Comparison of Machine Learning Algorithms to Increase Prediction Accuracy of COPD Domain

2017 ◽  
pp. 247-254 ◽  
Author(s):  
Lokman Saleh ◽  
Hamid Mcheick ◽  
Hicham Ajami ◽  
Hafedh Mili ◽  
Joumana Dargham
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals The Right Time for Crowd Communication during Campaigns for Sustainable Success of Crowdfunding: Evidence from Kickstarter Platform

2020 ◽  
Vol 12 (18) ◽  
pp. 7642 ◽  
Author(s):  
Michael J. Ryoba ◽  
Shaojian Qu ◽  
Ying Ji ◽  
Deqiang Qu
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Prediction Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Success Prediction ◽  
Combined Effects ◽  
Baseline Model ◽  
Predicting Success ◽  
The Right

Only a small percentage of crowdfunding projects succeed in securing funds, the fact of which puts the sustainability of crowdfunding platforms at risk. Researchers have examined the influences of phased aspects of communication, drawn from updates and comments, on success of crowdfunding campaigns, but in most cases they have focused on the combined effects of the aspects. This paper investigated campaign success contribution of various combinations of phased communication aspects from updates and comments, the best of which can help creators to successfully manage campaigns by focusing on the important communication aspects. Metaheuristic and machine learning algorithms were used to search and evaluate the best combination of phased communication aspects for predicting success using Kickstarter dataset. The study found that the number of updates in phase one, the polarity of comments in phase two, readability of updates and polarity of comments in phase three, and the polarity of comments in phase five are the most important communication aspects in predicting campaign success. Moreover, the success prediction accuracy with the aspects identified after phasing is more than the baseline model without phasing. Our findings can help crowdfunding actors to focus on the important communication aspects leading to improved likelihood of success.

scholarly journals Prediction Approaches for Smart Cultivation: A Comparative Study

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Amitabha Chakrabarty ◽  
Nafees Mansoor ◽  
Muhammad Irfan Uddin ◽  
Mosleh Hmoud Al-adaileh ◽  
Nizar Alsharif ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Crop Production ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Knowledge Based ◽  
The Neural Network ◽  
Long Time ◽  
Agriculture Extension

Crop cultivation is one of the oldest activities of civilization. For a long time, crop production was carried out based on knowledge passed from generation to generation. However, due to the rapid growth in the human population of the world, human knowledge-based cultivation is not enough to meet the demanding need. To address this issue, the usage of machine learning-based tools has been studied in this paper. An experiment has been carried out over 0.3 million data. This dataset identifies 46 prominent parameters for cultivation, which is collected from the Department of Agriculture Extension, Bangladesh. Comparison between neural networks and numbers of machine learning algorithms has been carried out in this research. It is observed that the neural network outperforms the other methods by maintaining an average prediction accuracy of 96.06% for six different crops. Other contemporary machine learning algorithms, namely, support vector machine, random forest, and logistic regression, have average prediction accuracy of around 68.9%, 91.2%, and 62.39%, respectively.

Predicting Porosity and Water Saturation from Well-Log Data Using Probabilistic Multi-Task Neural Network with Normalizing Flows

2021 ◽  
Author(s):  
Jinwoo Lee ◽  
Minsu Kwon ◽  
Youngjun Hong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Prediction Accuracy ◽  
Water Saturation ◽  
Learning Algorithms ◽  
Core Sample ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Well Log ◽  
Sample Data

Abstract In the oil and gas exploration process, understanding the hydrocarbon distribution of a reservoir is important. Well-log and core sample data such as porosity and water saturation are widely used for this purpose. With porosity and water saturation, we can calculate hydrocarbon volume more accurately than using well-log solely. However, as obtaining core sample data is expensive and time-consuming, predicting it with well-log can be a valuable solution for early-stage exploration since acquiring well-log is relatively economic and swift. Recently, numerous studies applied machine learning algorithms to predict core data from well-log. To the best of our knowledge, most works provide point estimation without probabilistic distribution modeling. In this paper, we developed a probabilistic deep neural network to provide uncertainty via confidence interval. Besides, we employed normalizing flows and multi-task learning to improve prediction accuracy. With this approach, we present the model's uncertainty that can be reliable information for decision making. Furthermore, we demonstrate our model outperforms other supervised machine learning algorithms regards to prediction accuracy.

scholarly journals Exploring Complementary Models Consisting of Machine Learning Algorithms for Landslide Susceptibility Mapping

2021 ◽  
Vol 10 (10) ◽  
pp. 639
Author(s):  
Han Hu ◽  
Changming Wang ◽  
Zhu Liang ◽  
Ruiyuan Gao ◽  
Bailong Li
Keyword(s):  
Machine Learning ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Prediction Accuracy ◽  
Learning Algorithms ◽  
Specific Area ◽  
Machine Learning Algorithms ◽  
Landslide Susceptibility Mapping ◽  
Human Beings ◽  
Statistical Measures

Landslides frequently occur because of natural or human factors. Landslides cause huge losses to the economy as well as human beings every year around the globe. Landslide susceptibility prediction (LSP) plays a key role in the prevention of landslides and has been under investigation for years. Although new machine learning algorithms have achieved excellent performance in terms of prediction accuracy, a sufficient quantity of training samples is essential. In contrast, it is hard to obtain enough landslide samples in most the areas, especially for the county-level area. The present study aims to explore an optimization model in conjunction with conventional unsupervised and supervised learning methods, which performs well with respect to prediction accuracy and comprehensibility. Logistic regression (LR), fuzzy c-means clustering (FCM) and factor analysis (FA) were combined to establish four models: LR model, FCM coupled with LR model, FA coupled with LR model, and FCM, FA coupled with LR model and applied in a specific area. Firstly, an inventory with 114 landslides and 10 conditioning factors was prepared for modeling. Subsequently, four models were applied to LSP. Finally, the performance was evaluated and compared by k-fold cross-validation based on statistical measures. The results showed that the coupled model by FCM, FA and LR achieved the greatest performance among these models with the AUC (Area under the curve) value of 0.827, accuracy of 85.25%, sensitivity of 74.96% and specificity of 86.21%. While the LR model performed the worst with an AUC value of 0.736, accuracy of 77%, sensitivity of 62.52% and specificity of 72.55%. It was concluded that both the dimension reduction and sample size should be considered in modeling, and the performance can be enhanced by combining complementary methods. The combination of models should be more flexible and purposeful. This work provides reference for related research and better guidance to engineering activities, decision-making by local administrations and land use planning.

scholarly journals Anomaly detection and missing data imputation in building energy data for automated data pre-processing

2021 ◽  
Vol 2069 (1) ◽  
pp. 012144
Author(s):  
K Takahashi ◽  
R Ooka ◽  
S Ikeda
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Missing Data ◽  
Anomaly Detection ◽  
Prediction Accuracy ◽  
Energy Savings ◽  
Predictive Accuracy ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Detection Accuracy

Abstract A new trend in building automation is the implementation of smart energy management systems to measure and control building systems without a need for decision-making by human operators. Artificial intelligence can optimize these systems by predicting future demand to make informed decisions about how to efficiently operate individual equipment. These machine learning algorithms use historical data to learn demand trends and require high quality datasets in order to make accurate predictions. But because of issues with data transmission or sensor errors, real world datasets often contain outliers or have data missing. In most research settings, these values can be simply omitted, but in practice, anomalies compromise the automation system’s prediction accuracy, rendering it unable to maximize energy savings. This study explores different machine learning algorithms for anomaly detection for automatically pre-processing incoming data using a case study on an actual electrical demand in a hospital building in Japan, namely cluster-based techniques such as k-means clustering and neural network-based approaches such as the autoencoder. Once anomalies were identified, the missing data was filled with prediction values from a deep neural network model. The newly composed data was then evaluated based on detection accuracy, prediction accuracy and training time. The proposed method of processing anomaly values allows the prediction model to process collected data without interruption, and shows similar predictive accuracy as manually processing the data. These predictions allow energy systems to optimize HVAC equipment control, increasing energy savings and reducing peak building loads.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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