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Implementing Artificial Intelligence in H-BIM Using the j48 algorithm to Manage Historic Buildings

INTERNATIONAL jOURNAL OF ARCHITECTURAL HERITAGE 2020年第8期14卷 1148-1160页

作者： Bienvenido-Huertas, David Enrique Nieto-julian, juan jose Moyano, juan Manuel Macias-Bernal, juan Castro, jose Univ Seville Dept Graph Express & Bldg Engn Seville Spain Univ Seville Dept Bldg Construct 2 Seville Spain

The preservation of the architectural heritage is characterized by the intervention of different technicians, who may disagree on decision-making criteria. In recent years, the H-BIM methodology has emerged to manage these buildings, although the multidisciplinary technical personnel make the decision-taking something of a challenge. In this regard, artificial intelligence may be an opportunity to establish automatic responses, thus optimizing the process. This article proposes a methodology to implement models of classification using the j48 algorithm in a H-BIM model. The case study was focused on a tiles panel from a building which belongs to the Real Alcazar of Seville. First, a model of classification was developed to estimate the degree of intervention with an adequate degree of adjustment. Then, the model was implemented in the H-BIM software by programming using GDL. This methodology automates the decision-making and reduces times of assessments, visualizing and managing the information in the H-BIM model.

关键词： atificial intelligence decision trees geometric description language j48 algorithm H-BIM project Pavilion of Charles V

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Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using j48 algorithm of WEKA in ANN based prediction model

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RENEWABLE ENERGY 2015年 75卷 675-693页

作者： Yadav, Amit Kumar Chandel, S. S. Natl Inst Technol Ctr Energy & Environm Hamirpur Himachal Prades India

Solar potential of western Himalayan Indian state of Himachal Pradesh is assessed using Artificial Neural Network (ANN) based global solar radiation (GSR) prediction model. j48 algorithm in Waikato Environment for Knowledge Analysis (WEKA)is used for the selection of input parameters for ANN model for predicting GSR. Most relevant input parameters are found to be temperature, altitude and sunshine hours whereas latitude, longitude, clearness index and extraterrestrial radiation are found to be least influencing variables. The usefulness of j48 algorithm in variable selection is checked by developing five ANN models: ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5. The maximum mean absolute percentage error (MAPE) for ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5 are found to be 16.91%, 16.89%, 16.38%, 6.89% and 9.04% respectively. ANN-5 model is used to develop the solar maps of Himachal Pradesh. The estimated GSR varies from 3.59 to 5.38 kWh/m(2)/day indicating good solar potential for solar energy applications. A correlation is developed between NASA satellite data and ground measured GSR data to find values close to ground measured GSR for different locations. The correlation coefficient is found to be 0.97. Models developed can be used to assess solar potential of any location worldwide. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Solar potential Global solar radiation Artificial neural network j48 algorithm Western Himalayas

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Improving Effectiveness of Honeypots: Predicting Targeted Destination Port Numbers During Attacks Using j48 algorithm 2nd

Improving Effectiveness of Honeypots: Predicting Targeted De...

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2nd International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM)

作者： Gangabissoon, Tanveer Nathoo, Amaan Ramhith, Rakshay Gopee, Bhooneshwar Bekaroo, Girish Middlesex Univ Mauritius Sch Sci & Technol Coastal Rd Uniciti Flic En Flac Mauritius

ISBN: (纸本)9783030182403;9783030182397

During recent years, there has been an increase in cyber-crime and cybercriminal activities around the world and as countermeasures, effective attack prevention and detection mechanisms are needed. A popular tool to augment existing attack detection mechanisms is the Honeypot. It serves as a decoy for luring attackers, with the purpose to accumulate essential details about the intruder and techniques used to compromise systems. In this endeavor, such tools need to effectively listen and keep track of ports on hosts such as servers and computers within networks. This paper investigates, analyzes and predicts destination port numbers targeted by attackers in order to improve the effectiveness of honeypots. To achieve the purpose of this paper, the j48 decision tree classifier was applied on a database containing information on cyber-attacks. Results revealed insightful information on key destination port numbers targeted by attackers, in addition to how these targeted ports vary within different regions around the world.

关键词： Destination port Honeypot Prediction j48 algorithm Decision tree

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Fault Diagnosis of Single-Stage Bevel Gearbox by Energy Operator and j48 algorithm 2nd

Fault Diagnosis of Single-Stage Bevel Gearbox by Energy Oper...

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2nd World Congress on Condition Monitoring, WCCM 2019

作者： Kumar, V. Verma, A.K. Sarangi, S. Department of Mechanical Engineering Indian Institute of Technology Patna Patna India Rolls-Royce@NTU Corporate Lab Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9789811591983

The main objective of this paper is to detect and classify the faults in a single-stage bevel gearbox. In this paper, energy operator (EO) is combined with the spectrum and cepstrum analysis is used for detection of faults and for classification purpose, j48 algorithm is used. Usually, the vibration signal is contaminated with noises which suppress the fault frequencies in spectrum analysis. So, a filter-free, simple, and efficient EO method is used to reduce noise and interferences of vibration signal coming from the gearbox test rig. All the experiments are carried out by varying the speed and load under three health conditions of gearbox such as healthy, chipped, and missing tooth. The vibration signal is preprocessed by EO, which is further analyzed by spectrum and cepstrum analysis in MATLAB to detect the location of faults. j48 decision tree algorithm is used to select the dominant feature among 12 statistical features which are extracted from the processed signal and based on these chosen features;the overall classification accuracy is approx. 89% with a precision of 0.917. The obtained results show that the presented method can detect and classify the faults of gearbox efficiently. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Bevel gearbox Cepstrum analysis Energy operator Fault diagnosis j48 algorithm Spectrum analysis

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Fault diagnosis of air compressor set-up using decision tree based j48 classification algorithm

JOURNAL OF ENGINEERING RESEARCH

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jOURNAL OF ENGINEERING RESEARCH 2024年第4期12卷 1011-1025页

作者： Dhakar, Atul Singh, Bhagat Gupta, Pankaj Jaypee Univ Engn & Technol Guna MP India

This paper describes an air compressor fault diagnosis method based on the acquisition of audio signals pertaining to seven faulty and one healthy condition. These audio signals are processed using Local Mean Decomposition (LMD) signal processing technique. Further, statistical indicators have been evaluated for feature extraction considering the decomposed signals. From different statistical indictors mean, variance, root mean square (RMS), root mean amplitude (RMA), absolute mean amplitude (AMA), kurtosis, peak to peak index, waveform index, peak index, impulse index, margin index, skewness, Shannon entropy, standard deviation, log energy entropy, log detector and CPT has been selected for decision tree based j48 classification algorithm. Decision tree based j48 classification algorithm more accurately identified healthy and faulty bearing state in an air compressor with the help of all statistical indicators. Data set consists of 360 instances having 17 attributes with 2 classes (healthy and bearing fault). Higher classification accuracy of j48 algorithm (96.66 %) has been obtained for healthy and faulty bearing conditions. LMD along with decision tree based j48 classification algorithm is quite suitable for processing and monitoring in situ fault features in air compressor set-up.

关键词： Air compressor Fault diagnostics Kurtosis index Decision tree j48 algorithm

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Fault diagnosis in reciprocating air compressors using ensemble voting classifier

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL jOURNAL 2025年

作者： Reddy, Suhash Sridharan, Naveen Venkatesh Sivakumar, Aravinth Vaithiyanathan, Sugumaran Vellore Inst Technol Sch Mech Engn SMEC Vandalur Kelambakkam Rd Chennai 600127 India Lulea Univ Technol Div Operat & Maintenance Engn Lulea Sweden Sri Ranganathar Inst Engn & Technol Deparment Mech Engn Coimbatore India

Reciprocating air compressors play a pivotal role in various industrial applications, demanding a steadfast focus on their dependable operation. This research addresses the challenge of fault diagnosis in reciprocating air compressors by leveraging the potential of ensemble voting classifiers. Vibration signals were captured under five compressor conditions, covering both normal operation and multiple fault scenarios, such as inlet valve fluttering, outlet valve fluttering, check valve faults, and combined inlet and outlet valve issues. A range of features, including statistical and autoregressive moving average features were extracted from the acquired vibration signals to effectively differentiate among these conditions. Feature selection was executed using the j48 algorithm to identify the most pertinent indicators. During the classification phase, an exhaustive evaluation of individual classifiers, such as K-Nearest Neighbors, j48, support vector machine, multilayer perceptron (MLP), Na & iuml;ve Bayes, logistic regression, random forest (RF), and logistic model tree, was conducted. The top-performing classifiers were subsequently selected for incorporation into ensemble voting systems. Within the ensemble voting framework, various configurations were explored, ranging from two to five classifiers, employing multiple voting strategies to optimize fault diagnosis performance. The findings of this study showcase that the combination of MLP and RF classifiers for statistical features produced the maximum classification accuracy of 98.67%.

关键词： Air compressors fault diagnosis voting algorithm j48 algorithm valve fault

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Predicting Faculty Research Productivity using j48 Decision Tree algorithm 2021

Predicting Faculty Research Productivity using J48 Decision ...

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2021 The 9th International Conference on Information Technology: IoT and Smart City

作者： Ranie Baul Canlas Keno Cruz Piad Ace Carpio Lagman joseph David Espino jayson Marcellana Victoriano Isagani Mirador Tano Nicanor San Gabriel Graduate School Don Honorio Ventura State University Philippines College of Information and Communications Technology Bulacan State University Philippines Institute of Technology Far Eastern University Philippines Graduate Studies La Consolacion University Philippines Philippines Information Technology Sarmiento Campus Bulacan State University Philippines College of Computer Science and Information Technology Quezon City University Philippines

ISBN: (纸本)9781450384971

Research productivity is seen as a shared issue present in most academic institutions, as research output requires skills, time and patience. In the Philippines, higher education institutions are mandated to undertake research and other similar investigations in various academic areas since they are one of the key institutions who play major role in the generation and dissemination of knowledge. This study generally aimed to come up with prediction model for faculty research productivity. Specifically, it tried to seek solutions to the following: identification of necessary attributes that have significant relationship to faculty research productivity, generate computational model for predicting research productivity of faculty through j48 Decision Tree algorithm and validate the performance of computational model using confusion matrix analysis, precision, recall and f-measure. Results showed that attributes such as paper proposal, length of service, age, teaching loads, academic ranks, designation/s, civil status, academic qualification, sex and status of appointment has vital influence to research productivity of faculty. The computational model generated had an acceptable computed accuracy, precision, recall and f-measure results. Based on the data model performance results, the system can be used and can be implemented in an actual working system.

关键词： Faculty research productivity j48 algorithm Mathematics of computing Decision tree

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An intelligent model based on data mining and fuzzy logic for fault diagnosis of external gear hydraulic pumps

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INSIGHT 2009年第11期51卷 594-600页

作者： Mollazade, K. Ahmadi, H. Omid, M. Alimardani, R. Univ Tehran Dept Agr Machinery Engn Fac Biosyst Engn Karaj *** Iran

This paper presents a fault diagnosis method based on a fuzzy inference system (FIS) in combination with decision trees. Experiments were conducted on an external gear hydraulic pump. The vibration signal from a piezoelectric transducer is captured for the following conditions: 'Normal pump' (GOOD), 'journal-bearing with inner face wear' (BIFW), 'Gear with tooth face wear' (GTFW) and 'journal-bearing with inner face wear and Gear with tooth face wear' (G&BW), for three working levels of pump speed (1000, 1500 and 2000 r/min). The features of signal were extracted using descriptive statistic parameters. The j48 algorithm is used as a feature selection procedure to select pertinent features from the data set. The output of the j48 algorithm is a decision tree that was employed to produce the crisp if-then rule and membership function sets. The structure of the FIS classifier was then defined based on the crisp sets. In order to evaluate the proposed j48-FIS model, the data sets obtained from vibration signals of the pump were used. Results showed that the total classification accuracy for 1000, 1500 and 2000 r/min conditions were 100%, 96.42% and 89.28%, respectively. The results indicate that the combined j48-FIS model has the potential for fault diagnosis of hydraulic pumps.

关键词： Intelligent fault diagnosis j48 algorithm hydraulic pump FIS

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Stability assessment of hard rock pillars using two intelligent classification techniques: A comparative study

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2017年 68卷 32-37页

作者： Ghasemi, Ebrahim Kalhori, Hamid Bagherpour, Raheb Isfahan Univ Technol Dept Min Engn Esfahan *** Iran

One of the most challenging safety problems in underground hard rock mines is pillar stability during mining operation. This paper presents an assessment of j48 and SVC application for pillar stability prediction in underground hard rock mines. Based on a database compiled from various hard rock mines and using these algorithms, two stability graphs are developed. The performance assessment of models indicates that both models can predict pillar stability with acceptable accuracy. In comparison with logistic regression model, the prediction capability of j48 and SVC models is better, but the j48 model shows superiority over two other models.

关键词： Pillar stability Underground hard rock mines j48 algorithm Support vector classification (SVC)

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Design of an expert system for sorting pistachio nuts through decision tree and fuzzy logic classifier

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 4339-4347页

作者： Omid, Mahmoud Univ Tehran Sch Agr & Nat Resources Fac Agr Engn & Technol Dept Agr Machinery Karaj Iran

This paper presents an expert system for sorting open and closed shell pistachio nuts. A prototype was set up to detect closed shell pistachio nuts by dropping them onto a steel plate and recording the acoustic signals that was generated when a kernel hit the plate. To determine the important characteristics and to unravel the significance of these signals, further analysis or processing was required. j48 decision tree (DT) is used for both feature selection and classification. Initially, the j48 DT was used for selecting the best statistical features that will discriminate among two classes from impact acoustic signals. The output of j48 DT algorithm was then converted into crisp IF-THEN rules and membership function sets of the fuzzy classifier. Four IF-THEN rules, generated from the extracted features of j48 DT, were required by the fuzzy classifier. To evaluate the performance of the expert system, data on 300 nuts of open and closed shells were used. The data were initially divided into two parts: 210 instances (70%) for training and the remaining 90 instances (30%) for testing the classifier. The correct classification rate and RMSE for the training set were 99.52% and 0.07, and for the test set were 95.56% and 0.21, respectively. These encouraging results as well as the robustness of the FIS based expert system makes the approach ideal for automated inspection systems. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Pistachio nuts Sorting Classification Expert system Fuzzy classifier Decision tree j48 algorithm

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