scholarly journals Comparative Performance of Machine Learning Algorithms in the Prediction of Indoor Daylight Illuminances

2020 ◽  
Vol 12 (11) ◽  
pp. 4471 ◽  
Author(s):  
Jack Ngarambe ◽  
Amina Irakoze ◽  
Geun Young Yun ◽  
Gon Kim
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Indoor Spaces

The performance of machine learning (ML) algorithms depends on the nature of the problem at hand. ML-based modeling, therefore, should employ suitable algorithms where optimum results are desired. The purpose of the current study was to explore the potential applications of ML algorithms in modeling daylight in indoor spaces and ultimately identify the optimum algorithm. We thus developed and compared the performance of four common ML algorithms: generalized linear models, deep neural networks, random forest, and gradient boosting models in predicting the distribution of indoor daylight illuminances. We found that deep neural networks, which showed a determination of coefficient (R2) of 0.99, outperformed the other algorithms. Additionally, we explored the use of long short-term memory to forecast the distribution of daylight at a particular future time. Our results show that long short-term memory is accurate and reliable (R2 = 0.92). Our findings provide a basis for discussions on ML algorithms’ use in modeling daylight in indoor spaces, which may ultimately result in efficient tools for estimating daylight performance in the primary stages of building design and daylight control schemes for energy efficiency.

scholarly journals Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model

2021 ◽  
Vol 7 ◽  
pp. e645
Author(s):  
Ramish Jamil ◽  
Imran Ashraf ◽  
Furqan Rustam ◽  
Eysha Saad ◽  
Arif Mehmood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

Sarcasm emerges as a common phenomenon across social networking sites because people express their negative thoughts, hatred and opinions using positive vocabulary which makes it a challenging task to detect sarcasm. Although various studies have investigated the sarcasm detection on baseline datasets, this work is the first to detect sarcasm from a multi-domain dataset that is constructed by combining Twitter and News Headlines datasets. This study proposes a hybrid approach where the convolutional neural networks (CNN) are used for feature extraction while the long short-term memory (LSTM) is trained and tested on those features. For performance analysis, several machine learning algorithms such as random forest, support vector classifier, extra tree classifier and decision tree are used. The performance of both the proposed model and machine learning algorithms is analyzed using the term frequency-inverse document frequency, bag of words approach, and global vectors for word representations. Experimental results indicate that the proposed model surpasses the performance of the traditional machine learning algorithms with an accuracy of 91.60%. Several state-of-the-art approaches for sarcasm detection are compared with the proposed model and results suggest that the proposed model outperforms these approaches concerning the precision, recall and F1 scores. The proposed model is accurate, robust, and performs sarcasm detection on a multi-domain dataset.

scholarly journals SEO Ministration : Content Related Tag Suggestion Using One vs Rest

2021 ◽  
pp. 245-253
Author(s):  
Dyapa Sravan Reddy ◽  
Lakshmi Prasanna Reddy ◽  
Kandibanda Sai Santhosh ◽  
Virrat Devaser
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

SEO Analyst pays a lot of time finding relevant tags for their articles and in some cases, they are unaware of the content topics. The current proposed ML model will recommend content-related tags so that the Content writers/SEO analyst will be having an overview regarding the content and minimizes their time spent on unknown articles. Machine Learning algorithms have a plethora of applications and the extent of their real-life implementations cannot be estimated. Using algorithms like One vs Rest (OVR), Long Short-Term Memory (LSTM), this study has analyzed how Machine Learning can be useful for tag suggestions for a topic. The training of the model with One vs Rest turned out to deliver more accurate results than others. This Study certainly answers how One vs Rest is used for tag suggestions that are needed to promote a website and further studies are required to suggest keywords required.

scholarly journals A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM

2020 ◽  
Vol 10 (19) ◽  
pp. 6755
Author(s):  
Carlos Iturrino Garcia ◽  
Francesco Grasso ◽  
Antonio Luchetta ◽  
Maria Cristina Piccirilli ◽  
Libero Paolucci ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Validation Dataset ◽  
Short Term ◽  
Current Time ◽  
Term Memory ◽  
Long Short Term Memory

The use of electronic loads has improved many aspects of everyday life, permitting more efficient, precise and automated process. As a drawback, the nonlinear behavior of these systems entails the injection of electrical disturbances on the power grid that can cause distortion of voltage and current. In order to adopt countermeasures, it is important to detect and classify these disturbances. To do this, several Machine Learning Algorithms are currently exploited. Among them, for the present work, the Long Short Term Memory (LSTM), the Convolutional Neural Networks (CNN), the Convolutional Neural Networks Long Short Term Memory (CNN-LSTM) and the CNN-LSTM with adjusted hyperparameters are compared. As a preliminary stage of the research, the voltage and current time signals are simulated using MATLAB Simulink. Thanks to the simulation results, it is possible to acquire a current and voltage dataset with which the identification algorithms are trained, validated and tested. These datasets include simulations of several disturbances such as Sag, Swell, Harmonics, Transient, Notch and Interruption. Data Augmentation techniques are used in order to increase the variability of the training and validation dataset in order to obtain a generalized result. After that, the networks are fed with an experimental dataset of voltage and current field measurements containing the disturbances mentioned above. The networks have been compared, resulting in a 79.14% correct classification rate with the LSTM network versus a 84.58% for the CNN, 84.76% for the CNN-LSTM and a 83.66% for the CNN-LSTM with adjusted hyperparameters. All of these networks are tested using real measurements.

ClickbaitTR: Dataset for clickbait detection from Turkish news sites and social media with a comparative analysis via machine learning algorithms

2021 ◽  
pp. 016555152110077
Author(s):  
Şura Genç ◽  
Elif Surer
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Logistic Regression ◽  
Random Forest ◽  
Short Term Memory ◽  
Ensemble Classifier ◽  
Machine Learning Algorithms ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Clickbait is a strategy that aims to attract people’s attention and direct them to specific content. Clickbait titles, created by the information that is not included in the main content or using intriguing expressions with various text-related features, have become very popular, especially in social media. This study expands the Turkish clickbait dataset that we had constructed for clickbait detection in our proof-of-concept study, written in Turkish. We achieve a 48,060 sample size by adding 8859 tweets and release a publicly available dataset – ClickbaitTR – with its open-source data analysis library. We apply machine learning algorithms such as Artificial Neural Network (ANN), Logistic Regression, Random Forest, Long Short-Term Memory Network (LSTM), Bidirectional Long Short-Term Memory (BiLSTM) and Ensemble Classifier on 48,060 news headlines extracted from Twitter. The results show that the Logistic Regression algorithm has 85% accuracy; the Random Forest algorithm has a performance of 86% accuracy; the LSTM has 93% accuracy; the ANN has 93% accuracy; the Ensemble Classifier has 93% accuracy; and finally, the BiLSTM has 97% accuracy. A thorough discussion is provided for the psychological aspects of clickbait strategy focusing on curiosity and interest arousal. In addition to a successful clickbait detection performance and the detailed analysis of clickbait sentences in terms of language and psychological aspects, this study also contributes to clickbait detection studies with the largest clickbait dataset in Turkish.

scholarly journals Alzheimer’s Disease Prediction Using Long Short-Term Memory with Early-Phase 18F-Florbetaben Imaging Data

2021 ◽  
Author(s):  
Hyeon Kang ◽  
Kyung Won Park ◽  
Do-Young Kang
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Dual Phase ◽  
Imaging Data ◽  
Short Term ◽  
Term Memory ◽  
Imaging Test ◽  
Long Short Term Memory

Abstract Single amyloid-beta (Aβ) imaging test is not enough to rise to the challenge of making AD diagnosis because of Aβ-negative AD or positive cognitively normal (CN). We aimed to distinguish AD from CN with dual-phase 18F-Florbetaben (FBB) via machine learning algorithms and evaluate the AD positivity scores compared to delay-phase FBB (dFBB) which is currently adopted for AD diagnosis.A total of 264 patients (74 CN and 190 AD), who underwent FBB imaging test and neuropsychological tests were retrospectively analyzed. We compared three kinds of machine learning-based models and evaluated their performance with 4-fold cross validation.AD positivity scores estimated from dual-phase FBB showed better accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) for AD detection (ACC: 84.091 %, AUROC: 0.900) than those from dFBB imaging (ACC: 81.364 %, AUROC: 0.890). The association between predicted AD positivity and the AD occurrence were compared, the use of dual-phase FBB was highest (OR: 56.333), followed by dFBB (OR: 35.182).These results show that the combined model which interpret dual-phase FBB with long short-term memory can be used to provide a more accurate AD positivity score, which shows a closer association with AD, than the prediction with only single-phase FBB.

scholarly journals Using Machine Learning Methods in the Financial Market for Technical Analysis Based on Hybrid Models

2020 ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hybrid Model ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Input Structure ◽  
Long Short Term Memory

In this study, it is presented a new hybrid model based on deep neural networks to predict the direction and magnitude of the Forex market movement in the short term. The overall model presented is based on the scalping strategy and is provided for high frequency transactions. The proposed hybrid model is based on a combination of three models based on deep neural networks. The first model is a deep neural network with a multi-input structure consisting of a combination of Long Short Term Memory layers. The second model is a deep neural network with a multi-input structure made of a combination of one-dimensional Convolutional Neural network layers. The third model has a simpler structure and is a multi-input model of the Multi-Layer Perceptron layers. The overall model was also a model based on the majority vote of three top models. This study showed that models based on Long Short-Term Memory layers provided better results than the other models and even hybrid models with more than 70% accurate.

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