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New energy industry investment risk assessment method based on fuzzy AHP

INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2023年第4-5期45卷 436-447页

作者： Mo, Lianguang Hunan City Univ Sch Management Yi yang 413000 Peoples R China

In order to improve the accuracy, efficiency and comprehensiveness of investment risk assessment, a new energy industry investment risk assessment method based on fuzzy AHP is proposed. Firstly, the random forest algorithm is used to predict the investment risk of new energy industry. Secondly, the fuzzy AHP method is used to construct the risk assessment system, the normalisation and consistency test are used to deal with the assessment indicators, and the weight of the assessment indicators is calculated. Finally, based on the evaluation index system, a new energy industry investment risk evaluation model based on multiple regression analysis is established to realise the new energy industry investment risk evaluation. The experimental results show that the highest accuracy of the evaluation results of the proposed method is more than 80%, the evaluation efficiency is high, and the evaluation results are more comprehensive.

关键词： fuzzy AHP risk assessment random forest algorithm cart algorithm multiple regression analysis

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Detecting menarcheal status through dental mineralization stages?

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AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2016年第2期161卷 367-373页

作者： Jeanson, Alize Lacoste Santos, Frederic Bruzek, Jaroslav Urzel, Vanessa Univ Bordeaux CNRS UMR 5199 PACEA Batiment B8Allee Geoffroy St Hilaire CS 50023 F-33400 Talence France Charles Univ Prague Fac Nat Sci Dept Anthropol & Human Genet Vinicna 7 Prague 12843 2 Czech Republic

Menarche is an indicator frequently used to study variation in growth, development, and related health conditions among members of living populations. As a life event, menarche is often associated with changes in an individual's social identity. The reproductive lifespan, which for females starts with menarche, is a paramount feature of palaeodemographic studies. Determination of menarche status from the skeletal remains of individuals of past populations can be obtained by assessing the developmental status of the iliac crest, as well as the hand and wrist bones, which are, unlike teeth, often poorly recovered in bioarchaeological contexts. The present study seeks to evaluate the link between dental mineralization and menarche in a population of known menarche status. The relationship between permanent teeth mineralization and menarche status was investigated by using data of developing permanent teeth (167 radiographs) rated in accordance with the well-known standards of Demirjian et al. and Moorrees et al. collected among 73 living French females of known menarcheal status. Using correlation ratios, GLMM and cart algorithm, menarcheal status is correlated with mineralization of the premolars. Menarcheal status is predicted correctly for 92 and 77% of radiographs of the learning and validation samples, respectively. Although promising, the results require caution prior to generalization to other populations. The age of menarche in this particular sample may simply coincide with the development of the premolars in this particular sample. Therefore, further investigation applied to populations with various mean ages of menarche is required in order to provide new evidence of variation in human growth and development from the correspondence between the mineralization of the permanent teeth and menarche.

关键词： cart algorithm GLMM menarche puberty teeth development

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Unleashing an ML-based selection criteria for economic lot sizing in a smart batch-type production system

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TQM JOURNAL 2024年第1期36卷 90-107页

作者： Kumar, Narinder Singh, Bikram Jit Khope, Pravin Indian Inst Technol Dept Mech Engn Ropar India Maharishi Markandeshwar Deemed Be Univ MM Engn Coll Dept Mech Engn Mullana India Priyadarshini Coll Engn Dept Mech Engn Nagpur Maharashtra India

Purpose Inventory models are quantitative ways of calculating low-cost operating systems. These models can be either deterministic or stochastic. A deterministic model hypothesizes variable quantities like demand and lead time, as certain. However, various types of research have revealed that the value of demand and lead time is still ambiguous and vary unanimously. The main purpose of this research piece is to reduce the uncertainties in such a dynamic environment of Industry 4.0. Design/methodology/approach The current study tackles the multiperiod single-item inventory lot-size problem with varying demands. The three lot sizing policies - Lot for Lot, Silver-Meal heuristic and Wagner-Whitin algorithm - are reviewed and analyzed. The suggested machine learning (ML)-based technique implies the criteria, when and which of these inventory models (with varying demands and safety stock) are best fit (or suitable) for economical production. Findings When demand surpasses a predicted value, variance in demand comes into the picture. So the current work considers these things and formulates the proper lot size, which can fix this dynamic situation. To deduce sufficient lot size, all three considered stochastic models are explored exclusively, as per respective protocols, and have been analyzed collectively through suitable regression analysis. Further, the ML-based Classification And Regression Tree (cart) algorithm is used strategically to predict which model would be economical (or have the least inventory cost) with continuously varying demand and other inventory attributes. Originality/value The ML-based cart algorithm has rarely been seen to provide logical assistance to inventory practitioners in making wise-decision, while selecting inventory control models in dynamic batch-type production systems.

关键词： Material requirement planning Wagner-Whitin algorithm Lot for Lot model Silver-Meal heuristic cart algorithm Regression Safety stock Lot size policy

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Decision Tree Model for Predicting Exposure Temperature and Retention Period-Dependent Behavior of Blended Concrete

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2023年第6期47卷 3277-3289页

作者： Kulkarni, Kishor S. Babu Narayan, K. S. Yaragal, Subhash C. Cent Bldg Res Inst CSIR Roorkee 247667 Uttarakhand India Natl Inst Technol Karnataka Dept Civil Engn Surathkal Karnataka India

The major objective of the study is to estimate the behavior of blended concrete at various sustained exposure temperatures and retention times. The study examines the properties of four different types of concrete mixes, including unblended and blended mixes with fly ash and ground granulated blast furnace slag used to partially replace cement at exposure temperatures between 100 degrees C and 800 degrees C for varying exposure times of 1, 2, and 3 h. Concrete quality has been evaluated using measurements of density, porosity, and ultrasonic pulse velocity. Residual compressive and splitting tensile strengths have also been determined. The experimental study indicates that blended concrete has better fire-endurance characteristics than unblended concrete. The exposure temperature and retention time dependent behavior of unblended and blended concrete is predicted using classification and regression decision tree techniques.

关键词： Blended concrete Elevated temperature Mechanical properties ANOVA Decision tree cart algorithm

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Evaluation of some factors on birth and weaning weights in Awassi sheep by using GLM and cart analysis

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TROPICAL ANIMAL HEALTH AND PRODUCTION 2022年第6期54卷 400-400页

作者： Sahin, Ozcan Selcuk Univ Fac Agr Dept Anim Sci Konya Turkey

Birth weight (BW), which is the first physiological record of the postnatal period, is an important indicator that affects growth and development, reproduction, and some yields of living things in the future. The present study was carried out using the birth records of 5417 heads of Awassi lambs from 2013 to 2018 in MeMuTa dairy sheep farm in Zengen town of Eregli district of Konya city in Tiirkiye. In this study, the effects of some environmental and genetic factors on birth and weaning weight in Awassi lambs were evaluated using both general linear model (GLM) and classification and regression tree (cart) analysis. As a result of the GLM analysis, the least squares' means of birth and weaning weights (BW and WW) were determined as 4.15 +/- 0.05 kg and 16.39 +/- 0.13 kg, respectively. Fixed genetic factors such as birth type and sex, and environmental factors such as season and year were found to be significant on birth weight (P < 0.01). As for weaning weight, other factors except birth type were found to be similarly important like birth weight (P < 0.01). Also, linear regression of birth weight on weaning weight was found to be significant (P < 0.01). In GLM and cart analyses, the determination coefficient (R-2) was determined to be 23.80% and 11.70% for BW, and 24.11% and 13.18% for WW, respectively. The findings of the analysis results showed that the factors in the model have a similar tendency both in terms of their relative ratios in the total variation in the GLM analysis and in their relative importance in the cart algorithm. It was seen that cart data mining algorithm in the prediction of some genetic and environmental factors on birth and weaning weights using some factors in Awassi lambs produced successful results in predictive performance. Briefly, it was determined that there is a similar tendency to GLM analysis according to cart algorithm analysis in the current study.

关键词： Awassi Birth weight Weaning weight Environment factors GLM cart algorithm

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Presenting the Regression Tree Method and its application in a large-scale educational dataset

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INTERNATIONAL JOURNAL OF RESEARCH & METHOD IN EDUCATION 2020年第2期43卷 201-221页

作者： Assis Gomes, Cristiano Mauro Jelihovschi, Enio Fed Univ Minas Gerais Brazil Lab Cognit Architecture Mapping LAICO Dept Psychol Av Antonio Carlos6627Sala 4010 Belo Horizonte MG Brazil Univ Estadual Santa Cruz Dept Sci & Technol Ilheus Brazil

Regression Tree Method is not yet a mainstream method in Education, despite of being a traditional approach in Machine Learning. We advocate that this method should become mainstream in Education, since, in our point of view, it is the most suitable method to analyse complex datasets, very common in Education. This is, for example, the case of educational governmental large-scale databases, in particular those where the information: (1) have large quantity and types of variables;(2) exhibit many categorical variables with many categories;(3) have many non-linear relationships among variables;(4) are guided or supported by management goals, instead of a specific theory. In this paper we show its rationale, focusing on the Classification And Regression Trees algorithm (cart). We also apply this algorithm to a complex large-scale educational dataset, the microdata of the National Examination for Secondary Education (Exame Nacional do Ensino Medio [ENEM]). Our general goal is to disseminate the use of the Regression Tree Method in Education, particularly in complex datasets and on the substantial and interpretative aspects of this method.

关键词： Tree regression prediction National Examination for Secondary Education large-scale datasets exploratory approach cart algorithm

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Improvement of Power Transformer Faults Diagnosis Using Random Search with Classification and Regression Tree 5

Improvement of Power Transformer Faults Diagnosis Using Rand...

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5th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Wang, Tonglei Yang, Jinggang Shao, Jian Wu, Peng Ma, Yong Liang, Jiabi State Grid Jiangsu Elect Power Res Inst Nanjing Peoples R China

ISBN: (纸本)9781665490542

Dissolved gas in transformer oil is an important parameter to analyze the operating condition of transformers. Dissolved gas analysis (DGA) is also a commonly used method for transformer fault diagnosis. Compared to the traditional IEC Ratios, Rogers, Duval Triangle and Pentagon methods, the artificial intelligence algorithms improve the efficiency of transformer fault diagnosis, and also reduce the requirements and reliance on application experience. In order to explore more feature information in the DGA data and the accuracy of diagnosis results, a transformer fault diagnosis model based on random search and classification regression tree is built in the paper. Based on the interrelationship of the dissolved gases, the paper expands the number of features of DGA. In addition, the random search algorithm is used to realize the parameter optimization of cart model so as to improve the accuracy of fault diagnosis results. Based on the collected DGA sample dataset in the paper, the improvement effect of the RS algorithm on the cart model is verified and discussed. It is found that the median accuracy rate exceeds 92.3% for the power transformer diagnosis, demonstrating the effectiveness of the proposed technique.

关键词： DGA fault diagnosis power transformer RS algorithm cart algorithm

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QoE Prediction Model for IPTV based on Machine Learning 8

QoE Prediction Model for IPTV based on Machine Learning

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8th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Meng, Hao Huang, Ruochen Wei, Xin Qian, Yi Liu, Qifeng Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Jiangsu Peoples R China Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing 210003 Jiangsu Peoples R China

ISBN: (纸本)9781509028603

IPTV is a new multimedia service over the Internet. In order to improve the quality of IPTV service and the user's satisfaction, telecom operators are interested in studying and improving user's Quality of Experience (QoE). In this paper, we study the relationship between the viewing records from IPTV set top box and the user's QoE based on IPTV service. Firstly, data processing is performed. After the procedure, the important attributes influencing QoE are selected. Then we propose a new attribute called user's viewing custom from the user's point of view. We also create a mapping between viewing time ratio and user's QoE for subjective video quality evaluation. In addition, we improve the cart algorithm with the idea of weighted mean. Experimental results show that the proposed methods can indeed improve the prediction accuracy of QoE model when comparing with original method.

关键词： IPTV QoE user's viewing custom cart algorithm

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Dynamic Remote Sensing Monitoring of Land Use Change in Xiangxi River Watershed by Decision Tree Method

Dynamic Remote Sensing Monitoring of Land Use Change in Xian...

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18th International Conference on Geoinformatics

作者： Cao, Xue Li, Manchun Luo, Ping Ke, Changqing Nanjing Univ Sch Geog & Oceanog Sci Nanjing 210093 Peoples R China Ctr Assessment & Dev Land & Real Estate Shenzhen 518034 Peoples R China

ISBN: (纸本)9781424473021

Monitoring land use status timely is the basis of rational management of land resource, and the important gist for planning agriculture or even the whole national economy. Possessing the advantages of macroscopic information, real time, periodicity and integrity, remote sensing provide the possibility for monitoring land use quickly, objectively and precisely. This research uses cart algorithm to draw knowledge automatically, and extracts classification image of the trial zone to analyze land use change in Xiangxi river watershed. It was concluded that Land use was unreasonable utilized at Xiangxi River basin in recently years, vegetation and the maintenance of water and soil were destroyed in a certain extent.

关键词： xiangxi river basin land use change remote sensing decision tree cart algorithm

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Tuning Hyperparameters of Decision Tree Classifiers using Computationally Efficient Schemes 1

Tuning Hyperparameters of Decision Tree Classifiers using Co...

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1st IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Alawad, Wedad Zohdy, Mohamed Debnath, Debatosh Oakland Univ Sch Engn & Comp Sci Rochester MI 48309 USA

ISBN: (纸本)9781538695555

Attack types and patterns are constantly evolving which makes frequent detection system updates an urgent need. In contrast, the computation cost of developing machine learning-based detection models such as decision tree classifiers is expensive which can be an obstacle to frequently updating detection models. Tuning classifiers' hyperparameters is a key factor in selecting the best detection model but it significantly increases the computation overhead of the developing procedure. In this research, we have presented a computationally efficient strategy and an algorithm for tuning decision tree classification algorithms' hyperparameters with less budget and time.

关键词： Supervised learning decision tree cart algorithm hyperparameter tuning

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