scholarly journals Modeling trihalomethanes concentrations in water treatment plants using machine learning techniques

2018 ◽  
Vol 111 ◽  
pp. 125-133 ◽  
Author(s):  
Jongkwan Park ◽  
Chan ho Lee ◽  
Kyung Hwa Cho ◽  
Seongho Hong ◽  
Young Mo Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Treatment ◽  
Machine Learning Techniques ◽  
Water Treatment Plants ◽  
Learning Techniques

Offshore Water Treatment KPIs Using Machine Learning Techniques

2021 ◽  
Author(s):  
Lauren Flores ◽  
Martin Morles ◽  
Cheng Chen
Keyword(s):  
Machine Learning ◽  
Water Treatment ◽  
Membrane Fouling ◽  
Membrane System ◽  
Water Quality Monitoring ◽  
Machine Learning Techniques ◽  
Process Conditions ◽  
Operation Conditions ◽  
Sulfate Removal ◽  
Learning Techniques

Abstract New water treatment facilities in the Gulf of Mexico include a seawater Sulfate Removal Unit (SRU) to mitigate reservoir souring and scaling. The general industry sulfate target for offshore SRU is usually 20 mg/L or even 40 mg/L; however, some facilities may require <10 mg/L of sulfate in injection water, which makes water quality monitoring more critical and challenging. Current industrial practice relies on only pressure drop and a constant cleaning interval frequency to perform SRU maintenance which may result in reduced membrane life due to frequency cleaning or severe membrane fouling without the capability to predict fouling based on process conditions. The machine learning techniques applied will fill the gap and deliver a prediction model based on both simulation and real-time field data. This model will track and monitor the system key performance indicators (KPIs) including pressure, membrane fouling factor (FF), permeate sulfate concentration etc. The monitoring and prediction of these KPIs provide estimates on when the next maintenance procedure is required, track membrane system status for troubleshooting and actions, and optimize membrane performance by tuning operation conditions.

scholarly journals Dissolved oxygen modeling of an urban stream using grid partitioning and subtractive clustering fuzzy techniques

2021 ◽  
Author(s):  
Sameer Arora
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Treatment ◽  
Dissolved Oxygen ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Machine Learning Techniques ◽  
Subtractive Clustering ◽  
Grid Partitioning ◽  
Learning Techniques

Dissolved oxygen is one of the prime parameters for assessing the water quality of any stream and the health status of aquatic life. The dissolved oxygen present in the water body plays an essential role in deciding water treatment processes to enhance water quality up to the design standards for the specified water use. Thus, the accurate estimation of dissolved oxygen concentration is necessary to evolve measures for maintaining the riverine ecosystem and designing the appropriate water treatment plants. Machine learning techniques are becoming useful tools for the prediction and simulation of water quality parameters. With these viewpoints, a study was carried out in the Delhi stretch of Yamuna River, India, and physiochemical parameters were examined for five years to simulate the dissolved oxygen using different machine learning techniques. Simulation and prediction competencies of ANFIS grid partitioning (ANFIS-GP) and ANFIS subtractive clustering (ANFIS-SC) were tested on various water quality parameters. Variation in dissolved oxygen was examined on various combinations of parameters. ANFIS-GP has been designed using the Gaussian function, and ANFIS-SC works on the likelihood of cluster centers. Results obtained from the models were evaluated using root mean square error (RMSE) and coefficient of determination (R2) to identify the optimum solution and appropriate combination of parameters that simulate the observed dissolved oxygen. Results of ANFIS-GP and ANFIS-SC indicate that both the models produce suitable solutions for the prediction; however, ANFIS-GP outperforms the ANFIS-SC and could act as a useful tool for defining, planning, and management of water quality parameters.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

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