Path Planning Optimization in SDN Using Machine Learning Techniques

2021 ◽  
Author(s):  
Marlon Rodriguez ◽  
Ricardo Flores Moyano ◽  
Noel Perez ◽  
Daniel Riofrio ◽  
Diego Benitez
Keyword(s):  
Machine Learning ◽  
Path Planning ◽  
Machine Learning Techniques ◽  
Planning Optimization ◽  
Learning Techniques

scholarly journals Kaip sukurti intelektualesnę maršrutų planavimo programą?

2009 ◽  
Vol 50 ◽  
pp. 311-315
Author(s):  
Akira Imada
Keyword(s):  
Machine Learning ◽  
Path Planning ◽  
Intelligent Agent ◽  
Machine Learning Techniques ◽  
Human Intelligence ◽  
Learning Techniques ◽  
The Way

Šiame straipsnyje svarstoma, kiek intelektualu yra tai, kas vadinama dirbtiniu intelektu. Žmogaus intelektas ne visada yra efektyvus, ne visada optimalus; dažnai yra daugiau ar mažiau spontaniškas, nenuspėjamas. Net patekus į panašias situacijas, kokiose jau teko būti, mūsų elgsena gali būti kitokia nei praeityje. Siekdami realizuoti analogiškai lanksčius dirbtinio intelekto agentus, siūlome bandomąją užduotį, kurioje turime begalinį kiekį vienodai svarbių sprendinių, ir ištiriame, ar programinis agentas, apmokytas mašininio mokymo metodais, elgsis taip pat intelektualiai, kaip ir žmonės.How Can We Design a More Intelligent Path-Planning?Akira Imada SummaryWe contemplate in this paper on how it would be intelligent when we say artifi cial intelligence. Human intelligence is not always effi cient, nor worthy enough to be called optimized but rather spontaneous or unpredictable more or less. Even when we come across a similar situation as before our behavior may be different than in the way we had reacted then. Aiming such a fl exibility in an artifi cial intelligent agent, we propose here a benchmark in which we have infi nite number of equally valuable solutions. And then we observe if an agent trained by some of the machine learning techniques will behave in such an intelligent way as human’s.

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.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

Sign in / Sign up

Export Citation Format

Share Document