A machine learning approach to predict the activity of smart home inhabitant

2021 ◽  
pp. 1-13
Author(s):  
Mohammad Marufuzzaman ◽  
Teresa Tumbraegel ◽  
Labonnah Farzana Rahman ◽  
Lariyah Mohd Sidek
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Execution Time ◽  
Smart Home ◽  
General Purpose ◽  
Raspberry Pi ◽  
Learning Approach ◽  
Intelligent Network ◽  
Machine Learning Approach ◽  
Home Appliance

A smart home inhabitant performs a unique pattern or sequence of tasks repeatedly. Thus, a machine learning approach will be required to build an intelligent network of home appliances, and the algorithm should respond quickly to execute the decision. This study proposes a decision tree-based machine learning approach for predicting the activities using different appliances such as state, locations and time. A noise filter is employed to remove unwanted data and generate task sequences, and dual state properties of a home appliance are utilized to extract episodes from the sequence. An incremental decision tree approach was taken to reduce execution time. The algorithm was tested using a well-known smart home dataset from MavLab. The experimental results showed that the algorithm successfully extracted 689 predictions and their location at 90% accuracy, and the total execution time was 94 s, which is less than that of existing methods. A hardware prototype was designed using Raspberry Pi 2 B to validate the proposed prediction system. The general-purpose input-output (GPIO) interfaces of Raspberry Pi 2 B were used to communicate with the prototype testbed and showed that the algorithm successfully predicted the next activities.

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.

Machine learning approach for multi-perspective volcanic eruption recognition using thermal infrared images

2020 ◽  
Author(s):  
Claudia Corradino ◽  
Gaetana Ganci ◽  
Giuseppe Bilotta ◽  
Annalisa Cappello ◽  
Ciro Del Negro
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Real Time ◽  
Mount Etna ◽  
Learning Approach ◽  
Bag Of Words ◽  
Infrared Images ◽  
Machine Learning Approach ◽  
Thermal Infrared Images ◽  
Thermal Cameras

<p>Detect, locate and characterize eruptions in real-time is fundamental to monitor volcanic activity. Here we present an automatic system able to discover and identify the main types of eruptive activities by exploiting infrared images acquired by the thermal cameras installed around Mount Etna volcano. The system, which employs the machine learning approach, is based on a decision tree tool and a bag of words-based classifier. The decision tree provides information on the visibility level of the monitored area, while the bag of words-based classifiers detects the onset of the eruptive activity and recognize the eruption type among either explosion and/or lava flow or plume. Thus, applied to each image of all thermal cameras over Etna in real-time, the proposed system provides two outputs, namely the visibility level and the recognized activity status. By merging the outcomes coming from each thermal camera, the monitored phenomena can be fully described from different perspectives getting deeper information in real-time and in an automatic way.   </p>

scholarly journals A Machine Learning Approach for Predicting Execution Time of Spark Jobs

2018 ◽  
Vol 57 (4) ◽  
pp. 3767-3778 ◽  
Author(s):  
Sara Mustafa ◽  
Iman Elghandour ◽  
Mohamed A. Ismail
Keyword(s):  
Machine Learning ◽  
Execution Time ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Handwritten digits recognition with decision tree classification: a machine learning approach

2019 ◽  
Vol 9 (5) ◽  
pp. 4446
Author(s):  
Tsehay Admassu Assegie ◽  
Pramod Sekharan Nair
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Classification Model ◽  
Learning Approach ◽  
Data Sets ◽  
The Past ◽  
Decision Tree Classification ◽  
Machine Learning Approach ◽  
Future Data ◽  
Class Labels

Handwritten digits recognition is an area of machine learning, in which a machine is trained to identify handwritten digits. One method of achieving this is with decision tree classification model. A decision tree classification is a machine learning approach that uses the predefined labels from the past known sets to determine or predict the classes of the future data sets where the class labels are unknown. In this paper we have used the standard kaggle digits dataset for recognition of handwritten digits using a decision tree classification approach. And we have evaluated the accuracy of the model against each digit from 0 to 9.

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