scholarly journals Sign language dactyl recognition based on machine learning algorithms

2021 ◽  
Vol 4 (2(112)) ◽  
pp. 58-72
Author(s):  
Chingiz Kenshimov ◽  
Zholdas Buribayev ◽  
Yedilkhan Amirgaliyev ◽  
Aisulyu Ataniyazova ◽  
Askhat Aitimov
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Sign Language ◽  
Gesture Recognition ◽  
Research Work ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

In the course of our research work, the American, Russian and Turkish sign languages were analyzed. The program of recognition of the Kazakh dactylic sign language with the use of machine learning methods is implemented. A dataset of 5000 images was formed for each gesture, gesture recognition algorithms were applied, such as Random Forest, Support Vector Machine, Extreme Gradient Boosting, while two data types were combined into one database, which caused a change in the architecture of the system as a whole. The quality of the algorithms was also evaluated. The research work was carried out due to the fact that scientific work in the field of developing a system for recognizing the Kazakh language of sign dactyls is currently insufficient for a complete representation of the language. There are specific letters in the Kazakh language, because of the peculiarities of the spelling of the language, problems arise when developing recognition systems for the Kazakh sign language. The results of the work showed that the Support Vector Machine and Extreme Gradient Boosting algorithms are superior in real-time performance, but the Random Forest algorithm has high recognition accuracy. As a result, the accuracy of the classification algorithms was 98.86 % for Random Forest, 98.68 % for Support Vector Machine and 98.54 % for Extreme Gradient Boosting. Also, the evaluation of the quality of the work of classical algorithms has high indicators. The practical significance of this work lies in the fact that scientific research in the field of gesture recognition with the updated alphabet of the Kazakh language has not yet been conducted and the results of this work can be used by other researchers to conduct further research related to the recognition of the Kazakh dactyl sign language, as well as by researchers, engaged in the development of the international sign language

scholarly journals Modelos de machine learning para predição do sucesso de startups

2021 ◽  
Vol 12 (2) ◽  
pp. 28-55
Author(s):  
Fabiano Rodrigues ◽  
Francisco Aparecido Rodrigues ◽  
Thelma Valéria Rocha Rodrigues
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Decision Tree ◽  
Initial Public Offering ◽  
Gradient Boosting ◽  
Support Vector ◽  
Trade Offs ◽  
Extreme Gradient Boosting ◽  
Public Offering

Este estudo analisa resultados obtidos com modelos de machine learning para predição do sucesso de startups. Como proxy de sucesso considera-se a perspectiva do investidor, na qual a aquisição da startup ou realização de IPO (Initial Public Offering) são formas de recuperação do investimento. A revisão da literatura aborda startups e veículos de financiamento, estudos anteriores sobre predição do sucesso de startups via modelos de machine learning, e trade-offs entre técnicas de machine learning. Na parte empírica, foi realizada uma pesquisa quantitativa baseada em dados secundários oriundos da plataforma americana Crunchbase, com startups de 171 países. O design de pesquisa estabeleceu como filtro startups fundadas entre junho/2010 e junho/2015, e uma janela de predição entre junho/2015 e junho/2020 para prever o sucesso das startups. A amostra utilizada, após etapa de pré-processamento dos dados, foi de 18.571 startups. Foram utilizados seis modelos de classificação binária para a predição: Regressão Logística, Decision Tree, Random Forest, Extreme Gradiente Boosting, Support Vector Machine e Rede Neural. Ao final, os modelos Random Forest e Extreme Gradient Boosting apresentaram os melhores desempenhos na tarefa de classificação. Este artigo, envolvendo machine learning e startups, contribui para áreas de pesquisa híbridas ao mesclar os campos da Administração e Ciência de Dados. Além disso, contribui para investidores com uma ferramenta de mapeamento inicial de startups na busca de targets com maior probabilidade de sucesso.   

scholarly journals Research on dairy products detection based on machine learning algorithm

2022 ◽  
Vol 355 ◽  
pp. 03008
Author(s):  
Yang Zhang ◽  
Lei Zhang ◽  
Yabin Ma ◽  
Jinsen Guan ◽  
Zhaoxia Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Electronic Nose ◽  
Milk Fat ◽  
Dairy Products ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

In this study, an electronic nose model composed of seven kinds of metal oxide semiconductor sensors was developed to distinguish the milk source (the dairy farm to which milk belongs), estimate the content of milk fat and protein in milk, to identify the authenticity and evaluate the quality of milk. The developed electronic nose is a low-cost and non-destructive testing equipment. (1) For the identification of milk sources, this paper uses the method of combining the electronic nose odor characteristics of milk and the component characteristics to distinguish different milk sources, and uses Principal Component Analysis (PCA) and Linear Discriminant Analysis , LDA) for dimensionality reduction analysis, and finally use three machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) to build a milk source (cow farm) Identify the model and evaluate and compare the classification effects. The experimental results prove that the classification effect of the SVM-LDA model based on the electronic nose odor characteristics is better than other single feature models, and the accuracy of the test set reaches 91.5%. The RF-LDA and SVM-LDA models based on the fusion feature of the two have the best effect Set accuracy rate is as high as 96%. (2) The three algorithms, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are used to construct the electronic nose odor data for milk fat rate and protein rate. The method of estimating the model, the results show that the RF model has the best estimation performance( R2 =0.9399 for milk fat; R2=0.9301for milk protein). And it prove that the method proposed in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of dairy products.

scholarly journals Machine learning as a successful approach for predicting complex spatio–temporal patterns in animal species abundance

2021 ◽  
pp. 289-301
Author(s):  
B. Martín ◽  
J. González–Arias ◽  
J. A. Vicente–Vírseda
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Animal Species ◽  
Temporal Patterns ◽  
Additive Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stochastic Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Spatio Temporal

Our aim was to identify an optimal analytical approach for accurately predicting complex spatio–temporal patterns in animal species distribution. We compared the performance of eight modelling techniques (generalized additive models, regression trees, bagged CART, k–nearest neighbors, stochastic gradient boosting, support vector machines, neural network, and random forest –enhanced form of bootstrap. We also performed extreme gradient boosting –an enhanced form of radiant boosting– to predict spatial patterns in abundance of migrating Balearic shearwaters based on data gathered within eBird. Derived from open–source datasets, proxies of frontal systems and ocean productivity domains that have been previously used to characterize the oceanographic habitats of seabirds were quantified, and then used as predictors in the models. The random forest model showed the best performance according to the parameters assessed (RMSE value and R2). The correlation between observed and predicted abundance with this model was also considerably high. This study shows that the combination of machine learning techniques and massive data provided by open data sources is a useful approach for identifying the long–term spatial–temporal distribution of species at regional spatial scales.

scholarly journals Techniques for Detecting Malware Traffic: A Comprehensive Approach to Feature Selection and Classification

2021 ◽  
Vol 9 (12) ◽  
pp. 1-10
Author(s):  
Harsha A K
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Steady Increase ◽  
Extreme Gradient Boosting

Abstract: Since the advent of encryption, there has been a steady increase in malware being transmitted over encrypted networks. Traditional approaches to detect malware like packet content analysis are inefficient in dealing with encrypted data. In the absence of actual packet contents, we can make use of other features like packet size, arrival time, source and destination addresses and other such metadata to detect malware. Such information can be used to train machine learning classifiers in order to classify malicious and benign packets. In this paper, we offer an efficient malware detection approach using classification algorithms in machine learning such as support vector machine, random forest and extreme gradient boosting. We employ an extensive feature selection process to reduce the dimensionality of the chosen dataset. The dataset is then split into training and testing sets. Machine learning algorithms are trained using the training set. These models are then evaluated against the testing set in order to assess their respective performances. We further attempt to tune the hyper parameters of the algorithms, in order to achieve better results. Random forest and extreme gradient boosting algorithms performed exceptionally well in our experiments, resulting in area under the curve values of 0.9928 and 0.9998 respectively. Our work demonstrates that malware traffic can be effectively classified using conventional machine learning algorithms and also shows the importance of dimensionality reduction in such classification problems. Keywords: Malware Detection, Extreme Gradient Boosting, Random Forest, Feature Selection.

scholarly journals A Comparative Analysis of Machine Learning Models for Prediction of Insurance Uptake in Kenya

2020 ◽  
Author(s):  
Nelson Yego ◽  
Juma Kasozi ◽  
Joseph Nkrunziza
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Characteristic Curve ◽  
Confusion Matrix ◽  
Gradient Boosting ◽  
Support Vector ◽  
Sampled Data ◽  
Learning Models ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models

The role of insurance in financial inclusion as well as in economic growth is immense. However, low uptake seems to impede the growth of the sector hence the need for a model that robustly predicts uptake of insurance among potential clients. In this research, we compared the performances of eight (8) machine learning models in predicting the uptake of insurance. The classifiers considered were Logistic Regression, Gaussian Naive Bayes, Support Vector Machines, K Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting Machines and Extreme Gradient boosting. The data used in the classification was from the 2016 Kenya FinAccess Household Survey. Comparison of performance was done for both upsampled and downsampled data due to data imbalance. For upsampled data, Random Forest classifier showed highest accuracy and precision compared to other classifiers but for down sampled data, gradient boosting was optimal. It is noteworthy that for both upsampled and downsampled data, tree-based classifiers were more robust than others in insurance uptake prediction. However, in spite of hyper-parameter optimization, the area under receiver operating characteristic curve remained highest for Random Forest as compared to other tree-based models. Also, the confusion matrix for Random Forest showed least false positives, and highest true positives hence could be construed as the most robust model for predicting the insurance uptake. Finally, the most important feature in predicting uptake was having a bank product hence bancassurance could be said to be a plausible channel of distribution of insurance products.

scholarly journals Forest Vertical Structure Mapping Using Two-Seasonal Optic Images and LiDAR DSM Acquired from UAV Platform through Random Forest, XGBoost, and Support Vector Machine Approaches

2021 ◽  
Vol 13 (21) ◽  
pp. 4282
Author(s):  
Jin-Woo Yu ◽  
Young-Woong Yoon ◽  
Won-Kyung Baek ◽  
Hyung-Sup Jung
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Forest Structure ◽  
Vertical Structure ◽  
Performance Comparison ◽  
Support Vector ◽  
Surface Model ◽  
Extreme Gradient Boosting ◽  
Autumn And Winter

Research on the forest structure classification is essential, as it plays an important role in assessing the vitality and diversity of vegetation. However, classifying forest structure involves in situ surveying, which requires considerable time and money, and cannot be conducted directly in some instances; also, the update cycle of the classification data is very late. To overcome these drawbacks, feasibility studies on mapping the forest vertical structure from aerial images using machine learning techniques were conducted. In this study, we investigated (1) the performance improvement of the forest structure classification, using a high-resolution LiDAR-derived digital surface model (DSM) acquired from an unmanned aerial vehicle (UAV) platform and (2) the performance comparison of results obtained from the single-seasonal and two-seasonal data, using random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM). For the performance comparison, the UAV optic and LiDAR data were divided into three cases: (1) only used autumn data, (2) only used winter data, and (3) used both autumn and winter data. From the results, the best model was XGBoost, and the F1 scores achieved using this method were approximately 0.92 in the autumn and winter cases. A remarkable improvement was achieved when both two-seasonal images were used. The F1 score improved by 35.3% from 0.68 to 0.92. This implies that (1) the seasonal variation in the forest vertical structure can be more important than the spatial resolution, and (2) the classification performance achieved from the two-seasonal UAV optic images and LiDAR-derived DSMs can reach 0.9 with the application of an optimal machine learning approach.

scholarly journals Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-20
Author(s):  
Nihad Brahimi ◽  
Huaping Zhang ◽  
Lin Dai ◽  
Jianzi Zhang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Research Work ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Models ◽  
Car Sharing ◽  
K Nearest Neighbors ◽  
Extreme Gradient Boosting ◽  
On The Road

The car-sharing system is a popular rental model for cars in shared use. It has become particularly attractive due to its flexibility; that is, the car can be rented and returned anywhere within one of the authorized parking slots. The main objective of this research work is to predict the car usage in parking stations and to investigate the factors that help to improve the prediction. Thus, new strategies can be designed to make more cars on the road and fewer in the parking stations. To achieve that, various machine learning models, namely vector autoregression (VAR), support vector regression (SVR), eXtreme gradient boosting (XGBoost), k-nearest neighbors (kNN), and deep learning models specifically long short-time memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), CNN-LSTM, and multilayer perceptron (MLP), were performed on different kinds of features. These features include the past usage levels, Chongqing’s environmental conditions, and temporal information. After comparing the obtained results using different metrics, we found that CNN-LSTM outperformed other methods to predict the future car usage. Meanwhile, the model using all the different feature categories results in the most precise prediction than any of the models using one feature category at a time

scholarly journals Performance of Statistical and Machine Learning-Based Methods for Predicting Biogeographical Patterns of Fungal Productivity in Forest Ecosystems

2020 ◽  
Author(s):  
Albert Morera ◽  
Juan Martínez de Aragón ◽  
José Antonio Bonet ◽  
Jingjing Liang ◽  
Sergio de-Miguel
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Models ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models ◽  
Modelling Approaches

Abstract BackgroundThe prediction of biogeographical patterns from a large number of driving factors with complex interactions, correlations and non-linear dependences require advanced analytical methods and modelling tools. This study compares different statistical and machine learning models for predicting fungal productivity biogeographical patterns as a case study for the thorough assessment of the performance of alternative modelling approaches to provide accurate and ecologically-consistent predictions.MethodsWe evaluated and compared the performance of two statistical modelling techniques, namely, generalized linear mixed models and geographically weighted regression, and four machine learning models, namely, random forest, extreme gradient boosting, support vector machine and deep learning to predict fungal productivity. We used a systematic methodology based on substitution, random, spatial and climatic blocking combined with principal component analysis, together with an evaluation of the ecological consistency of spatially-explicit model predictions.ResultsFungal productivity predictions were sensitive to the modelling approach and complexity. Moreover, the importance assigned to different predictors varied between machine learning modelling approaches. Decision tree-based models increased prediction accuracy by ~7% compared to other machine learning approaches and by more than 25% compared to statistical ones, and resulted in higher ecological consistence at the landscape level.ConclusionsWhereas a large number of predictors are often used in machine learning algorithms, in this study we show that proper variable selection is crucial to create robust models for extrapolation in biophysically differentiated areas. When dealing with spatial-temporal data in the analysis of biogeographical patterns, climatic blocking is postulated as a highly informative technique to be used in cross-validation to assess the prediction error over larger scales. Random forest was the best approach for prediction both in sampling-like environments as well as in extrapolation beyond the spatial and climatic range of the modelling data.

scholarly journals Machine-Learning-Based Prediction of Corrosion Behavior in Additively Manufactured Inconel 718

Data ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 80
Author(s):  
O. V. Mythreyi ◽  
M. Rohith Srinivaas ◽  
Tigga Amit Kumar ◽  
R. Jayaganthan
Keyword(s):  
Machine Learning ◽  
Corrosion Behavior ◽  
Inconel 718 ◽  
Polynomial Regression ◽  
Research Work ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

This research work focuses on machine-learning-assisted prediction of the corrosion behavior of laser-powder-bed-fused (LPBF) and postprocessed Inconel 718. Corrosion testing data of these specimens were collected and fit into the following machine learning algorithms: polynomial regression, support vector regression, decision tree, and extreme gradient boosting. The model performance, after hyperparameter optimization, was evaluated using a set of established metrics: R2, mean absolute error, and root mean square error. Among the algorithms, the extreme gradient boosting algorithm performed best in predicting the corrosion behavior, closely followed by other algorithms. Feature importance analysis was executed in order to determine the postprocessing parameters that influenced the most the corrosion behavior in Inconel 718 manufactured by LPBF.

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