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IEEE Aerospace Conference

作者： Seale, Maria Hines, Amanda Nabholz, Grace Ruvinsky, Alicia Eslinger, Owen Rigoni, Nathan Vega-Maisonet, Luis Us Army Engineer Res & Dev Ctr 3909 Halls Ferry Rd Vicksburg MS 39180 USA Aviat & Missile Res Dev & Engn Ctr Huntsville AL USA Univ Puerto Rico PR-108 Mayaguez PR 00682 USA

ISBN: (纸本)9781538668542

The US Army Aviation and Missile Research, Development, and Engineering Center (AMRDEC), in collaboration with the US Army Engineer Research and Development Center (ERDC), is using machine learning (ML) to transform the way rotorcraft maintenance logbook event data is scored for reporting purposes. Traditionally, human analysts manually inspect data fields completed by maintenance personnel and, using published guidelines in conjunction with their personal expertise, provide sets of labels or scores for each event. These labels are stored with the data and provide valuable insight into maintenance event histories. However, the inequity between the enormous volume of maintenance data generated daily and the ability of analysts to score the data results in only 10% of all data receiving scores;therefore, 90% of the recorded data does not contain this important value added feature. Classification algorithms for automating this scoring process trained on existing labeled data sets have been implemented with promising results. A particularly challenging element of this problem, however, involves the classification of the specific component on which maintenance was performed. Greater than 1200 unique labels exist that can be used to describe a rotorcraft component that is the subject of a maintenance action. Furthermore, the component labels are hierarchically structured, resulting in the occurrence of multiple levels of precision in identifying a component in the expert-labeled data used for training. Although computational efficiency of common classification algorithms has improved considerably, it is still quite challenging to harness these methods for problems that include large numbers of unique class labels. This paper describes several novel strategies for solving this problem, based on hierarchical ensemble models and strategic label set segmentation. Through these approaches, a best overall total component classification accuracy of 96% was achieved, in conjuncti

关键词： Maintenance engineering Classification algorithms Prediction algorithms machine learning algorithms Reliability Aircraft machine learning

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Evaluation of machine learning algorithms for Intrusion Detection System 15

Evaluation of Machine Learning Algorithms for Intrusion Dete...

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15th IEEE International Symposium on Intelligent Systems and Informatics (SISY)

作者： Almseidin, Mohammad Alzubi, Maen Kovacs, Szilveszter Alkasassbeh, Mouhammd Univ Miskolc Dept Informat Technol H-3515 Miskolc Hungary Mutah Univ Dept Informat Technol Amman Jordan

ISBN: (纸本)9781538638552

Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate.

关键词： machine learning algorithms Intrusion detection Vegetation Algorithm design and analysis Prediction algorithms Testing Neural networks

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Forecasting Solar Photovoltaic Power Output in the German Jordanian University in Amman Using Artificial Intelligence and machine learning algorithms 10

Forecasting Solar Photovoltaic Power Output in the German Jo...

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10th International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Qaadan, Sahar Alshare, Aiman German Jordanian Univ Mechatron Engn Dept Madaba Jordan German Jordanian Univ Mech & Maintenance Engn Madaba Jordan

ISBN: (纸本)9781665490481

Forecasting solar photovoltaic power output is a difficult task, but it is essential for effective solar energy planning. Artificial intelligence and machine learning algorithms are promising to improve the forecasting accuracy. In this work, we discuss how different types of data (e.g. weather, solar irradiance, etc.) can be used to improve the accuracy of the forecast. We evaluate the effect of using machine learning algorithms, including artificial neural networks, support vector machines, and gradient boost to design the model that effectively represent the output power of a photovoltaic solar system in Jordan. Experimental results show that algorithms such as gradient boost perform the best compared to simulation and other machine learning algorithms. Best model selection applies 10-fold cross validation using data from weather stations to forecast PV power output. The best model has been trained using imputed and pre-processed data from 2018-2022 and has shown excellent results. The Gradient Boost algorithm outperformed other algorithms, with an RMSE value 0.808 and 0.921 R2. This suggests that the Gradient Boost algorithm is a promising tool for forecasting PV power output.

关键词： forecasting machine learning algorithms Climatic conditions photovoltaic cells gradient boosting support vector machines neural network

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Using Attribute-based Feature Selection Approaches and machine learning algorithms for Detecting Fraudulent Website URLs 10

Using Attribute-based Feature Selection Approaches and Machi...

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10th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Aydin, Mustafa Butun, Ismail Bicakci, Kemal Baykal, Nazife Middle East Tech Univ METU Informat Inst Ankara Turkey Banking Regulat & Supervis Agcy Istanbul Turkey Chalmers Univ Technol Dept Comp Sci & Engn Gothenburg Sweden TOBB Univ Econ & Technol Comp Engn Dept Ankara Turkey METU Comp Engn Dept Northern Cyprus Campus Guzelyurt Cyprus

ISBN: (纸本)9781728137834

Phishing is a malicious form of online theft and needs to be prevented in order to increase the overall trust of the public on the Internet. In this study, for that purpose, the authors present their findings on the methods of detecting phishing websites. Data mining algorithms along with classifier algorithms are used in order to achieve a satisfactory result. In terms of classifiers, the Naive Bayes, SMO, and J48 algorithms are used. As for the feature selection algorithm;Gain Ratio Attribute and ReliefF Attribute are selected. The results are provided in a comparative way. Accordingly;SMO and J48 algorithms provided satisfactory results in the detection of phishing websites, however, Naive Bayes performed poor and is the least recommended method among all.

关键词： Attribute-based feature selection Cyber theft Data analysis Fraudulent website detection machine learning algorithms

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Travel Time Prediction: Comparison of machine learning algorithms in a Case Study 20

Travel Time Prediction: Comparison of Machine Learning Algor...

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20th IEEE International Conference on High Performance Computing and Communications (HPCC) / 16th IEEE International Conference on Smart City (SmartCity) / 4th IEEE International Conference on Data Science and Systems (DSS)

作者： Goudarzi, Forough Brunel Univ London Coll Engn Design & Phys Sci London England

ISBN: (纸本)9781538666142

Travel time prediction has important applications within the field of intelligent transportation, such as vehicle routing, congestion and traffic management. A challenging task in travel time prediction is obtaining data that is not readily available, as a clear majority of links in roads network are not equipped with traffic sensors. In this paper, data of travel time is collected for a link using Google Maps Application Programming Interface (API). Then, travel times are predicted for short horizons of up to one hour on the link by applying machine learning algorithms. The Mean Absolute Error (MAE) of predictions are compared. The study indicates that a shallow Artificial Neural Network (ANN) can provide more accurate prediction than the other algorithms.

关键词： Roads Google Artificial neural networks Predictive models machine learning algorithms Data models

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Impact Analysis of Stacked machine learning algorithms Based Feature Selections for Deep learning Algorithm Applied to Regression Analysis

Impact Analysis of Stacked Machine Learning Algorithms Based...

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SoutheastCon Conference

作者： Kulkarni, Shrirang Ambaji Gurupur, Varadraj P. King, Christian Univ Cent Florida Sch Global Hlth Management & Informat Orlando FL 32816 USA

ISBN: (纸本)9781665406529

Ensemble learning algorithms have proved to be one of the best machine learning algorithms towards optimal performances in terms of regression and classification tasks for a variety of applications. When applied for small or medium structured datasets, eXtreme Gradient Boosting (XGBoost) has emerged as a popular ensemble learning technique based on its performance merits. In recent years Light Gradient Boosting machine (LightGBM) has emerged as a promising ensemble strategy that is competing with XGBoost in terms of performance. Also, Lasso Regression has proven capabilities in terms of feature selections and applications for small datasets. This paper illustrates experimentation performed on a diabetes dataset where the authors tested the hypothesis that feature selection has relatively no impact on the performances of Deep learning algorithms as they have built-in capabilities in terms of layers to perform feature selection on their own. Therefore, the hypothesis tested stacking using Deep learning - Multi-Layer Perceptron (DMLP) with optimal algorithms like XGBoost, LightGBM, and Lasso Regression. In the present work, DMLP with all feature variables (DMLP-ALL) outscored DMLP with stacked selected features (DMLP-MS) by 8.78 % in terms of R-2. Also, DMLP-ALL outperformed the benchmarked algorithm Automated machine learning (AML) by 10.25% in terms of R-2. The validation of the proposed stacking models by applying a moderate-sized dataset provides promising results for deep learning models stacked with a powerful Level-0 learner.

关键词： Stacking models machine learning algorithms Deep learning algorithms Regression metrics

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Defining machine learning algorithms as accident prediction models for Italian two-lane rural, suburban, and urban roads

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INTERNATIONAL JOURNAL OF INJURY CONTROL AND SAFETY PROMOTION 2022年第4期29卷 450-462页

作者： Fiorentini, Nicholas Leandri, Pietro Losa, Massimo Univ Pisa Engn Sch Dept Civil & Ind Engn DICI Pisa Italy

Four Accident Prediction Models have been defined for Italian two-lane rural, suburban, and urban roads by exploiting different machine learning algorithms. Specifically, a Classification and Regression Tree, a Boosted Regression Tree, a Random Forest, and a Support Vector machine have been implemented to predict the number of Fatal and Injury crashes on a 905-km network, which experienced 5,802 FI crashes in 2008-2016. The dataset incorporates geometrical, functional, and environmental information. Several performance metrics have been computed, such as Determination Coefficient, Mean Absolute Error, Root Mean Square Error, and scatterplots. Outcomes suggest that Support Vector machine outperforms the other machine learning algorithms for predicting Fatal and Injury crashes. In Addition, the computation of Predictor Importance shows that traffic flow, the density of intersections, driveway density, and type of area are the most impacting factors on crash likelihood. Road authorities may use these findings for conducting reliable safety analyses.

关键词： Accident prediction models crash prediction models machine learning algorithms Fatal and injury crashes two-lane roads road safety

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Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms

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Asian Journal of Civil Engineering 2024年第1期25卷 253-264页

作者： Al-Rawashdeh, Mohammad Al Nawaiseh, Moh’d Yousef, Isam Bisharah, Majdi Alkhadrawi, Sajeda Al-Bdour, Hamza Department of Civil Engineering Faculty of Engineering Al-Balqa Applied University As-Salt Jordan Department of Civil Engineering Faculty of Engineering Amman Arab University Amman Jordan Department of Civil Engineering Munib and Angela Masri Faculty Engineering Aqaba University of Technology Aqaba Jordan Department of Civil Engineering University Putra Malaysia Selangor Serdang Malaysia Water and Environmental Engineering Department Al-Balqa Applied University Huson College Huson Jordan Master of Engineering Project Management Faculty of Engineering Jadara University Irbid Jordan

This study compares Bayesian Optimization-based machine learning systems that anticipate earthquake-damaged buildings and to evaluates building damage classification models. Using metrics, this study evaluates Random Forest, ElasticNet, and Decision Tree algorithms. This study showed damage level asymmetry. Fifth grade is the most prevalent and first grade the least. The class imbalance makes estimating building damage grades difficult, emphasizing the necessity for careful modeling. Bayesian Optimization optimizes machine learning algorithm hyperparameters to solve this problem. The optimization technique maximizes the receiver operating characteristic curve (AUROC), which measures the models’ ability to discern between damage levels. Convergence shows that Bayesian Optimization improves model discrimination. The optimized models classified building damage grades with an AUROC of 0.9952. Comparing machine learning algorithms yields insights. The ElasticNet model predicts building damage grade with 92.56% test accuracy and 92.67% train accuracy. With 89.39% test accuracy and 99.82% train accuracy, the Random Forest model performs well. The Decision Tree model has 89.19% test and 99.94% train accuracy. Mean Squared Error (MSE) shows that the Random Forest model makes more accurate predictions than the others. In this study, machine learning techniques forecast building deterioration. Research should address class imbalance, because it affects model performance. Bayesian Optimization helps models acquire data patterns, improving classification accuracy. This study shows that machine learning and optimization can forecast building seismic damage grades. The proposed model can successfully use for earthquake risk assessment and mitigation. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Bayesian optimization Building damage grade prediction Class imbalance Comparative analysis machine learning algorithms

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ESTIMATING SOIL MOISTURE FROM C AND X BAND SAR USING machine learning algorithms AND COMPACT POLARIMETRY 38

ESTIMATING SOIL MOISTURE FROM C AND X BAND SAR USING MACHINE...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Santi, E. Pettinato, S. Paloscia, S. Dabboor, M. Notarnicola, C. Padovano, A. Greifeneder, F. Cuozzo, G. CNR IFAC Via Madonna del Piano 10 I-50019 Florence Italy Govt Canada Environm & Climate Change Canada Sci & Technol Branch Dorval PQ Canada EURAC Res Inst Earth Observat Viale Druso 1 I-39100 Bolzano Italy

ISBN: (纸本)9781538671504

This research aims at exploiting the integration of C- and X-band SAR data for the monitoring of Soil Moisture Content (SMC). Time series of Radarsat2 (RS2) and COSMOSkyMed (CSK) images are collected on two test areas, located in Italy and in Canada. The backscattering sensitivity to SMC measured by in- situ stations is investigated considering the available sensor frequencies and polarizations. In addition, for exploiting the potential of fully polarimetric acquisitions of RS2, simulated Compact Polarimetric (CP) data are computed by using a Radarsat Constellation Mission (RCM) data simulator, and their sensitivity to the target SMC is examined. Based on the experimental findings, two machine learning (ML) approaches to the SMC retrieval, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN) are implemented and tested on the two areas. Looking at the preliminary results, the integration of X- and C- band images does provide valuable information for the retrieval of SMC, while the simulated CP parameters exhibit a certain sensitivity to SMC. On the South Tyrol test area, both SVR and ANN tested with different combinations of RS2 and CSK data were able to retrieve SMC with a RMSE between 2% and 4% of SMC and correlation coefficient R between 0.85 and 0.97, depending on the combination of inputs. The application of the ML algorithms to the other available images on the Mazia test area and the implementation of the ML retrieval algorithm using CP data are still under investigation.

关键词： COSMO-SkyMed Radarsat2 agricultural areas soil moisture retrieval machine learning algorithms RCM compact polarimetry

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Software Defect Estimation Using machine learning algorithms 4

Software Defect Estimation Using Machine Learning Algorithms

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4th International Conference on Computer Science and Engineering (UBMK)

作者： Yalciner, Burcu Ozdes, Merve Hacettepe Univ Comp Engn Ankara Turkey

ISBN: (纸本)9781728139647

Software Engineering is a comprehensive domain since it requires a tight communication between system stakeholders and delivering the system to be developed within a determinate time and a limited budget. Delivering the customer requirements include procuring high performance by minimizing the system. Thanks to effective prediction of system defects on the front line of the project life cycle, the project's resources and the effort or the software developers can be allocated more efficiently for system development and quality assurance activities. The main aim of this paper is to evaluate the capability of machine learning algorithms in software defect prediction and find the best category while comparing seven machine learning algorithms within the context of four NASA datasets obtained from public PROMISE repository [12]. All in all, the results of ensemble learners category consisting of Random Forests (RF) and Bagging in defect prediction is pretty much its counterparts.

关键词： Software quality metrics Software defect predic-tion Software fault prediction machine learning algorithms

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