scholarly journals Tối ưu hóa ước tính mức tiêu thụ năng lượng trong các tòa nhà dựa trên các thuật toán trí tuệ nhân tạo

2020 ◽  
Vol 14 (1V) ◽  
pp. 35-45
Author(s):  
Trần Đức Học ◽  
Lê Tấn Tài
Keyword(s):  
Support Vector Machine ◽  
Linear Regression ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Support Vector ◽  
Ensemble Model ◽  
Chi Square ◽  
Chi Squared ◽  
Classification And Regression ◽  
Automatic Interaction Detector

Mô phỏng và dự báo năng lượng tiêu thụ đóng vai trò quan trọng trong việc thiết lập chính sách năng lượng và đưa ra quyết định theo hướng phát triển bền vững. Nghiên cứu này sử dụng phương pháp kỹ thuật thống kê và công cụ trí tuệ nhân tạo bao gồm mạng nơ-ron thần kinh (ANNs – Artificial neutral networks), máy hỗ trợ véc tơ (SVM – Support vector machine), cây phân loại và hồi quy (CART - Classification and regression trees), hồi quy tuyến tính (LR - Linear regression), hồi quy tuyến tính tổng quát (GENLIN - Generalized linear regression), tự động phát hiện tương tác Chi-squared (CHAID - Chi-square automatic interaction detector) và mô hình tổng hợp (Ensemble model) để dự đoán mức tiêu thụ năng lượng trong các căn hộ tòa nhà chung cư. Bộ dữ liệu để xây dựng mô hình gồm 200 mẫu được khảo sát ở nhiều chung cư tại TP. Hồ Chí Minh. Mô hình đơn có hiệu quả tốt nhất trong quá trình dự đoán là CART, trong khi đó mô hình được tổng hợp tốt nhất là CART + GENLIN. Từ khóa: ước tính; tòa nhà; tiêu thụ năng lượng; khai phá dữ liệu, trí tuệ nhân tạo.

scholarly journals Support Vector Machine Berbasis Feature Selection Untuk Sentiment Analysis Kepuasan Pelanggan Terhadap Pelayanan Warung dan Restoran Kuliner Kota Tegal

2018 ◽  
Vol 5 (5) ◽  
pp. 537 ◽  
Author(s):  
Oman Somantri ◽  
Dyah Apriliani
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Information Gain ◽  
Support Vector ◽  
Chi Square ◽  
Proposed Model ◽  
Chi Squared ◽  
The Difference ◽  
Increase In Accuracy

<p class="Judul2"><strong>Abstrak</strong></p><p class="Judul2"> </p><p class="Abstrak">Setiap pelanggan pasti menginginkan sebuah pendukung keputusan dalam menentukan pilihan ketika akan mengunjungi sebuah tempat makan atau kuliner yang sesuai dengan keinginan salah satu contohnya yaitu di Kota Tegal. <em>Sentiment analysis</em> digunakan untuk memberikan sebuah solusi terkait dengan permasalahan tersebut, dengan menereapkan model algoritma S<em>upport Vector Machine</em> (SVM). Tujuan dari penelitian ini adalah mengoptimalisasi model yang dihasilkan dengan diterapkannya <em>feature selection</em> menggunakan algoritma <em>Informatioan Gain</em> (IG) dan <em>Chi Square</em> pada hasil model terbaik yang dihasilkan oleh SVM pada klasifikasi tingkat kepuasan pelanggan terhadap warung dan restoran kuliner di Kota Tegal sehingga terjadi peningkatan akurasi dari model yang dihasilkan. Hasil penelitian menunjukan bahwa tingkat akurasi terbaik dihasilkan oleh model SVM-IG dengan tingkat akurasi terbaik sebesar 72,45% mengalami peningkatan sekitar 3,08% yang awalnya 69.36%. Selisih rata-rata yang dihasilkan setelah dilakukannya optimasi SVM dengan <em>feature selection</em> adalah 2,51% kenaikan tingkat akurasinya. Berdasarkan hasil penelitian bahwa <em>feature selection</em> dengan menggunakan <em>Information Gain (IG)</em> (SVM-IG) memiliki tingkat akurasi lebih baik apabila dibandingkan SVM dan <em>Chi Squared</em> (SVM-CS) sehingga dengan demikian model yang diusulkan dapat meningkatkan tingkat akurasi yang dihasilkan oleh SVM menjadi lebih baik.</p><p class="Abstrak"><strong><em><br /></em></strong></p><p class="Abstrak"><strong><em>Abstract</em></strong></p><p class="Judul2"> </p><p class="Judul2"><em>The Customer needs to get a decision support in determining a choice when they’re visit a culinary restaurant accordance to their wishes especially at Tegal City. Sentiment analysis is used to provide a solution related to this problem by applying the Support Vector Machine (SVM) algorithm model. The purpose of this research is to optimize the generated model by applying feature selection using Informatioan Gain (IG) and Chi Square algorithm on the best model produced by SVM on the classification of customer satisfaction level based on culinary restaurants at Tegal City so that there is an increasing accuracy from the model. The results showed that the best accuracy level produced by the SVM-IG model with the best accuracy of 72.45% experienced an increase of about 3.08% which was initially 69.36%. The difference average produced after SVM optimization with feature selection is 2.51% increase in accuracy. Based on the results of the research, the feature selection using Information Gain (SVM-IG) has a better accuracy rate than SVM and Chi Squared (SVM-CS) so that the proposed model can improve the accuracy of SVM better.</em></p>

scholarly journals KLASIFIKASI STATUS KEMISKINAN RUMAH TANGGA DENGAN METODE SUPPORT VECTOR MACHINES (SVM) DAN CLASSIFICATION AND REGRESSION TREES (CART) MENGGUNAKAN GUI R (Studi Kasus di Kabupaten Wonosobo Tahun 2018)

2020 ◽  
Vol 9 (4) ◽  
pp. 525-534
Author(s):  
Lutfia Nuzula ◽  
Alan Prahutama ◽  
Arief Rachman Hakim
Keyword(s):  
Support Vector Machines ◽  
Classification Accuracy ◽  
Regression Trees ◽  
Poverty Line ◽  
Classification And Regression Trees ◽  
Support Vector ◽  
Poor People ◽  
Central Java ◽  
Vector Machines ◽  
Classification And Regression

The poor are people who have average monthly expenditures per capita below the poverty line. Wonosobo District became the poorest district in Central Java in 2011-2018, although the percentage of poor people has decreased every year. It cannot be separated from the efforts of the Wonosobo District Government to overcome poverty through various programs. This study classified households in Wonosobo District in 2018 as poor and non-poor based on influencing factors. This study used the Support Vector Machines (SVM) method to be compared with the Classification and Regression Trees (CART) method. It used the data from the 2018 National Socio-Economic Survey of Central Java with a total of 795 observations. Result of the research using the SVM method and the RBF kernel, the classification accuracy reaches 89.82% then the classification accuracy using the CART method reaches 87.08%. GUI designed by RShiny package can make easier for users to analyze the SVM and CART with the valid output. 

scholarly journals Machine learning approaches to classify melon landraces based on phenotypic traits

Genetika ◽  
2020 ◽  
Vol 52 (3) ◽  
pp. 1021-1029
Author(s):  
Rad Naroui ◽  
Gholamali Keykha ◽  
Jahangir Abbaskoohpayegani ◽  
Ramin Rafezi
Keyword(s):  
Machine Learning ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Partial Least Square ◽  
Support Vector ◽  
Phenotypic Traits ◽  
Learning Approaches ◽  
Operating Characteristics ◽  
Kappa Value ◽  
Classification And Regression

Phenotyping of native cultivars is becoming more essential, as they are an important for breeders as a genetic source for breeding. The variability of morphological properties plays critical role in melon breeding. In this paper various machine learning approaches were implemented to identify melon accession classes. A field experiment was conducted in Zahak Agriculture station to differentiate 144 melon accessions based on 14 traits. For this, Partial Least Square Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN) and Classification And Regression Trees (CART) were compared. The most commonly used performance values comprise overall accuracy, kappa value, Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) were performed to identify accuracy of the models. The results showed the best performance for CART than others. The AUC and kappa value were 0.85 and 0.80 and fruit weight was the most important trait that affecting diversity in melon accessions. Regarding to these results Classification And Regression Trees (CART) is reliable for identification of melon accessions classes.

Preoperative hematocrit and platelet count are associated with blood loss during spinal fusion for children with neuromuscular scoliosis

2021 ◽  
pp. 175045892096263
Author(s):  
Margaret O Lewen ◽  
Jay Berry ◽  
Connor Johnson ◽  
Rachael Grace ◽  
Laurie Glader ◽  
...  
Keyword(s):  
Platelet Count ◽  
Blood Loss ◽  
Spinal Fusion ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Neuromuscular Scoliosis ◽  
Independent Variables ◽  
Estimated Blood Loss ◽  
Laboratory Results ◽  
Classification And Regression

Aim To assess the relationship of preoperative hematology laboratory results with intraoperative estimated blood loss and transfusion volumes during posterior spinal fusion for pediatric neuromuscular scoliosis. Methods Retrospective chart review of 179 children with neuromuscular scoliosis undergoing spinal fusion at a tertiary children’s hospital between 2012 and 2017. The main outcome measure was estimated blood loss. Secondary outcomes were volumes of packed red blood cells, fresh frozen plasma, and platelets transfused intraoperatively. Independent variables were preoperative blood counts, coagulation studies, and demographic and surgical characteristics. Relationships between estimated blood loss, transfusion volumes, and independent variables were assessed using bivariable analyses. Classification and Regression Trees were used to identify variables most strongly correlated with outcomes. Results In bivariable analyses, increased estimated blood loss was significantly associated with higher preoperative hematocrit and lower preoperative platelet count but not with abnormal coagulation studies. Preoperative laboratory results were not associated with intraoperative transfusion volumes. In Classification and Regression Trees analysis, binary splits associated with the largest increase in estimated blood loss were hematocrit ≥44% vs. <44% and platelets ≥308 vs. <308 × 109/L. Conclusions Preoperative blood counts may identify patients at risk of increased bleeding, though do not predict intraoperative transfusion requirements. Abnormal coagulation studies often prompted preoperative intervention but were not associated with increased intraoperative bleeding or transfusion needs.

scholarly journals Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model

2021 ◽  
Vol 13 (12) ◽  
pp. 2300
Author(s):  
Samy Elmahdy ◽  
Tarig Ali ◽  
Mohamed Mohamed
Keyword(s):  
Machine Learning ◽  
Regional Scale ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Groundwater Potential ◽  
Machine Learning Algorithms ◽  
Conditioning Factors ◽  
Potential Mapping ◽  
Classification And Regression ◽  
Groundwater Potential Mapping

Mapping of groundwater potential in remote arid and semi-arid regions underneath sand sheets over a very regional scale is a challenge and requires an accurate classifier. The Classification and Regression Trees (CART) model is a robust machine learning classifier used in groundwater potential mapping over a very regional scale. Ten essential groundwater conditioning factors (GWCFs) were constructed using remote sensing data. The spatial relationship between these conditioning factors and the observed groundwater wells locations was optimized and identified by using the chi-square method. A total of 185 groundwater well locations were randomly divided into 129 (70%) for training the model and 56 (30%) for validation. The model was applied for groundwater potential mapping by using optimal parameters values for additive trees were 186, the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps are effective at enhancing the mapping of groundwater potential over a very regional scale obtained using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert.

Melanoma survival with Classification and Regression Trees Analysis: a complement for the communication of prognosis to patients

2021 ◽  
Vol 156 (4) ◽  
Author(s):  
Silvia MANCINI ◽  
Emanuele CROCETTI ◽  
Lauro BUCCHI ◽  
Nicola PIMPINELLI ◽  
Rosa VATTIATO ◽  
...  
Keyword(s):  
Regression Trees ◽  
Classification And Regression Trees ◽  
Classification And Regression

Reply from Authors re: Michael W. Kattan. Classification and Regression Trees Versus Nomograms: A Bone Scan Positivity Example. Eur Urol 2010;57:559–60

2010 ◽  
Vol 57 (4) ◽  
pp. 560-561
Author(s):  
Alberto Briganti ◽  
Umberto Capitanio ◽  
Nazareno Suardi ◽  
Andrea Gallina ◽  
Patrizio Rigatti ◽  
...  
Keyword(s):  
Bone Scan ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Classification And Regression

scholarly journals Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Triticum aestivum L.)

2021 ◽  
Author(s):  
Lance F Merrick ◽  
Dennis N Lozada ◽  
Xianming Chen ◽  
Arron H Carter
Keyword(s):  
Support Vector Machine ◽  
Winter Wheat ◽  
Genomic Selection ◽  
Stripe Rust ◽  
Regression Models ◽  
Prediction Models ◽  
Support Vector ◽  
Classification Models ◽  
Breeding Lines ◽  
Classification And Regression

Most genomic prediction models are linear regression models that assume continuous and normally distributed phenotypes, but responses to diseases such as stripe rust (caused by Puccinia striiformis f. sp. tritici) are commonly recorded in ordinal scales and percentages. Disease severity (SEV) and infection type (IT) data in germplasm screening nurseries generally do not follow these assumptions. On this regard, researchers may ignore the lack of normality, transform the phenotypes, use generalized linear models, or use supervised learning algorithms and classification models with no restriction on the distribution of response variables, which are less sensitive when modeling ordinal scores. The goal of this research was to compare classification and regression genomic selection models for skewed phenotypes using stripe rust SEV and IT in winter wheat. We extensively compared both regression and classification prediction models using two training populations composed of breeding lines phenotyped in four years (2016-2018, and 2020) and a diversity panel phenotyped in four years (2013-2016). The prediction models used 19,861 genotyping-by-sequencing single-nucleotide polymorphism markers. Overall, square root transformed phenotypes using rrBLUP and support vector machine regression models displayed the highest combination of accuracy and relative efficiency across the regression and classification models. Further, a classification system based on support vector machine and ordinal Bayesian models with a 2-Class scale for SEV reached the highest class accuracy of 0.99. This study showed that breeders can use linear and non-parametric regression models within their own breeding lines over combined years to accurately predict skewed phenotypes.

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