scholarly journals A Machine Learning Approach to Interpolating Indoors Trajectories

2021 ◽  
Author(s):  
Daniel Carvalho ◽  
Daniel Sullivan ◽  
Rafael Almeida ◽  
Carlos Caminha
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forest ◽  
Continuous Monitoring ◽  
Learning Approach ◽  
Ensemble Models ◽  
Server Side ◽  
Machine Learning Approach ◽  
Network Overload

In this article we propose a machine learning-based modeling to solve network overload problems caused by continuous monitoring of the trajectories of multiple tracked devices indoors. The proposed modeling was evaluated with hundreds of object coordinate locations tracked in three synthetic environments and one real environment. We show that it is possible to solve the problem of network overload increasing latency in sending data and predicting as server-side trajectories with ensemble models, such as the Random Forest, and using Artificial Neural Networks. We also show that it is possible to predict at least fifteen intermediate coordinates of the paths of the tracked objects with R2 greater than 0.95.

Artificial neural networks for the prediction of solvation energies based on experimental and computational data

2020 ◽  
Vol 22 (42) ◽  
pp. 24359-24364
Author(s):  
Jiyoung Yang ◽  
Matthias J. Knape ◽  
Oliver Burkert ◽  
Virginia Mazzini ◽  
Alexander Jung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ion Pair ◽  
Learning Approach ◽  
Solvation Energies ◽  
Machine Learning Approach ◽  
Computational Data ◽  
Artificial Neural

We present a machine learning approach based on artificial neural networks for the prediction of ion pair solvation energies.

scholarly journals Identification of Mouth Cancer laceration Using Machine Learning Approach

2018 ◽  
Vol 7 (03) ◽  
pp. 01-07
Author(s):  
Prof. Barry Wiling
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Algorithm ◽  
Histogram Equalization ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
Segmented Image ◽  
Svm Algorithm ◽  
Machine Learning Approach

This Paper describes about Identification of Mouth Cancer laceration Using Machine Learning Approach .The SVM algorithm is used for this purpose. Image segmentation operations are performed using: Resizing an image, Gray scale conversion, Histogram equalization and Classifying the Segmented image using SVM. SVM is used to reduce the complexity faced in the existing system comprising of Texture Segmentation and ANN (Artificial Neural Networks) Algorithm. SVM is a simple Machine Learning algorithm when compared to ANN. The outcome of the paper is to segment and classify the Malignancy from the Non-Malignant region using the classifier SVM. SVM performs the classification based on the dataset that contains the trained images.

scholarly journals Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

scholarly journals Machine-learning and statistical methods for DDoS attack detection and defense system in software defined networks

2021 ◽  
Author(s):  
Merlin James Rukshan Dennis
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Statistical Approach ◽  
Denial Of Service ◽  
Attack Detection ◽  
Learning Approach ◽  
Ddos Attack ◽  
Machine Learning Approach ◽  
Ddos Detection ◽  
Ddos Attack Detection

Distributed Denial of Service (DDoS) attack is a serious threat on today’s Internet. As the traffic across the Internet increases day by day, it is a challenge to distinguish between legitimate and malicious traffic. This thesis proposes two different approaches to build an efficient DDoS attack detection system in the Software Defined Networking environment. SDN is the latest networking approach which implements centralized controller, which is programmable. The central control and the programming capability of the controller are used in this thesis to implement the detection and mitigation mechanisms. In this thesis, two designed approaches, statistical approach and machine-learning approach, are proposed for the DDoS detection. The statistical approach implements entropy computation and flow statistics analysis. It uses the mean and standard deviation of destination entropy, new flow arrival rate, packets per flow and flow duration to compute various thresholds. These thresholds are then used to distinguish normal and attack traffic. The machine learning approach uses Random Forest classifier to detect the DDoS attack. We fine-tune the Random Forest algorithm to make it more accurate in DDoS detection. In particular, we introduce the weighted voting instead of the standard majority voting to improve the accuracy. Our result shows that the proposed machine-learning approach outperforms the statistical approach. Furthermore, it also outperforms other machine-learning approach found in the literature.

scholarly journals Application of FURIA for Finding the Faults in a Hydraulic Brake System Using a Vibration Analysis through a Machine Learning Approach

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Alamelu Manghai T. M ◽  
Jegadeeshwaran R
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Continuous Monitoring ◽  
Selection Process ◽  
Brake System ◽  
Learning Approach ◽  
Vibration Signals ◽  
Hydraulic Brake ◽  
Machine Learning Approach

Vibration-based continuous monitoring system for fault diagnosis of automobile hydraulic brake system is presented in this study. This study uses a machine learning approach for the fault diagnosis study. A hydraulic brake system test rig was fabricated. The vibration signals were acquired from the brake system under different simulated fault conditions using a piezoelectric transducer. The histogram features were extracted from the acquired vibration signals. The feature selection process was carried out using a decision tree. The selected features were classified using fuzzy unordered rule induction algorithm ( FURIA ) and Repeated Incremental Pruning to Produce Error Reduction ( RIPPER ) algorithm. The classification results of both algorithms for fault diagnosis of a hydraulic brake system were presented. Compared to RIPPER and J48 decision tree, the FURIA performs better and produced 98.73 % as the classification accuracy.

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