Nitrogen removal in subsurface constructed wetland: Assessment of the influence and prediction by data mining and machine learning

Constructed wetland technology is currently evolving into an acceptable, economically competitive alternative for many wastewater treatment applications. Although showing great promise for removing carbonaceous materials from wastewater, wetland systems have not been as successful at nitrification. This is primarily due to oxygen limitations. Nitrification does occur in conventional wetland treatment systems, but typically requires long hydraulic retention times. This paper describes a study that first evaluated the capability of subsurface flow constructed wetlands to treat a high strength seafood processor wastewater and then evaluated passive aeration configurations and effluent recirculation with respect to nitrogen treatment efficiency. The first stage of a 2-stage wetland treatment system exhibited a relatively short hydraulic retention time and was designed for BOD removal only. The second stage wetland employed an unsaturated inlet zone and effluent recirculation to enhance nitrification. Results indicate that organic loading, and thus BOD removal, in the first stage wetland is key to optimal nitrification. Passive aeration through an unsaturated inlet zone and recirculation achieved up to 65-70 per cent ammonia nitrogen removal at hydraulic retention times of about 3.5 days. Inlet zone configuration and effluent recirculation is shown to enhance the nitrogen removal capability of constructed wetland treatment systems.

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Classification of Operational and Financial Variables Affecting the Bullwhip Effect in Indian Sectors: A Machine Learning Approach

Recent Patents on Computer Science ◽

10.2174/2213275911666181012121059 ◽

2019 ◽

Vol 12 (3) ◽

pp. 171-179 ◽

Cited By ~ 6

Author(s):

Sachin Gupta ◽

Anurag Saxena

Keyword(s):

Machine Learning ◽

Data Mining ◽

Supply Chain ◽

Supply Chain Management ◽

Product Life Cycle ◽

Consumer Preference ◽

Bullwhip Effect ◽

Machine Learning Techniques ◽

Chain Management ◽

Financial Variables

Background: The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study. Methods: Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification. Results: Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation. Conclusion: Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.

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Data mining techniques with machine learning algorithm to predict patients of heart disease

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1088/1/012035 ◽

2021 ◽

Vol 1088 (1) ◽

pp. 012035

Author(s):

Mulyawan ◽

Agus Bahtiar ◽

Githera Dwilestari ◽

Fadhil Muhammad Basysyar ◽

Nana Suarna

Keyword(s):

Machine Learning ◽

Data Mining ◽

Heart Disease ◽

Learning Algorithm ◽

Machine Learning Algorithm ◽

Data Mining Techniques

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Effects of sulfamethoxazole on nitrogen removal and molecular ecological network in integrated vertical-flow constructed wetland

Ecotoxicology and Environmental Safety ◽

10.1016/j.ecoenv.2021.112292 ◽

2021 ◽

Vol 219 ◽

pp. 112292

Author(s):

Shaoyong Lu ◽

Yaru Zhang ◽

Xiaohui Liu ◽

Jiamin Xu ◽

Ying Liu ◽

...

Keyword(s):

Nitrogen Removal ◽

Constructed Wetland ◽

Vertical Flow ◽

Ecological Network ◽

Vertical Flow Constructed Wetland ◽

Molecular Ecological Network

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Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

Global Business Review ◽

10.1177/0972150920984857 ◽

2021 ◽

pp. 097215092098485

Author(s):

Sonika Gupta ◽

Sushil Kumar Mehta

Keyword(s):

Machine Learning ◽

Data Mining ◽

Literature Review ◽

Systematic Literature Review ◽

Classification Accuracy ◽

Meta Analysis ◽

Financial Statement ◽

Research Articles ◽

Financial Statement Fraud ◽

Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

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Algorithms and software for data mining and machine learning: a critical comparative view from a systematic review of the literature

The Journal of Supercomputing ◽

10.1007/s11227-021-03708-5 ◽

2021 ◽

Author(s):

Gilda Taranto-Vera ◽

Purificación Galindo-Villardón ◽

Javier Merchán-Sánchez-Jara ◽

Julio Salazar-Pozo ◽

Alex Moreno-Salazar ◽

...

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Data Mining ◽

Review Of The Literature

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