scholarly journals Machine Learning Based Indoor Localisation Using Wi-Fi And Smartphone

10.31645/06 ◽  
2020 ◽  
Author(s):  
Zulqarnain Khokhar ◽  
◽  
Murtaza Ahmed Siddiqi ◽  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Indoor Localization ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
Gradient Boosting ◽  
Learning Techniques ◽  
Indoor Localisation

Wi-Fi based indoor positioning with the help of access points and smart devices have become an integral part in finding a device or a person’s location. Wi-Fi based indoor localization technology has been among the most attractive field for researchers for a number of years. In this paper, we have presented Wi-Fi based in-door localization using three different machine-learning techniques. The three machine learning algorithms implemented and compared are Decision Tree, Random Forest and Gradient Boosting classifier. After making a fingerprint of the floor based on Wi-Fi signals, mentioned algorithms were used to identify device location at thirty different positions on the floor. Random Forest and Gradient Boosting classifier were able to identify the location of the device with accuracy higher than 90%. While Decision Tree was able to identify the location with accuracy a bit higher than 80%.

scholarly journals Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 403
Author(s):  
Muhammad Waleed ◽  
Tai-Won Um ◽  
Tariq Kamal ◽  
Syed Muhammad Usman
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Farm Machinery ◽  
Learning Techniques

In this paper, we apply the multi-class supervised machine learning techniques for classifying the agriculture farm machinery. The classification of farm machinery is important when performing the automatic authentication of field activity in a remote setup. In the absence of a sound machine recognition system, there is every possibility of a fraudulent activity taking place. To address this need, we classify the machinery using five machine learning techniques—K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB). For training of the model, we use the vibration and tilt of machinery. The vibration and tilt of machinery are recorded using the accelerometer and gyroscope sensors, respectively. The machinery included the leveler, rotavator and cultivator. The preliminary analysis on the collected data revealed that the farm machinery (when in operation) showed big variations in vibration and tilt, but observed similar means. Additionally, the accuracies of vibration-based and tilt-based classifications of farm machinery show good accuracy when used alone (with vibration showing slightly better numbers than the tilt). However, the accuracies improve further when both (the tilt and vibration) are used together. Furthermore, all five machine learning algorithms used for classification have an accuracy of more than 82%, but random forest was the best performing. The gradient boosting and random forest show slight over-fitting (about 9%), but both algorithms produce high testing accuracy. In terms of execution time, the decision tree takes the least time to train, while the gradient boosting takes the most time.

Ionospheric VTEC Forecasting using Machine Learning

2021 ◽  
Author(s):  
Randa Natras ◽  
Michael Schmidt
Keyword(s):  
Magnetic Field ◽  
Machine Learning ◽  
Random Forest ◽  
Space Weather ◽  
Early Warning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Learning Techniques

<p>The accuracy and reliability of Global Navigation Satellite System (GNSS) applications are affected by the state of the Earth‘s ionosphere, especially when using single frequency observations, which are employed mostly in mass-market GNSS receivers. In addition, space weather can be the cause of strong sudden disturbances in the ionosphere, representing a major risk for GNSS performance and reliability. Accurate corrections of ionospheric effects and early warning information in the presence of space weather are therefore crucial for GNSS applications. This correction information can be obtained by employing a model that describes the complex relation of space weather processes with the non-linear spatial and temporal variability of the Vertical Total Electron Content (VTEC) within the ionosphere and includes a forecast component considering space weather events to provide an early warning system. To develop such a model is challenging but an important task and of high interest for the GNSS community.</p><p>To model the impact of space weather, a complex chain of physical dynamical processes between the Sun, the interplanetary magnetic field, the Earth's magnetic field and the ionosphere need to be taken into account. Machine learning techniques are suitable in finding patterns and relationships from historical data to solve problems that are too complex for a traditional approach requiring an extensive set of rules (equations) or for which there is no acceptable solution available yet.</p><p>The main objective of this study is to develop a model for forecasting the ionospheric VTEC taking into account physical processes and utilizing state-of-art machine learning techniques to learn complex non-linear relationships from the data. In this work, supervised learning is applied to forecast VTEC. This means that the model is provided by a set of (input) variables that have some influence on the VTEC forecast (output). To be more specific, data of solar activity, solar wind, interplanetary and geomagnetic field and other information connected to the VTEC variability are used as input to predict VTEC values in the future. Different machine learning algorithms are applied, such as decision tree regression, random forest regression and gradient boosting. The decision trees are the simplest and easiest to interpret machine learning algorithms, but the forecasted VTEC lacks smoothness. On the other hand, random forest and gradient boosting use a combination of multiple regression trees, which lead to improvements in the prediction accuracy and smoothness. However, the results show that the overall performance of the algorithms, measured by the root mean square error, does not differ much from each other and improves when the data are well prepared, i.e. cleaned and transformed to remove trends. Preliminary results of this study will be presented including the methodology, goals, challenges and perspectives of developing the machine learning model.</p>

scholarly journals Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1089
Author(s):  
Sung-Hee Kim ◽  
Chanyoung Jeong
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Manufacturing ◽  
Gradient Boosting ◽  
Experimental Conditions ◽  
Learning Techniques ◽  
Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

scholarly journals Learning from Imbalanced Educational Data Using Ensemble Machine Learning Algorithms

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 01) ◽  
pp. 183-195
Author(s):  
Thingbaijam Lenin ◽  
N. Chandrasekaran
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Missing Values ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Adaptive Boosting ◽  
Stochastic Gradient Boosting ◽  
Ensemble Machine Learning ◽  
Learning Techniques ◽  
Student’S Performance

Student’s academic performance is one of the most important parameters for evaluating the standard of any institute. It has become a paramount importance for any institute to identify the student at risk of underperforming or failing or even drop out from the course. Machine Learning techniques may be used to develop a model for predicting student’s performance as early as at the time of admission. The task however is challenging as the educational data required to explore for modelling are usually imbalanced. We explore ensemble machine learning techniques namely bagging algorithm like random forest (rf) and boosting algorithms like adaptive boosting (adaboost), stochastic gradient boosting (gbm), extreme gradient boosting (xgbTree) in an attempt to develop a model for predicting the student’s performance of a private university at Meghalaya using three categories of data namely demographic, prior academic record, personality. The collected data are found to be highly imbalanced and also consists of missing values. We employ k-nearest neighbor (knn) data imputation technique to tackle the missing values. The models are developed on the imputed data with 10 fold cross validation technique and are evaluated using precision, specificity, recall, kappa metrics. As the data are imbalanced, we avoid using accuracy as the metrics of evaluating the model and instead use balanced accuracy and F-score. We compare the ensemble technique with single classifier C4.5. The best result is provided by random forest and adaboost with F-score of 66.67%, balanced accuracy of 75%, and accuracy of 96.94%.

Machine Learning Techniques for IoT-Based Indoor Tracking and Localization

2022 ◽  
pp. 123-145
Author(s):  
Pelin Yildirim Taser ◽  
Vahid Khalilpour Akram
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Learning Algorithms ◽  
Distance Estimation ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Traditional Methods ◽  
Learning Techniques ◽  
Localization Systems

The GPS signals are not available inside the buildings; hence, indoor localization systems rely on indoor technologies such as Bluetooth, WiFi, and RFID. These signals are used for estimating the distance between a target and available reference points. By combining the estimated distances, the location of the target nodes is determined. The wide spreading of the internet and the exponential increase in small hardware diversity allow the creation of the internet of things (IoT)-based indoor localization systems. This chapter reviews the traditional and machine learning-based methods for IoT-based positioning systems. The traditional methods include various distance estimation and localization approaches; however, these approaches have some limitations. Because of the high prediction performance, machine learning algorithms are used for indoor localization problems in recent years. The chapter focuses on presenting an overview of the application of machine learning algorithms in indoor localization problems where the traditional methods remain incapable.

scholarly journals Estimating Warehouse Rental Price using Machine Learning Techniques

2018 ◽  
Vol 13 (2) ◽  
pp. 235-250 ◽  
Author(s):  
Yixuan Ma ◽  
Zhenji Zhang ◽  
Alexander Ihler ◽  
Baoxiang Pan
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Estate ◽  
Rapid Development ◽  
Supply And Demand ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Logistics Industry ◽  
Real Estate Price ◽  
Learning Techniques

Boosted by the growing logistics industry and digital transformation, the sharing warehouse market is undergoing a rapid development. Both supply and demand sides in the warehouse rental business are faced with market perturbations brought by unprecedented peer competitions and information transparency. A key question faced by the participants is how to price warehouses in the open market. To understand the pricing mechanism, we built a real world warehouse dataset using data collected from the classified advertisements websites. Based on the dataset, we applied machine learning techniques to relate warehouse price with its relevant features, such as warehouse size, location and nearby real estate price. Four candidate models are used here: Linear Regression, Regression Tree, Random Forest Regression and Gradient Boosting Regression Trees. The case study in the Beijing area shows that warehouse rent is closely related to its location and land price. Models considering multiple factors have better skill in estimating warehouse rent, compared to singlefactor estimation. Additionally, tree models have better performance than the linear model, with the best model (Random Forest) achieving correlation coefficient of 0.57 in the test set. Deeper investigation of feature importance illustrates that distance from the city center plays the most important role in determining warehouse price in Beijing, followed by nearby real estate price and warehouse size.

scholarly journals Machine Learning Algorithms For Understanding The Determinants of Under-Five Mortality

2021 ◽  
Author(s):  
Rakesh Kumar Saroj ◽  
Pawan Kumar Yadav ◽  
Rajneesh Singh ◽  
Obvious Nchimunya Chilyabanyama
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Information Gain ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Mortality Data ◽  
Mortality Factors ◽  
Under Five ◽  
Learning Techniques

Abstract Background: The death rate of under-five children in India declined last few decades, but few bigger states have poor performance. This is a matter of serious concern for the child's health as well as social development. Nowadays, machine learning techniques play a crucial role in the smart health care system to capture the hidden factors and patterns of outcomes. In this paper, we used machine learning techniques to predict the important factors of under-five mortality.This study aims to explore the importance of machine learning techniques to predict under-five mortality and to find the important factors that cause under-five mortality.The data was taken from the National Family Health Survey-IV of Uttar Pradesh. We used four machine learning techniques like decision tree, support vector machine, random forest, and logistic regression to predict under-five mortality factors and model accuracy of each model. We have also used information gain to rank to know the important variables for accurate predictions in under-five mortality data.Result: Random Forest (RF) predicts the child mortality factors with the highest accuracy of 97.5 %, and the number of living children, births in the last five years, educational level, birth order, total children ever born, currently breastfeeding, and size of child at birth that identifying as essential factors for under-five mortality.Conclusion: The study focuses on machine learning techniques to predict and identify important factors for under-five mortality. The random forest model provides an excellent predictive result for estimating the risk factors of under-five mortality. Based on the resulting outcome, policymakers can make policies and plans to reduce under-five mortality.

scholarly journals Sport analytics for cricket game results using machine learning: An experimental study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kumash Kapadia ◽  
Hussein Abdel-Jaber ◽  
Fadi Thabtah ◽  
Wael Hadi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Predictive Models ◽  
Information Gain ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Technology ◽  
Home Team ◽  
Feature Sets ◽  
Learning Techniques

Indian Premier League (IPL) is one of the more popular cricket world tournaments, and its financial is increasing each season, its viewership has increased markedly and the betting market for IPL is growing significantly every year. With cricket being a very dynamic game, bettors and bookies are incentivised to bet on the match results because it is a game that changes ball-by-ball. This paper investigates machine learning technology to deal with the problem of predicting cricket match results based on historical match data of the IPL. Influential features of the dataset have been identified using filter-based methods including Correlation-based Feature Selection, Information Gain (IG), ReliefF and Wrapper. More importantly, machine learning techniques including Naïve Bayes, Random Forest, K-Nearest Neighbour (KNN) and Model Trees (classification via regression) have been adopted to generate predictive models from distinctive feature sets derived by the filter-based methods. Two featured subsets were formulated, one based on home team advantage and other based on Toss decision. Selected machine learning techniques were applied on both feature sets to determine a predictive model. Experimental tests show that tree-based models particularly Random Forest performed better in terms of accuracy, precision and recall metrics when compared to probabilistic and statistical models. However, on the Toss featured subset, none of the considered machine learning algorithms performed well in producing accurate predictive models.

scholarly journals A Novel Approach for Detecting DGA-Based Botnets in DNS Queries Using Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ali Soleymani ◽  
Fatemeh Arabgol
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Text Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Detection Accuracy ◽  
Domain Name ◽  
Botnet Detection ◽  
Learning Techniques

In today’s security landscape, advanced threats are becoming increasingly difficult to detect as the pattern of attacks expands. Classical approaches that rely heavily on static matching, such as blacklisting or regular expression patterns, may be limited in flexibility or uncertainty in detecting malicious data in system data. This is where machine learning techniques can show their value and provide new insights and higher detection rates. The behavior of botnets that use domain-flux techniques to hide command and control channels was investigated in this research. The machine learning algorithm and text mining used to analyze the network DNS protocol and identify botnets were also described. For this purpose, extracted and labeled domain name datasets containing healthy and infected DGA botnet data were used. Data preprocessing techniques based on a text-mining approach were applied to explore domain name strings with n-gram analysis and PCA. Its performance is improved by extracting statistical features by principal component analysis. The performance of the proposed model has been evaluated using different classifiers of machine learning algorithms such as decision tree, support vector machine, random forest, and logistic regression. Experimental results show that the random forest algorithm can be used effectively in botnet detection and has the best botnet detection accuracy.

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