Analyze the impact of the epidemic on New York taxis by machine learning algorithms and recommendations for optimal prediction algorithms

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.

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Prediction of chronic obstructive pulmonary disease stages using machine learning algorithms

International Journal of Decision Support System Technology ◽

10.4018/ijdsst.286693 ◽

2022 ◽

Vol 14 (1) ◽

pp. 0-0

Keyword(s):

Machine Learning ◽

Chronic Obstructive Pulmonary Disease ◽

Pulmonary Disease ◽

Predictive Value ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Chronic Obstructive ◽

Optimal Prediction ◽

Obstructive Pulmonary Disease ◽

Disease Stages

Identifying chronic obstructive pulmonary disease (COPD) severity stages is of great importance to control the related mortality rates and reduce the associated costs. This study aims to build prediction models for COPD stages and, to compare the relative performance of five machine learning algorithms to determine the optimal prediction algorithm. This research is based on data collected from a private hospital in Egypt for the two calendar years 2018 and 2019. Five machine learning algorithms were used for the comparison. The F1 score, specificity, sensitivity, accuracy, positive predictive value and negative predictive value were the performance measures used for algorithms comparison. Analysis included 211 patients’ records. Our results show that the best performing algorithm in most of the disease stages is the PNN with the optimal prediction accuracy and hence it can be considered as a powerful prediction tool used by decision makers in predicting severity stages of COPD.

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An analytical survey on the role of machine learning algorithms in case of intrusion detection

ACCENTS Transactions on Information Security ◽

10.19101/tis.2020.517002 ◽

2020 ◽

Vol 5 (19) ◽

pp. 32-35

Author(s):

Anand Vijay ◽

Kailash Patidar ◽

Manoj Yadav ◽

Rishi Kushwah

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Handling Mechanism ◽

The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

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Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: the Evaluation of Text Classification Algorithms Using Machine Learning

10.20944/preprints201912.0220.v1 ◽

2019 ◽

Author(s):

Francesc López Seguí ◽

Ricardo Ander Egg Aguilar ◽

Gabriel de Maeztu ◽

Anna García-Altés ◽

Francesc García Cuyàs ◽

...

Keyword(s):

Machine Learning ◽

Primary Care ◽

Text Classification ◽

Learning Strategy ◽

Care Service ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Face To Face ◽

Classification Tool ◽

The Impact

Background: the primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: the study was intended to examine the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: 20 machine learning algorithms (based on 5 types of algorithms and 4 text representation techniques)were trained using a sample of 3,559 messages (169,102 words) corresponding to 2,268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: the best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables "avoiding the need of a face-to-face visit" and "increased demand" (precision = 0.98 and 0.97, respectively) rather than the variable "type of query"(precision = 0.48). Conclusion: to the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals.

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Comparison of Machine Learning Algorithms in the Interpolation and Extrapolation of Flame Describing Functions

Volume 4B: Combustion, Fuels, and Emissions ◽

10.1115/gt2019-91319 ◽

2019 ◽

Author(s):

Michael McCartney ◽

Matthias Haeringer ◽

Wolfgang Polifke

Keyword(s):

Machine Learning ◽

Gaussian Processes ◽

Spline Interpolation ◽

Learning Algorithms ◽

Predictive Performance ◽

Machine Learning Algorithms ◽

Test Time ◽

Minimal Amount ◽

Data Points ◽

The Impact

Abstract This paper examines and compares commonly used Machine Learning algorithms in their performance in interpolation and extrapolation of FDFs, based on experimental and simulation data. Algorithm performance is evaluated by interpolating and extrapolating FDFs and then the impact of errors on the limit cycle amplitudes are evaluated using the xFDF framework. The best algorithms in interpolation and extrapolation were found to be the widely used cubic spline interpolation, as well as the Gaussian Processes regressor. The data itself was found to be an important factor in defining the predictive performance of a model, therefore a method of optimally selecting data points at test time using Gaussian Processes was demonstrated. The aim of this is to allow a minimal amount of data points to be collected while still providing enough information to model the FDF accurately. The extrapolation performance was shown to decay very quickly with distance from the domain and so emphasis should be put on selecting measurement points in order to expand the covered domain. Gaussian Processes also give an indication of confidence on its predictions and is used to carry out uncertainty quantification, in order to understand model sensitivities. This was demonstrated through application to the xFDF framework.

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The impact of Negative to Positive Training Dataset Ratio on Atrial Fibrillation Classification Machine Learning Algorithms Performance

Journal of Physics Conference Series ◽

10.1088/1742-6596/1500/1/012131 ◽

2020 ◽

Vol 1500 ◽

pp. 012131

Author(s):

Firdaus ◽

Andre Herviant Juliano ◽

Naufal Rachmatullah ◽

Sarifah Putri Rafflesia ◽

Dinna Yunika Hardiyanti ◽

...

Keyword(s):

Machine Learning ◽

Atrial Fibrillation ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Dataset ◽

The Impact

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Performance of three machine learning algorithms for predicting soil organic carbon in German agricultural soil

10.5194/soil-2021-107 ◽

2021 ◽

Author(s):

Ali Sakhaee ◽

Anika Gebauer ◽

Mareike Ließ ◽

Axel Don

Keyword(s):

Machine Learning ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Agricultural Soil ◽

Learning Algorithms ◽

Model Performance ◽

Machine Learning Algorithms ◽

Support Vector ◽

Organic Soils ◽

The Impact

Abstract. Soil organic carbon (SOC), as the largest terrestrial carbon pool, has the potential to influence climate change and mitigation, and consequently SOC monitoring is important in the frameworks of different international treaties. There is therefore a need for high resolution SOC maps. Machine learning (ML) offers new opportunities to do this due to its capability for data mining of large datasets. The aim of this study, therefore, was to test three commonly used algorithms in digital soil mapping – random forest (RF), boosted regression trees (BRT) and support vector machine for regression (SVR) – on the first German Agricultural Soil Inventory to model agricultural topsoil SOC content. Nested cross-validation was implemented for model evaluation and parameter tuning. Moreover, grid search and differential evolution algorithm were applied to ensure that each algorithm was tuned and optimised suitably. The SOC content of the German Agricultural Soil Inventory was highly variable, ranging from 4 g kg−1 to 480 g kg−1. However, only 4 % of all soils contained more than 87 g kg−1 SOC and were considered organic or degraded organic soils. The results show that SVR provided the best performance with RMSE of 32 g kg−1 when the algorithms were trained on the full dataset. However, the average RMSE of all algorithms decreased by 34 % when mineral and organic soils were modeled separately, with the best result from SVR with RMSE of 21 g kg−1. Model performance is often limited by the size and quality of the available soil dataset for calibration and validation. Therefore, the impact of enlarging the training data was tested by including 1223 data points from the European Land Use/Land Cover Area Frame Survey for agricultural sites in Germany. The model performance was enhanced for maximum 1 % for mineral soils and 2 % for organic soils. Despite the capability of machine learning algorithms in general, and particularly SVR, in modelling SOC on a national scale, the study showed that the most important to improve the model performance was separate modelling of mineral and organic soils.

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The Impact of Selecting a Validation Method in Machine Learning on Predicting Basketball Game Outcomes

Symmetry ◽

10.3390/sym12030431 ◽

2020 ◽

Vol 12 (3) ◽

pp. 431 ◽

Cited By ~ 1

Author(s):

Tomislav Horvat ◽

Ladislav Havaš ◽

Dunja Srpak

Keyword(s):

Machine Learning ◽

Cross Validation ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Test Validation ◽

Sporting Events ◽

Validation Method ◽

Validation Methods ◽

Independent Events ◽

The Impact

Interest in sports predictions as well as the public availability of large amounts of structured and unstructured data are increasing every day. As sporting events are not completely independent events, but characterized by the influence of the human factor, the adequate selection of the analysis process is very important. In this paper, seven different classification machine learning algorithms are used and validated with two validation methods: Train&Test and cross-validation. Validation methods were analyzed and critically reviewed. The obtained results are analyzed and compared. Analyzing the results of the used machine learning algorithms, the best average prediction results were obtained by using the nearest neighbors algorithm and the worst prediction results were obtained by using decision trees. The cross-validation method obtained better results than the Train&Test validation method. The prediction results of the Train&Test validation method by using disjoint datasets and up-to-date data were also compared. Better results were obtained by using up-to-date data. In addition, directions for future research are also explained.

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A Statistical Study to Analyze the Impact of External Weather Change on Chronic Pulmonary Infection in South Norway with Machine Learning Algorithms

Communications in Computer and Information Science - Intelligent Technologies and Applications ◽

10.1007/978-3-030-71711-7_10 ◽

2021 ◽

pp. 113-124

Author(s):

Ayan Chatterjee ◽

Martin W. Gerdes ◽

Andreas Prinz ◽

Santiago Martinez

Keyword(s):

Machine Learning ◽

Pulmonary Infection ◽

Statistical Study ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

The Impact ◽

Weather Change

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Review Paper on Prediction of Heart Disease using Machine Learning Algorithms

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35626 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 2937-2940

Author(s):

Aadar Pandita

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Review Paper ◽

Heart Diseases ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

The World ◽

Medical Researchers ◽

High Level ◽

The Impact

: Heart disease has been one of the ruling causes for death for quite some time now. About 31% of all deaths every year in the world take place as a result of cardiovascular diseases [1]. A majority of the patients remain uninformed of their symptoms until quite late while others find it difficult to minimise the effects of risk factors that cause heart diseases. Machine Learning Algorithms have been quite efficacious in producing results with a high level of correctness thereby preventing the onset of heart diseases in many patients and reducing the impact in the ones that are already affected by such diseases. It has helped medical researchers and doctors all over the world in recognising patterns in the patients resulting in early detections of heart diseases.

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