scholarly journals Performance Comparison of Artificial Intelligence Techniques for Non-intrusive Electrical Load Monitoring

2018 ◽  
Vol 7 (2) ◽  
pp. 143-152
Author(s):  
Khairuddin Khalid ◽  
Azah Mohamed ◽  
Ramizi Mohamed ◽  
Hussain Shareef
Keyword(s):  
Artificial Intelligence ◽  
Reactive Power ◽  
Performance Comparison ◽  
Support Vector ◽  
Fluorescent Light ◽  
Time Interval ◽  
Smart Meter ◽  
Air Conditioner ◽  
Artificial Intelligence Techniques ◽  
Load Monitoring

The increased awareness in reducing energy consumption and encouraging response from the use of smart meters have triggered the idea of non-intrusive load monitoring (NILM). The purpose of NILM is to obtain useful information about the usage of electrical appliances usually measured at the main entrance of electricity to obtain aggregate power signal by using a smart meter. The load operating states based on the on/off loads can be detected by analysing the aggregate power signals. This paper presents a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types that are usually available in commercial buildings, such as fluorescent light, air-conditioner and personal computer. In this NILM study, experiments were carried out to collect information of the load usage pattern by using a commercial smart meter. From the power parameters captured by the smart meter, effective signal analysis has been done using the time time (TT)-transform to achieve accurate load disaggregation. Load feature selection is also considered by using three power parameters which are real power, reactive power and the TT-transform parameters. These three parameters are used as inputs for training the artificial intelligence techniques in classifying the type and operating states of the loads. The load classification results showed that the proposed extreme learning machine (ELM) technique has successfully achieved high accuracy and fast learning compared with artificial neural network and support vector machine. Based on validation results, ELM achieved the highest load classification with 100% accuracy for data sampled at 1 minute time interval.

scholarly journals A Multidisciplinary Approach for Evaluating Spatial and Temporal Variations in Water Quality

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 853 ◽  
Author(s):  
Viet Thang Le ◽  
Nguyen Hong Quan ◽  
Ho Huu Loc ◽  
Nguyen Thi Thanh Duyen ◽  
Tran Duc Dung ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Water Quality ◽  
Confusion Matrix ◽  
Temporal Variations ◽  
Machine Learning Techniques ◽  
Spatial And Temporal Variations ◽  
Support Vector ◽  
Domestic Water ◽  
Supply Source ◽  
Artificial Intelligence Techniques

The primary goal of this study is to investigate the classification capability of several artificial intelligence techniques, including the decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for evaluating spatial and temporal variations in water quality. The application case is the Song Quao-Ca Giang (SQ-CG) water system, a main domestic water supply source of the city of Phan Thiet in Binh Thuan province, Vietnam. To evaluate the water quality condition of the source, the government agency has initiated an extensive sampling project, collecting samples from 43 locations covering the SQ reservoir, the main canals, and the surrounding areas during 2015–2016. Different classifying models based on artificial intelligence techniques were developed to analyze the sampling data after the performances of the models were evaluated and compared using the confusion matrix, accuracy rate, and several error indexes. The results show that machine-learning techniques can be used to explicitly evaluate spatial and temporal variations in water quality.

scholarly journals Performance analysis of pre-trained transfer learning models for the classification of the rolling bearing faults

2021 ◽  
Vol 2070 (1) ◽  
pp. 012141
Author(s):  
Pavan Sharma ◽  
Hemant Amhia ◽  
Sunil Datt Sharma
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Manufacturing Industry ◽  
Rolling Bearing ◽  
Performance Comparison ◽  
Learning Methods ◽  
Training Time ◽  
Bearing Faults ◽  
Artificial Intelligence Techniques

Abstract Nowadays, artificial intelligence techniques are getting popular in modern industry to diagnose the rolling bearing faults (RBFs). The RBFs occur in rotating machinery and these are common in every manufacturing industry. The diagnosis of the RBFs is highly needed to reduce the financial and production losses. Therefore, various artificial intelligence techniques such as machine and deep learning have been developed to diagnose the RBFs in the rotating machines. But, the performance of these techniques has suffered due the size of the dataset. Because, Machine learning and deep learning methods based methods are suitable for the small and large datasets respectively. Deep learning methods have also been limited to large training time. In this paper, performance of the different pre-trained models for the RBFs classification has been analysed. CWRU Dataset has been used for the performance comparison.

Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks

2012 ◽  
pp. 414-427 ◽  
Author(s):  
Marco Vannucci ◽  
Valentina Colla ◽  
Silvia Cateni ◽  
Mirko Sgarbi
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machines ◽  
Decision Trees ◽  
Real World ◽  
Support Vector ◽  
Artificial Intelligence Techniques ◽  
Vector Machines ◽  
Classification Tasks

In this chapter a survey on the problem of classification tasks in unbalanced datasets is presented. The effect of the imbalance of the distribution of target classes in databases is analyzed with respect to the performance of standard classifiers such as decision trees and support vector machines, and the main approaches to improve the generally not satisfactory results obtained by such methods are described. Finally, two typical applications coming from real world frameworks are introduced, and the uses of the techniques employed for the related classification tasks are shown in practice.

Comparative Study on Artificial Intelligence Techniques in Crime Forecasting

2019 ◽  
Vol 892 ◽  
pp. 94-100
Author(s):  
Alif Ridzuan Khairuddin ◽  
Razana Alwee ◽  
Habibollah Harun
Keyword(s):  
Artificial Intelligence ◽  
Statistical Technique ◽  
Support Vector ◽  
Prevention Measures ◽  
Artificial Intelligence Technique ◽  
Artificial Intelligence Techniques ◽  
The Past ◽  
Forecasting Performance ◽  
Performance Capability ◽  
Artificial Neural Network Ann

An application of efficient crime analysis is beneficial and helpful to understand the behavior of trend and pattern of crimes. Crime forecasting is an area of research that assists authorities in enforcing early crime prevention measures. Statistical technique has been widely applied in the past to develop crime forecasting models. However, it has been observed that researchers have begun to shift their research interests from statistical model to artificial intelligence model in crime forecasting. Thus, this study is conducted to observe the capabilities of artificial intelligence technique in improving crime forecasting. The main objective of this study is to conduct a comparative analysis on forecasting performance capabilities of four artificial intelligence techniques, namely, artificial neural network (ANN), support vector regression (SVR), random forest (RF), and gradient tree boosting (GTB) in forecasting crime rate. Forecasting capability of each technique was assessed in terms of measurement of errors. From the result obtained, GTB showed the highest performance capability where it scored the lowest measurement of errors compared to SVR, RF, and ANN.

scholarly journals Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1172
Author(s):  
Purushottam Agrawal ◽  
Alok Sinha ◽  
Satish Kumar ◽  
Ankit Agarwal ◽  
Ashes Banerjee ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Traditional Approach ◽  
Human Life ◽  
Large Data ◽  
Quality Parameters ◽  
Support Vector ◽  
Artificial Intelligence Techniques

Freshwater quality and quantity are some of the fundamental requirements for sustaining human life and civilization. The Water Quality Index is the most extensively used parameter for determining water quality worldwide. However, the traditional approach for the calculation of the WQI is often complex and time consuming since it requires handling large data sets and involves the calculation of several subindices. We investigated the performance of artificial intelligence techniques, including particle swarm optimization (PSO), a naive Bayes classifier (NBC), and a support vector machine (SVM), for predicting the water quality index. We used an SVM and NBC for prediction, in conjunction with PSO for optimization. To validate the obtained results, groundwater water quality parameters and their corresponding water quality indices were found for water collected from the Pindrawan tank area in Chhattisgarh, India. Our results show that PSO–NBC provided a 92.8% prediction accuracy of the WQI indices, whereas the PSO–SVM accuracy was 77.60%. The study’s outcomes further suggest that ensemble machine learning (ML) algorithms can be used to estimate and predict the Water Quality Index with significant accuracy. Thus, the proposed framework can be directly used for the prediction of the WQI using the measured field parameters while saving significant time and effort.

Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks

2011 ◽  
pp. 551-565 ◽  
Author(s):  
Marco Vannucci ◽  
Valentina Colla ◽  
Silvia Cateni ◽  
Mirko Sgarbi
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machines ◽  
Decision Trees ◽  
Real World ◽  
Support Vector ◽  
Artificial Intelligence Techniques ◽  
Vector Machines ◽  
Classification Tasks

In this chapter a survey on the problem of classification tasks in unbalanced datasets is presented. The effect of the imbalance of the distribution of target classes in databases is analyzed with respect to the performance of standard classifiers such as decision trees and support vector machines, and the main approaches to improve the generally not satisfactory results obtained by such methods are described. Finally, two typical applications coming from real world frameworks are introduced, and the uses of the techniques employed for the related classification tasks are shown in practice.

scholarly journals A Corporate Credit Rating Model Using Support Vector Domain Combined with Fuzzy Clustering Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Xuesong Guo ◽  
Zhengwei Zhu ◽  
Jia Shi
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Credit Rating ◽  
Computational Cost ◽  
Support Vector ◽  
Artificial Intelligence Techniques ◽  
Corporate Credit ◽  
Fuzzy Clustering Algorithm ◽  
Corporate Credit Rating

Corporate credit-rating prediction using statistical and artificial intelligence techniques has received considerable attentions in the literature. Different from the thoughts of various techniques for adopting support vector machines as binary classifiers originally, a new method, based on support vector domain combined with fuzzy clustering algorithm for multiclassification, is proposed in the paper to accomplish corporate credit rating. By data preprocessing using fuzzy clustering algorithm, only the boundary data points are selected as training samples to accomplish support vector domain specification to reduce computational cost and also achieve better performance. To validate the proposed methodology, real-world cases are used for experiments, with results compared with conventional multiclassification support vector machine approaches and other artificial intelligence techniques. The results show that the proposed model improves the performance of corporate credit-rating with less computational consumption.

scholarly journals Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study

2017 ◽  
Vol 13 (3) ◽  
pp. 342 ◽  
Author(s):  
Alaá Rateb Mahmoud Al-shamasneh ◽  
Unaizah Hanum Binti Obaidellah
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Human Cancer ◽  
Imaging Techniques ◽  
Detection Methods ◽  
Support Vector ◽  
Inference System ◽  
Mortality And Morbidity ◽  
Artificial Intelligence Techniques

Cancer is the general name for a group of more than 100 diseases. Although cancer includes different types of diseases, they all start because abnormal cells grow out of control. Without treatment, cancer can cause serious health problems and even loss of life. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for lung, breast, and brain cancers. These methods used for diagnosis include artificial intelligence techniques, such as support vector machine neural network, artificial neural network, fuzzy logic, and adaptive neuro-fuzzy inference system, with medical imaging like X-ray, ultrasound, magnetic resonance imaging, and computed tomography scan images. Imaging techniques are the most important approach for precise diagnosis of human cancer. We investigated all these techniques to identify a method that can provide superior accuracy and determine the best medical images for use in each type of cancer.

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