scholarly journals An Effective Hybrid Approach Based on Machine Learning Techniques for Auto-Translation: Japanese to English

2021 ◽  
Author(s):  
Mhd Saeed Sharif ◽  
Bilyaminu Auwal Romo ◽  
Harry Maltby ◽  
Ali Al-Bayatti
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Hybrid Machine Learning Approach for Freeway Traffic Speed Estimation

2020 ◽  
Vol 2674 (10) ◽  
pp. 68-78
Author(s):  
Zhao Zhang ◽  
Yun Yuan ◽  
Xianfeng (Terry) Yang
Keyword(s):  
Machine Learning ◽  
Flow Model ◽  
Hybrid Approach ◽  
Traffic Monitoring ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Freeway Traffic ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Traffic Speed Estimation

Accurate and timely estimation of freeway traffic speeds by short segments plays an important role in traffic monitoring systems. In the literature, the ability of machine learning techniques to capture the stochastic characteristics of traffic has been proved. Also, the deployment of intelligent transportation systems (ITSs) has provided enriched traffic data, which enables the adoption of a variety of machine learning methods to estimate freeway traffic speeds. However, the limitation of data quality and coverage remain a big challenge in current traffic monitoring systems. To overcome this problem, this study aims to develop a hybrid machine learning approach, by creating a new training variable based on the second-order traffic flow model, to improve the accuracy of traffic speed estimation. Grounded on a novel integrated framework, the estimation is performed using three machine learning techniques, that is, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). All three models are trained with the integrated dataset including the traffic flow model estimates and the iPeMS and PeMS data from the Utah Department of Transportation (DOT). Further using the PeMS data as the ground truth for model evaluation, the comparisons between the hybrid approach and pure machine learning models show that the hybrid approach can effectively capture the time-varying pattern of the traffic and help improve the estimation accuracy.

Arabic tweets sentiment analysis – a hybrid scheme

2016 ◽  
Vol 42 (6) ◽  
pp. 782-797 ◽  
Author(s):  
Haifa K. Aldayel ◽  
Aqil M. Azmi
Keyword(s):  
Machine Learning ◽  
Saudi Arabia ◽  
Hybrid Approach ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Good Source ◽  
Learning Classifier ◽  
Learning Techniques ◽  
Semantic Orientation ◽  
F Measure

The fact that people freely express their opinions and ideas in no more than 140 characters makes Twitter one of the most prevalent social networking websites in the world. Being popular in Saudi Arabia, we believe that tweets are a good source to capture the public’s sentiment, especially since the country is in a fractious region. Going over the challenges and the difficulties that the Arabic tweets present – using Saudi Arabia as a basis – we propose our solution. A typical problem is the practice of tweeting in dialectical Arabic. Based on our observation we recommend a hybrid approach that combines semantic orientation and machine learning techniques. Through this approach, the lexical-based classifier will label the training data, a time-consuming task often prepared manually. The output of the lexical classifier will be used as training data for the SVM machine learning classifier. The experiments show that our hybrid approach improved the F-measure of the lexical classifier by 5.76% while the accuracy jumped by 16.41%, achieving an overall F-measure and accuracy of 84 and 84.01% respectively.

NETWORK ANOMALIES DETECTION APPROACH BASED ON WEIGHTED VOTING

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Information Systems ◽  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
Computationally Efficient ◽  
Weighted Voting ◽  
Detection Systems ◽  
Learning Techniques ◽  
Detection Approach ◽  
Accuracy Increase

To avoid information systems malfunction, their integrity disruption, availability violation as well as data confidentiality, it is necessary to detect anomalies in information system operation as quickly as possible. The anomalies are usually caused by malicious activity – information systems attacks. However, the current approaches to detect anomalies in information systems functioning have never been perfect. In particular, statistical and signature-based techniques do not allow detection of anomalies based on modifications of well-known attacks, dynamic approaches based on machine learning techniques result in false responses and frequent anomaly miss-outs. Therefore, various hybrid solutions are being frequently offered on the basis of those two approaches. The paper suggests a hybrid approach to detect anomalies by combining computationally efficient classifiers of machine learning with accuracy increase due to weighted voting. Pilot evaluation of the developed approach proved its feasibility for anomaly detection systems.

scholarly journals Machine Learning-Based Code Auto-Completion Implementation for Firmware Developers

2020 ◽  
Vol 10 (23) ◽  
pp. 8520
Author(s):  
Junghyun Kim ◽  
Kyuman Lee ◽  
Sanghyun Choi
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Prediction Accuracy ◽  
Language Model ◽  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
Specific Domain ◽  
Efficient Manner ◽  
Source Codes ◽  
Learning Techniques

With the advent of artificial intelligence, the research paradigm in natural language processing has been transitioned from statistical methods to machine learning-based approaches. One application is to develop a deep learning-based language model that helps software engineers write code faster. Although there have already been many attempts to develop code auto-completion functionality from different research groups, a need to establish an in-house code has been identified for the following reasons: (1) a security-sensitive company (e.g., Samsung Electronics) may not want to utilize commercial tools given that there is a risk of leaked source codes and (2) commercial tools may not be applicable to the specific domain (e.g., SSD firmware development) especially if one needs to predict unique code patterns and style. This research proposes a hybrid approach that harnesses the synergy between machine learning techniques and advanced design methods aiming to develop a code auto-completion framework that helps firmware developers write code in a more efficient manner. The sensitivity analysis results show that the deterministic design results in reducing prediction accuracy as it generates output in some unexpected ways, while the probabilistic design provides a list of reasonable next code elements in which one could select it manually to increase prediction accuracy.

Automated Harmonization of Bass Lines from Bach Chorales: A Hybrid Approach

2020 ◽  
Vol 43 (2-3) ◽  
pp. 142-157
Author(s):  
Gilbert Wassermann ◽  
Mark Glickman
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Generative Models ◽  
Machine Learning Techniques ◽  
Weighted Linear Combination ◽  
Harmonic Structure ◽  
Primary Input ◽  
Music Student ◽  
Learning Techniques ◽  
Individual Voice

In this article, a combination of two novel approaches to the harmonization of chorales in the style of J. S. Bach is proposed, implemented, and profiled. The first is the use of the bass line, as opposed to the melody, as the primary input into a chorale-harmonization algorithm. The second is a compromise between methods guided by music knowledge and by machine-learning techniques, designed to mimic the way a music student learns. Specifically, our approach involves learning harmonic structure through a hidden Markov model, and determining individual voice lines by optimizing a Boltzmann pseudolikelihood function incorporating musical constraints through a weighted linear combination of constraint indicators. Although previous generative models have focused only on codifying musical rules or on machine learning without any rule specification, by using a combination of musicologically sound constraints with weights estimated from chorales composed by Bach, we were able to produce musical output in a style that closely resembles Bach's chorale harmonizations. A group of test subjects was able to distinguish which chorales were computer generated only 51.3% of the time, a rate not significantly different from guessing.

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