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 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.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

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.

Pattern Recognition Techniques for the Evaluation of Spatial and Temporal Variations in Water Quality. A Case Study:

2001 ◽  
Vol 35 (12) ◽  
pp. 2881-2894 ◽  
Author(s):  
Wunderlin Daniel Alberto ◽  
Dı́az Marı́a del Pilar ◽  
Amé Marı́a Valeria ◽  
Pesce Silvia Fabiana ◽  
Hued Andrea Cecilia ◽  
...  
Keyword(s):  
Water Quality ◽  
Pattern Recognition ◽  
Temporal Variations ◽  
Spatial And Temporal Variations ◽  
Pattern Recognition Techniques

scholarly journals Formation of a cooperation network in Mato Grosso on Machine Learning and Image Analysis: Diagnosis of COVID-19 in X-ray images

2021 ◽  
Author(s):  
Jeniffer Luz ◽  
Scenio De Araujo ◽  
Caio Abreu ◽  
Juvenal Silva Neto ◽  
Carlos Gulo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Early Diagnosis ◽  
Critical Mass ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Mato Grosso ◽  
X Ray ◽  
Occurrence Matrix

Since the beginning of the COVID-19 outbreak, the scientific communityhas been making efforts in several areas, either by seekingvaccines or improving the early diagnosis of the disease to contributeto the fight against the SARS-CoV-2 virus. The use of X-rayimaging exams becomes an ally in early diagnosis and has been thesubject of research by the medical image processing and analysiscommunity. Although the diagnosis of diseases by image is a consolidatedresearch theme, the proposed approach aims to: a) applystate-of-the-art machine learning techniques in X-ray images forthe COVID-19 diagnosis; b) identify COVID-19 features in imagingexamination; c) to develop an Artificial Intelligence model toreduce the disease diagnosis time; in addition to demonstrating thepotential of the Artificial Intelligence area as an incentive for theformation of critical mass and encouraging research in machinelearning and processing and analysis of medical images in the Stateof Mato Grosso, in Brazil. Initial results were obtained from experimentscarried out with the SVM (Support Vector Machine) classifier,induced on a publicly available image dataset from Kaggle repository.Six attributes suggested by Haralick, calculated on the graylevel co-occurrence matrix, were used to represent the images. Theprediction model was able to achieve 82.5% accuracy in recognizingthe disease. The next stage of the studies includes the study of deeplearning models.

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