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Approaches to Building a Detection Model for Water Quality: A Case Study
TLDR
In this paper, we evaluate some popular classification algorithms to model a water quality detection system. Expand
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Machine learning approaches for anomaly detection of water quality on a real-world data set*
TLDR
This paper extends the ACIIDS 2018 paper Muharemi, Logofătu, Andersson, and Leon (2018a) and proposes a solution to some challenges when dealing with time series data. Expand
  • 13
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Applying Tree Ensemble to Detect Anomalies in Real-World Water Composition Dataset
TLDR
This paper proposes a general data driven approach to construct an automated online event detention system for drinking water. Expand
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Platform for Simulation and Improvement of Swarm Behavior in Changing Environments
TLDR
This paper describes a framework which allows wide flexibility on the moving algorithms of the swarm particles as well as an easy modification of the surrounding constrictions. Expand
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A Simulation-Based Analysis of Interdependent Populations in a Dynamic Ecological Environment
TLDR
We analyze a simple agent-based model and a computational simulation of a simple, yet analytically intractable problem scenario from the field of ecology and show that even with a seemingly simple model and simulation, one could obtain plausible results regarding a system's real life behavior. Expand
  • 4
Review on General Techniques and Packages for Data Imputation in R on a Real World Dataset
TLDR
This paper provides an overview of a considerable dataset imputation by applying three different algorithms under a missing completely at random assumption. Expand
  • 7
Social Web-Based Anxiety Index's Predictive Information on S&P 500 Revisited
TLDR
We propose a new, non-linear statistical approach that accounts for nonlinearity and heteroscedasticity in the stock market, and extend the approach and the results presented in the paper. Expand
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  • PDF
Sentiment and stock market volatility predictive modelling — A hybrid approach
TLDR
This paper proposes a new approach to developing stock market volatility predictive models by incorporating a hybrid GARCH and artificial neural network framework, and proves the advantage of this framework over a GARCH only based framework. Expand
  • 5
  • PDF
Parallel Evolutionary Approach of Compaction Problem Using MapReduce
TLDR
We introduce a distributed evolutionary algorithm (MapReduce Parallel Evolutionary Algorithm-MRPEA) and compare it with two greedy approaches. Expand
  • 6
An Evaluation of Regression Algorithms Performance for the Chemical Process of Naphthalene Sublimation
TLDR
The LMNNR algorithm is compared to other well-established regression algorithms, such as support vector regression, multilayer perceptron neural networks, classical k-nearest neighbor, random forest, and others. Expand
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