scholarly journals Research Ancient Artifact Identification Methods under Intelligent Perception and Recognition Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qiang Zhao
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
South China ◽  
Archaeological Site ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Decision Tree Algorithm ◽  
Chinese People ◽  
Identification Methods ◽  
Challenging Tasks

Over the last two decades, the identification of ancient artifacts has been regarded as one of the most challenging tasks for archaeologists. Chinese people consider these artifacts as symbols of their cultural heritage. The development of technology has helped in the identification of ancient artifacts to a greater extent. The study preferred machine-learning algorithms to identify the ancient artifacts found throughout China. The major cities of China were selected for the study and classified the cities based on different features like temple, modern city, harbour, battle, and South China. The study used a decision tree algorithm for recognition and gradient boosting for perception aspects. According to the findings of the study, the algorithms produced 98% accuracy and prediction in detecting ancient artifacts in China. The proposed models provide a good indicator for detecting archaeological site locations.

Machine learning models for prediction of postoperative ileus in patients underwent laparoscopic colorectal surgery

2020 ◽  
Author(s):  
Xueyan Li ◽  
Genshan Ma ◽  
Xiaobo Qian ◽  
Yamou Wu ◽  
Xiaochen Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Colorectal Surgery ◽  
Decision Tree ◽  
Risk Prediction ◽  
Postoperative Ileus ◽  
Laparoscopic Colorectal Surgery ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Malignant Lesions

Abstract Background: We aimed to assess the performance of machine learning algorithms for the prediction of risk factors of postoperative ileus (POI) in patients underwent laparoscopic colorectal surgery for malignant lesions. Methods: We conducted analyses in a retrospective observational study with a total of 637 patients at Suzhou Hospital of Nanjing Medical University. Four machine learning algorithms (logistic regression, decision tree, random forest, gradient boosting decision tree) were considered to predict risk factors of POI. The total cases were randomly divided into training and testing data sets, with a ratio of 8:2. The performance of each model was evaluated by area under receiver operator characteristic curve (AUC), precision, recall and F1-score. Results: The morbidity of POI in this study was 19.15% (122/637). Gradient boosting decision tree reached the highest AUC (0.76) and was the best model for POI risk prediction. In addition, the results of the importance matrix of gradient boosting decision tree showed that the five most important variables were time to first passage of flatus, opioids during POD3, duration of surgery, height and weight. Conclusions: The gradient boosting decision tree was the optimal model to predict the risk of POI in patients underwent laparoscopic colorectal surgery for malignant lesions. And the results of our study could be useful for clinical guidelines in POI risk prediction.

scholarly journals Selección de tutores académicos en la educación superior usando árboles de decisión

2018 ◽  
Vol 29 (1) ◽  
pp. 108
Author(s):  
Argelia B. Urbina Nájera ◽  
Jorge De la Calleja
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Community ◽  
Machine Learning Algorithms ◽  
Voluntary Participation ◽  
Educación Superior ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
Educacion Superior ◽  
Real World Problems

RESUMEN  En este documento se presenta un método para mejorar el proceso de tutoría académica en la educación superior. El método incluye a identificación de las habilidades principales de los tutores de forma automática utilizando el algoritmo árboles de decisión, uno de los algoritmos más utilizados en la comunidad de aprendizaje automático para resolver problemas del mundo real con gran precisión. En el estudio, el algoritmo arboles de decisión fue capaz de identificar las habilidades y afinidades entre estudiantes y tutores. Los experimentos se llevaron a cabo utilizando un conjunto de datos de 277 estudiantes y 19 tutores, mismos que fueron seleccionados por muestreo aleatorio simple y participación voluntaria en el caso de los tutores. Los resultados preliminares muestran que los atributos más importantes para los tutores son la comunicación, la autodirección y las habilidades digitales. Al mismo tiempo, se presenta un proceso de tutoría en el que la asignación del tutor se basa en estos atributos, asumiendo que puede ayudar a fortalecer las habilidades de los estudiantes que demanda la sociedad actual. De la misma forma, el árbol de decisión obtenido se puede utilizar para agrupar a tutores y estudiantes basados en sus habilidades y afinidades personales utilizando otros algoritmos de aprendizaje automático. La aplicación del proceso de tutoría sugerido podría dar la pauta para ver el proceso de tutoría de manera individual sin vincularla a procesos de desempeño académico o deserción escolar.ABSTRACTIn this paper, we present a method for the tutoring process in order to improve academic tutoring in higher education. The method includes identifying the main skills of tutors in an automated manner using decision trees, one of the most used algorithms in the machine learning community for solving several real-world problems with high accuracy. In our study, the decision tree algorithm was able to identify those skills and personal affinities between students and tutors. Experiments were carried out using a data set of 277 students and 19 tutors, which were selected by random sampling and voluntary participation, respectively. Preliminary results show that the most important attributes for tutors are communication, self-direction and digital skills. At the same time, we introduce a tutoring process where the tutor assignment is based on these attributes, assuming that it can help to strengthen the student's skills demanded by today's society. In the same way, the decision tree obtained can be used to create cluster of tutors and clusters of students based on their personal abilities and affinities using other machine learning algorithms. The application of the suggested tutoring process could set the tone to see the tutoring process individually without linking it to processes of academic performance or school dropout.

scholarly journals Internet of things based Early Detection of Diabetes using Machine Learning Algorithms: Dpa

2019 ◽  
Vol 8 (10) ◽  
pp. 1443-1447
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Early Detection ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Early Prediction ◽  
Decision Tree Algorithm ◽  
Diabetes Prediction ◽  
Prediction Of Diabetes

This paper introduces a new decision tree algorithm Diabetes Prediction Algorithm (DPA), for the early prediction of diabetes based on the datasets. The datasets are collected by using Internet of Things (IOT) Diabetes Sensors, comprises of 15000 records, out of which 11250 records are used for training purpose and 3750 are used for testing purpose. The proposed algorithm DPA yielded an accuracy of 90.02 %, specificity of 92.60 %, and precision of 89.17% and error rate of 9.98%. further, the proposed algorithm is compared with existing approaches. Currently there are numerous algorithms available which are not complete accurate and DPA helps.

scholarly journals Prediction of COVID-19 Diagnosis Based on OpenEHR Artefacts

2021 ◽  
Author(s):  
Daniela Oliveira ◽  
Diana Ferreira ◽  
Nuno Abreu ◽  
Pedro Leuschner ◽  
António Abelha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Structure ◽  
Decision Tree ◽  
Home Care ◽  
Predictive Model ◽  
Machine Learning Algorithms ◽  
Health Conditions ◽  
Evolutionary Status ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

Abstract The complexity and momentum of monitoring COVID-19 patients calls for the usage of agile and scalable data structure methodologies. A system for tracking symptoms and health conditions of suspected or confirmed SARS-CoV-2 infected patients was developed based on the openEHR architecture. All data on the evolutionary status of patients in home care as well as the results of their COVID-19 test were used to train different machine learning algorithms, with the aim of developing a predictive model capable of identifying COVID-19 infections according to the severity of symptoms identified by patients. The results obtained were promising, with the best model achieving an accuracy of 96.25%, a precision of 99.91%, a sensitivity of 92.58%, and a specificity of 99.92%, using the Decision Tree algorithm and the Split Validation method.

scholarly journals Utilizing the Genetic Algorithm to Pruning the C4.5 Decision Tree Algorithm

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Maad M. Mijwil ◽  
Rana A. Abttan
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Decision Tree ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Continuous Data ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
Confidence Factor ◽  
C4.5 Decision Tree

A decision tree (DTs) is one of the most popular machine learning algorithms that divide data repeatedly to form groups or classes. It is a supervised learning algorithm that can be used on discrete or continuous data for classification or regression. The most traditional classifier in this algorithm is the C4.5 decision tree, which is the point of this research. This classifier has the advantage of building a vast data set and does not stop until it reaches the desired goal. The problem with this classifier is that there are unnecessary nodes and branches leading to overfitting. This overfitting can negatively affect the classification process. In this context, the authors suggest utilizing a genetic algorithm to prune the effect of overfitting. This dataset study consists of four datasets: IRIS, Car Evaluation, GLASS, and WINE collected from UC Irvine (UCI) machine learning repository. The experimental results have confirmed the effectiveness of the genetic algorithm in pruning the effect of overfitting on the four datasets and optimizing confidence factor (CF) of the C4.5 decision tree. The proposed method has reached about 92% accuracy in this work.

scholarly journals Machine Learning Based Indoor Localisation Using Wi-Fi And Smartphone

10.31645/06 ◽  
2020 ◽  
Author(s):  
Zulqarnain Khokhar ◽  
◽  
Murtaza Ahmed Siddiqi ◽  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Indoor Localization ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
Gradient Boosting ◽  
Learning Techniques ◽  
Indoor Localisation

Wi-Fi based indoor positioning with the help of access points and smart devices have become an integral part in finding a device or a person’s location. Wi-Fi based indoor localization technology has been among the most attractive field for researchers for a number of years. In this paper, we have presented Wi-Fi based in-door localization using three different machine-learning techniques. The three machine learning algorithms implemented and compared are Decision Tree, Random Forest and Gradient Boosting classifier. After making a fingerprint of the floor based on Wi-Fi signals, mentioned algorithms were used to identify device location at thirty different positions on the floor. Random Forest and Gradient Boosting classifier were able to identify the location of the device with accuracy higher than 90%. While Decision Tree was able to identify the location with accuracy a bit higher than 80%.

scholarly journals Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques

2021 ◽  
Vol 6 (1) ◽  
pp. 40-56
Author(s):  
Dhyan Chandra Yadav ◽  
Saurabh Pal
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Ensemble Methods ◽  
Machine Learning Algorithms ◽  
Ensemble Method ◽  
Gradient Boosting ◽  
High Classification Accuracy

This paper has organized a heart disease-related dataset from UCI repository. The organized dataset describes variables correlations with class-level target variables. This experiment has analyzed the variables by different machine learning algorithms. The authors have considered prediction-based previous work and finds some machine learning algorithms did not properly work or do not cover 100% classification accuracy with overfitting, underfitting, noisy data, residual errors on base level decision tree. This research has used Pearson correlation and chi-square features selection-based algorithms for heart disease attributes correlation strength. The main objective of this research to achieved highest classification accuracy with fewer errors. So, the authors have used parallel and sequential ensemble methods to reduce above drawback in prediction. The parallel and serial ensemble methods were organized by J48 algorithm, reduced error pruning, and decision stump algorithm decision tree-based algorithms. This paper has used random forest ensemble method for parallel randomly selection in prediction and various sequential ensemble methods such as AdaBoost, Gradient Boosting, and XGBoost Meta classifiers. In this paper, the experiment divides into two parts: The first part deals with J48, reduced error pruning and decision stump and generated a random forest ensemble method. This parallel ensemble method calculated high classification accuracy 100% with low error. The second part of the experiment deals with J48, reduced error pruning, and decision stump with three sequential ensemble methods, namely AdaBoostM1, XG Boost, and Gradient Boosting. The XG Boost ensemble method calculated better results or high classification accuracy and low error compare to AdaBoostM1 and Gradient Boosting ensemble methods. The XG Boost ensemble method calculated 98.05% classification accuracy, but random forest ensemble method calculated high classification accuracy 100% with low error.

Prediction of longitudinal facial crack in steel thin slabs funnel mold using different machine learning algorithms

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kushalkumar Thakkar ◽  
Suhas Suresh Ambekar ◽  
Manoj Hudnurkar
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Manufacturing Companies ◽  
Content Type ◽  
Steel Manufacturing

Purpose Longitudinal facial cracks (LFC) are one of the major defects occurring in the continuous-casting stage of thin slab caster using funnel molds. Longitudinal cracks occur mainly owing to non-uniform cooling, varying thermal conductivity along mold length and use of high superheat during casting, improper casting powder characteristics. These defects are difficult to capture and are visible only in the final stages of a process or even at the customer end. Besides, there is a seasonality associated with this defect where defect intensity increases during the winter season. To address the issue, a model-based on data analytics is developed. Design/methodology/approach Around six-month data of steel manufacturing process is taken and around 60 data collection point is analyzed. The model uses different classification machine learning algorithms such as logistic regression, decision tree, ensemble methods of a decision tree, support vector machine and Naïve Bays (for different cut off level) to investigate data. Findings Proposed research framework shows that most of models give good results between cut off level 0.6–0.8 and random forest, gradient boosting for decision trees and support vector machine model performs better compared to other model. Practical implications Based on predictions of model steel manufacturing companies can identify the optimal operating range where this defect can be reduced. Originality/value An analytical approach to identify LFC defects provides objective models for reduction of LFC defects. By reducing LFC defects, quality of steel can be improved.

scholarly journals An Elderly Health Monitoring System Using Machine Learning and In-Depth Analysis Techniques on the NIH Stroke Scale

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1115
Author(s):  
Jaehak Yu ◽  
Sejin Park ◽  
Hansung Lee ◽  
Cheol-Sig Pyo ◽  
Yang Sun Lee
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Rapid Change ◽  
Machine Learning Algorithms ◽  
Semantic Interpretation ◽  
Stroke Severity ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Depth Analysis ◽  
C4.5 Decision Tree

Recently, with the rapid change to an aging society and the increased interest in healthcare, disease prediction and management through various healthcare devices and services is attracting much attention. In particular, stroke, represented by cerebrovascular disease, is a very dangerous disease, in which death or mental and physical aftereffects are very large in adults and the elderly. The sequelae of such stroke diseases are very dangerous, because they make social and economic activities difficult. In this paper, we propose a new system to prediction and in-depth analysis stroke severity of elderly over 65 years based on the National Institutes of Health Stroke Scale (NIHSS). In addition, we use the algorithm of decision tree of C4.5, which is a methodology of prediction and analysis of machine learning techniques. The C4.5 decision trees are machine learning algorithms that provide additional in-depth rules of the execution mechanism and semantic interpretation analysis. Finally, in this paper, it is verified that the C4.5 decision tree algorithm can be used to classify and predict stroke severity, and to obtain additional NIHSS features reduction effects. Therefore, during the operation of an actual system, the proposed model uses only 13 features out of the 18 stroke scale features, including age, so that it can provide faster and more accurate service support. Experimental results show that the system enables this by reducing the patient NIH stroke scale measurement time and making the operation more efficient, with an overall accuracy, using the C4.5 decision tree algorithm, of 91.11%.

Reinforced XGBoost machine learning model for sustainable intelligent agrarian applications

2020 ◽  
Vol 39 (5) ◽  
pp. 7605-7620 ◽  
Author(s):  
Dhivya Elavarasan ◽  
Durai Raj Vincent
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Mean Square Error ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Other ◽  
Gradient Boosting ◽  
Model Assessment ◽  
Mean Square ◽  
Extreme Gradient Boosting

The development in science and technical intelligence has incited to represent an extensive amount ofdata from various fields of agriculture. Therefore an objective rises up for the examination of the available data and integrating with processes like crop enhancement, yield prediction, examination of plant infections etc. Machine learning has up surged with tremendous processing techniques to perceive new contingencies in the multi-disciplinary agrarian advancements. In this pa- per a novel hybrid regression algorithm, reinforced extreme gradient boosting is proposed which displays essentially improved execution over traditional machine learning algorithms like artificial neural networks, deep Q-Network, gradient boosting, ran- dom forest and decision tree. Extreme gradient boosting constructs new models, which are essentially, decision trees learning from the mistakes of their predecessors by optimizing the gradient descent loss function. The proposed hybrid model performs reinforcement learning at every node during the node splitting process of the decision tree construction. This leads to effective utilizationofthesamplesbyselectingtheappropriatesplitattributeforenhancedperformance. Model’sperformanceisevaluated by means of Mean Square Error, Root Mean Square Error, Mean Absolute Error, and Coefficient of Determination. To assure a fair assessment of the results, the model assessment is performed on both training and test dataset. The regression diagnostic plots from residuals and the results obtained evidently delineates the fact that proposed hybrid approach performs better with reduced error measure and improved accuracy of 94.15% over the other machine learning algorithms. Also the performance of probability density function for the proposed model delineates that, it can preserve the actual distributional characteristics of the original crop yield data more approximately when compared to the other experimented machine learning models.

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