Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.

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Performance of Statistical and Machine Learning-Based Methods for Predicting Biogeographical Patterns of Fungal Productivity in Forest Ecosystems

10.21203/rs.3.rs-122045/v1 ◽

2020 ◽

Author(s):

Albert Morera ◽

Juan Martínez de Aragón ◽

José Antonio Bonet ◽

Jingjing Liang ◽

Sergio de-Miguel

Keyword(s):

Machine Learning ◽

Random Forest ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Learning Approaches ◽

Learning Models ◽

Extreme Gradient Boosting ◽

Machine Learning Models ◽

Modelling Approaches

Abstract BackgroundThe prediction of biogeographical patterns from a large number of driving factors with complex interactions, correlations and non-linear dependences require advanced analytical methods and modelling tools. This study compares different statistical and machine learning models for predicting fungal productivity biogeographical patterns as a case study for the thorough assessment of the performance of alternative modelling approaches to provide accurate and ecologically-consistent predictions.MethodsWe evaluated and compared the performance of two statistical modelling techniques, namely, generalized linear mixed models and geographically weighted regression, and four machine learning models, namely, random forest, extreme gradient boosting, support vector machine and deep learning to predict fungal productivity. We used a systematic methodology based on substitution, random, spatial and climatic blocking combined with principal component analysis, together with an evaluation of the ecological consistency of spatially-explicit model predictions.ResultsFungal productivity predictions were sensitive to the modelling approach and complexity. Moreover, the importance assigned to different predictors varied between machine learning modelling approaches. Decision tree-based models increased prediction accuracy by ~7% compared to other machine learning approaches and by more than 25% compared to statistical ones, and resulted in higher ecological consistence at the landscape level.ConclusionsWhereas a large number of predictors are often used in machine learning algorithms, in this study we show that proper variable selection is crucial to create robust models for extrapolation in biophysically differentiated areas. When dealing with spatial-temporal data in the analysis of biogeographical patterns, climatic blocking is postulated as a highly informative technique to be used in cross-validation to assess the prediction error over larger scales. Random forest was the best approach for prediction both in sampling-like environments as well as in extrapolation beyond the spatial and climatic range of the modelling data.

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Performance Evaluation Of Machine Learning Techniques On Rpas Remote Sensing Images

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1197.0886s19 ◽

2019 ◽

Vol 8 (6S) ◽

pp. 1035-1039

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Random Forest ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Kappa Index ◽

Remote Sensing Images ◽

Accuracy Measure

Recent advancements in remote sensing platforms from satellites to close-range Remotely Piloted Aircraft System (RPAS), is principal to a growing demand for innovative image processing and classification tools. Where, Machine learning approaches are very prevailing group of data driven implication tools that provide a broader scope when applied to remote sensed data. In this paper, applying different machine learning approaches on the remote sensing images with open source packages in R, to find out which algorithm is more efficient for obtaining better accuracy. We carried out a rigorous comparison of four machine learning algorithms-Support vector machine, Random forest, regression tree, Classification and Naive Bayes. These algorithms are evaluated by Classification accurateness, Kappa index and curve area as accuracy metrics. Ten runs are done to obtain the variance in the results on the training set. Using k-fold cross validation the validation is carried out. This theme identifies Random forest approach as the best method based on the accuracy measure under different conditions. Random forest is used to train efficient and highly stable with respect to variations in classification representation parameter values and significantly more accurate than other machine learning approaches trailed

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Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep

Sensors ◽

10.3390/s18103532 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3532 ◽

Cited By ~ 16

Author(s):

Nicola Mansbridge ◽

Jurgen Mitsch ◽

Nicola Bollard ◽

Keith Ellis ◽

Giuliana Miguel-Pacheco ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Time Budget ◽

Learning Algorithms ◽

Eating Behaviour ◽

Machine Learning Algorithms ◽

Support Vector ◽

Optimum Number ◽

Eating Behaviours ◽

Adaptive Boosting

Grazing and ruminating are the most important behaviours for ruminants, as they spend most of their daily time budget performing these. Continuous surveillance of eating behaviour is an important means for monitoring ruminant health, productivity and welfare. However, surveillance performed by human operators is prone to human variance, time-consuming and costly, especially on animals kept at pasture or free-ranging. The use of sensors to automatically acquire data, and software to classify and identify behaviours, offers significant potential in addressing such issues. In this work, data collected from sheep by means of an accelerometer/gyroscope sensor attached to the ear and collar, sampled at 16 Hz, were used to develop classifiers for grazing and ruminating behaviour using various machine learning algorithms: random forest (RF), support vector machine (SVM), k nearest neighbour (kNN) and adaptive boosting (Adaboost). Multiple features extracted from the signals were ranked on their importance for classification. Several performance indicators were considered when comparing classifiers as a function of algorithm used, sensor localisation and number of used features. Random forest yielded the highest overall accuracies: 92% for collar and 91% for ear. Gyroscope-based features were shown to have the greatest relative importance for eating behaviours. The optimum number of feature characteristics to be incorporated into the model was 39, from both ear and collar data. The findings suggest that one can successfully classify eating behaviours in sheep with very high accuracy; this could be used to develop a device for automatic monitoring of feed intake in the sheep sector to monitor health and welfare.

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Predicting Student’s Performance Using Machine Learning Algorithm

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1209 ◽

2021 ◽

pp. 53-58

Author(s):

Sheela Rani P ◽

Dhivya S ◽

Dharshini Priya M ◽

Dharmila Chowdary A

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Prediction Model ◽

Naive Bayes ◽

Learning Algorithm ◽

Naïve Bayes ◽

Machine Learning Algorithms ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

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Techniques for Detecting Malware Traffic: A Comprehensive Approach to Feature Selection and Classification

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39088 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1-10

Author(s):

Harsha A K

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Random Forest ◽

Learning Algorithms ◽

Malware Detection ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Steady Increase ◽

Extreme Gradient Boosting

Abstract: Since the advent of encryption, there has been a steady increase in malware being transmitted over encrypted networks. Traditional approaches to detect malware like packet content analysis are inefficient in dealing with encrypted data. In the absence of actual packet contents, we can make use of other features like packet size, arrival time, source and destination addresses and other such metadata to detect malware. Such information can be used to train machine learning classifiers in order to classify malicious and benign packets. In this paper, we offer an efficient malware detection approach using classification algorithms in machine learning such as support vector machine, random forest and extreme gradient boosting. We employ an extensive feature selection process to reduce the dimensionality of the chosen dataset. The dataset is then split into training and testing sets. Machine learning algorithms are trained using the training set. These models are then evaluated against the testing set in order to assess their respective performances. We further attempt to tune the hyper parameters of the algorithms, in order to achieve better results. Random forest and extreme gradient boosting algorithms performed exceptionally well in our experiments, resulting in area under the curve values of 0.9928 and 0.9998 respectively. Our work demonstrates that malware traffic can be effectively classified using conventional machine learning algorithms and also shows the importance of dimensionality reduction in such classification problems. Keywords: Malware Detection, Extreme Gradient Boosting, Random Forest, Feature Selection.

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Cereal yield forecasting combining satellite drought-based indices, regional climate and weather data using machine learning approaches in Morocco

10.5194/egusphere-egu21-14590 ◽

2021 ◽

Author(s):

El houssaine Bouras ◽

Lionel Jarlan ◽

Salah Er-Raki ◽

Riad Balaghi ◽

Abdelhakim Amazirh ◽

...

Keyword(s):

Machine Learning ◽

Regional Climate ◽

Model Development ◽

Machine Learning Algorithms ◽

Weather Data ◽

Drought Indices ◽

Support Vector ◽

Learning Approaches ◽

Climate Data ◽

Yield Forecasting

<p>Cereals are the main crop in Morocco. Its production exhibits a high inter-annual due to uncertain rainfall and recurrent drought periods. Considering the importance of this resource to the country's economy, it is thus important for decision makers to have reliable forecasts of the annual cereal production in order to pre-empt importation needs. In this study, we assessed the joint use of satellite-based drought indices, weather (precipitation and temperature) and climate data (pseudo-oscillation indices including NAO and the leading modes of sea surface temperature -SST- in the mid-latitude and in the tropical area) to predict cereal yields at the level of the agricultural province using machine learning algorithms (Support Vector Machine -SVM-, Random forest -FR- and eXtreme Gradient Boost -XGBoost-) in addition to Multiple Linear Regression (MLR). Also, we evaluate the models for different lead times along the growing season from January (about 5 months before harvest) to March (2 months before harvest). The results show the combination of data from the different sources outperformed the use of a single dataset; the highest accuracy being obtained when the three data sources were all considered in the model development. In addition, the results show that the models can accurately predict yields in January (5 months before harvesting) with an R&#178; = 0.90 and RMSE about 3.4 Qt.ha<sup>-1</sup>. &#160;When comparing the model&#8217;s performance, XGBoost represents the best one for predicting yields. Also, considering specific models for each province separately improves the statistical metrics by approximately 10-50% depending on the province with regards to one global model applied to all the provinces. The results of this study pointed out that machine learning is a promising tool for cereal yield forecasting. Also, the proposed methodology can be extended to different crops and different regions for crop yield forecasting.</p>

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Abstract 15895: Machine Learning Algorithms to Predict Major Adverse Cardiovascular Events in Patients Undergoing Orthotopic Liver Transplantation: A Retrospective Cohort Study

Circulation ◽

10.1161/circ.142.suppl_3.15895 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

vardhmaan jain ◽

Vikram Sharma ◽

Agam Bansal ◽

Cerise Kleb ◽

Chirag Sheth ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Cardiovascular Events ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Major Adverse Cardiovascular Events ◽

Support Vector ◽

Post Transplant ◽

Extreme Gradient Boosting ◽

All Cause Mortality

Background: Post-transplant major adverse cardiovascular events (MACE) are amongst the leading cause of death amongst orthotopic liver transplant(OLT) recipients. Despite years of guideline directed therapy, there are limited data on predictors of post-OLT MACE. We assessed if machine learning algorithms (MLA) can predict MACE and all-cause mortality in patients undergoing OLT. Methods: We tested three MLA: support vector machine, extreme gradient boosting(XG-Boost) and random forest with traditional logistic regression for prediction of MACE and all-cause mortality on a cohort of consecutive patients undergoing OLT at our center between 2008-2019. The cohort was randomly split into a training (80%) and testing (20%) cohort. Model performance was assessed using c-statistic or AUC. Results: We included 1,459 consecutive patients with mean ± SD age 54.2 ± 13.8 years, 32% female who underwent OLT. There were 199 (13.6%) MACE and 289 (20%) deaths at a mean follow up of 4.56 ± 3.3 years. The random forest MLA was the best performing model for predicting MACE [AUC:0.78, 95% CI: 0.70-0.85] as well as mortality [AUC:0.69, 95% CI: 0.61-0.76], with all models performing better when predicting MACE vs mortality. See Table and Figure. Conclusion: Random forest machine learning algorithms were more predictive and discriminative than traditional regression models for predicting major adverse cardiovascular events and all-cause mortality in patients undergoing OLT. Validation and subsequent incorporation of MLA in clinical decision making for OLT candidacy could help risk stratify patients for post-transplant adverse cardiovascular events.

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Combination of NIR spectroscopy and machine learning for monitoring chili sauce adulterated with ripened papaya

E3S Web of Conferences ◽

10.1051/e3sconf/202018704001 ◽

2020 ◽

Vol 187 ◽

pp. 04001

Author(s):

Ravipat Lapcharoensuk ◽

Kitticheat Danupattanin ◽

Chaowarin Kanjanapornprapa ◽

Tawin Inkawee

Keyword(s):

Machine Learning ◽

Food Industry ◽

Partial Least Squares Regression ◽

Nir Spectroscopy ◽

Machine Learning Algorithms ◽

Support Vector ◽

Learning Approaches ◽

Least Squares Regression ◽

Validation Set ◽

Global Food

This research aimed to study the combination of NIR spectroscopy and machine learning for monitoring chilli sauce adulterated with papaya smoothie. The chilli sauce was produced by the famous community enterprise of chilli sauce processing in Thailand. The ingredients of the chilli sauce consisted of 45% chilli, 25% sugar, 20% garlic, 5% vinegar, and 5% salt. The chilli sauce sample was mixed with ripened papaya (Khaek Dam variety) smoothie with 9 levels from 10 to 90 %w/w. The NIR spectra of pure chilli sauce, papaya smoothie and 9 adulterated chilli sauce samples were recorded using FT-NIR spectrometer in the wavenumber range of 12500 and 4000 cm-1. Three machine learning algorithms were applied to develop a model for monitoring adulterated chilli sauce, including partial least squares regression (PLS), support vector machine (SVM), and backpropagation neural network (BPNN). All model presented performance of prediction in the validation set with R2al = 0.99 while RMSEP of PLS, SVM and BPNN were 1.71, 2.18 and 3.27% w/w respectively. This finding indicated that NIR spectroscopy coupled with machine learning approaches were shown to be an alternative technique to monitor papaya smoothie adulterated in chilli sauce in the global food industry.

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Damage Classification of Composites Using Machine Learning

Volume 13: Safety Engineering, Risk, and Reliability Analysis ◽

10.1115/imece2019-11851 ◽

2019 ◽

Author(s):

Shweta Dabetwar ◽

Stephen Ekwaro-Osire ◽

João Paulo Dias

Keyword(s):

Machine Learning ◽

Composite Materials ◽

Random Forest ◽

Condition Monitoring ◽

Machine Learning Algorithms ◽

Support Vector ◽

Damage Classification ◽

Combining Data ◽

Ultrasonic Measurements

Abstract Composite materials have tremendous and ever-increasing applications in complex engineering systems; thus, it is important to develop non-destructive and efficient condition monitoring methods to improve damage prediction, thereby avoiding catastrophic failures and reducing standby time. Nondestructive condition monitoring techniques when combined with machine learning applications can contribute towards the stated improvements. Thus, the research question taken into consideration for this paper is “Can machine learning techniques provide efficient damage classification of composite materials to improve condition monitoring using features extracted from acousto-ultrasonic measurements?” In order to answer this question, acoustic-ultrasonic signals in Carbon Fiber Reinforced Polymer (CFRP) composites for distinct damage levels were taken from NASA Ames prognostics data repository. Statistical condition indicators of the signals were used as features to train and test four traditional machine learning algorithms such as K-nearest neighbors, support vector machine, Decision Tree and Random Forest, and their performance was compared and discussed. Results showed higher accuracy for Random Forest with a strong dependency on the feature extraction/selection techniques employed. By combining data analysis from acoustic-ultrasonic measurements in composite materials with machine learning tools, this work contributes to the development of intelligent damage classification algorithms that can be applied to advanced online diagnostics and health management strategies of composite materials, operating under more complex working conditions.

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Prediction of novel mouse TLR9 agonists using a random forest approach

BMC Molecular and Cell Biology ◽

10.1186/s12860-019-0241-0 ◽

2019 ◽

Vol 20 (S2) ◽

Author(s):

Varun Khanna ◽

Lei Li ◽

Johnson Fung ◽

Shoba Ranganathan ◽

Nikolai Petrovsky

Keyword(s):

Machine Learning ◽

Random Forest ◽

Correlation Coefficient ◽

Matthews Correlation Coefficient ◽

Learning Algorithms ◽

Ensemble Classifier ◽

Innate Immune ◽

Machine Learning Algorithms ◽

Support Vector ◽

Random Forest Algorithm

Abstract Background Toll-like receptor 9 is a key innate immune receptor involved in detecting infectious diseases and cancer. TLR9 activates the innate immune system following the recognition of single-stranded DNA oligonucleotides (ODN) containing unmethylated cytosine-guanine (CpG) motifs. Due to the considerable number of rotatable bonds in ODNs, high-throughput in silico screening for potential TLR9 activity via traditional structure-based virtual screening approaches of CpG ODNs is challenging. In the current study, we present a machine learning based method for predicting novel mouse TLR9 (mTLR9) agonists based on features including count and position of motifs, the distance between the motifs and graphically derived features such as the radius of gyration and moment of Inertia. We employed an in-house experimentally validated dataset of 396 single-stranded synthetic ODNs, to compare the results of five machine learning algorithms. Since the dataset was highly imbalanced, we used an ensemble learning approach based on repeated random down-sampling. Results Using in-house experimental TLR9 activity data we found that random forest algorithm outperformed other algorithms for our dataset for TLR9 activity prediction. Therefore, we developed a cross-validated ensemble classifier of 20 random forest models. The average Matthews correlation coefficient and balanced accuracy of our ensemble classifier in test samples was 0.61 and 80.0%, respectively, with the maximum balanced accuracy and Matthews correlation coefficient of 87.0% and 0.75, respectively. We confirmed common sequence motifs including ‘CC’, ‘GG’,‘AG’, ‘CCCG’ and ‘CGGC’ were overrepresented in mTLR9 agonists. Predictions on 6000 randomly generated ODNs were ranked and the top 100 ODNs were synthesized and experimentally tested for activity in a mTLR9 reporter cell assay, with 91 of the 100 selected ODNs showing high activity, confirming the accuracy of the model in predicting mTLR9 activity. Conclusion We combined repeated random down-sampling with random forest to overcome the class imbalance problem and achieved promising results. Overall, we showed that the random forest algorithm outperformed other machine learning algorithms including support vector machines, shrinkage discriminant analysis, gradient boosting machine and neural networks. Due to its predictive performance and simplicity, the random forest technique is a useful method for prediction of mTLR9 ODN agonists.

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