Zero-Shot Machine Learning Technique for Classification of Multi-User Big Data Workloads

Official statistics on monthly export values have a publicity lag between the current period and the published publication. None of the previous researchers estimated the value of exports for the monthly period. This circumstance is due to limitations in obtaining supporting data that can predict the criteria for the current export value of goods. AIS data is one type of big data that can provide solutions in producing the latest indicators to forecast export values. Statistical Methods and Conventional Machine Learning are implemented as forecasting methods. Seasonal ARIMA and Artificial Neural Network (ANN) methods are both used in research to forecast the value of Indonesia’s exports. However, ANN has a weakness that requires high computational costs to obtain optimal parameters. Genetic Algorithm (GA) is effective in increasing ANN accuracy. Based on these backgrounds, this paper aims to develop and select an AIS indicator to predict the monthly export value in Indonesia and optimize ANN performance by combining the ANN algorithm with the genetic algorithm (GA-ANN). The research successfully established five indicators that can be used as predictors in the forecasting model. According to the model evaluation results, the genetic algorithm has succeeded in improving the performance of the ANN model as indicated by the resulting RMSE GA-ANN value, which is smaller than the RMSE of the ANN model.

Download Full-text

An Ensemble Machine Learning Technique for Functional Requirement Classification

Symmetry ◽

10.3390/sym12101601 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1601

Author(s):

Nouf Rahimi ◽

Fathy Eassa ◽

Lamiaa Elrefaei

Keyword(s):

Machine Learning ◽

Requirement Engineering ◽

Support Vector ◽

Functional Requirement ◽

Machine Learning Technique ◽

Engineering Software ◽

Ensemble Machine Learning ◽

Learning Technique ◽

Combined Models

In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifying FR statements to improve their accuracy and availability. This technique combines different ML models and uses enhanced accuracy as a weight in the weighted ensemble voting approach. The five combined models are Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, and Support Vector Classification (SVC). The technique was implemented, trained, and tested using a collected dataset. The accuracy of classifying FR was 99.45%, and the required time was 0.7 s.

Download Full-text

Polarity-based Classification of Subjective Micro-posts using Machine Learning Technique

International Journal of Scientific and Engineering Research ◽

10.14299/ijser.2018.07.02 ◽

2018 ◽

Vol 9 (7) ◽

pp. 373

Author(s):

Arshad Arain ◽

Rajesh kumar ◽

Nudra Siddiquie ◽

Komal Naz ◽

Sabeen gul ◽

...

Keyword(s):

Machine Learning ◽

Machine Learning Technique ◽

Learning Technique

Download Full-text

Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns

Data Science Journal ◽

10.2481/dsj.8.s59 ◽

2009 ◽

Vol 8 ◽

pp. S59-S67 ◽

Cited By ~ 1

Author(s):

H Kimura ◽

H Kawashima ◽

H Kusaka ◽

H Kitagawa

Keyword(s):

Machine Learning ◽

Machine Learning Technique ◽

Learning Technique

Download Full-text

A Weight Based Labeled Classifier Using Machine Learning Technique for Classification of Medical Data

Revue d intelligence artificielle ◽

10.18280/ria.350104 ◽

2021 ◽

Vol 35 (1) ◽

pp. 39-46

Author(s):

Mohammed Zaheer Ahmed ◽

Chitraivel Mahesh

Keyword(s):

Machine Learning ◽

Medical Data ◽

Machine Learning Technique ◽

Learning Technique

Download Full-text

Classification of specialty coffees using machine learning techniques

Research Society and Development ◽

10.33448/rsd-v10i5.14732 ◽

2021 ◽

Vol 10 (5) ◽

pp. e13110514732

Author(s):

Paulo César Ossani ◽

Diogo Francisco Rossoni ◽

Marcelo Ângelo Cirillo ◽

Flávio Meira Borém

Keyword(s):

Machine Learning ◽

Supervised Classification ◽

Consumer Acceptance ◽

Machine Learning Techniques ◽

Machine Learning Technique ◽

Learning Techniques ◽

Learning Technique ◽

Evaluation Test ◽

New Methodologies

Specialty coffees have a big importance in the economic scenario, and its sensory quality is appreciated by the productive sector and by the market. Researches have been constantly carried out in the search for better blends in order to add value and differentiate prices according to the product quality. To accomplish that, new methodologies must be explored, taking into consideration factors that might differentiate the particularities of each consumer and/or product. Thus, this article suggests the use of the machine learning technique in the construction of supervised classification and identification models. In a sensory evaluation test for consumer acceptance using four classes of specialty coffees, applied to four groups of trained and untrained consumers, features such as flavor, body, sweetness and general grade were evaluated. The use of machine learning is viable because it allows the classification and identification of specialty coffees produced in different altitudes and different processing methods.

Download Full-text