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PAMMELA: Policy Administration Methodology using Machine Learning
In recent years, Attribute-Based Access Control (ABAC) has become quite popular and effective for enforcing access control in dynamic and collaborative environments.
Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor
In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHVp) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidized bed reactor.
Performance Analysis of Robust Stable PID Controllers Using Dominant Pole Placement for SOPTD Process Models
The effect of different types of non-dominant poles and the corresponding stability regions are obtained for nine test-bench processes indicating different levels of open-loop damping and lag to delay ratio.
Evolving Chaos: Identifying New Attractors of the Generalised Lorenz Family
In a recent paper, we presented an intelligent evolutionary search technique through genetic programming (GP) for finding new analytical expressions of nonlinear dynamical systems, similar to the classical Lorenz attractor's which also exhibit chaotic behaviour in the phase space.
Comparison of Decision Tree Based Classification Strategies to Detect External Chemical Stimuli from Raw and Filtered Plant Electrical Response
Plants monitor their surrounding environment and control their physiological functions by producing an electrical response.
Multi-objective Active Control Policy Design for Commensurate and Incommensurate Fractional Order Chaotic Financial Systems
In this paper, an active control policy design for a fractional order (FO) financial system is attempted, considering multiple conflicting objectives.
On the Existence of Synchrostates in Multichannel EEG Signals during Face-perception Tasks
However, here we report a similar observation of quasi-stable phase synchronised states in multichannel EEG.
Fractional Order Fuzzy Control of Hybrid Power System with Renewable Generation Using Chaotic PSO
This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme.
Fractional Order AGC for Distributed Energy Resources Using Robust Optimization
The applicability of fractional order (FO) automatic generation control (AGC) for power system frequency oscillation damping is investigated in this paper, employing distributed energy generation.
Fractional Order Load-Frequency Control of Interconnected Power Systems Using Chaotic Multi-objective Optimization
The chaotic versions of the NSGA-II algorithm are compared with the standard NSGA-II in terms of solution quality and computational time.
Exploring Strategies for Classification of External Stimuli Using Statistical Features of the Plant Electrical Response
Plants sense their environment by producing electrical signals which in essence represent changes in underlying physiological processes.
Artifact reduction in multichannel pervasive EEG using hybrid WPT-ICA and WPT-EMD signal decomposition techniques
In order to reduce the muscle artifacts in multi-channel pervasive Electroencephalogram (EEG) signals, we here propose and compare two hybrid algorithms by combining the concept of wavelet packet transform (WPT), empirical mode decomposition (EMD) and Independent Component Analysis (ICA).
Classification of Autism Spectrum Disorder Using Supervised Learning of Brain Connectivity Measures Extracted from Synchrostates
The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects.
Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models
This paper proposes a stochastic model using the concept of Markov chains for the inter-state transitions of the millisecond order quasi-stable phase synchronized patterns or synchrostates, found in multi-channel Electroencephalogram (EEG) signals.
When Darwin meets Lorenz: Evolving new chaotic attractors through genetic programming
In this paper, we propose a novel methodology for automatically finding new chaotic attractors through a computational intelligence technique known as multi-gene genetic programming (MGGP).
Global solar irradiation prediction using a multi-gene genetic programming approach
In this paper, a nonlinear symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for a data-driven modelling between the dependent and the independent variables.
