Combining machine learning algorithms and an incremental capacity analysis on 18650 cell under different cycling temperature and SOC range

A novel way to apply machine learning algorithms on the incremental capacity analysis (dQ/dV) is developed to identify battery cycling conditions under different temperatures and working SOC ranges. Batteries are cycled under each combination of temperatures (-10oC, 25oC, 60oC) and SOC ranges (0-10%, 25-75%, 90-100%, 0-100%) up to 60 equivalent cycles. The discharge data is transformed into dQ/dV-V curve and its features of the peaks and valleys are further taken for machine learning. Both supervised and unsupervised machine learning algorithms (PCA and LDA) are applied to classify batteries in terms of temperature or SOC range. The results reveal that batteries cycled under different temperatures can be identified separately regardless of the working SOC range. When splitting 60 samples with a ratio of training set equals to 0.85, the remaining test set gives an identification accuracy of 89% in temperature and 67% in working SOC range.

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Towards Near-Real-Time Intrusion Detection for IoT Devices using Supervised Learning and Apache Spark

Electronics ◽

10.3390/electronics9030444 ◽

2020 ◽

Vol 9 (3) ◽

pp. 444 ◽

Cited By ~ 1

Author(s):

Valerio Morfino ◽

Salvatore Rampone

Keyword(s):

Machine Learning ◽

Random Forest ◽

Learning Algorithms ◽

Hybrid Approach ◽

Cyber Attacks ◽

Machine Learning Algorithms ◽

Apache Spark ◽

Identification Accuracy ◽

Supervised Machine Learning ◽

Iot Devices

In the fields of Internet of Things (IoT) infrastructures, attack and anomaly detection are rising concerns. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing proportionally. In this paper the performances of several machine learning algorithms in identifying cyber-attacks (namely SYN-DOS attacks) to IoT systems are compared both in terms of application performances, and in training/application times. We use supervised machine learning algorithms included in the MLlib library of Apache Spark, a fast and general engine for big data processing. We show the implementation details and the performance of those algorithms on public datasets using a training set of up to 2 million instances. We adopt a Cloud environment, emphasizing the importance of the scalability and of the elasticity of use. Results show that all the Spark algorithms used result in a very good identification accuracy (>99%). Overall, one of them, Random Forest, achieves an accuracy of 1. We also report a very short training time (23.22 sec for Decision Tree with 2 million rows). The experiments also show a very low application time (0.13 sec for over than 600,000 instances for Random Forest) using Apache Spark in the Cloud. Furthermore, the explicit model generated by Random Forest is very easy-to-implement using high- or low-level programming languages. In light of the results obtained, both in terms of computation times and identification performance, a hybrid approach for the detection of SYN-DOS cyber-attacks on IoT devices is proposed: the application of an explicit Random Forest model, implemented directly on the IoT device, along with a second level analysis (training) performed in the Cloud.

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A Review on Various Algorithms used in Machine Learning

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1952248 ◽

2019 ◽

pp. 915-920

Author(s):

Divya Chaudhary ◽

Er. Richa Vasuja

Keyword(s):

Machine Learning ◽

Data Mining ◽

New Technologies ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Learning System ◽

Training Set ◽

Learning Data ◽

Do So

In today's scenario all of data is being generated by everyone of us . so it becomes vital for us to handle this data. To do so new technologies are being developed such as machine learning, data mining etc. This paper gives the study related to machine learning(ML).Precise approximations are repetitively being produced by Machine Learning algorithms. Machine learning system effectively “learns” how to guess from training set of completed jobs. The main purpose of the review is to give a jagged estimate or overview about the mostly used algorithms in machine learning.

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Comparison of Machine Learning Algorithms for Discharge Prediction of Multipurpose Dam

Water ◽

10.3390/w13233369 ◽

2021 ◽

Vol 13 (23) ◽

pp. 3369

Author(s):

Jiyeong Hong ◽

Seoro Lee ◽

Gwanjae Lee ◽

Dongseok Yang ◽

Joo Hyun Bae ◽

...

Keyword(s):

Machine Learning ◽

Mean Squared Error ◽

Learning Algorithms ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Physical Models ◽

Gradient Boosting ◽

Activity Schedules ◽

Discharge Data ◽

Dam Inflow

For effective water management in the downstream area of a dam, it is necessary to estimate the amount of discharge from the dam to quantify the flow downstream of the dam. In this study, a machine learning model was constructed to predict the amount of discharge from Soyang River Dam using precipitation and dam inflow/discharge data from 1980 to 2020. Decision tree, multilayer perceptron, random forest, gradient boosting, RNN-LSTM, and CNN-LSTM were used as algorithms. The RNN-LSTM model achieved a Nash–Sutcliffe efficiency (NSE) of 0.796, root-mean-squared error (RMSE) of 48.996 m3/s, mean absolute error (MAE) of 10.024 m3/s, R of 0.898, and R2 of 0.807, showing the best results in dam discharge prediction. The prediction of dam discharge using machine learning algorithms showed that it is possible to predict the amount of discharge, addressing limitations of physical models, such as the difficulty in applying human activity schedules and the need for various input data.

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Comparison of common machine learning algorithms trained with multi-zone models for identifying the location and strength of indoor pollutant sources

Indoor and Built Environment ◽

10.1177/1420326x20931576 ◽

2020 ◽

pp. 1420326X2093157

Author(s):

Yu Huang ◽

Zhi Gao ◽

Hongguang Zhang

Keyword(s):

Machine Learning ◽

Meteorological Parameters ◽

Human Life ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Identification Accuracy ◽

Sensor Data ◽

Support Vector ◽

Accurate Identification ◽

Pollutant Sources

The accurate identification of the characteristics of pollutant sources can effectively prevent the loss of human life and property damage caused by the sudden release of harmful chemicals in emergency situations. Machine learning algorithms, artificial neural network (ANN), support vector machine (SVM), k-nearest neighbour (KNN) and naive Bayesian (NB) classification can be used to identify the location of pollutant sources with limited sensor data inputs. In this study, the identification accuracy of the four above-mentioned machine learning algorithms was investigated and compared, considering the different sensor layouts, eigenvector inputs, meteorological parameters and number of samples. The results show that the collection of pollutant concentrations over an extended period of time could improve identification accuracy. Additional sensors were required to reach the same identification accuracy after the introduction of distributed meteorological parameters. Increasing the number of trained samples by a factor of five improved the identification accuracy of KNN by 22% and that of SVM by 1.7%; however, ANN and NB classification remained basically unchanged. When identifying the release mass of the pollutant source, multiple linear, ANN and SVM regression models were adopted. Results show that ANN performs best, whereas SVM provides the least optimal performance.

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Supplemental Material for One Model to Rule Them All? Using Machine Learning Algorithms to Determine the Number of Factors in Exploratory Factor Analysis

Psychological Methods ◽

10.1037/met0000262.supp ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Factor Analysis ◽

Exploratory Factor Analysis ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Number Of Factors

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Forecasting US movies box office performances in Turkey using machine learning algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189120 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6579-6590

Author(s):

Sandy Çağlıyor ◽

Başar Öztayşi ◽

Selime Sezgin

Keyword(s):

Machine Learning ◽

Global Economy ◽

Learning Algorithms ◽

Forecast Model ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

High Stakes ◽

Box Office ◽

Industry Forecast ◽

The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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Intelligent system of English composition scoring model based on improved machine learning algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189235 ◽

2020 ◽

pp. 1-11

Author(s):

Jie Liu ◽

Lin Lin ◽

Xiufang Liang

Keyword(s):

Machine Learning ◽

Evaluation System ◽

Intelligent System ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Assessment System ◽

English Composition ◽

Region Extraction ◽

Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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The Unlearnable Checkerboard Pattern

Communications of the Blyth Institute ◽

10.33014/issn.2640-5652.1.2.holloway.1 ◽

2019 ◽

Vol 1 (2) ◽

pp. 78-80

Author(s):

Eric Holloway

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Checkerboard Pattern ◽

Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms. Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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