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作者： Jessica R. Werling Air Force Institute of Technology

学位级别：硕士

Mixed traffic networks containing both traditional information and communications technology (ICT) network traffic and supervisory control and data acquisition (SCADA) network traffic are more commonplace now due to the desire for remote control and monitoring of industrial processes. The ability to identify SCADA devices on a mixed traffic network with zero prior knowledge, such as port, protocol or IP address, is desirable since SCADA devices are communicating over corporate networks but typically use non-standard ports and proprietary protocols. Inspired by previous research success of machine learning (ML) in accurately classifying various protocols in Internet traffic, this research uses the following four supervised ML algorithms to identify SCADA network traffic within a mixed traffic trace: Na" ive Bayes, NBTree, J4.8, and BayesNet. Using packet timing, packet size and data throughput as traffic behavior categories, this research calculates 24 attributes from each device dataflow within a mixed traffic trace and introduces a novel approach of using these attributes to identify SCADA network traffic, achieving at least a.99 true positive rate (TPR). This research goes further by utilizing two attribute reduction functions to identify an optimal attribute subset, while maintaining the desired TPR of.99 for SCADA network traffic. The attributes and ML algorithms chosen for experimentation successfully demonstrate that a TPR of.9935 for SCADA network traffic is feasible on a given network. This research also successfully identifies an optimal attribute subset, while maintaining the.99 TPR. The optimal attribute subset identified in this research provides the SCADA network traffic behaviors that most effectively differentiating them from traditional ICT network traffic.

关键词： Supervisory Control and Data Acquisition Network Analysis Industrial Control Systems Machine learning Network Traffic Behavior Network Traffic Characterization Dataflow Characterization supervised learning algorithms

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CREDIT CARD FRAUD DETECTION (Machine learning algorithms)

CREDIT CARD FRAUD DETECTION (Machine learning algorithms)

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作者： Fredrik Westerlund Umea University

学位级别：硕士

Credit card fraud is a field with perpetrators performing illegal actions that may affect other individuals or companies negatively. For instance, a criminal can steal credit card information from an account holder and then conduct fraudulent transactions. The activities are a potential contributory factor to how illegal organizations such as terrorists and drug traffickers support themselves financially. Within the machine learning area, there are several methods that possess the ability to detect credit card fraud transactions; supervised learning and unsupervised learning algorithms. This essay investigates the supervised approach, where two algorithms (Hellinger Distance Decision Tree (HDDT) and Random Forest) are evaluated on a real life dataset of 284,807 transactions. Under those circumstances, the main purpose is to develop a "well-functioning" model with a reasonable capacity to categorize transactions as fraudulent or legit. As the data is heavily unbalanced, reducing the false-positive rate is also an important part when conducting research in the chosen area. In conclusion, evaluated algorithms present a fairly similar outcome, where both models have the capability to distinguish the classes from each other. However, the Random Forest approach has a better performance than HDDT in all measures of interest.

关键词： Credit card fraud detection Machine learning supervised learning algorithms Classification Unbalanced data

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Data Fusion for Predictive Maintenance of Industrial Assets Using Digital Twin 9

Data Fusion for Predictive Maintenance of Industrial Assets ...

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9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022

作者： Avornu, Christian Akama, Samuel Dodzi Avornu, Eric Kofi Aveyom, Valentine School of Engineering Huzhou University Huzhou China C.K. Tedam University of Technology and Applied Sciences Department of Computer Science Navrongo Ghana Logistics and Supply Chain Ho Technical University Ho Ghana

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

The amount of data we produce each day in every sector keeps booming. This is attributable to the accelerating growth of the Internet of Things (IoT) technologies. Data fusion has a lot of applications in the area of complex industrial process control. The concept of data fusion can also be used to measure and detect a fault in ultrahigh-precision manufacturing assets. Predictive maintenance owes much of its promise and success to effective machine learning, artificial intelligence, big data analytics, *** are widely applied to offer digital twin simulation, analysis, and prediction. The aim of this research is to study how data from multiple sensors could be analytically explored and integrated for the purpose of predictive maintenance. In other to understand the concept behind this article, public data from Microsoft Azure is used to build a predictive maintenance model using supervised learning algorithms. These algorithms can be used for predictive maintenance of machines and equipment in many manufacturing industries. © 2022 Institute of Advanced Engineering and Science (IAES).

关键词： Data Fusion Digital Twin IoT Technology Machine learning Manufacturing Industry Predictive Maintenance supervised learning algorithms

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Hybrid model approach in data mining

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2024年第10期53卷 4998-5007页

作者： Bakirarar, Batuhan Cosgun, Erdal Elhan, Atilla Halil Ankara Univ Sch Med Dept Biostat Ankara Turkiye Microsoft Seattle WA USA Ankara Univ Tip Fak Dekanligi Morfoloji Yerles kesi Hacettepe Mahallesi AAdnan Saygun Cad 35 Ankara Turkiye

Studies on hybrid data mining approach has been increasing in recent years. Hybrid data mining is defined as an effective combination of various data mining techniques to use the power of each technique and compensate for each other's weaknesses. The purpose of this study is to present state-of-the-art data mining algorithms and applications and to propose a new hybrid data mining approach for classifying medical data. In addition, in the study, it was aimed to calculate performance metrics of data mining methods and to compare these metrics with the metrics obtained from the hybrid model. The study utilized simulated datasets produced on the basis of various scenarios and hepatitis dataset obtained from the UCI database. supervised learning algorithms were used. In addition, hybrid models were created by combining these algorithms. In simulated datasets, it was observed that MCC values increased with a higher sample size and higher correlation between the independent variables. In addition, as the correlation between independent variables increased in imbalanced datasets, a noticeable increase was observed in the performance metrics of the group with lower sample size. A similar case was observed with the actual datasets.

关键词： Data mining Hybrid models Machine learning Performance metrics supervised learning algorithms

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Human Lower Limb Motion Capture and Recognition Based on Smartphones

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SENSORS 2022年第14期22卷 5273-5273页

作者： Duan, Lin-Tao Lawo, Michael Wang, Zhi-Guo Wang, Hai-Ying Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Peoples R China Chengdu Univ Sch Comp Sci Chengdu 610106 Peoples R China Bremen Univ Int Grad Sch Dynam Logist D-28359 Bremen Germany

Human motion recognition based on wearable devices plays a vital role in pervasive computing. Smartphones have built-in motion sensors that measure the motion of the device with high precision. In this paper, we propose a human lower limb motion capture and recognition approach based on a Smartphone. We design a motion logger to record five categories of limb activities (standing up, sitting down, walking, going upstairs, and going downstairs) using two motion sensors (tri-axial accelerometer, tri-axial gyroscope). We extract the motion features and select a subset of features as a feature vector from the frequency domain of the sensing data using Fast Fourier Transform (FFT). We classify and predict human lower limb motion using three supervised learning algorithms: Naive Bayes (NB), K-Nearest Neighbor (KNN), and Artificial Neural Networks (ANNs). We use 670 lower limb motion samples to train and verify these classifiers using the 10-folder cross-validation technique. Finally, we design and implement a live detection system to validate our motion detection approach. The experimental results show that our low-cost approach can recognize human lower limb activities with acceptable accuracy. On average, the recognition rate of NB, KNN, and ANNs are 97.01%, 96.12%, and 98.21%, respectively.

关键词： human motion recognition motion sensor smartphone supervised learning algorithms

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Assessing occupational risk of heat stress at construction: A worker-centric wearable sensor-based approach

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SAFETY SCIENCE 2021年 142卷 105395-105395页

作者： Shakerian, Shahrad Habibnezhad, Mahmoud Ojha, Amit Lee, Gaang Liu, Yizhi Jebelli, Houtan Lee, SangHyun Penn State Univ Dept Architectural Engn 201 Engn Unit B State Coll PA 16802 USA Penn State Univ Dept Architectural Engn 202 Engn Unit B State Coll PA 16802 USA Univ Michigan Dept Civil & Environm Engn Tishman Construct Management Program 2350 Hayward StGG Brown Bldg Ann Arbor MI 48109 USA Penn State Univ Dept Architectural Engn 224 Engn Unit A State Coll PA 16802 USA Univ Michigan Dept Civil & Environm Engn Tishman Construct Management Program 2350 Hayward StSuite 2340 GG Brown Bldg Ann Arbor MI 48109 USA

Construction workers are at a high risk of exposure to excessive heat generated by several factors such as intensive physical activities, personal protective clothing, and frequent heat events at construction sites. Previous studies attempted to evaluate the occupational risk of heat stress by concentrating on environmental variables or the self-assessment measures of perceived heat. Despite their potentials, most of these approaches were intrusive, inaccurate, and intermittent. More importantly, they mainly overlooked the disparities in workers' physical and physiological characteristics. To address these limitations, this study proposes a heat-stress risk-assessment process to evaluate workers' bodily responses to heat - heat strain - based on the continuous measurement of their physiological signals. To this end, workers' physiological signals were captured using a wristband-type biosensor. Subsequently, their physiological signals were decontaminated from noises, resampled into an array of informative features, and finally interpreted into distinct states of individuals' heat strain by employing several supervised learning algorithms. To examine the performance of the proposed process, physiological signals were collected from 18 subjects while performing specific construction tasks under three predetermined environmental conditions with a different probability of exposure to heat stress. The analysis results revealed the proposed process could predict the risk of heat strain with more than 92% accuracy, illuminating the potentials of wearable biosensors to continuously assess workers' heat strain. The long-term implications of this study can be capitalized as guidelines to improve systematic evaluation of heat strain and promote workers' occupational safety and well-being through early detection of heat strain at construction sites.

关键词： Data-driven health monitoring Construction Workers' safety Occupational Heat strain risk Wearable biosensors Signal processing supervised learning algorithms Electrodermal activity (EDA) Skin temperature (ST)

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Segmentation process and spectral characteristics in the determination of musical genres

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Procedia Computer Science 2020年 175卷 96-101页

作者： Amelec Viloria Omar Bonerge Pineda Lezama Danelys Cabrera Universidad Tecnológica Centroamericana (UNITEC) San Pedro Sula Honduras

Over the past few years there has been a tendency to store audio tracks for later use on CD-DVDs, HDD-SSDs as well as on the internet, which makes it challenging to classify the information either online or offline. For this purpose, the audio tracks must be tagged. Tags are said to be texts based on the semantic information of the sound [1]. Thus, music analysis can be done in several ways [2] since music is identified by its genre, artist, instruments and structure, by a tagging system that can be manual or automatic. The manual tagging allows the visualization of the behavior of an audio track either in time domain or in frequency domain as in the spectrogram, making it possible to classify the songs without listening to them. However, this process is very time consuming and labor intensive, including health problems [3] which shows that "the volume, sound sensitivity, time and cost required for a manual labeling process is generally prohibitive. Three fundamental steps are required to carry out automatic labelling: pre-processing, feature extraction and classification [4]. The present study developed an algorithm for performing automatic classification of music genres using a segmentation process employing spectral characteristics such as centroid (SC), flatness (SF) and spread (SS), as well as a time spectral characteristic.

关键词： supervised learning algorithms Music genres classification Centroid (SC) Flatness (SF) Spread (SS)

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Medical emergency department triage data processing using a machine-learning solution

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HELIYON 2023年第8期9卷 e18402页

作者： Vantu, Andreea Vasilescu, Anca Baicoianu, Alexandra Transilvania Univ Brasov Fac Math & Comp Sci Brasov Romania Transilvania Univ Brasov Dept Math & Comp Sci Brasov Romania

Over the years, artificial intelligence has demonstrated its ability to overcome many challenges in our day-to-day life. The evolution of it inquired more studies about Machine learning possible solutions for different domains, including health care. The increasing demand for artificial intelligence solutions has brought accessibility to loads of data, including clinical data. The availability of medical records facilitates new opportunities to explore Machine learning models and their abilities to process a significant amount of data and to identify patterns with the purpose of solving a medical problem. Understanding the applicability of artificial intelligence on this type of data has to be a compelling aim for emergency medicine clinicians. This paper focuses on the general clinical problem of the complex correlation between medical records and later diagnosis and, especially, on the process of emergency department triage which uses the Emergency Severity Index (ESI) as triage protocol. This study presents a comparison between three different Machine learning models, such as Logistic Regression, Random Forest Tree and NN-Sequentail, with the purpose of classifying patients with an emergency code. We conducted four experiments because of imbalanced data. A web-based application was developed to improve the triage process after our theoretical and exploratory results. Overall, in all experiments, the NN-Sequential model had better results, having, in the first experiment, a ROC-AUC score for each ESI emergency code of: 0.59%, 0.76%, 0.71%, 0.78% 0.64%. After applying methods to balance the data, the model yielded a ROC-AUC score for each emergency code of 0.72%, 0.75%, 0.69%, 0.74%, 0.78%. In the last experiment consisting of a three-class classification problem, the NN Sequential and Random Forest Tree models had similar metric outcomes, and the NN-Sequential algorithm had a ROC-AUC score for each emergency code of: 0.76%, 0.72%, 0.84%. Without any doubt, our res

关键词： Emergency medicine Triage Clinical decision support Patient medical record Medical data processing Machine learning supervised learning algorithms

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Development of Artificial Intelligence Techniques for Solar PV Power Forecasting for Dehradun Region of India

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JOURNAL OF ELECTRICAL SYSTEMS 2021年第3期17卷 324-337页

作者： Verma, Amit Upadhyay, K. G. Tripathi, M. M. Uttarakhand Tech Univ Fac Technol Dehra Dun Uttarakhand India MMMUT Elect Engn Dept Gorakhpur Uttar Pradesh India Delhi Technol Univ Elect Engn Dept Delhi India

The need of reducing emission of carbon dioxide became possible with the increased penetration of solar photovoltaic (PV) power generation. The variable and intermittent nature of solar PV power generation affects the stability of the distribution grid. It comes to be necessary to forecast the generated PV power to avoid such type of uncertain conditions. This paper presents the empirical comparisons of six developed supervised learning algorithms to predict the solar power generation for the Dehradun region in India. The algorithms namely multiple linear regression (MLP), ridge regression, decision tree (DT), random forest (RF), support vector machine (SVM) and K-nearest neighbor (KNN) are modified in accordance with the higher prediction accuracy. The detailed empirical comparisons of results are discussed on the basis of mean absolute percentage error (MAPE) and root mean squared error (RMSE). It is found that the best performance of RF method with MAPE as 2.2790% and RMSE as 0.8792%. Tree based algorithms have shown the improved performance among all the methods while SVM and ridge techniques perform quietly low.

关键词： supervised learning algorithms Random forest Decision Tree mean absolute percentage error (MAPE) root mean squared error (RMSE)

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A decision support system for selecting the most suitable machine learning in healthcare using user parameters and requirements

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Healthcare Analytics 2022年 2卷 100117-100117页

作者： Ketkar, Yashodhan Gawade, Sushopti Department of Information Technology Engineering Pillai College of Engineering New Panvel 410206 India Department of Computer Engineering Pillai College of Engineering New Panvel 410206 India

The application of machine learning in the medical field is still limited. The main reason behind the lack of use is the unavailability of an easy-to-use machine learning system that targets non-technical users. The objective of this paper is to propose an automated machine learning system to aid non-technical users. The proposed system provides the user with simple choices to provide suggestions to the system. The system uses the combination of the user's choices and performance evaluation to select the most suited model from available options. In this study, we employed the system on a Parkinson's disease dataset. The templates for support vector machine and random forest algorithms are provided to the system. Support vector machines and random forests were able to produce 80% and 75% accuracy, respectively. The system used performance parameters of the system and user choices to select the most suited models for each test case. The support vector machine was selected as the most suited model in three test cases, while random forest was selected as the most suited for one test case. The test cases also showed that the weighted time parameter impacted the results heavily. © 2022 The Author(s)

关键词： Automated selection system General prediction system Healthcare industry Parkinson's disease detection supervised learning algorithms

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