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A Comparison of One-Class Versus Two-Class Machine learning Models for Wildfire Prediction in California 21st

A Comparison of One-Class Versus Two-Class Machine Learning...

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21st Australasian Conference on Data Science and Machine learning, AusDM 2023

作者： Ismail, Fathima Nuzla Sengupta, Abira Woodford, Brendon J. Licorish, Sherlock A. Department of Biochemistry University of Otago Dunedin New Zealand School of Computing University of Otago Dunedin New Zealand

ISBN: (纸本)9789819986958

Due to climate change, forest regions in California are increasingly experiencing severe wildfires, with other issues affecting the rest of the world. Machine learning (ML) and artificial intelligence (AI) models have emerged to predict wildfire hazards and aid mitigation efforts. However, the wildfire prediction modelling domain faces inconsistencies due to database manipulations for multi-class classification. To help to address this issue, our paper focuses on creating wildfire prediction models through One-class classification algorithms: Support Vector Machine, Isolation Forest, AutoEncoder, Variational AutoEncoder, Deep Support Vector Data Description, and Adversarially learned Anomaly Detection. To minimise bias in the selection of the training and testing data, Five-Fold Cross-Validation was used to validate all One-class ML models. These One-class ML models outperformed Two-class ML models using the same ground truth data, with mean accuracy levels between 90 and 99 percent. Shapley values were used to derive the most important features affecting the wildfire prediction model, which is a novel contribution to the field of wildfire prediction. Among the most important factors were the seasonal maximum and mean dew point temperatures. In providing access to our algorithms, using Python Flask and a web-based tool, the top-performing models were operationalized for deployment as a REST API, with the potential to strengthen wildfires mitigation strategies. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： ANN-AutoEncoder ANN-Variational Auto-Encoder Isolation Forest One-class SVM PyOD scikit-learn

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A Machine learning Scheme for Tool Wear Monitoring and Replacement in IoT-Enabled Smart Manufacturing 1st

A Machine Learning Scheme for Tool Wear Monitoring and Repla...

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1st International Conference on Innovative Product Design and Intelligent Manufacturing System, ICIPDIMS 2019

作者： Patel, Zeel Bharatkumar Muthuswamy, Sreekumar Centre for AI IoT and Robotics Department of Mechanical Engineering Indian Institute of Information Technology Design and Manufacturing Vandalur-Kelambakkam Road Kancheepuram Chennai600127 India

ISBN: (纸本)9789811526954

Tool wear monitoring is an important task in a smart manufacturing industry. Detecting worn-out tools and replacing them in time can increase the efficiency significantly. Various sensors are being used in machine tools to integrate them into a smart manufacturing setup. Continuously decreasing the cost of the sensors is encouraging the use of low-cost indirect methods for the task. Using multiple sensors increases the precision of estimating tool health over the single sensor-based approach. Appropriate mathematical models relating tool wear parameters and sensors data can be used here, but machine learning models become more suitable in a large variety of applications over normal mathematical models. This paper proposes a methodology for multi-sensor-based indirect tool wear monitoring system and presents a comparison of accuracy among various machine learning models. Standard references are used to generate dummy training and testing data. Python is used to create and test the models. In the end, it has been found that Naïve Bayes and support vector machine algorithms are yielding up to 97% accuracy. This is the initial work in the development of an IoT enabled and fully automated manufacturing setup. © 2020, Springer Nature Singapore Pte Ltd.

关键词： AI and IoT Machine learning scikit-learn Smart manufacturing Tool wear monitoring

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Mastering Numerical Computing with NumPy: Master scientific computing and perform complex operations with ease

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2018年

作者： Umit Mert Cakmak Mert Cuhadaroglu

ISBN: (数字)9781788996846

ISBN: (纸本)9781788993357

Enhance the power of NumPy and start boosting your scientific computing capabilities About This Book • Grasp all aspects of numerical computing and understand NumPy • Explore examples to learn exploratory data analysis (EDA), regression, and clustering • Access NumPy libraries and use performance benchmarking to select the right tool Who This Book Is For Mastering Numerical Computing with NumPy is for you if you are a Python programmer, data analyst, data engineer, or a data science enthusiast, who wants to master the intricacies of NumPy and build solutions for your numeric and scientific computational problems. You are expected to have familiarity with mathematics to get the most out of this book. What You Will learn • Perform vector and matrix operations using NumPy • Perform exploratory data analysis (EDA) on US housing data • Develop a predictive model using simple and multiple linear regression • Understand unsupervised learning and clustering algorithms with practical use cases • Write better NumPy code and implement the algorithms from scratch • Perform benchmark tests to choose the best configuration for your system In Detail NumPy is one of the most important scientific computing libraries available for Python. Mastering Numerical Computing with NumPy teaches you how to achieve expert level competency to perform complex operations, with in-depth coverage of advanced concepts. Beginning with NumPy's arrays and functions, you will familiarize yourself with linear algebra concepts to perform vector and matrix math operations. You will thoroughly understand and practice data processing, exploratory data analysis (EDA), and predictive modeling. You will then move on to working on practical examples which will teach you how to use NumPy statistics in order to explore US housing data and develop a predictive model using simple and multiple linear regression techniques. Once you have got to grips with the basics, you will explore unsupervised learning and

关键词： Pandas scikit-learn NumPy Linear Regression EDA SciPy NumPy Arrays Numerical Computing Vector matrix linear algebra

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Automating Data Analysis with Python: A Comparative Study of Popular Libraries and their Application 3

Automating Data Analysis with Python: A Comparative Study of...

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Bhardwaj, Pooja Choudhury, Chahil Batra, Priya Chandigarh University Department of Cse Punjab Mohali India

ISBN: (纸本)9798350342338

In areas like businesses, finance, and science, data analysis plays a very important role. Data analysis needs to be automated to improve productivity and eliminate human error as data volumes and complexity grow. Python is a well-liked language for automated data analysis because of its ease of use and broad library support. For automated data analysis, we are evaluating many well-known Python libraries, such as Python and Pandas, Matplotlib, Seaborn scikit-learn, and TensorFlow is used in this text. We evaluate the usability, computational efficiency, and accuracy of these libraries when performing various data analysis tasks. We also go into great length on the uses of each library in various sectors, as well as its benefits and drawbacks. Our study shows that the user's level of experience and the type of data analysis task both affect the library choice. We came to the conclusion that choosing the right library is crucial for automating data analysis using Python and significantly affects the accuracy and efficacy of the study. © 2023 IEEE.

关键词： Automating Data Analysis Matplotlib NumPy Pandas Python scikit-learn Seaborn TensorFlow

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A Clinical Decision Support System to Help the Interpretation of Laboratory Results and to Elaborate a Clinical Diagnosis in Blood Coagulation Domain 15th

A Clinical Decision Support System to Help the Interpretatio...

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15th International Work-Conference on Artificial Neural Networks (IWANN)

作者： Lasson, Francois Delamarre, Alban Redou, Pascal Buche, Cedric FIU VISAGE Lab Miami FL USA ENIB LAB STICC Brest France LATIM Brest France

ISBN: (纸本)9783030205188;9783030205171

Hemophilia is a rare hemorrhagic disorder caused by clotting factor deficiencies that leads to a less efficient coagulation system. Treatments of this pathology rely on a patient's subjective assessment which reflects a need for a laboratory assay able to predict the clinical patient phenotype. According to the literature, global assays such as thrombin generation (TG), are good predictors of bleeding episodes and therefore seem to be good candidates to fit this need. Nevertheless, the result of the TG assay, known as thrombogram, is difficult to interpret for nonexpert clinicians. In this paper, we present a machine learning-based clinical decision support system which goal is to help clinical decision making. In doing so, we have adopted several approaches in order to evaluate well-known machine learning algorithms, in terms of accuracy and robustness, on a thrombogram database generated using numerical simulations. Obtained results, 95.57% of accuracy using a cascade of a SVM and MLPs to classify all categories and 98.10% of accuracy for the binary case hemophilia A/B, prove that our proposal can efficiently diagnose hemophilia.

关键词： Machine learning-based clinical decision support system Thrombin generation assay Hemophilia diagnosis scikit-learn

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Python: Real-World Data Science: Real-World Data Science

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丛书名： Curated Course

2016年

作者： Fabrizio Romano Dusty Phillips Phuong Vo.T.H Martin Czygan

ISBN: (数字)9781786465160

ISBN: (纸本)9781786465160

Unleash the power of Python and its robust data science capabilities Key Features: * Unleash the power of Python 3 objects * learn to use powerful Python libraries for effective data processing and analysis * Harness the power of Python to analyze data and create insightful predictive models * Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Book Description: The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you’ll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it’s time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls. What you will learn: * Install and setup Python * Implement objects in Python by creating classes and defining methods * Get acquainted with NumPy to use it with arrays and array-oriented computing in data analysis * Create effective visualizations for presenting your data using M

关键词： Python data science NumPy pandas data mining machine learning data analysis IPython data visualization SciPy scikit-learn matplotlib object-oriented programming OOP deep learning big data decision trees predictive analysis

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Using Natural Language Processing and Machine learning Techniques to Characterize Configuration Bug Reports: A Study

Using Natural Language Processing and Machine Learning Techn...

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作者： Wei Wen University of Kentucky

学位级别：硕士

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Automatic Grading of Programming Exams

Automatic Grading of Programming Exams

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作者： Jørgen Sirhaug Norwegian University of Science and Technology

学位级别：硕士

The fields of Computer Science and IT are more needed than ever before, and the number of students enrolled in programming courses is rising. An increase in students leads to an increased demand for more teachers and student assistants, and for the final exam, more graders. To resolve this need, this thesis explored the possibility of an automated grading system, which would learn grading patterns from human graders by extracting features from students' exam submissions and using them to train a machine learning classification system. A variety of source code evaluation strategies, classification algorithms, parameter ranges and feature sets were assessed, and an experiment was conducted using the continuation exam of 2017 for the course TDT4100 – Object-Oriented Programming as a dataset. The experiment lays the foundation for further research and development within the fields of source code analysis and quality checks, while results were inconclusive because of what we believe to be a sub-optimal dataset.

关键词： Machine learning classification Linear SVC Naive Bayes feature extraction scikit-learn

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Estimação e previsão de produtividade de soja por redes neurais no MATOPIBA

Estimação e previsão de produtividade de soja por redes n...

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作者： Santos, Valter Barbosa dos Universidade Estadual Paulista

A demanda por alimento se torna cada vez maior e para atendê-la em tempo hábil e quantidade suficiente, utilizamos redes neurais artificiais para previsão de produtividade. Dentre essas técnicas, a mais comumente empregada para prever a produtividade da soja é a Rede Neural Artificial. O objetivo dessa pesquisa foi estimar e prever a produtividade de soja utilizando redes neurais artificiais e informações climáticas mensais da temperatura do ar, precipitação, radiação global, e componentes do balanço hídrico como evapotranspiração de cultivo, armazenamento, evapotranspiração real de cultivo, deficiência e excedentes hídricos durante o ciclo do cultivo para os estados do Maranhão, Tocantins, Piauí e Bahia, que juntos formam a fronteira agrícola do MATOPIBA no Brasil, produtividade média para a região durante o período avaliado é de 2.575 kg ha-1. Os resultados mostram que a RNA ajustada para estimação apresentou erros de até 100 kg ha-1 (sub e superestimando) em grande parte (27) das localidades, 10 municípios apresentam produtividade subestimada, e 15 apresentam resultados superestimados. Enquanto que a RNA ajustada para previsão mostrou que 30 localidades apresentam produtividades com erros até 100 kg ha-1, 8 e 14 munícipios com produtividade subestimada e superestimada, *** demand for food becomes increasing and to meet it in a timely and sufficient amount, we use artificial neural networks to predict productivity. Among these techniques, the most commonly used to predict soybean yield is the Artificial Neural Network. The objective of this research was to estimate and predict soybean yield using artificial neural networks and monthly climatic information of air temperature, precipitation, global radiation, and water balance components such as crop evapotranspiration , storage, real crop evapotranspiration, deficiency and water surpluses during the cultivation cycle for the states of Maranhão, Tocantins, Piauí and Bahia, which together form the agr

关键词： Balanço hídrico Soja Produtividade agrícola Inteligência artificial Redes neurais artificiais Modelo de cultivos Agrometeorologia MATOPIBA Python scikit-learn Dissertação de mestrado

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Využití umělé inteligence v technické diagnostice

Využití umělé inteligence v technické diagnostice

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作者： Konečný, Antonín Brno University of Technology

Diplomová práce je zaměřena na využití metod umělé inteligence pro vyhodnocení poruchového stavu strojního zařízení. Vyhodnocovaná data jsou z vibrodiagnostické... 详细信息

Diplomová práce je zaměřena na využití metod umělé inteligence pro vyhodnocení poruchového stavu strojního zařízení. Vyhodnocovaná data jsou z vibrodiagnostického modelu pro simulaci statické a dynamické nevývahy. Aplikovány jsou metody strojového učení, konkrétně učení s učitelem. V práci je popsáno softwarové prostředí Spyder, jeho alternativy, a programovací jazyk Python, ve kterém jsou skripty napsány. Obsahuje přehled s popisem použitých knihoven scikit-learn, SciPy, Pandas ...) a metod, kterými jsou klasifikace metodou nejbližšího souseda (KNN), metoda podpůrných vektorů (SVM), rozhodovací stromy (DT) a klasifikace metodou náhodného lesa (RF). Výsledky úspěšnosti klasifikace jsou vizualizovány ve výsledné klasifikační matici pro každou metodu. Součástí přílohy jsou napsané skripty pro zpracování a výpočet prediktorů, hledání nejvhodnějších parametrů modelu, hodnocení úspěšnosti učení a klasifikace s vizualizací výsledku.

关键词： umělá inteligence strojové učení učení s učitelem klasifikace extrakce charakteristických rysů Spyder Python scikit-learn sklearn hledání nejvhodnějších parametrů metoda nejbližšího souseda metoda podpůrných vektorů rozhodovací stromy klasifi

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