Integrating machine-learning techniques in knowledge-based systems verification

1993 ◽  
pp. 405-414
Author(s):  
Hakim Lounis
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Learning Techniques

Short or Long Review? - Text Analytics and Machine Learning Approaches to Online Reputation

2021 ◽  
Vol 9 (1) ◽  
pp. 28-40
Author(s):  
Xiangming Samuel Li
Keyword(s):  
Machine Learning ◽  
Sharing Economy ◽  
Machine Learning Techniques ◽  
Reputation Systems ◽  
Learning Approaches ◽  
Text Analytics ◽  
Knowledge Based ◽  
Learning Techniques ◽  
Internet Platform ◽  
Online Reputation

This paper first constructs a numerical text review score by applying text analytics and machine learning techniques to more than three million online text reviews collected from the Airbnb platform. Next, we employ the text review score to analyze the effect of review length on text review score and obtain insights on the interplay between the text review length and online reputation. The main contributions of this paper include: experimenting with advanced text analytics and machine learning approaches to assess online reputation; constructing an innovative text review score as a new online reputation measure; building a large knowledge-based review corpus with labels; and obtaining important insights about the effects of text review length on online reputation. Further, it has managerial and business implications for all internet platform markets and the sharing economy players seeking to build more effective online reputation systems.

MACHINE APPROACHES TO PROTEIN FEATURE PREDICTION

1992 ◽  
Vol 03 (supp01) ◽  
pp. 183-193 ◽  
Author(s):  
David K. Tcheng ◽  
Shankar Subramaniam
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Protein Structure ◽  
Homology Modeling ◽  
Three Dimensional ◽  
Antibody Fragments ◽  
Machine Learning Techniques ◽  
Knowledge Based ◽  
Learning Techniques ◽  
Protein Feature

Knowledge-based approaches are being increasingly used in predicting protein structure and motifs. Machine learning techniques such as neural networks and decision-trees have become invaluable tools for these approaches. This paper describes the use of machine learning in predicting sequence-based motifs in antibody fragments. Given the limited number of three dimensional structures and the plethora of sequences, this technique is useful for homology modeling of three dimensional structures of antibody fragments.

Artificial intellugence and knowledge based systems in molecular biology

1994 ◽  
Vol 9 (3) ◽  
pp. 287-300
Author(s):  
John Fox ◽  
Christopher J. Rawlings
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Molecular Data ◽  
Genetic Maps ◽  
Machine Learning Techniques ◽  
Knowledge Based Systems ◽  
Complex Molecules ◽  
Knowledge Based ◽  
Learning Techniques ◽  
Computer Scientists

AbstractOver the last ten years, molecular biologists and computer scientists have experimented with various artificial intelligence techniques, notably knowledge based and expert systems, qualitative simulation, natural language processing and various machine learning techniques. These techniques have been applied to problems in molecular data analysis, construction of advanced databases and modelling of biological systems. Practical results are now being obtained, notably in the representation and recognition of genetically significant structures, the assembly of genetic maps and prediction of the structure of complex molecules such as proteins. The paper outlines the principal methods used, surveys the findings to date, and identifies promising trends and current limitations.

scholarly journals Special Issue: Machine Learning in Design

1994 ◽  
Vol 8 (2) ◽  
pp. 81-82 ◽  
Author(s):  
Mary Lou Maher ◽  
David C. Brown ◽  
Alex Duffy
Keyword(s):  
Machine Learning ◽  
The Other ◽  
Machine Learning Techniques ◽  
Design Knowledge ◽  
Special Issue ◽  
Perceived Need ◽  
Knowledge Based ◽  
Learning Techniques ◽  
Knowledge Based Design

The linking of research in machine learning with research in knowledge-based design is such that each of the two areas benefit from the consideration of the other. The use of machine learning in design addresses the perceived need to support the capture and representation of design knowledge, because handcrafting a representation is a difficult and time-consuming task. In addition, design provides a task with which to investigate the usefulness of existing machine learning techniques, and, perhaps, to discover new ones.

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.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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