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Python-based scikit-learn machine learning models for thermal and electrical performance prediction of high-capacity lithium-ion battery

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2022年第2期46卷 786-794页

作者： Tran, Manh-Kien Panchal, Satyam Chauhan, Vedang Brahmbhatt, Niku Mevawalla, Anosh Fraser, Roydon Fowler, Michael Univ Waterloo Dept Chem Engn 200 Univ Ave West Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Mech & Mechatron Engn Waterloo ON Canada Western New England Univ Dept Mech Engn Springfield MA USA JG Coll Comp Applicat ASIA Campus Ahmadabad Gujarat India

With the increasing popularity of electric vehicles (EVs), the demands for rechargeable and high-performance batteries like lithium-ion (Li-ion) batteries have soared. Li-ion battery systems require the use of a battery management system (BMS) to perform safely and efficiently. Accurate and reliable battery modeling is important for the BMS to function properly. Currently, many BMS applications use the equivalent circuit model due to its simplicity. However, with the development of a cloud BMS, machine learning battery models can be utilized, which can potentially improve the accuracy and reliability of the BMS. This work investigates the performance of four different machine learning models used to predict the thermal (temperature) and electrical (voltage) behaviors of Li-ion battery cells. A prismatic Li-ion battery cell with a capacity of 25 Ah was cycled under a constant current profile at three different ambient temperatures, and the surface temperature and voltage of the battery were measured. The four machine learning regression models-linear regression, k-nearest neighbors, random forest, and decision tree-were developed using the scikit-learn library in Python and validated with experimental data. The results of their performance were reported and compared using the R-2 metric. The decision tree-based model, with an R-2 score of 0.99, was determined to be the best model in this case study.

关键词： battery modeling lithium-ion battery machine learning multivariate multioutput regression scikit-learn thermal modeling

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Thermogravimetric experiments based prediction of biomass pyrolysis behavior: A comparison of typical machine learning regression models in scikit-learn

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MARINE POLLUTION BULLETIN 2024年 202卷 116361页

作者： Zhong, Yu Liu, Fahang Huang, Guozhe Zhang, Juan Li, Changhai Ding, Yanming China Univ Geosci Fac Engn Wuhan 430074 Peoples R China China Univ Geosci Inst Nat Disaster Risk Prevent & Emergency Managem Wuhan 430074 Peoples R China Univ Sci & Technol China State Key Lab Fire Sci Hefei 230027 Peoples R China

A variety of machine learning (ML) models have been extensively utilized in predicting biomass pyrolysis owing to their prowess in deciphering complex non-linear relationships between inputs and outputs, but there is still a lack of consensus on the optimal methods. This study elaborates on the development, optimization, and evaluation of three ML methodologies, namely, artificial neural networks, random forest (RF), and support vector machines, aimed to determine the optimal model for accurate prediction of biomass pyrolysis behavior using thermogravimetric data. This work assesses the utility of thermal data derived from these models in the computation of kinetic and thermodynamic parameters, alongside an analysis of their statistical performance. Eventually, the RF model exhibits superior physical interpretability and the least discrepancy in predicting kinetic and thermodynamic parameters. Furthermore, a feature importance analysis conducted within the RF model framework quantitatively reveals that temperature and heating rate account for 98.5 % and 1.5 %, respectively.

关键词： Biomass pyrolysis Prediction Machine learning Comparison scikit-learn

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scikit-learn Pipelines Meet Knowledge Graphs The Python kgextension Package 18th

scikit-learn Pipelines Meet Knowledge Graphs The Python kgex...

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18th Extended Semantic Web Conference (ESWC)

作者： Bucher, Tabea-Clara Jiang, Xuehui Meyer, Ole Waitz, Stephan Hertling, Sven Paulheim, Heiko Univ Mannheim Data & Web Sci Grp Mannheim Germany

ISBN: (纸本)9783030804183;9783030804176

Python is currently the most used platform for data science and machine learning. At the same time, public knowledge graphs have been identified as a valuable source of background knowledge in many data science tasks. In this paper, we introduce the kgextension package for Python, which allows for using knowledge graph in data science pipelines built in Python. The demo shows how data from public knowledge graphs such as DBpedia and Wikidata can be used in data mining pipelines based on the popular Python package scikit-learn. We demonstrate the package's utility by showing that the prediction accuracy on a popular Kaggle task can be significantly increased by using background knowledge from DBpedia.

关键词： Python scikit-learn Knowledge graph Background knowledge Data mining

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scikit-ANFIS: A scikit-learn Compatible Python Implementation for Adaptive Neuro-Fuzzy Inference System

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2024年第6期26卷 2039-2057页

作者： Zhang, Dongsong Chen, Tianhua Xinyang Coll Sch Big Data & Artificial Intelligence Xinyang 464000 Henan Peoples R China Univ Huddersfield Sch Comp & Engn Huddersfield HD1 3DH England

The Adaptative neuro-fuzzy inference system (ANFIS) has shown great potential in processing practical data from control, prediction, and inference applications, reflecting advantages in both high performance and system interpretability as a result of the hybridization of neural networks and fuzzy systems. Matlab has been a prevalent platform that allows to utilize and deploy ANFIS conveniently. On the other hand, due to the recent popularity of machine learning and deep learning, which are predominantly Python-based, implementations of ANFIS in Python have attracted recent attention. Although there are a few Python-based ANFIS implementations, none of them are directly compatible with scikit-learn, one of the most frequently used libraries in machine learning. As such, this paper proposes scikit-ANFIS, a novel scikit-learn compatible Python implementation for ANFIS by adopting a uniform format such as fit() and predict() functions to provide the same interface as scikit-learn. Our scikit-ANFIS is designed in a user-friendly way to not only manually generate a general fuzzy system and train it with the ANFIS method but also to automatically create an ANFIS fuzzy system. We also provide four kinds of representative cases to show that scikit-ANFIS represents a valuable addition to the scikit-learn compatible Python software that supports ANFIS fuzzy reasoning. Experimental results on four datasets show that our scikit-ANFIS outperforms recent Python-based implementations while achieving parallel performance to ANFIS in Matlab, a standard implementation officially realized by Matlab, which indicates the performance advantages and application convenience of our software.

关键词： Neuro-fuzzy Fuzzy system Anfis Python scikit-learn PyTorch

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scikit-learn : Machine learning Simplified: Implement scikit-learn into every step of the data science pipeline

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2017年

作者： Guillermo Moncecchi Raul G Tompson Trent Hauck Gavin Hackeling

ISBN: (数字)9781788833479

ISBN: (纸本)9781788833479

Implement scikit-learn into every step of the data science pipeline Key Features: * [*]Use Python and scikit-learn to create intelligent applications * [*]Discover how to apply algorithms in a variety of situations to tackle common and not-so common challenges in the machine learning domain * [*]A practical, example-based guide to help you gain expertise in implementing and evaluating machine learning systems using scikit-learn * Book Description: Machine learning, the art of creating applications that learn from experience and data, has been around for many years. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility; moreover, within the Python data space, scikit-learn is the unequivocal choice for machine learning. The course combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. The course starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets. You will learn to incorporate machine learning in your applications. Ranging from handwritten digit recognition to document classification, examples are solved step-by-step using scikit-learn and Python. By the end of this course you will have learned how to build applications that learn from experience, by applying the main concepts and techniques of machine learning. What you will learn: * [*] Review fundamental concepts including supervised and unsupervised experiences, common tasks, and performance

关键词： SVM scikit-learn python data processing classification regression linear models machine learning supervised learning unsupervised learning image recognition

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IMAGE CLASSIFICATION FOR MAPPING OIL PALM DISTRIBUTION VIA SUPPORT VECTOR MACHINE USING scikit-learn MODULE

IMAGE CLASSIFICATION FOR MAPPING OIL PALM DISTRIBUTION VIA S...

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International Conference on Geomatic and Geospatial Technology (GGT) - Geospatial and Disaster Risk Managemen

作者： Shaharum, N. S. N. Shafri, H. Z. M. Ghani, W. A. W. A. K. Samsatli, S. Yusuf, B. Al-Habshi, M. M. A. Prince, H. M. Univ Putra Malaysia Fac Engn Dept Civil Engn Serdang 43400 Selangor Malaysia Univ Putra Malaysia Fac Engn GISRC Serdang 43400 Selangor Malaysia Univ Putra Malaysia Fac Engn SPERC Dept Chem & Environm Engn Upm Serdang 43400 Selangor Malaysia Univ Bath Dept Chem Engn Bath BA2 7AY Avon England

The world has been alarmed with the global warming effects. Global warming has been a distress towards the environment, thus shorten the Earth's lifespan. It is a challenging task to reduce the global warming effects in a short period, knowing that the human population is increasing along with the electricity and energy demand. In order to reduce the effects, renewable energy is presented as an alternative method to produce energy in a way that will not harm the environment. Oil palm is one of the agricultural crops that produces huge amount of biomass which can be processed and used as a renewable energy source. In 2016, Malaysia has reported over 5 million hectares of land were covered by oil palm plantations. Placing Malaysia as the second largest country of oil palm producer in the world has given it an advantage to produce renewable energy source. However, there is a need to monitor the sustainability of oil palm plantations in Malaysia via effective mapping approaches. This study utilised two different platforms (open source and commercial) using a machine learning algorithm namely Support Vector Machine (SVM) to perform oil palm mapping. An open source Python programming-based technique utilising scikit-learn module was performed to map the oil palm distribution and the result produced had an overall accuracy of 91.39%. To support and validate the efficiency of the Python programming-based image classification, a commercial remote sensing software (ENVI) was used and compared by implementing the same SVM algorithm and the result showed an overall accuracy of 98.21%.

关键词： Landsat oil palm Python remote sensing scikit-learn support vector machine

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Evolution of scikit-learn Pipelines with Dynamic Structured Grammatical Evolution 1

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23rd European International Conference on the Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Assuncao, Filipe Lourenco, Nuno Ribeiro, Bernardete Machado, Penousal Univ Coimbra Dept Informat Engn CISUC Coimbra Portugal Univ Lisbon Fac Ciencias Dept Informat LASIGE Lisbon Portugal

ISBN: (数字)9783030437220

ISBN: (纸本)9783030437213;9783030437220

The deployment of Machine learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice of the ML algorithm and its parameterisation. The task is even more challenging considering that the design of features is in many cases problem specific, and thus requires domain-expertise. To overcome these limitations Automated Machine learning (AutoML) methods seek to automate, with few or no human-intervention, the design of pipelines, i.e., automate the selection of the sequence of methods that have to be applied to the raw data. These methods have the potential to enable non-expert users to use ML, and provide expert users with solutions that they would unlikely consider. In particular, this paper describes AutoML-DSGE - a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of scikit-learn classification pipelines. The experimental results include comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient Classification Pipeline Evolution (RECIPE), and show that the average performance of the classification pipelines generated by AutoML-DSGE is always superior to the average performance of RECIPE;the differences are statistically significant in 3 out of the 10 used datasets.

关键词： Automated Machine learning scikit-learn Dynamic Structured Grammatical Evolution

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A low-cost proximate sensing method for early detection of nematodes in walnut using Walabot and scikit-learn classification algorithms 5

A low-cost proximate sensing method for early detection of n...

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Conference on Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V

作者： Niu, Haoyu Zhao, Tiebiao Westphal, Andreas Chen, YangQuan Univ Calif Merced 5200 Lake Rd Merced CA 95340 USA Kearney Agr Res & Extens Ctr 9240 S Riverbend Ave Parlier CA 93648 USA

ISBN: (纸本)9781510636064

Soil-borne plant-parasitic nematodes exist in many soils. Some of them can cause up to 15 to 20 percent annual yield losses. Walnut has high economic value, and most edible walnuts in the US are produced in the fertile soils of the California Central Valley. Soil-dwelling nematode parasites are a significant threat, and cause severe root damage and affect the walnut yields. Early detection of plant-parasitic nematodes is critical to design management strategies. In this study, we proposed use of a new low-cost proximate radio frequency tridimensional sensor "Walabot" and machine learning classification algorithms. This pocket-sized device, unlike the remote sensing tools such as unmanned aerial vehicles (UAVs), is not limited by flight time and payload capability. It can work flexibly in the field and provide data information more promptly and accurately than UAVs or satellite. Walnut leaves from trees of different nematodes infestation levels were placed on this sensor, to test if the Walabot can detect small changes of the nematode infestation levels. Hypothetically, waveforms generated by different signals may be useful to estimate the damage caused by nematodes. scikit-learn classification algorithms, such as Neural Networks, Random forest, Adam optimizer, and Gaussian processing, were applied for data processing. Results showed that the Walabot predicted nematodes infestation levels with an accuracy of 72% so far.

关键词： Nematodes walnut Walabot scikit-learn classification

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Machine learning Made Easy: A Review of scikit-learn Package in Python Programming Language

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JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS 2019年第3期44卷 348-361页

作者： Hao, Jiangang Ho, Tin Kam Educ Testing Serv Ctr Next Generat Psychometr & Data Sci Rosedale Rd Princeton NJ 08540 USA IBM Watson Artificial Intelligence Res & Dev Yorktown Hts NY 10598 USA

Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review scikit-learn, a machine learning package in the Python programming language that is widely used in data science. The scikit-learn package includes implementations of a comprehensive list of machine learning methods under unified data and modeling procedure conventions, making it a convenient toolkit for educational and behavior statisticians.

关键词： machine learning Python scikit-learn

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Sentiment Analysis of Twitter Data Using Machine learning Techniques and scikit-learn 18

Sentiment Analysis of Twitter Data Using Machine Learning Te...

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International Conference on Algorithms, Computing and Artificial Intelligence (ACAI)

作者： Elbagir, Shihab Yang, Jing Harbin Engn Univ Coll Comp Sci & Technol Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781450366250

Sentiment analysis of Twitter data is an area that has experienced significant growth in recent years. The ability to identify sentiment from tweets using machine learning techniques has attracted researchers because of the simple efficiency of machine learning techniques. This paper tackles the use of machine learning algorithms and scikit-learn in sentiment analysis of Twitter data. To do this, we perform analyses on Twitter datasets made publicly available by NLTK Corpora and create an efficient feature by using a feature extraction technique. We train and test various machine learning classifiers such as MultinomialNB, BernoulliNB, LogisticRegression, SGD classifier, SVC, LinearSVC, and NuSVC. Experimental results demonstrate that BernoulliNB, LogisticRegression, and SGD classifier reached accuracy as high as 75%.

关键词： Twitter sentiment analysis Machine learning Techniques scikit-learn

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