A Novel Approach for Prediction of Warts Disease Treatment Methods: Machine Learning Techniques

2019 ◽  
Author(s):  
Lavanya P. ◽  
Priyanka Prakash ◽  
Manasa M. ◽  
R. G. Babukarthik ◽  
Bonduvenkat B.
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Disease Treatment ◽  
Treatment Methods ◽  
Novel Approach ◽  
Learning Techniques

scholarly journals Reaction Wheels Fault Isolation Onboard 3-Axis Controlled Satel-lite using Enhanced Random Forest with Multidomain Features

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Afshin Rahimi ◽  
Mofiyinoluwa O. Folami
Keyword(s):  
Machine Learning ◽  
Fault Isolation ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Alternative Methods ◽  
Model Parameters ◽  
High Fidelity ◽  
Novel Approach ◽  
Learning Techniques

As the number of satellite launches increases each year, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex, generating a high-fidelity model that accurately describes the system becomes complicated. Therefore, imploring a data-driven method can provide to be more beneficial for such applications. This research proposes a novel approach for data-driven machine learning techniques on the detection and isolation of nonlinear systems, with a case-study for an in-orbit closed loop-controlled satellite with reaction wheels as actuators. High-fidelity models of the 3-axis controlled satellite are employed to generate data for both nominal and faulty conditions of the reaction wheels. The generated simulation data is used as input for the isolation method, after which the data is pre-processed through feature extraction from a temporal, statistical, and spectral domain. The pre-processed features are then fed into various machine learning classifiers. Isolation results are validated with cross-validation, and model parameters are tuned using hyperparameter optimization. To validate the robustness of the proposed method, it is tested on three characterized datasets and three reaction wheel configurations, including standard four-wheel, three-orthogonal, and pyramid. The results prove superior performance isolation accuracy for the system under study compared to previous studies using alternative methods (Rahimi & Saadat, 2019, 2020).

scholarly journals Using Machine Learning for Decreasing State Uncertainty in Planning

2020 ◽  
Vol 69 ◽  
pp. 765-806
Author(s):  
Senka Krivic ◽  
Michael Cashmore ◽  
Daniele Magazzeni ◽  
Sandor Szedmak ◽  
Justus Piater
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Process ◽  
The State ◽  
Machine Learning Techniques ◽  
Planning Problem ◽  
Planning Techniques ◽  
Novel Approach ◽  
Learning Techniques ◽  
State Uncertainty

We present a novel approach for decreasing state uncertainty in planning prior to solving the planning problem. This is done by making predictions about the state based on currently known information, using machine learning techniques. For domains where uncertainty is high, we define an active learning process for identifying which information, once sensed, will best improve the accuracy of predictions. We demonstrate that an agent is able to solve problems with uncertainties in the state with less planning effort compared to standard planning techniques. Moreover, agents can solve problems for which they could not find valid plans without using predictions. Experimental results also demonstrate that using our active learning process for identifying information to be sensed leads to gathering information that improves the prediction process.

A Novel Approach to Detect Spam and Smishing SMS using Machine Learning Techniques

2021 ◽  
pp. 267-285
Author(s):  
Ankit Kumar Jain ◽  
Sumit Kumar Yadav ◽  
Neelam Choudhary
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Second Phase ◽  
Short Message ◽  
Machine Learning Classifiers ◽  
Novel Approach ◽  
Learning Techniques ◽  
Message Service ◽  
Novel Method ◽  
Two Phases

Smishing attack is generally performed by sending a fake short message service (SMS) that contains a link of the malicious webpage or application. Smishing messages are the subclass of spam SMS and these are more harmful compared to spam messages. There are various solutions available to detect the spam messages. However, no existing solution, filters the smishing message from the spam message. Therefore, this article presents a novel method to filter smishing message from spam message. The proposed approach is divided into two phases. The first phase filters the spam messages and ham messages. The second phase filters smishing messages from spam messages. The performance of the proposed method is evaluated on various machine learning classifiers using the dataset of ham and spam messages. The simulation results indicate that the proposed approach can detect spam messages with the accuracy of 94.9% and it can filter smishing messages with the accuracy of 96% on neural network classifier.

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