scholarly journals Correlating in vitro performance with physico-chemical characteristics of nanofibrous scaffolds for skin tissue engineering using supervised machine learning algorithms

2020 ◽  
Vol 7 (12) ◽  
pp. 201293
Author(s):  
Lakshmi Y. Sujeeun ◽  
Nowsheen Goonoo ◽  
Honita Ramphul ◽  
Itisha Chummun ◽  
Fanny Gimié ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tissue Engineering ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Cell Material ◽  
Nanofibrous Scaffolds ◽  
Polymeric Scaffolds ◽  
Physico Chemical

The engineering of polymeric scaffolds for tissue regeneration has known a phenomenal growth during the past decades as materials scientists seek to understand cell biology and cell–material behaviour. Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineering (TE) to guide through the complexity of experimental conditions. We have attempted using experimental in vitro data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TE, to model scaffold performance using machine learning (ML) approach. Fibre diameter, pore diameter, water contact angle and Young's modulus were used to find a correlation with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay of L929 fibroblasts cells on the scaffolds after 7 days. Six supervised learning algorithms were trained on the data using Seaborn/Scikit-learn Python libraries. After hyperparameter tuning, random forest regression yielded the highest accuracy of 62.74%. The predictive model was also correlated with in vivo data. This is a first preliminary study on ML methods for the prediction of cell–material interactions on nanofibrous scaffolds.

scholarly journals Crop price prediction using supervised machine learning algorithms

2021 ◽  
Vol 1916 (1) ◽  
pp. 012042
Author(s):  
Ranjani Dhanapal ◽  
A AjanRaj ◽  
S Balavinayagapragathish ◽  
J Balaji
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Price Prediction

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

Spatial Roadway Condition-Assessment Mapping Utilizing Smartphones and Machine Learning Algorithms

2021 ◽  
pp. 036119812110061
Author(s):  
Charalambos Kyriakou ◽  
Symeon E. Christodoulou ◽  
Loukas Dimitriou
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Condition Assessment ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Geographical Information ◽  
Related Field ◽  
Pavement Surface ◽  
Automated Method ◽  
Smartphone Technology

The paper presents a data-driven framework and related field studies on the use of supervised machine learning and smartphone technology for the spatial condition-assessment mapping of roadway pavement surface anomalies. The study explores the use of data, collected by sensors from a smartphone and a vehicle’s onboard diagnostic device while the vehicle is in movement, for the detection of roadway anomalies. The research proposes a low-cost and automated method to obtain up-to-date information on roadway pavement surface anomalies with the use of smartphone technology, artificial neural networks, robust regression analysis, and supervised machine learning algorithms for multiclass problems. The technology for the suggested system is readily available and accurate and can be utilized in pavement monitoring systems and geographical information system applications. Further, the proposed methodology has been field-tested, exhibiting accuracy levels higher than 90%, and it is currently expanded to include larger datasets and a bigger number of common roadway pavement surface defect types. The proposed system is of practical importance since it provides continuous information on roadway pavement surface conditions, which can be valuable for pavement engineers and public safety.

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