scholarly journals Design Design the temperature and humidity classification of the workspace by using a decision tree model.

2020 ◽  
Vol 6 (2) ◽  
pp. 169-178
Author(s):  
Wahyu Setiady ◽  
Y.B. Adyapaka Apatya
Keyword(s):  
Decision Tree ◽  
Decision Tree Model ◽  
Raspberry Pi ◽  
Decision Tree Classifier ◽  
Tree Model ◽  
Tree Classifier ◽  
Temperature And Humidity

Rancang bangun alat klasifikasi suhu dan kelembaban ruang kerja dengan menggunakan model decision tree. Berdasarkan tabel standar tata cara perencanaan teknis konservasi energi pada bangunan gedung, suhu nyaman optimal ada pada kisaran 22,8oC – 25,8 oC dengan ambang atas 28 oC dan kelembaban 70%. Dengan memanfaatkan decision tree classifier, suhu dan kelembaban ruangan yang dideteksi oleh sensor DHT11 diklasifikasikan berdasarkan model yang telah dibuat dengan menggunakan Raspberry Pi 3 dan node red. Penelitian ini dilaksanakan di laboratorium komputer Politeknik Industri ATMI yang juga digunakan sebagai laboratorium riset terapan yang bekerjasama dengan industri dalam bidang pengembangan perangkat lunak otomasi. Penelitian ini berhasil membuat alat klasifikasi suhu dan kelembaban ruang kerja dengan menggunakan model decision tree yang menghasilkan status dingin, sejuk nyaman, nyaman optimal, hangat nyaman dan panas dengan tingkat prediksi model 0,983.  

scholarly journals HYPER HEURISTIC EVOLUTIONARY APPROACH FOR CONSTRUCTING DECISION TREE CLASSIFIERS

2021 ◽  
Vol 20 (Number 2) ◽  
pp. 249-276
Author(s):  
Sunil Kumar ◽  
Saroj Ratnoo ◽  
Jyoti Vashishtha
Keyword(s):  
Decision Tree ◽  
Heuristic Approach ◽  
Decision Tree Model ◽  
Evolutionary Approach ◽  
Optimal Decision ◽  
Decision Tree Classifier ◽  
Tree Model ◽  
Tree Construction ◽  
Tree Classifier ◽  
Optimal Values

Decision tree models have earned a special status in predictive modeling since these are considered comprehensible for human analysis and insight. Classification and Regression Tree (CART) algorithm is one of the renowned decision tree induction algorithms to address the classification as well as regression problems. Finding optimal values for the hyper parameters of a decision tree construction algorithm is a challenging issue. While making an effective decision tree classifier with high accuracy and comprehensibility, we need to address the question of setting optimal values for its hyper parameters like the maximum size of the tree, the minimum number of instances required in a node for inducing a split, node splitting criterion and the amount of pruning. The hyper parameter setting influences the performance of the decision tree model. As researchers, we know that no single setting of hyper parameters works equally well for different datasets. A particular setting that gives an optimal decision tree for one dataset may produce a sub-optimal decision tree model for another dataset. In this paper, we present a hyper heuristic approach for tuning the hyper parameters of Recursive and Partition Trees (rpart), which is a typical implementation of CART in statistical and data analytics package R. We employ an evolutionary algorithm as hyper heuristic for tuning the hyper parameters of the decision tree classifier. The approach is named as Hyper heuristic Evolutionary Approach with Recursive and Partition Trees (HEARpart). The proposed approach is validated on 30 datasets. It is statistically proved that HEARpart performs significantly better than WEKA’s J48 algorithm in terms of error rate, F-measure, and tree size. Further, the suggested hyper heuristic algorithm constructs significantly comprehensible models as compared to WEKA’s J48, CART and other similar decision tree construction strategies. The results show that the accuracy achieved by the hyper heuristic approach is slightly less as compared to the other comparative approaches.

A gradient boosted decision tree-based sentiment classification of twitter data

2020 ◽  
Vol 18 (04) ◽  
pp. 2050027
Author(s):  
S. Neelakandan ◽  
D. Paulraj
Keyword(s):  
Decision Tree ◽  
Opinion Mining ◽  
Research Topic ◽  
Sentiment Classification ◽  
Decision Tree Classifier ◽  
Twitter Data ◽  
Tree Classifier ◽  
Boosted Decision Tree ◽  
Text Sentiment Analysis

People communicate their views, arguments and emotions about their everyday life on social media (SM) platforms (e.g. Twitter and Facebook). Twitter stands as an international micro-blogging service that features a brief message called tweets. Freestyle writing, incorrect grammar, typographical errors and abbreviations are some noises that occur in the text. Sentiment analysis (SA) centered on a tweet posted by the user, and also opinion mining (OM) of the customers review is another famous research topic. The texts are gathered from users’ tweets by means of OM and automatic-SA centered on ternary classifications, namely positive, neutral and negative. It is very challenging for the researchers to ascertain sentiments as a result of its limited size, misspells, unstructured nature, abbreviations and slangs for Twitter data. This paper, with the aid of the Gradient Boosted Decision Tree classifier (GBDT), proposes an efficient SA and Sentiment Classification (SC) of Twitter data. Initially, the twitter data undergoes pre-processing. Next, the pre-processed data is processed using HDFS MapReduce. Now, the features are extracted from the processed data, and then efficient features are selected using the Improved Elephant Herd Optimization (I-EHO) technique. Now, score values are calculated for each of those chosen features and given to the classifier. At last, the GBDT classifier classifies the data as negative, positive, or neutral. Experiential results are analyzed and contrasted with the other conventional techniques to show the highest performance of the proposed method.

scholarly journals Classification of Hydraulic Jump in Rough Beds

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2249
Author(s):  
Ghorban Mahtabi ◽  
Barkha Chaplot ◽  
Hazi Mohammad Azamathulla ◽  
Mahesh Pal
Keyword(s):  
Decision Tree ◽  
Hydraulic Jump ◽  
Decision Tree Classifier ◽  
Class A ◽  
Class D ◽  
Tree Classifier ◽  
Improved Performance ◽  
Bed Roughness ◽  
Rough Beds

This paper presents a classification using a decision tree algorithm of hydraulic jump over rough beds based on the approach Froude number, Fr1. Specifically, 581 datasets, from literature, were analyzed. Of these, 280 datasets were for natural rough beds and 301 were for artificial rough beds. The said dataset was divided into four classes based on the energy losses. To compare the performance of the decision tree classifier (J48), a multi-layer neural network (NN) was used. The results suggest an improved performance in terms of classification accuracy by the J48 algorithm in comparison to the NN classifier. Furthermore, the classifier model had only four leaves and achieved an accuracy of 91.56%. Furthermore, classification results showed that the first class (A) of hydraulic jump over the rough beds is approximately similar to that for the smooth bed. Moreover, in the next three classes (B, C, and D), upper values of Fr1 decreased with respect to the smooth bed classes. Lastly, in class D, the upper value of Fr1 reduced to 7.45, which indicates that the shear stress (i.e., the energy loss) grows sharply with increasing Fr1. Put simply, bed roughness effectively increases the energy dissipation with an increase in the Fr1.

scholarly journals Genetic Algorithm for Effective Fall Detection with Wrist Wearable Device

2019 ◽  
Vol 9 (1S3) ◽  
pp. 159-164
Keyword(s):  
Genetic Algorithm ◽  
Decision Tree ◽  
Real Time ◽  
Fall Detection ◽  
Wearable Device ◽  
Raspberry Pi ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
Supervised Learning Algorithms ◽  
Mental Trauma

Falls have always been a major cause of injury related deaths among the old aged population in our country. It causes mental trauma and severe fractures to the bones and spine which impacts their quality of life. Therefore a proper fall prediction and alert system along with a timely rapid response could enable us to tackle such serious fall events and decrease the fatality. Various sensors and embedded controllers are used in conjunction with various machine learning classifiers to help us predict and optimize the falls effectively. This work presents a wrist wearable device using MPU-6050 sensor and raspberry-pi controller with help of machine learn algorithm which help us to predict the falls. Five different supervised learning algorithms and one unsupervised algorithm was implemented and evaluated on the basis of their accuracy, sensitivity and specificity. Out of all these classifiers, the decision tree with an accuracy of 85% was implemented in the system which classified the fall from the real time non-fall data sets. Further the performance of system was increased using genetic algorithm which gave better classification results unlike the normal decision tree classifier. Once the falls are predicted we can give a real-time response which can be an added feature to this system

scholarly journals Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Majid Nour ◽  
Kemal Polat
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Random Forest ◽  
Decision Tree ◽  
Systolic Blood Pressure ◽  
Diastolic Blood Pressure ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
C4.5 Decision Tree

Hypertension (high blood pressure) is an important disease seen among the public, and early detection of hypertension is significant for early treatment. Hypertension is depicted as systolic blood pressure higher than 140 mmHg or diastolic blood pressure higher than 90 mmHg. In this paper, in order to detect the hypertension types based on the personal information and features, four machine learning (ML) methods including C4.5 decision tree classifier (DTC), random forest, linear discriminant analysis (LDA), and linear support vector machine (LSVM) have been used and then compared with each other. In the literature, we have first carried out the classification of hypertension types using classification algorithms based on personal data. To further explain the variability of the classifier type, four different classifier algorithms were selected for solving this problem. In the hypertension dataset, there are eight features including sex, age, height (cm), weight (kg), systolic blood pressure (mmHg), diastolic blood pressure (mmHg), heart rate (bpm), and BMI (kg/m2) to explain the hypertension status and then there are four classes comprising the normal (healthy), prehypertension, stage-1 hypertension, and stage-2 hypertension. In the classification of the hypertension dataset, the obtained classification accuracies are 99.5%, 99.5%, 96.3%, and 92.7% using the C4.5 decision tree classifier, random forest, LDA, and LSVM. The obtained results have shown that ML methods could be confidently used in the automatic determination of the hypertension types.

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