scholarly journals Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 311
Author(s):  
Christina Koutroumpina ◽  
Spyros Sioutas ◽  
Stelios Koutroubinas ◽  
Kostas Tsichlas
Keyword(s):  
Low Cost ◽  
Sampling Rate ◽  
Classification Performance ◽  
General Purpose ◽  
Supervised Machine Learning ◽  
Smart Meter ◽  
Decision Tree Classifier ◽  
Household Level ◽  
Tree Classifier ◽  
Energy Disaggregation

The problem of energy disaggregation is the separation of an aggregate energy signal into the consumption of individual appliances in a household. This is useful, since the goal of energy efficiency at the household level can be achieved through energy-saving policies towards changing the behavior of the consumers. This requires as a prerequisite to be able to measure the energy consumption at the appliance level. The purpose of this study is to present some initial results towards this goal by making heavy use of the characteristics of a particular din-rail meter, which is provided by Meazon S.A. Our thinking is that meter-specific energy disaggregation solutions may yield better results than general-purpose methods, especially for sophisticated meters. This meter has a 50 Hz sampling rate over 3 different lines and provides a rather rich set of measurements with respect to the extracted features. In this paper we aim at evaluating the set of features generated by the smart meter. To this end, we use well-known supervised machine learning models and test their effectiveness on certain appliances when selecting specific subsets of features. Three algorithms are used for this purpose: the Decision Tree Classifier, the Random Forest Classifier, and the Multilayer Perceptron Classifier. Our experimental study shows that by using a specific set of features one can enhance the classification performance of these algorithms.

scholarly journals Optimization of Physical Activity Recognition for Real-Time Wearable Systems: Effect of Window Length, Sampling Frequency and Number of Features

2019 ◽  
Vol 9 (22) ◽  
pp. 4833 ◽  
Author(s):  
Ardo Allik ◽  
Kristjan Pilt ◽  
Deniss Karai ◽  
Ivo Fridolin ◽  
Mairo Leier ◽  
...  
Keyword(s):  
Physical Activity ◽  
Feature Selection ◽  
Real Time ◽  
Sampling Frequency ◽  
Classification Performance ◽  
Window Length ◽  
Decision Tree Classifier ◽  
Acceleration Signal ◽  
Wearable Systems ◽  
Tree Classifier

The aim of this study was to develop an optimized physical activity classifier for real-time wearable systems with the focus on reducing the requirements on device power consumption and memory buffer. Classification parameters evaluated in this study were the sampling frequency of the acceleration signal, window length of the classification fragment, and the number of classification features, found with different feature selection methods. For parameter evaluation, a decision tree classifier was created based on the acceleration signals recorded during tests, where 25 healthy test subjects performed various physical activities. Overall average F1-score achieved in this study was about 0.90. Similar F1-scores were achieved with the evaluated window lengths of 5 s (0.92 ± 0.02) and 3 s (0.91 ± 0.02), while classification performance with 1 s were lower (0.87 ± 0.02). Tested sampling frequencies of 50 Hz, 25 Hz, and 13 Hz had similar results with most classified activity types, with an exception of outdoor cycling, where differences were significant. Using forward sequential feature selection enabled the decreasing of the number of features from initial 110 features to about 12 features without lowering the classification performance. The results of this study have been used for developing more efficient real-time physical activity classifiers.

scholarly journals A Low Rate Energy Disaggregation using Non-Intrusive Load Monitoring

2019 ◽  
Vol 8 (11) ◽  
pp. 4204-4209
Keyword(s):  
Energy Conservation ◽  
Low Cost ◽  
Sampling Rate ◽  
Energy Usage ◽  
Energy Meter ◽  
High Sampling Rate ◽  
Energy Disaggregation ◽  
Load Monitoring ◽  
The Cost ◽  
Conservation And Management

Nowadays, Energy conservation and management are a must practice due to the exponentially increasing energy usage. One solution for providing for energy conservation is appliance load monitoring. Load monitoring approach should be simple and of low cost in order to be massively deployable. Non-Intrusive load monitoring is a better approach since it can disaggregate energy at the cost of single energy meter. A low sampling rate energy meter incurs low cost compared to a high sampling rate energy meter. In this paper a less complex, low cost energy disaggregation approach has been proposed

scholarly journals Performance Evaluation of a Proposed Machine Learning Model for Chronic Disease Datasets Using an Integrated Attribute Evaluator and an Improved Decision Tree Classifier

2020 ◽  
Vol 10 (22) ◽  
pp. 8137
Author(s):  
Sushruta Mishra ◽  
Pradeep Kumar Mallick ◽  
Hrudaya Kumar Tripathy ◽  
Akash Kumar Bhoi ◽  
Alfonso González-Briones
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Heart Disease ◽  
Chronic Disease ◽  
Decision Tree ◽  
Classification Performance ◽  
Decision Tree Classifier ◽  
Accuracy Rate ◽  
Filter Methods ◽  
Tree Classifier

There is a consistent rise in chronic diseases worldwide. These diseases decrease immunity and the quality of daily life. The treatment of these disorders is a challenging task for medical professionals. Dimensionality reduction techniques make it possible to handle big data samples, providing decision support in relation to chronic diseases. These datasets contain a series of symptoms that are used in disease prediction. The presence of redundant and irrelevant symptoms in the datasets should be identified and removed using feature selection techniques to improve classification accuracy. Therefore, the main contribution of this paper is a comparative analysis of the impact of wrapper and filter selection methods on classification performance. The filter methods that have been considered include the Correlation Feature Selection (CFS) method, the Information Gain (IG) method and the Chi-Square (CS) method. The wrapper methods that have been considered include the Best First Search (BFS) method, the Linear Forward Selection (LFS) method and the Greedy Step Wise Search (GSS) method. A Decision Tree algorithm has been used as a classifier for this analysis and is implemented through the WEKA tool. An attribute significance analysis has been performed on the diabetes, breast cancer and heart disease datasets used in the study. It was observed that the CFS method outperformed other filter methods concerning the accuracy rate and execution time. The accuracy rate using the CFS method on the datasets for heart disease, diabetes, breast cancer was 93.8%, 89.5% and 96.8% respectively. Moreover, latency delays of 1.08 s, 1.02 s and 1.01 s were noted using the same method for the respective datasets. Among wrapper methods, BFS’ performance was impressive in comparison to other methods. Maximum accuracy of 94.7%, 95.8% and 96.8% were achieved on the datasets for heart disease, diabetes and breast cancer respectively. Latency delays of 1.42 s, 1.44 s and 132 s were recorded using the same method for the respective datasets. On the basis of the obtained result, a new hybrid Attribute Evaluator method has been proposed which effectively integrates enhanced K-Means clustering with the CFS filter method and the BFS wrapper method. Furthermore, the hybrid method was evaluated with an improved decision tree classifier. The improved decision tree classifier combined clustering with classification. It was validated on 14 different chronic disease datasets and its performance was recorded. A very optimal and consistent classification performance was observed. The mean values for accuracy, specificity, sensitivity and f-score metrics were 96.7%, 96.5%, 95.6% and 96.2% respectively.

scholarly journals Resnet Based Feature Extraction with Decision Tree Classifier for Classificaton of Mammogram Images

2021 ◽  
Vol 12 (2) ◽  
pp. 1147-1153
Author(s):  
T. Sathya Priya, Et. al.
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Decision Tree ◽  
Breast Cancer Diagnosis ◽  
Classification Performance ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
In The Beginning ◽  
Sensitivity Specificity ◽  
Mammogram Images

Right now, breast cancer is considered as a most important health problem among women over the world. The detection of breast cancer in the beginning stage can reduce the mortality rate to a considerable extent. Mammogram is an effective and regularly used technique for the detection and screening of breast cancer. The advanced deep learning (DL) techniques are utilized by radiologists for accurate finding and classification of medical images. This paper develops a new deep segmentation with residual network (DS-RN) based breast cancer diagnosis model using mammogram images. The presented DS-RN model involves preprocessing, Faster Region based Convolution Neural Network (R-CNN) (Faster R-CNN) with Inception v2 model based segmentation, feature extraction and classification. To classify the mammogram images, decision tree (DT) classifier model is used. A detailed simulation process is performed to ensure the betterment of the presented model on the Mini-MIAS dataset. The obtained experimental values stated that the DS-RN model has reached to a maximum classification performance with the maximum sensitivity, specificity, accuracy and F-Measure of 98.15%, 100%, 98.86% and 99.07% respectively.  

scholarly journals Expiry Prediction and Reducing Food Wastage using IoT and ML

2021 ◽  
Vol 12 (3) ◽  
pp. 155-162
Author(s):  
Kartik Nair ◽  
Bhavya Sekhani ◽  
Krina Shah ◽  
Sunil Karamchandani
Keyword(s):  
Binary Classification ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Expiry Date ◽  
Oxide Semiconductors ◽  
Tree Classifier ◽  
Sensor Temperature ◽  
Food Wastage

This paper details development of a low-cost, small-size, and portable electronic nose (E-nose) for the prediction of the expiry date of food products. The Sensor array is composed of commercially available metal oxide semiconductors sensors like MQ2 sensor, temperature sensor, and humidity sensor, which were interfaced with the help of ESP8266 and Arduino Uno for data acquisition, storage, and analysis of the dataset consisting of the odor from the fruit at different ripening stages. The developed system is used to analyze gas sensor values from various fruits like bananas and tomatoes. Responding signals of the e-nose were extracted and analyzed. Based on the obtained data we applied a few machine learning algorithms to predict if a banana is stale or not. Logistic regression, Decision Tree Classifier, Support Vector Classifier (SVC) & K-Nearest Neighbours (KNN) classifiers were the binary classification algorithms used to determine whether the fruit became stale or not. We achieved an accuracy of 97.05%. These results prove that e-nose has the potential of assessing fruits and vegetable freshness and predict their expiry date, thus reducing food wastage.

scholarly journals A Rebalancing Framework for Classification of Imbalanced Medical Appointment No-show Data

2021 ◽  
Vol 6 (1) ◽  
pp. 178-192
Author(s):  
Ulagapriya Krishnan ◽  
Pushpa Sangar
Keyword(s):  
Nearest Neighbor ◽  
Performance Metrics ◽  
Imbalanced Data ◽  
Classification Performance ◽  
Sampling Techniques ◽  
Decision Tree Classifier ◽  
Minority Class ◽  
Medical Appointment ◽  
Tree Classifier ◽  
Under Sampling

Abstract Purpose This paper aims to improve the classification performance when the data is imbalanced by applying different sampling techniques available in Machine Learning. Design/methodology/approach The medical appointment no-show dataset is imbalanced, and when classification algorithms are applied directly to the dataset, it is biased towards the majority class, ignoring the minority class. To avoid this issue, multiple sampling techniques such as Random Over Sampling (ROS), Random Under Sampling (RUS), Synthetic Minority Oversampling TEchnique (SMOTE), ADAptive SYNthetic Sampling (ADASYN), Edited Nearest Neighbor (ENN), and Condensed Nearest Neighbor (CNN) are applied in order to make the dataset balanced. The performance is assessed by the Decision Tree classifier with the listed sampling techniques and the best performance is identified. Findings This study focuses on the comparison of the performance metrics of various sampling methods widely used. It is revealed that, compared to other techniques, the Recall is high when ENN is applied CNN and ADASYN have performed equally well on the Imbalanced data. Research limitations The testing was carried out with limited dataset and needs to be tested with a larger dataset. Practical implications This framework will be useful whenever the data is imbalanced in real world scenarios, which ultimately improves the performance. Originality/value This paper uses the rebalancing framework on medical appointment no-show dataset to predict the no-shows and removes the bias towards minority class.

AISI 1030

Alloy Digest ◽  
1983 ◽  
Vol 32 (5) ◽  
Keyword(s):  
Fracture Toughness ◽  
Corrosion Resistance ◽  
Low Cost ◽  
Heat Treating ◽  
Plain Carbon Steel ◽  
General Purpose ◽  
Fine Grain ◽  
Steel Mills ◽  
Medium Strength ◽  
Hot Rolled

Abstract AISI 1030 is a plain carbon steel containing nominally 0.30% carbon. It is used in the hot-rolled, normalized, oil-quenched-and-tempered or water-quenched-and-tempered conditions for general-purpose engineering and construction. It provides medium strength and toughness at low cost. Among its many uses are axles, bolts, gears and building sections. All data are on a single heat of fine-grain steel. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties as well as fracture toughness. It also includes information on corrosion resistance as well as forming, heat treating, machining, joining, and surface treatment. Filing Code: CS-94. Producer or source: Carbon and alloy steel mills.

AISI 1040

Alloy Digest ◽  
1971 ◽  
Vol 20 (6) ◽  
Keyword(s):  
Fracture Toughness ◽  
Corrosion Resistance ◽  
Low Cost ◽  
Heat Treating ◽  
General Purpose ◽  
Medium Carbon Steel ◽  
Medium Carbon ◽  
Steel Mills ◽  
Medium Strength ◽  
Hot Rolled

Abstract AISI 1040 is a medium-carbon steel used in the hot-rolled, normalized, oil quenched and tempered or water quenched and tempered condition for general purpose engineering and construction. It provides medium strength and toughness at low cost. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties as well as fracture toughness and fatigue. It also includes information on corrosion resistance as well as forming, heat treating, machining, joining, and surface treatment. Filing Code: CS-41. Producer or source: Carbon and alloy steel mills.

SAE 1037

Alloy Digest ◽  
1979 ◽  
Vol 28 (4) ◽  
Keyword(s):  
Fracture Toughness ◽  
Carbon Steel ◽  
Low Cost ◽  
Heat Treating ◽  
General Purpose ◽  
Medium Carbon Steel ◽  
Medium Carbon ◽  
Steel Mills ◽  
Medium Strength ◽  
Hot Rolled

Abstract SAE 1037 is a carbon steel that provides medium strength and medium toughness at low cost. It is used in the hot-rolled, normalized, oil-quenched-and-tempered and water-quenched-and-tempered conditions. This medium-carbon steel is used for construction and for general-purpose engineering. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties as well as fracture toughness. It also includes information on corrosion resistance as well as forming, heat treating, machining, joining, and surface treatment. Filing Code: CS-76. Producer or source: Carbon steel mills.

SAE 1039

Alloy Digest ◽  
1977 ◽  
Vol 26 (2) ◽  
Keyword(s):  
Fracture Toughness ◽  
Low Cost ◽  
Manganese Content ◽  
Heat Treating ◽  
General Purpose ◽  
Carbon Steels ◽  
The Other ◽  
Steel Mills ◽  
Medium Strength ◽  
Hot Rolled

Abstract SAF 1039 steel can be used in the hot-rolled, normalized, oil-quenched-and-tempered or water-quenched-and-tempered condition for general-purpose construction and engineering. Its manganese content is a little higher than some of the other standard carbon steels with comparable carbon levels; this gives it slightly higher hardenability and hardness. It provides medium strength and toughness at low cost. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties as well as fracture toughness. It also includes information on corrosion resistance as well as forming, heat treating, machining, joining, and surface treatment. Filing Code: CS-66. Producer or source: Carbon steel mills.

Sign in / Sign up

Export Citation Format

Share Document