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Mine wind speed sensor location selection based on decision tree theory

INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 315-323页

作者： Huang, Xu Lei, Wulin Longdong Univ New Energy Coll 45 Lanzhou Rd Qingyang Gansu Peoples R China

In order to realize the scientific layout of underground roadway sensors, based on the information entropy theory, the decision tree algorithm is applied to the location selection of the underground wind speed sensor. Taking the air volume traversing the entire network branch as the goal, the node air volume balance and the characteristic relationship between air volume and wind pressure as the constraints, the conventional layout data of the mine wind speed sensor is selected as the training sample, and the reasonable decision node is selected by measuring the uncertainty of the characteristic attribute. The research shows that the smaller the information entropy of the layout elements, the greater the weight in determining the sensor location. The classification conditions obtained by the wind speed sensor are, in descending, order: airflow disturbance, roadway support, distance from the inlet (return) and outlet, and roadway type. algorithms are applied to effectively combine downhole sensor siting with historical data containing unambiguous results.

关键词： decision tree algorithm sensor location information entropy feature attribute measurement mine intelligence

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Predictive Analysis of the Building Damage From the 2011 Great East Japan Tsunami Using decision tree Classification Related algorithms

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IEEE ACCESS 2021年 9卷 31065-31077页

作者： Saengtabtim, Kumpol Leelawat, Natt Tang, Jing treeranurat, Wanit Wisittiwong, Narunporn Suppasri, Anawat Pakoksung, Kwanchai Imamura, Fumihiko Takahashi, Noriyuki Charvet, Ingrid Chulalongkorn Univ Dept Ind Engn Fac Engn Bangkok 10330 Thailand Chulalongkorn Univ Disaster & Risk Management Informat Syst Res Grp Bangkok 10330 Thailand Chulalongkorn Univ Int Sch Engn Fac Engn Bangkok 10330 Thailand Thammasat Univ Sirindhorn Int Inst Technol Sch Management Technol Pathum Thani 12121 Thailand Tohoku Univ Int Res Inst Disaster Sci Sendai Miyagi 9808572 Japan Tohoku Univ Sch Engn Dept Architecture & Bldg Sci Sendai Miyagi 9808579 Japan Praedicat Inc Los Angeles CA 90016 USA

When considering a tsunami disaster, many researchers have considered the tsunami's flow depth and velocity as the primary contributors to the building damage. Additionally, the majority of these studies have used the maximum value as the measure of each of these two factors. However, building damage may not occur when the maximum flow depth and the maximum flow velocity of the tsunami are reached. This study addressed two objectives based on the 2011 Great East Japan Earthquake and Tsunami. Firstly, to find out whether the maximum values of the flow depth and flow velocity are the same as their critical values and, secondly, to verify which combination of the parameters is the best predictor of the building damage level. The data from 18,000 buildings in Ishinomaki City, Japan, with the cooperation of the Japanese joint survey team, were analyzed using the decision tree related algorithms. The critical variables were the simulated data at the time when the buildings collapsed. The analysis showed the accuracy of the prediction based on the group of variables. Finally, the findings showed that the combination of the critical flow depth and maximum flow velocity provided the highest accuracy for classifying the level of building damage.

关键词： Buildings Tsunami Earthquakes decision trees Soil Prediction algorithms Classification algorithms Building damages data mining decision tree algorithm 2011 Great East Japan earthquake and tsunami

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Exploiting sound signals for fault diagnosis of bearings using decision tree

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MEASUREMENT 2013年第3期46卷 1250-1256页

作者： Amarnath, M. Sugumaran, V. Kumar, Hemantha Indian Inst Informat Technol Design & Mfg Jabalpur Madhya Pradesh India VIT Univ Chennai 600048 Tamil Nadu India Birla Inst Technol & Sci Pilani Goa 403726 India

Bearing is one of vital transmission elements, finding numerous applications in small, medium and large machineries. Sound and vibration signals of a rotating machine contain the dynamic information about its operating condition. There are many articles in the literature reporting suitability of vibration signals for fault diagnosis applications;however, the transducer used for vibration signals (accelerometers) is costly. This prevents small scale industries and low cost equipments from using diagnostic tools on affordability ground. On the other hand, transducers used for acquiring sound signals are relatively low cost or/and affordable. Hence, there is a need for studying the use of sound signal for fault diagnosis applications. This paper uses acoustic signals (sound) acquired from near field area of bearings in good and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The descriptive statistical features were extracted from sound signals and the important ones were selected using decision tree (dimensionality reduction). The selected features were then used for classification using C4.5 decision tree algorithm. The paper also discusses the effect of features, effect of various classifier parameters on classification accuracy. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Bearing fault diagnosis decision tree algorithm Feature selection Machine learning approach

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A decision tree-Driven IoT systems for improved pre-natal diagnostic accuracy

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BMC MEDICAL INFORMATICS AND decision MAKING 2024年第1期24卷 1-19页

作者： Yang, Xuewen Liu, Ling Wang, Yan Zhengzhou Univ Affiliated Hosp 3 Prenatal Diag Ctr Zhengzhou 450052 Peoples R China Cent China Normal Univ Sch Comp Wuhan 430079 Peoples R China

Prenatal diagnostics are vital for the woman as well as her unborn baby. The diagnostics help in the early identification of the possibility of complication and the initial measures that help to ameliorate the mother and the fetus health status are taken. Over the year's various techniques have been employed in diagnosing genetic disorders before birth that lack effectiveness in terms of cost, time, and places to access ultra-modern health facilities. To overcome these problems, this paper puts forward a diagnostic model that integrates Internet of Things innovation with a Machine Learning approach which is the decision tree algorithms. First, it implies the application of IOT devices in the collection of vital information like heart rate, blood pressure, glucose levels, and fetal movement. The data is structured in the form of a dataset and transmitted to a Big Data storage for warehousing and processing. Secondly, the DTA is employed to analyze the data and look for patterns and possibilities of future health complications. The DTA operates in that it divides the dataset into subsets considering specific features and formulates a tree-like model of decisions. At every node, the algorithm chooses the attribute which has the highest information gain, to partition the data into different classes. This process goes on until it reaches a decision node through which, it can decide probable health problems from the input data. To increase the reliability of the developed model this study fine-tunes the model by using a large database of pre-natal health records. The system is capable of collecting data in real-time and flagging data that needs attention in the case of any abnormality to the health professional. The above methodology was tested on a 1000-record database of pre-natal health records where the proposal achieved 95% possibility of potential health problems as against 85% by classical statistical analysis. Furthermore, the system scaled down the number of fals

关键词： Pre-natal diagnostics IoT technology decision tree algorithm Real-time monitoring Health data analysis Maternal-fetal health

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Private and Energy-Efficient decision tree-Based Disease Detection for Resource-Constrained Medical Users in Mobile Healthcare Network

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IEEE ACCESS 2022年 10卷 17098-17112页

作者： Alex, Sona Dhanaraj, K. J. Deepthi, P. P. Natl Inst Technol Calicut Dept Elect & Commun Engn Kozhikode 673601 India

In mobile healthcare networks (MHN), outsourced disease detection services demand the privacy preservation of medical users and health service providers (health clouds). This necessitates the use of a fully homomorphic encryption (FHE) while providing disease detection services, such as decision tree-based disease detection. However, the existing homomorphic encryption schemes utilized in decision tree-based disease detection that ensure the privacy of the medical user and health cloud are computationally-intensive and energy-hungry at the edge devices. Hence the medical user finds it difficult to exploit the existing private decision tree-based disease detection services due to restrictions on battery capacity and computing resources. Therefore, this work proposes a protocol for private decision tree classification with low resource consumption (PDTC-LRC) on edge devices of medical users by considering decision tree parameters as confidential to the health cloud. An energy-efficient, additively homomorphic, symmetric key-based FHE-compatible Rivest scheme (FCRS) is developed for implementing PDTC-LRC. FCRS can be decrypted homomorphically at the health cloud to support additive and multiplicative homomorphism. Also, an energy and bandwidth-efficient secure integer comparison protocol is developed for realizing PDTC-LRC. Experiments on the Raspberry Pi 3B+ board validate the improved energy efficiency and real-time applicability of the proposed secure integer comparison protocol and decision tree classifier compared with similar schemes available in the literature. Simulation and mathematical analysis ensure that user and health cloud privacy requirements are achieved by maintaining the classification accuracy same as that of decision tree classification in the plain domain.

关键词： decision trees Cryptography Diseases Privacy Ciphers Protocols Medical diagnostic imaging Mobile healthcare networks privacy decision tree algorithm homomorphic encryption

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decision tree-Based Electricity Optimization Using Intelligent Appliance Controller

Decision Tree-Based Electricity Optimization Using Intellige...

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International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Shaikh, Aman Shelke, Maya Rai, Satayush Mujawar, Md Sami Mulani, Dastagir Ranjan, Nihar M. JSPM Rajarshi Shahu Coll Engn Dept Informat Technol Pune Maharashtra India

ISBN: (纸本)9789819984756;9789819984763

With the growth of infrastructural development, the demand for electricity has exponentially increased. An average human consumes about 14,000 to 7000 KWH of electrical energy alone per. To meet this demand, 75% electricity is generated from conventional resources like thermal, nuclear and hydro, and 15% electricity is generated from renewable resources like wind, solar and biomass. The 3/4 th portion of electricity is generated from non-renewable resources which cannot be produced. Hence it needs to be used with utmost care. Unavailability of electricity not only stops the digital cycle of the world but also disturbs individual life. The first and the biggest impact of unavailability of electricity is on the health sector. One example of such an impact is the outage in Mumbai on October 12, 2020 where the major grid failure caused nearly a full day outage in some regions of Mumbai. Hospitals in Mumbai struggled to keep essential patient services running to provide necessary treatments causing loss of lives. Though there are many reasons like standby consumption, poor insulation, poor infrastructure, etc., responsible for electricity wastage, one of the smallest and most ignored but important reason is human tendency of inefficient use of electricity. Hence, we propose an intelligent electricity optimization solution which takes advantage of already installed CCTV systems to detect the number of people present in an area with accuracy of 98.33% and thus accordingly takes immediate and accurate decisions to optimize the usage of required appliances.

关键词： Energy optimization MQTT Object detection Embedded systems IOT decision tree algorithm

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Development of Converted Deterministic Finite Automaton of decision tree Rules of Student Graduation and Adaptive Learning Environment 2018

Development of Converted Deterministic Finite Automaton of D...

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6th International Conference on Information Technology - IoT and Smart City (ICIT)

作者： Lagman, Ace C. Ballera, Melvin A. Contreras, Jennifer O. Raviz, Jennalyn G. FEU Inst Technol Manila Philippines Technol Inst Philippines Manila Philippines FEU Inst Technol Technol Inst Philippines Manila Philippines

ISBN: (纸本)9781450366298

In theory of computation, a deterministic finite automaton (DFA) is a finite state machine that accepts/rejects finite strings of symbols and only produces a unique computation. This study aims to convert the extracted decision tree rules sets from decision tree algorithm and the learning path sequence of the learning management system. This paper converts decision tree and learning management system rules into deterministic finite automaton. The decision tree rule sets are rule sets used to predict the vulnerability of not having graduation on time. The learning management system rules are the backbones of the learning management system in order to provide individualized learning scheme tailored to the preferences of the learners. The conversion of decision tree rules of student graduation and adaptive learning environment lead to easier interpretation and visualization of the methods and processes involved.

关键词： Deterministic Finite Automata Student Graduation decision tree algorithm Learning Management System

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The Analysis of Customer Churns in E-commerce based on decision tree

The Analysis of Customer Churns in E-commerce based on Decis...

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2nd International Conference on Computer Science and Applications (CSA)

作者： Guo, Feng Qin, Hui-lin Beijing Wuzi Univ Sch Informat Beijing Peoples R China Beijing Wuzi Univ Beijing Peoples R China

ISBN: (纸本)9781479999613

Holding and attracting customers are the keys to improve the competitiveness of e-commerce, researching customer relationship management can help us analyze the different types of customers, providing strong support for business decisions. Using decision tree algorithm in data mining techniques to analyze the basic information of e-commerce business customers, and to find out the features of customer churns, in this case, we can help companies improve customer relationships in a targeted manner, and avoid customer churns.

关键词： e-commerce decision tree algorithm customer churns model

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Correlation Method of English Vocabularies Learning on Internet Based On decision tree 3

Correlation Method of English Vocabularies Learning on Inter...

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3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Guan, Zhenbin Xiong, Zhan Zhang, Bo Hubei Polytech Univ Sch Foreign Languages Huangshi Hubei Peoples R China Wuhan Donghu Univ Sch Comp Sci Wuhan Hubei Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Fac Engn Sydney NSW Australia

ISBN: (纸本)9781538684818

In consideration of the particularity and the complexity of English vocabulary memory and the uncertainty of affecting factors, the paper proposed a correlation model of English words learning on Internet based on decision tree, aimed to solve the difficulties in learning English words. The model combined the vocabulary learning with the internet tools, resulting in a better effect of English words learning and providing English learners with a learning environment anytime and anywhere.

关键词： decision tree algorithm internet English words correlation

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The Application of decision tree in security audit

The Application of decision tree in security audit

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3rd International Conference on Advanced Design and Manufacturing Engineering (ADME 2013)

作者： Dan, Shao Bai, Wang Yan Changchun Univ Dept Comp Changchun Peoples R China

ISBN: (纸本)9783037858431

Network security audit is an important part in network security system, which, to some extent, makes up for the deficiencies in firewalls and intrusion detections. According to the characteristics of the network data, the traditional decision tree model is modified, and the application pattern in which the decision tree model is used to network data security audit is also proposed. Simulations show that the decision tree classification model not only is adaptive to the characteristics of network data, but also can prevent malicious attacks effectively. The effects of the attributes such as IP, port, and protocol type on classification are also uncovered.

关键词： Network data Security audit decision tree algorithm Classification of data

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