scholarly journals Forecasting Exchange Rates Better than the Random Walk Thanks to Machine Learning Techniques

2014 ◽  
Author(s):  
Christophe Amat ◽  
Tomasz Kamil Michalski ◽  
Gilles Stoltz
Keyword(s):  
Machine Learning ◽  
Random Walk ◽  
Exchange Rates ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Better Than

Application of Machine Learning Algorithms in Stock Market Prediction

2020 ◽  
pp. 153-180
Author(s):  
Sumit Kumar ◽  
Sanlap Acharya
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Better Than

The prediction of stock prices has always been a very challenging problem for investors. Using machine learning techniques to predict stock prices is also one of the favourite topics for academics working in this domain. This chapter discusses five supervised learning techniques and two unsupervised learning techniques to solve the problem of stock price prediction and has compared the performances of all the algorithms. Among the supervised learning techniques, Long Short-Term Memory (LSTM) algorithm performed better than the others whereas, among the unsupervised learning techniques, Restricted Boltzmann Machine (RBM) performed better. RBM is found to be performing even better than LSTM.

Story Point-Based Effort Estimation Model with Machine Learning Techniques

2020 ◽  
Vol 30 (01) ◽  
pp. 43-66
Author(s):  
Muaz Gultekin ◽  
Oya Kalipsiz
Keyword(s):  
Machine Learning ◽  
Empirical Evaluation ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Effort Estimation ◽  
Estimation Model ◽  
Software Projects ◽  
Learning Techniques ◽  
Better Than

Until now, numerous effort estimation models for software projects have been developed, most of them producing accurate results but not providing the flexibility to decision makers during the software development process. The main objective of this study is to objectively and accurately estimate the effort when using the Scrum methodology. A dynamic effort estimation model is developed by using regression-based machine learning algorithms. Story point as a unit of measure is used for estimating the effort involved in an issue. Projects are divided into phases and the phases are respectively divided into iterations and issues. Effort estimation is performed for each issue, then the total effort is calculated with aggregate functions respectively for iteration, phase and project. This architecture of our model provides flexibility to decision makers in any case of deviation from the project plan. An empirical evaluation demonstrates that the error rate of our story point-based estimation model is better than others.

scholarly journals Predicting students’ academic performance using a modified kNN algorithm

2021 ◽  
Author(s):  
Moohanad Jawthari ◽  
Veronika Stoffová
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Nearest Neighbors ◽  
Machine Learning Techniques ◽  
Categorical Variables ◽  
Ratio Scale ◽  
K Nearest Neighbors ◽  
Learning Techniques ◽  
Nominal Variables ◽  
Better Than

AbstractThe target (dependent) variable is often influenced not only by ratio scale variables, but also by qualitative (nominal scale) variables in classification analysis. Majority of machine learning techniques accept only numerical inputs. Hence, it is necessary to encode these categorical variables into numerical values using encoding techniques. If the variable does not have relation or order between its values, assigning numbers will mislead the machine learning techniques. This paper presents a modified k-nearest-neighbors algorithm that calculates the distances values of categorical (nominal) variables without encoding them. A student’s academic performance dataset is used for testing the enhanced algorithm. It shows that the proposed algorithm outperforms standard one that needs nominal variables encoding to calculate the distance between the nominal variables. The results show the proposed algorithm preforms 14% better than standard one in accuracy, and it is not sensitive to outliers.

scholarly journals Steganalysis Using Yedrodj-net net's Convolutional Neural Networks (CNN) Method on Steganography Tools

2020 ◽  
Vol 3 ◽  
pp. 207-211
Author(s):  
Nurmi Hidayasari ◽  
Imam Riadi ◽  
Yudi Prayudi
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Learning Performance ◽  
Machine Learning Techniques ◽  
Detection Model ◽  
Learning Techniques ◽  
Net Method ◽  
Blind Steganalysis ◽  
Better Than

Steganalysis method is used to detect the presence or absence of steganography files or can be referred to anti-steganography. Steganalysis can be used for positive purposes, which is to know the weaknesses of a steganography method, so that improvements can be made. One category of steganalysis is blind steganalysis, which is a way to detect secret files without knowing what steganography method is used. Blind steganalysis is difficult to implement, but then machine learning techniques emerged that could be used to create a detection model using experimental data, one of which is Convolutional Neural Networks (CNN). A study proposes that the CNN method can detect steganography files using the latest method with a low error probability value compared to other methods, CNN Yedroudj-net. As one of the steganalysis methods with the latest machine learning steganalysis techniques, an experiment is needed to find out whether Yedroudj-net can be a steganalysis for the output of many tools commonly used for steganography applications. Knowing the performance of CNN Yedroudj-net on several steganography tools is very important, to measure the level of ability in terms of steganalysis of some of these tools. Especially so far, machine learning performance is still doubtful in blind steganalysis. Plus some previous research only focused on certain methods to prove the performance of the proposed technique, including Yedroudj-net. This study will use five tools that are Hide In Picture (HIP), OpenStego, SilentEye, Steg and S-Tools, which are not known exactly what steganography methods are used on the tools. Yedroudj-net method will be implemented in the steganography file from the output of the five tools. Then a comparison with the popular steganalysis tool is used, StegSpy. The results show that Yedroudj-net is quite capable of detecting the presence of steganography files, slightly better than StegSpy.

Effectiveness of Domain-Based Lexicons vis-à-vis General Lexicon for Aspect-Level Sentiment Analysis: A Comparative Analysis

2019 ◽  
Vol 18 (03) ◽  
pp. 1950033
Author(s):  
Madan Lal Yadav ◽  
Basav Roychoudhury
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Actual Performance ◽  
Learning Techniques ◽  
Improved Performance ◽  
Text Content ◽  
Better Than

One can either use machine learning techniques or lexicons to undertake sentiment analysis. Machine learning techniques include text classification algorithms like SVM, naive Bayes, decision tree or logistic regression, whereas lexicon-based sentiment analysis uses either general or domain-based lexicons. In this paper, we investigate the effectiveness of domain lexicons vis-à-vis general lexicon, wherein we have performed aspect-level sentiment analysis on data from three different domains, viz. car, guitar and book. While it is intuitive that domain lexicons will always perform better than general lexicons, the actual performance however may depend on the richness of the concerned domain lexicon as well as the text analysed. We used the general lexicon SentiWordNet and the corresponding domain lexicons in the aforesaid domains to compare their relative performances. The results indicate that domain lexicon used along with general lexicon performs better as compared to general lexicon or domain lexicon, when used alone. They also suggest that the performance of domain lexicons depends on the text content; and also on whether the language involves technical or non-technical words in the concerned domain. This paper makes a case for development of domain lexicons across various domains for improved performance, while gathering that they might not always perform better. It further highlights that the importance of general lexicons cannot be underestimated — the best results for aspect-level sentiment analysis are obtained, as per this paper, when both the domain and general lexicons are used side by side.

scholarly journals Artificial intelligence in education and assessment methods

2020 ◽  
Vol 9 (5) ◽  
pp. 1998-2007
Author(s):  
Balqis Al Braiki ◽  
Saad Harous ◽  
Nazar Zaki ◽  
Fady Alnajjar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Daily Life ◽  
Machine Learning Techniques ◽  
Educational Context ◽  
Course Assessment ◽  
Teaching Community ◽  
Learning Techniques ◽  
Artificial Intelligence In Education ◽  
Better Than

Today, artificial intelligence has proliferated to reach almost every wing of daily life, perhaps one of the most sensitive of these being education. While teaching, insofar as it involves training human minds, is still mostly a form of art rather than a regular science, the taking up of this elitist job by computers has triggered much debate and controversy, involving the teaching community as much as the select corporate AI giants who strive to create computers capable of teaching better than humans. This paper surveys the most relevant studies carried out in this field to date. First, it introduces AI and describes the different AI applications in field of education and course assessment. It then goes on to list the most common topics in the educational context that have been resolved through AI and machine learning techniques, and finally, some of the most promising future lines of research are discussed. 

scholarly journals EMG BASED DIAGNOSIS OF MYOPATHY AND NEUROPATHY USING MACHINE LEARNING TECHNIQUES

2020 ◽  
Vol 4 (4) ◽  
pp. 38-45
Author(s):  
Anisha C. D ◽  
Arulanand N
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Time Domain ◽  
Neuromuscular Disorders ◽  
Bayesian Optimization ◽  
Machine Learning Techniques ◽  
Diagnostic Model ◽  
Learning Techniques ◽  
The Individual ◽  
Better Than

Myopathy and Neuropathy are non-progressive and progressive neuromuscular disorders which weakens the muscles and nerves respectively. Electromyography (EMG) signals are bio signals obtained from the individual muscle cells. EMG based diagnosis for neuromuscular disorders is a safe and reliable method. Integrating the EMG signals with machine learning techniques improves the diagnostic accuracy. The proposed system performs analysis on the clinical raw EMG dataset which is obtained from the publicly available PhysioNet database. The two-channel raw EMG dataset of healthy, myopathy and neuropathy subjects are divided into samples. The Time Domain (TD) features are extracted from divided samples of each subject. The extracted features are annotated with the class label representing the state of the individual. The annotated features split into training and testing set in the standard ratio 70: 30. The comparative classification analysis on the complete annotated features set and prominent features set procured using Pearson correlation technique is performed. The features are scaled using standard scaler technique. The analysis on scaled annotated features set and scaled prominent features set is also implemented. The hyperparameter space of the classifiers are given by trial and error method. The hyperparameters of the classifiers are tuned using Bayesian optimization technique and the optimal parameters are obtained. and are fed to the tuned classifier. The classification algorithms considered in the analysis are Random Forest and Multi-Layer Perceptron Neural Network (MLPNN). The performance evaluation of the classifiers on the test data is computed using the Accuracy, Confusion Matrix, F1 Score, Precision and Recall metrics. The evaluation results of the classifiers states that Random Forest performs better than MLPNN wherein it provides an accuracy of 96 % with non-scaled Time Domain (TD) features and MLPNN outperforms better than Random Forest with an accuracy of 97% on scaled Time Domain (TD) features which is higher than the existing systems. The inferences from the evaluation results is that Bayesian optimization tuned classifiers improves the accuracy which provides a robust diagnostic model for neuromuscular disorder diagnosis.

scholarly journals Combining ensemble Kalman filter and reservoir computing to predict spatiotemporal chaotic systems from imperfect observations and models

2021 ◽  
Vol 14 (9) ◽  
pp. 5623-5635 ◽  
Author(s):  
Futo Tomizawa ◽  
Yohei Sawada
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Chaotic Systems ◽  
Weather Prediction ◽  
Machine Learning Techniques ◽  
Reservoir Computing ◽  
Learning Techniques ◽  
Ensemble Transform Kalman Filter ◽  
Lorenz 96 ◽  
Better Than

Abstract. Prediction of spatiotemporal chaotic systems is important in various fields, such as numerical weather prediction (NWP). While data assimilation methods have been applied in NWP, machine learning techniques, such as reservoir computing (RC), have recently been recognized as promising tools to predict spatiotemporal chaotic systems. However, the sensitivity of the skill of the machine-learning-based prediction to the imperfectness of observations is unclear. In this study, we evaluate the skill of RC with noisy and sparsely distributed observations. We intensively compare the performances of RC and local ensemble transform Kalman filter (LETKF) by applying them to the prediction of the Lorenz 96 system. In order to increase the scalability to larger systems, we applied a parallelized RC framework. Although RC can successfully predict the Lorenz 96 system if the system is perfectly observed, we find that RC is vulnerable to observation sparsity compared with LETKF. To overcome this limitation of RC, we propose to combine LETKF and RC. In our proposed method, the system is predicted by RC that learned the analysis time series estimated by LETKF. Our proposed method can successfully predict the Lorenz 96 system using noisy and sparsely distributed observations. Most importantly, our method can predict better than LETKF when the process-based model is imperfect.

Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques

2015 ◽  
Vol 34 (7) ◽  
pp. 560-573 ◽  
Author(s):  
Vasilios Plakandaras ◽  
Theophilos Papadimitriou ◽  
Periklis Gogas
Keyword(s):  
Machine Learning ◽  
Exchange Rates ◽  
Machine Learning Techniques ◽  
Learning Techniques
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