scholarly journals Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 928
Author(s):  
Janggoon Lee ◽  
Chanhee Park ◽  
Heejun Roh
Keyword(s):  
Machine Learning ◽  
Spectrum Sensing ◽  
State Of The Art ◽  
Low Cost ◽  
Frequency Hopping ◽  
Classification Techniques ◽  
Machine Learning Classifiers ◽  
Learning Classifier ◽  
Learning Classifiers ◽  
And Training

Thanks to the frequency hopping nature of Bluetooth, sniffing Bluetooth traffic with low-cost devices has been considered as a challenging problem. To this end, BlueEar, a state-of-the-art low-cost sniffing system with two Bluetooth radios proposes a set of novel machine learning-based subchannel classification techniques for adaptive frequency hopping (AFH) map prediction by collecting packet statistics and spectrum sensing. However, there is no explicit evaluation results on the accuracy of BlueEar’s AFH map prediction. To this end, in this paper, we revisit the spectrum sensing-based classifier, one of the subchannel classification techniques in BlueEar. At first, we build an independent implementation of the spectrum sensing-based classifier with one Ubertooth sniffing radio. Using the implementation, we conduct a subchannel classification experiment with several machine learning classifiers where spectrum features are utilized. Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.

A Comparison of Machine Learning Classifiers

2011 ◽  
Vol 271-273 ◽  
pp. 149-153 ◽  
Author(s):  
Phani Srikanth ◽  
Amarjot Singh ◽  
Devinder Kumar ◽  
Aditya Nagrare ◽  
Vivek Angoth
Keyword(s):  
Machine Learning ◽  
Time Analysis ◽  
Cancer Dataset ◽  
Classification Techniques ◽  
Training Time ◽  
Machine Learning Classification ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Precision And Accuracy

A number of different classifiers have been used to improve the precision and accuracy and give better classification results. Machine learning classifiers have proven to be the most successful techniques in majority of the fields. This paper presents a comparison of the three most successful machine learning classification techniques SVM, boosting and Local SVM applied to a cancer dataset. The comparison is made on the basis of precision and accuracy along with the training time analysis. Finally, the efficacy of the classifiers is found.

scholarly journals Application of Machine Learning Classifier to Candida auris Drug Resistance Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Dingchen Li ◽  
Yaru Wang ◽  
Wenjuan Hu ◽  
Fangyan Chen ◽  
Jingya Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Resistance ◽  
Phylogenetic Tree ◽  
Resistance Mutations ◽  
Candida Auris ◽  
Test Set ◽  
Machine Learning Classifiers ◽  
Drug Resistance Mutations ◽  
Learning Classifier ◽  
Learning Classifiers

Candida auris (C. auris) is an emerging fungus associated with high morbidity. It has a unique transmission ability and is often resistant to multiple drugs. In this study, we evaluated the ability of different machine learning models to classify the drug resistance and predicted and ranked the drug resistance mutations of C. auris. Two C. auris strains were obtained. Combined with other 356 strains collected from the European Bioinformatics Institute (EBI) databases, the whole genome sequencing (WGS) data were analyzed by bioinformatics. Machine learning classifiers were used to build drug resistance models, which were evaluated and compared by various evaluation methods based on AUC value. Briefly, two strains were assigned to Clade III in the phylogenetic tree, which was consistent with previous studies; nevertheless, the phylogenetic tree was not completely consistent with the conclusion of clustering according to the geographical location discovered earlier. The clustering results of C. auris were related to its drug resistance. The resistance genes of C. auris were not under additional strong selection pressure, and the performance of different models varied greatly for different drugs. For drugs such as azoles and echinocandins, the models performed relatively well. In addition, two machine learning algorithms, based on the balanced test and imbalanced test, were designed and evaluated; for most drugs, the evaluation results on the balanced test set were better than on the imbalanced test set. The mutations strongly be associated with drug resistance of C. auris were predicted and ranked by Recursive Feature Elimination with Cross-Validation (RFECV) combined with a machine learning classifier. In addition to known drug resistance mutations, some new resistance mutations were predicted, such as Y501H and I466M mutation in the ERG11 gene and R278H mutation in the ERG10 gene, which may be associated with fluconazole (FCZ), micafungin (MCF), and amphotericin B (AmB) resistance, respectively; these mutations were in the “hot spot” regions of the ergosterol pathway. To sum up, this study suggested that machine learning classifiers are a useful and cost-effective method to identify fungal drug resistance-related mutations, which is of great significance for the research on the resistance mechanism of C. auris.

Automating the Clinical Assessment of Independent Wheelchair Sitting Pivot Transfer Techniques

2021 ◽  
Vol 27 (3) ◽  
pp. 1-11
Author(s):  
Lin Wei ◽  
Cheng-Shiu Chung ◽  
Alicia M. Koontz
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Objective Assessment ◽  
Assessment Tools ◽  
Assessment Instrument ◽  
Decision Threshold ◽  
Full Time ◽  
Test Set ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Background: Using proper transfer technique can help to reduce forces and prevent secondary injuries. However, current assessment tools rely on the ability to subjectively identify harmful movement patterns. Objectives: The purpose of the study was to determine the accuracy of using a low-cost markerless motion capture camera and machine learning methods to evaluate the quality of independent wheelchair sitting pivot transfers. We hypothesized that the algorithms would be able to discern proper (low risk) and improper (high risk) wheelchair transfer techniques in accordance with component items on the Transfer Assessment Instrument (TAI). Methods: Transfer motions of 91 full-time wheelchair users were recorded and used to develop machine learning classifiers that could be used to discern proper from improper technique. The data were labeled using the TAI item scores. Eleven out of 18 TAI items were evaluated by the classifiers. Motion variables from the Kinect were inputted as the features. Random forests and k-nearest neighbors algorithms were chosen as the classifiers. Eighty percent of the data were used for model training and hyperparameter turning. The validation process was performed using 20% of the data as the test set. Results: The area under the receiver operating characteristic curve of the test set for each item was over 0.79. After adjusting the decision threshold, the precisions of the models were over 0.87, and the model accuracies were over 71%. Conclusion: The results show promise for the objective assessment of the transfer technique using a low cost camera and machine learning classifiers.

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