia with considering a combination of Machine Learning Techniques. Random Forest is mainly used in for feature extraction for the dataset that is trained and tested followed by passing the updated dataset to a combination of different Machine Learning Techniques in order to provide accurate training and testing accuracy results from the dataset received. The use of the proposed model is in hospitals that have huge amount of dataset, with recursive training and testing of the model with the right Machine Learning Algorithm for huge amount of dataset it yields results fast in a short span of time, that can help save several life forms in a very short period of time

scholarly journals Node Behavior Classification for Traffic Prediction in Optical Burst Switched Networks using Machine Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1964-1968
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Traffic Prediction ◽  
Support Vector ◽  
Huge Amount ◽  
Switched Networks ◽  
Switched Network ◽  
Learning Techniques ◽  
Use Of Internet ◽  
Optical Burst

Currently due to massive use of internet there is need of huge amount of bandwidth. The utilization of bandwidth can be managed up with optical burst switched networks. These networks cannot provide good QoS due to problems like wavelength contention and congestion problem. Also it is not necessary that contention in a network leads to congestion. It can be due to nodes behavior which affects the flow of traffic from source to destination. Hence there is a need to classify the traffic through the node at correct juncture to avoid congestion. This can be achieved using machine learning techniques. In this paper, support vector machine, AdaBoost classifier and Bagging classifier are evaluated .Experimental work is carried on Optical Burst Switched network dataset using 22 attributes which is available on UCI repository. The results show that bagging classifier performed better with accuracy of 95% in classifying the nodes behavior.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Sign in / Sign up

Export Citation Format

Share Document