scholarly journals Predicting Long-Term Deformation of Soundproofing Resilient Materials Subjected to Compressive Loading: Machine Learning Approach

Materials ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4133
Author(s):  
Seungbum Koo ◽  
Jongkwon Choi ◽  
Changhyuk Kim
Keyword(s):  
Machine Learning ◽  
Residential Buildings ◽  
Learning Algorithms ◽  
Ethylene Vinyl Acetate ◽  
Machine Learning Algorithms ◽  
Test Method ◽  
Compressive Deformation ◽  
Test Results ◽  
Four Levels

Soundproofing materials are widely used within structural components of multi-dwelling residential buildings to alleviate neighborhood noise problems. One of the critical mechanical properties for the soundproofing materials to ensure its appropriate structural and soundproofing performance is the long-term compressive deformation under the service loading conditions. The test method in the current test specifications only evaluates resilient materials for a limited period (90-day). It then extrapolates the test results using a polynomial function to predict the long-term compressive deformation. However, the extrapolation is universally applied to materials without considering the level of loads; thus, the calculated deformation may not accurately represent the actual compressive deformation of the materials. In this regard, long-term compressive deformation tests were performed on the selected soundproofing resilient materials (i.e., polystyrene, polyethylene, and ethylene-vinyl acetate). Four levels of loads were chosen to apply compressive loads up to 350 to 500 days continuously, and the deformations of the test specimens were periodically monitored. Then, three machine learning algorithms were used to predict long-term compressive deformations. The predictions based on machine learning and ISO 20392 method are compared with experimental test results, and the accuracy of machine learning algorithms and ISO 20392 method are discussed.

scholarly journals Long-Term-Based Road Blackspot Screening Procedures by Machine Learning Algorithms

2020 ◽  
Vol 12 (15) ◽  
pp. 5972
Author(s):  
Nicholas Fiorentini ◽  
Massimo Losa
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Rural Roads ◽  
Priority List ◽  
Screening Procedures ◽  
Accident Case

Screening procedures in road blackspot detection are essential tools for road authorities for quickly gathering insights on the safety level of each road site they manage. This paper suggests a road blackspot screening procedure for two-lane rural roads, relying on five different machine learning algorithms (MLAs) and real long-term traffic data. The network analyzed is the one managed by the Tuscany Region Road Administration, mainly composed of two-lane rural roads. An amount of 995 road sites, where at least one accident occurred in 2012–2016, have been labeled as “Accident Case”. Accordingly, an equal number of sites where no accident occurred in the same period, have been randomly selected and labeled as “Non-Accident Case”. Five different MLAs, namely Logistic Regression, Classification and Regression Tree, Random Forest, K-Nearest Neighbor, and Naïve Bayes, have been trained and validated. The output response of the MLAs, i.e., crash occurrence susceptibility, is a binary categorical variable. Therefore, such algorithms aim to classify a road site as likely safe (“Accident Case”) or potentially susceptible to an accident occurrence (“Non-Accident Case”) over five years. Finally, algorithms have been compared by a set of performance metrics, including precision, recall, F1-score, overall accuracy, confusion matrix, and the Area Under the Receiver Operating Characteristic. Outcomes show that the Random Forest outperforms the other MLAs with an overall accuracy of 73.53%. Furthermore, all the MLAs do not show overfitting issues. Road authorities could consider MLAs to draw up a priority list of on-site inspections and maintenance interventions.

scholarly journals Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse

Cancers ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 606 ◽  
Author(s):  
Pablo Sala Elarre ◽  
Esther Oyaga-Iriarte ◽  
Kenneth H. Yu ◽  
Vicky Baudin ◽  
Leire Arbea Moreno ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pancreatic Cancer ◽  
Learning Algorithms ◽  
Small Sample ◽  
Machine Learning Algorithms ◽  
Individual Risk ◽  
Relapse Risk ◽  
Grade 3 ◽  
Risk Of Relapse

Background: Although surgical resection is the only potentially curative treatment for pancreatic cancer (PC), long-term outcomes of this treatment remain poor. The aim of this study is to describe the feasibility of a neoadjuvant treatment with induction polychemotherapy (IPCT) followed by chemoradiation (CRT) in resectable PC, and to develop a machine-learning algorithm to predict risk of relapse. Methods: Forty patients with resectable PC treated in our institution with IPCT (based on mFOLFOXIRI, GEMOX or GEMOXEL) followed by CRT (50 Gy and concurrent Capecitabine) were retrospectively analyzed. Additionally, clinical, pathological and analytical data were collected in order to perform a 2-year relapse-risk predictive population model using machine-learning techniques. Results: A R0 resection was achieved in 90% of the patients. After a median follow-up of 33.5 months, median progression-free survival (PFS) was 18 months and median overall survival (OS) was 39 months. The 3 and 5-year actuarial PFS were 43.8% and 32.3%, respectively. The 3 and 5-year actuarial OS were 51.5% and 34.8%, respectively. Forty-percent of grade 3-4 IPCT toxicity, and 29.7% of grade 3 CRT toxicity were reported. Considering the use of granulocyte colony-stimulating factors, the number of resected lymph nodes, the presence of perineural invasion and the surgical margin status, a logistic regression algorithm predicted the individual 2-year relapse-risk with an accuracy of 0.71 (95% confidence interval [CI] 0.56–0.84, p = 0.005). The model-predicted outcome matched 64% of the observed outcomes in an external dataset. Conclusion: An intensified multimodal neoadjuvant approach (IPCT + CRT) in resectable PC is feasible, with an encouraging long-term outcome. Machine-learning algorithms might be a useful tool to predict individual risk of relapse. A small sample size and therapy heterogeneity remain as potential limitations.

scholarly journals Embedding knowledge on ontology into the corpus by topic to improve the performance of deep learning methods in sentiment analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Duy Ngoc Nguyen ◽  
Tuoi Thi Phan ◽  
Phuc Do
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Sentiment Classification ◽  
Test Results ◽  
High Confidence ◽  
Knowledge Processing ◽  
Learning Methods ◽  
Novel Approach

AbstractSentiment classification, which uses deep learning algorithms, has achieved good results when tested with popular datasets. However, it will be challenging to build a corpus on new topics to train machine learning algorithms in sentiment classification with high confidence. This study proposes a method that processes embedding knowledge in the ontology of opinion datasets called knowledge processing and representation based on ontology (KPRO) to represent the significant features of the dataset into the word embedding layer of deep learning algorithms in sentiment classification. Unlike the methods that lexical encode or add information to the corpus, this method adds presentation of raw data based on the expert’s knowledge in the ontology. Once the data has a rich knowledge of the topic, the efficiency of the machine learning algorithms is significantly enhanced. Thus, this method is appliable to embed knowledge in datasets in other languages. The test results show that deep learning methods achieved considerably higher accuracy when trained with the KPRO method’s dataset than when trained with datasets not processed by this method. Therefore, this method is a novel approach to improve the accuracy of deep learning algorithms and increase the reliability of new datasets, thus making them ready for mining.

scholarly journals Improving the Performance of the Prediction by Machine Learning Algorithms

2019 ◽  
Vol 8 (4S3) ◽  
pp. 418-424
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Ready Time ◽  
Commercial Enterprise ◽  
Mathematics Facts ◽  
Type Studies

in the course of latest a long term, system gaining knowledge of (ML) has superior from the task of barely any laptop devotees abusing the plausibility of desktops figuring out a way to play around, and a chunk of mathematics (facts) that superb occasionally belief to be computational methodologies, to a free research area that has now not just given the essential base to measurable computational requirements of studying systems, yet moreover has created wonderful calculations which can be generally carried out for content material material translation, layout acknowledgment, and a numerous excellent commercial enterprise features or has precipitated a simply one among a type studies eagerness for data removal to differentiate wearing a veil regularities or abnormality within group facts that developing via way of next. This thesis centers round clarifying the concept and development of device learning, a portion of the mainstream device gaining knowledge of calculations and attempt to reflect onconsideration on 3 most ordinary calculations relying on a few essential thoughts. Sentiment140 dataset turn out to be utilized and execution of each estimate concerning getting ready time, expectation time and precision of forecast had been archived and analyzed.

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