Online learning behavior analysis based on machine learning

Purpose The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data. Design/methodology/approach The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues. Findings Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper. Originality/value This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

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A dependency-based machine learning approach to the identification of research topics: a case in COVID-19 studies

Library Hi Tech ◽

10.1108/lht-01-2021-0051 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Haoran Zhu ◽

Lei Lei

Keyword(s):

Machine Learning ◽

Noun Phrases ◽

New Method ◽

Learning Models ◽

Linguistic Features ◽

Research Topics ◽

Content Type ◽

Research Areas ◽

Information Retrieval Methods ◽

Machine Learning Models

PurposePrevious research concerning automatic extraction of research topics mostly used rule-based or topic modeling methods, which were challenged due to the limited rules, the interpretability issue and the heavy dependence on human judgment. This study aims to address these issues with the proposal of a new method that integrates machine learning models with linguistic features for the identification of research topics.Design/methodology/approachFirst, dependency relations were used to extract noun phrases from research article texts. Second, the extracted noun phrases were classified into topics and non-topics via machine learning models and linguistic and bibliometric features. Lastly, a trend analysis was performed to identify hot research topics, i.e. topics with increasing popularity.FindingsThe new method was experimented on a large dataset of COVID-19 research articles and achieved satisfactory results in terms of f-measures, accuracy and AUC values. Hot topics of COVID-19 research were also detected based on the classification results.Originality/valueThis study demonstrates that information retrieval methods can help researchers gain a better understanding of the latest trends in both COVID-19 and other research areas. The findings are significant to both researchers and policymakers.

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Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks

mSystems ◽

10.1128/msystems.00774-19 ◽

2020 ◽

Vol 5 (1) ◽

Cited By ~ 6

Author(s):

D. Aytan-Aktug ◽

P. T. L. C. Clausen ◽

V. Bortolaia ◽

F. M. Aarestrup ◽

O. Lund

Keyword(s):

Machine Learning ◽

Escherichia Coli ◽

Mycobacterium Tuberculosis ◽

Antimicrobial Resistance ◽

Bacterial Species ◽

Individual Species ◽

Learning Models ◽

Content Type ◽

Multiple Species ◽

Machine Learning Models

ABSTRACT Machine learning has proven to be a powerful method to predict antimicrobial resistance (AMR) without using prior knowledge for selected bacterial species-antimicrobial combinations. To date, only species-specific machine learning models have been developed, and to the best of our knowledge, the inclusion of information from multiple species has not been attempted. The aim of this study was to determine the feasibility of including information from multiple bacterial species to predict AMR for an individual species, since this may make it easier to train and update resistance predictions for multiple species and may lead to improved predictions. Whole-genome sequence data and susceptibility profiles from 3,528 Mycobacterium tuberculosis, 1,694 Escherichia coli, 658 Salmonella enterica, and 1,236 Staphylococcus aureus isolates were included. We developed machine learning models trained by the features of the PointFinder and ResFinder programs detected to predict binary (susceptible/resistant) AMR profiles. We tested four feature representation methods to determine the most efficient way for introducing features into the models. When training the model only on the Mycobacterium tuberculosis isolates, high prediction performances were obtained for the six AMR profiles included. By adding information on ciprofloxacin from the additional 3,588 isolates, there was no reduction in performance for the other antimicrobials but an increased performance for ciprofloxacin AMR profile prediction for Mycobacterium tuberculosis and Escherichia coli. In conclusion, the species-independent models can predict multi-AMR profiles for multiple species without losing any robustness. IMPORTANCE Machine learning is a proven method to predict AMR; however, the performance of any machine learning model depends on the quality of the input data. Therefore, we evaluated different methods of representing information about mutations as well as mobilizable genes, so that the information can serve as input for a robust model. We combined data from multiple bacterial species in order to develop species-independent machine learning models that can predict resistance profiles for multiple antimicrobials and species with high performance.

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GPT-3 AI language tool calls for cautious optimism

Emerald Expert Briefings ◽

10.1108/oxan-db256373 ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Public Funding ◽

Computational Effort ◽

Learning Models ◽

Content Type ◽

Business Applications ◽

Machine Learning Model ◽

Explainable Ai ◽

Advance Research ◽

Machine Learning Models

Significance It required arguably the single largest computational effort for a machine learning model to date, and is it capable of producing text at times indistinguishable from the work of a human author. This has generated considerable excitement about potentially transformative business applications -- and concerns about the system's weaknesses and possible misuse. Impacts Stereotypes and biases in machine learning models will become increasingly problematic as they are adopted by businesses and governments. The use of flawed AI tools that result in embarrassing failures risk cuts to public funding for AI research. Academia and industry face pressure to advance research into explainable AI, but progress is slow.

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Estimation of reference evapotranspiration using machine learning models with limited data

AIMS Geosciences ◽

10.3934/geosci.2021016 ◽

2021 ◽

Vol 7 (3) ◽

pp. 268-290

Author(s):

Adeeba Ayaz ◽

◽

Maddu Rajesh ◽

Shailesh Kumar Singh ◽

Shaik Rehana ◽

...

Keyword(s):

Machine Learning ◽

Reference Evapotranspiration ◽

Limited Data ◽

Learning Models ◽

Machine Learning Models

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An Evaluation of Wearable Inertial Sensor Configuration and Supervised Machine Learning Models for Automatic Punch Classification in Boxing

IoT ◽

10.3390/iot1020021 ◽

2020 ◽

Vol 1 (2) ◽

pp. 360-381

Author(s):

Matthew T. O. Worsey ◽

Hugo G. Espinosa ◽

Jonathan B. Shepherd ◽

David V. Thiel

Keyword(s):

Machine Learning ◽

Prediction Accuracy ◽

Inertial Sensors ◽

Inertial Sensor ◽

Supervised Machine Learning ◽

Learning Models ◽

Significant Statistical Difference ◽

Sensor Configuration ◽

Wearable Inertial Sensors ◽

Machine Learning Models

Machine learning is a powerful tool for data classification and has been used to classify movement data recorded by wearable inertial sensors in general living and sports. Inertial sensors can provide valuable biofeedback in combat sports such as boxing; however, the use of such technology has not had a global uptake. If simple inertial sensor configurations can be used to automatically classify strike type, then cumbersome tasks such as video labelling can be bypassed and the foundation for automated workload monitoring of combat sport athletes is set. This investigation evaluates the classification performance of six different supervised machine learning models (tuned and untuned) when using two simple inertial sensor configurations (configuration 1—inertial sensor worn on both wrists; configuration 2—inertial sensor worn on both wrists and third thoracic vertebrae [T3]). When trained on one athlete, strike prediction accuracy was good using both configurations (sensor configuration 1 mean overall accuracy: 0.90 ± 0.12; sensor configuration 2 mean overall accuracy: 0.87 ± 0.09). There was no significant statistical difference in prediction accuracy between both configurations and tuned and untuned models (p > 0.05). Moreover, there was no significant statistical difference in computational training time for tuned and untuned models (p > 0.05). For sensor configuration 1, a support vector machine (SVM) model with a Gaussian rbf kernel performed the best (accuracy = 0.96), for sensor configuration 2, a multi-layered perceptron neural network (MLP-NN) model performed the best (accuracy = 0.98). Wearable inertial sensors can be used to accurately classify strike-type in boxing pad work, this means that cumbersome tasks such as video and notational analysis can be bypassed. Additionally, automated workload and performance monitoring of athletes throughout training camp is possible. Future investigations will evaluate the performance of this algorithm on a greater sample size and test the influence of impact window-size on prediction accuracy. Additionally, supervised machine learning models should be trained on data collected during sparring to see if high accuracy holds in a competition setting. This can help move closer towards automatic scoring in boxing.

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An explainable prediction framework for engineering problems: case studies in reinforced concrete members modeling

Engineering Computations ◽

10.1108/ec-02-2021-0096 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Amirhessam Tahmassebi ◽

Mehrtash Motamedi ◽

Amir H. Alavi ◽

Amir H. Gandomi

Keyword(s):

Machine Learning ◽

Case Studies ◽

Failure Modes ◽

Statistical Significance ◽

Deep Understanding ◽

Machine Learning Algorithms ◽

Learning Models ◽

Content Type ◽

Engineering Problems ◽

Machine Learning Models

PurposeEngineering design and operational decisions depend largely on deep understanding of applications that requires assumptions for simplification of the problems in order to find proper solutions. Cutting-edge machine learning algorithms can be used as one of the emerging tools to simplify this process. In this paper, we propose a novel scalable and interpretable machine learning framework to automate this process and fill the current gap.Design/methodology/approachThe essential principles of the proposed pipeline are mainly (1) scalability, (2) interpretibility and (3) robust probabilistic performance across engineering problems. The lack of interpretibility of complex machine learning models prevents their use in various problems including engineering computation assessments. Many consumers of machine learning models would not trust the results if they cannot understand the method. Thus, the SHapley Additive exPlanations (SHAP) approach is employed to interpret the developed machine learning models.FindingsThe proposed framework can be applied to a variety of engineering problems including seismic damage assessment of structures. The performance of the proposed framework is investigated using two case studies of failure identification in reinforcement concrete (RC) columns and shear walls. In addition, the reproducibility, reliability and generalizability of the results were validated and the results of the framework were compared to the benchmark studies. The results of the proposed framework outperformed the benchmark results with high statistical significance.Originality/valueAlthough, the current study reveals that the geometric input features and reinforcement indices are the most important variables in failure modes detection, better model can be achieved with employing more robust strategies to establish proper database to decrease the errors in some of the failure modes identification.

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A Dynamic Convolutional Neural Network Based Shared-Bike Demand Forecasting Model

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3447988 ◽

2021 ◽

Vol 12 (6) ◽

pp. 1-24

Author(s):

Shaojie Qiao ◽

Nan Han ◽

Jianbin Huang ◽

Kun Yue ◽

Rui Mao ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Prediction Accuracy ◽

Demand Forecasting ◽

Forecasting Model ◽

Learning Models ◽

Bike Sharing ◽

Demand Forecasting Model ◽

Machine Learning Models

Bike-sharing systems are becoming popular and generate a large volume of trajectory data. In a bike-sharing system, users can borrow and return bikes at different stations. In particular, a bike-sharing system will be affected by weather, the time period, and other dynamic factors, which challenges the scheduling of shared bikes. In this article, a new shared-bike demand forecasting model based on dynamic convolutional neural networks, called SDF , is proposed to predict the demand of shared bikes. SDF chooses the most relevant weather features from real weather data by using the Pearson correlation coefficient and transforms them into a two-dimensional dynamic feature matrix, taking into account the states of stations from historical data. The feature information in the matrix is extracted, learned, and trained with a newly proposed dynamic convolutional neural network to predict the demand of shared bikes in a dynamical and intelligent fashion. The phase of parameter update is optimized from three aspects: the loss function, optimization algorithm, and learning rate. Then, an accurate shared-bike demand forecasting model is designed based on the basic idea of minimizing the loss value. By comparing with classical machine learning models, the weight sharing strategy employed by SDF reduces the complexity of the network. It allows a high prediction accuracy to be achieved within a relatively short period of time. Extensive experiments are conducted on real-world bike-sharing datasets to evaluate SDF. The results show that SDF significantly outperforms classical machine learning models in prediction accuracy and efficiency.

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A Genome-Based Model to Predict the Virulence of Pseudomonas aeruginosa Isolates

mBio ◽

10.1128/mbio.01527-20 ◽

2020 ◽

Vol 11 (4) ◽

Author(s):

Nathan B. Pincus ◽

Egon A. Ozer ◽

Jonathan P. Allen ◽

Marcus Nguyen ◽

James J. Davis ◽

...

Keyword(s):

Machine Learning ◽

Pseudomonas Aeruginosa ◽

Core Genome ◽

Learning Models ◽

Single Nucleotide Variants ◽

Bacterial Genomes ◽

Content Type ◽

Accessory Genome ◽

A Genome ◽

Machine Learning Models

ABSTRACT Variation in the genome of Pseudomonas aeruginosa, an important pathogen, can have dramatic impacts on the bacterium’s ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine learning approach to a genetically and phenotypically diverse collection of 115 clinical P. aeruginosa isolates using genomic information and corresponding virulence phenotypes in a mouse model of bacteremia. We defined the accessory genome of these isolates through the presence or absence of accessory genomic elements (AGEs), sequences present in some strains but not others. Machine learning models trained using AGEs were predictive of virulence, with a mean nested cross-validation accuracy of 75% using the random forest algorithm. However, individual AGEs did not have a large influence on the algorithm’s performance, suggesting instead that virulence predictions are derived from a diffuse genomic signature. These results were validated with an independent test set of 25 P. aeruginosa isolates whose virulence was predicted with 72% accuracy. Machine learning models trained using core genome single-nucleotide variants and whole-genome k-mers also predicted virulence. Our findings are a proof of concept for the use of bacterial genomes to predict pathogenicity in P. aeruginosa and highlight the potential of this approach for predicting patient outcomes. IMPORTANCE Pseudomonas aeruginosa is a clinically important Gram-negative opportunistic pathogen. P. aeruginosa shows a large degree of genomic heterogeneity both through variation in sequences found throughout the species (core genome) and through the presence or absence of sequences in different isolates (accessory genome). P. aeruginosa isolates also differ markedly in their ability to cause disease. In this study, we used machine learning to predict the virulence level of P. aeruginosa isolates in a mouse bacteremia model based on genomic content. We show that both the accessory and core genomes are predictive of virulence. This study provides a machine learning framework to investigate relationships between bacterial genomes and complex phenotypes such as virulence.

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Machine Learning Method for TOC Prediction: Taking Wufeng and Longmaxi Shales in the Sichuan Basin, Southwest China as an Example

Geofluids ◽

10.1155/2021/6794213 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Jia Rong ◽

Zongyuan Zheng ◽

Xiaorong Luo ◽

Chao Li ◽

Yuping Li ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Shale Gas ◽

Sichuan Basin ◽

Prediction Accuracy ◽

Random Search ◽

Organic Carbon Content ◽

Learning Models ◽

The Sichuan Basin ◽

Machine Learning Models

The total organic carbon content (TOC) is a core indicator for shale gas reservoir evaluations. Machine learning-based models can quickly and accurately predict TOC, which is of great significance for the production of shale gas. Based on conventional logs, the measured TOC values, and other data of 9 typical wells in the Jiaoshiba area of the Sichuan Basin, this paper performed a Bayesian linear regression and applied a random forest machine learning model to predict TOC values of the shale from the Wufeng Formation and the lower part of the Longmaxi Formation. The results showed that the TOC value prediction accuracy was improved by more than 50% by using the well-trained machine learning models compared with the traditional Δ Log R method in an overmature and tight shale. Using the halving random search cross-validation method to optimize hyperparameters can greatly improve the speed of building the model. Furthermore, excluding the factors that affect the log value other than the TOC and taking the corrected data as input data for training could improve the prediction accuracy of the random forest model by approximately 5%. Data can be easily updated with machine learning models, which is of primary importance for improving the efficiency of shale gas exploration and development.

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An Exploration of Machine Learning Methods for Biometric Identification Based on Keystroke Dynamics

Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-0301-0.ch014 ◽

2020 ◽

pp. 259-270

Author(s):

Didem Filiz ◽

Ömer Özgür Tanrıöver

Keyword(s):

Machine Learning ◽

Mobile Phones ◽

Machine Learning Algorithms ◽

Biometric Identification ◽

Classification Methods ◽

Limited Data ◽

Learning Models ◽

Keystroke Dynamics ◽

Touch Screen Devices ◽

Machine Learning Models

In this chapter, authors explore keystroke dynamics as behavioral biometrics and effectiveness of state-of-the-art machine learning algorithms for identifying and authenticating users based on keystroke data. One of the motivations is to explore the use of classifiers to the field keystroke dynamics. In different settings, recent machine learning models have been effective with limited data and computationally relatively inexpensive. Therefore, authors conducted experiments with two different keystroke dynamics datasets with limited data. They demonstrated the effectiveness of models on dataset obtained from touch screen devices (mobile phones) and also on normal keyboard. Although there are similar recent studies which explore different classification algorithms, their main aim has been anomaly detection. But authors experimented with classification methods for user identification and authentication using two different keystroke datasets from touchscreens and keyboards.

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