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Prediction of university enrollment using computational intelligence

Prediction of university enrollment using computational inte...

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ieee Swarm intelligence symposium, SIS

作者： Ryan Stallings Biswanath Samanta Department of Mechanical Engineering Georgia Southern University Statesboro GA USA

This work presents a study on prediction of university enrollment using three computational intelligence (CI) techniques. The enrollment forecasting has been considered as a form of time series prediction using CI techniques that include an artificial neural network (ANN), a neuro-fuzzy inference system (ANFIS) and an aggregated fuzzy time series model. A novel form of ANN, namely, single multiplicative neuron (SMN), as an alternative to traditional multi-layer perceptron (MLP), has been used for time series prediction. A variation of population based heuristic optimization approach, namely, co-operative particle swarm optimization (COPSO), has been used to estimate the parameters for the SMN, the combination is termed here as COPSO-SMN. The second CI technique used for time series prediction is adaptive neuro fuzzy inference system (ANFIS) which combines the advantages of ANN and fuzzy logic (FL). The third technique is based on an aggregated fuzzy time series model that utilizes both global trend of the past data and the local fuzzy fluctuations. The first two CI models have been developed for one-step-ahead prediction of time series using the data of the current time and three previous time steps. The models based on these three techniques have been trained using a previously published dataset. The models have been further trained and tested using enrollment data of Georgia Southern University for the period of 1924-2012. The training and test performances of all three CI techniques have been compared for the datasets.

关键词： Mathematical model Educational institutions Time series analysis Training computational modeling Predictive models data models

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Alzheimer's disease patients classification through EEG signals processing

Alzheimer's disease patients classification through EEG sign...

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ieee symposium on computational intelligence and data Mining (CIDM)

作者： Giulia Fiscon Emanuel Weitschek Giovanni Felici Paola Bertolazzi Simona De Salvo Placido Bramanti Maria Cristina De Cola Institute of Systems Analysis and Computer Science National Research Council Rome Italy Department of Computer Control and Management Engineering Sapienza University Rome Italy Department of Engineering Roma Tre University Rome Italy IRCCS Centro Neurolesi “Bonino-Pulejo” Messina Italy

Alzheimer's Disease (AD) and its preliminary stage - Mild Cognitive Impairment (MCI) - are the most widespread neurodegenerative disorders, and their investigation remains an open challenge. ElectroEncephalography (EEG) appears as a non-invasive and repeatable technique to diagnose brain abnormalities. Despite technical advances, the analysis of EEG spectra is usually carried out by experts that must manually perform laborious interpretations. computational methods may lead to a quantitative analysis of these signals and hence to characterize EEG time series. The aim of this work is to achieve an automatic patients classification from the EEG biomedical signals involved in AD and MCI in order to support medical doctors in the right diagnosis formulation. The analysis of the biological EEG signals requires effective and efficient computer science methods to extract relevant information. data mining, which guides the automated knowledge discovery process, is a natural way to approach EEG data analysis. Specifically, in our work we apply the following analysis steps: (i) pre-processing of EEG data; (ii) processing of the EEG-signals by the application of time-frequency transforms; and (iii) classification by means of machine learning methods. We obtain promising results from the classification of AD, MCI, and control samples that can assist the medical doctors in identifying the pathology.

关键词： Electroencephalography Feature extraction data mining computational modeling Electrodes Diseases Support vector machines

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Distributed boosting algorithm for classification of text documents

Distributed boosting algorithm for classification of text do...

引用

International symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Martin Sarnovsky Michal Vronc Department of cybernetics and artificial intelligence Technical University in Kosice Kosice Slovak Republic

Presented paper focuses on the area of analysis and classification of textual documents. We present the classification of documents based on boosting method applied on the decision tree algorithm. Main objective of the paper is to present the implementation of distributed boosting algorithm based on Map Reduce paradigm. We have used the GridGain framework as a platform for distributed data processing and have tested the implemented solution on two different dataset within our testing environment.

关键词： Boosting Classification algorithms computational modeling Algorithm design and analysis Informatics Text mining Training

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Batch linear least squares-based learning algorithm for MLMVN with soft margins

Batch linear least squares-based learning algorithm for MLMV...

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ieee symposium on computational intelligence and data Mining (CIDM)

作者： Evgeni Aizenberg Igor Aizenberg Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands Computational Intelligence Lab Texas A&M University-Texarkana Texarkana TX USA

In this paper, we consider a batch learning algorithm for the multilayer neural network with multi-valued neurons (MLMVN) and its soft margins variant (MLMVN-SM). MLMVN is a neural network with a standard feedforward organization based on the multi-valued neuron (MVN). MVN is a neuron with complex-valued weights and inputs/output located on the unit circle. Standard MLMVN has a derivative-free learning algorithm based on the error-correction learning rule. Recently, this algorithm was modified for MLMVN with discrete outputs by using soft margins (MLMVN-SM). This modification improves classification results when MLMVN is used as a classifier. Another recent development in MLMVN is the use of batch acceleration step for MLMVN with a single output neuron. Complex QR-decomposition was used to adjust the output neuron weights for all learning samples simultaneously, while the hidden neuron weights were adjusted in a regular way. In this paper, we merge the soft margins approach with batch learning. We suggest a batch linear least squares (LLS) learning algorithm for MLMVN-SM. We also expand the batch technique to multiple output neurons and hidden neurons. This new learning technique drastically reduces the number of learning iterations and learning time when solving classification problems (compared to MLMVN-SM), while maintaining the classification accuracy of MLMVN-SM.

关键词： Neurons Biological neural networks Feedforward neural networks Classification algorithms Backpropagation Indexes Artificial neural networks

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Service oriented architecture for remote machine control in ICS

Service oriented architecture for remote machine control in ...

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International symposium on Applied Machine intelligence and Informatics (SAMI)

作者： T. Lojka M. Miškuf I. Zolotová Department of Cybernetics and Artificial intelligence Technical University of Košice Košice Slovakia

Innovative approaches in the field of information technology gets easily inside automation and creates better platform for integration and cooperation of information and control systems (ICS) components. This innovations lead to the ability of ICS for automatic agile reconfiguration, according to market requirements. To implement this ability has become SOA (service orientated architecture) a part of ICS. This paper deals about SOA with the meaning of communication and integration facility in SCADA/HMI (Supervisory Control And data Acquisition / Human-Machine Interface) for remote control through mobile devices. Mobile devices integrate big potential for increasing the agility of ICS and can be involved into SOA. Main contribution is increasing agility of ICS with remote control via mobile devices and SOA.

关键词： Servers Mobile handsets Service-oriented architecture Semiconductor optical amplifiers Mobile communication Computer architecture data visualization

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The effectiveness of persuasion in The Settlers of Catan

The effectiveness of persuasion in The Settlers of Catan

引用

ieee symposium on computational intelligence and Games, CIG

作者： Markus Guhe Alex Lascarides School of Informatics University of Edinburgh Edinburgh Scotland

We present a method for obtaining a useful symbolic model of persuasion in a complex game, where players' preferences over the outcomes of negotiations over resources are incomplete, uncertain, and dynamic. We focus on the problem of identifying the stage in the game where successfully persuading an agent to perform a particular action will have the most impact on one's chances to win. Our approach exploits empirical data from game simulations, where the set up allows us to investigate individual aspects of the policy in suitably controlled ways. We demonstrate the effectiveness of the approach within the domain of The Settlers of Catan and present some specific lessons that can be learned for this particular game, e.g. that a tipping point in the game occurs for persuasion moves that are made when the leading player reaches 7 Victory Points.

关键词： Lead Law computational modeling

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Multiple Rotor UAVs Formation Flight and Its Application on Level Evaluation of Earthquake Damage

Multiple Rotor UAVs Formation Flight and Its Application on ...

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International symposium on computational intelligence and Design, ISCID

作者： Haoting Liu Wei Wang Xuefeng Wang Wenpeng Yu Ming Lv Beijing Institute of Aerospace Control Device China Academy of Aerospace Electronics Technology Beijing China Department of Medical Equipment General Hospital of the Chinese People's Armed Police Forces Beijing China

ISBN: (纸本)9781479970063

A multiple rotor UAVs (Unmanned Aerial Vehicle) based photograph and image analysis system for earthquake damage evaluation is proposed. In contrast to other system, our multiple UAVs can fly with typical rectangle, triangle, or pentagonal formation shape in vertical or horizontal hovering mode, splice images to construct a big scene picture, and make a texture analysis of the ground earthquake images. The image mosaic method of projective transformation, the photograph visual field model of signal UAV, and an integrated texture analysis method are all proposed to analyze and evaluate the damage level of the typical target on ground. The Gray Level Co-occurrence Matrix (GLCM) features, the Tamura texture feature, the Gabor wavelet feature, and the basic line and circle elements, are all considered in our system. An application of earthquake damage evaluation is shown in this paper. Many experiment results have proved the correctness and the validity of our proposed system and method.

关键词： Earthquakes Visualization Cameras Shape Rotors Buildings ieee Press

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Learning features and their transformations from natural videos

Learning features and their transformations from natural vid...

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ieee symposium on computational intelligence in Dynamic and Uncertain Environments (CIDUE)

作者： Jayanta K. Dutta Bonny Banerjee Department of Electrical & Computer Engineering University of Memphis Memphis USA

Learning features invariant to arbitrary transformations in the data is a requirement for any recognition system, biological or artificial. It is now widely accepted that simple cells in the primary visual cortex respond to features while the complex cells respond to features invariant to different transformations. We present a novel two-layered feedforward neural model that learns features in the first layer by spatial spherical clustering and invariance to transformations in the second layer by temporal spherical clustering. Learning occurs in an online and unsupervised manner following the Hebbian rule. When exposed to natural videos acquired by a camera mounted on a cat's head, the first and second layer neurons in our model develop simple and complex cell-like receptive field properties. The model can predict by learning lateral connections among the first layer neurons. A topographic map to their spatial features emerges by exponentially decaying the flow of activation with distance from one neuron to another in the first layer that fire in close temporal proximity, thereby minimizing the pooling length in an online manner simultaneously with feature learning.

关键词： Neurons Feedforward neural networks Radio frequency Videos computational modeling Biological system modeling Predictive models

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Immersive virtual reality environment of a subway evacuation on a cloud for disaster preparedness and response training

Immersive virtual reality environment of a subway evacuation...

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ieee symposium on computational intelligence for Human-like intelligence (CIHLI)

作者： Sharad Sharma Shanmukha Jerripothula Stephon Mackey Oumar Soumare Department of Computer Science Bowie State University Bowie Maryland USA

Virtual Reality (VR) based training and evacuation drills in disaster preparedness has been increasingly recognized as an alternative to traditional real-life drills and table-top exercises. Immersive collaborative VR evacuation drills offer a unique way for training in emergencies. The participants can enter the collaborative VR environment setup on the cloud and participate in the evacuation drill which leads to considerable cost advantages over large-scale real-life exercises. This paper presents an experimental design approach to gather data on human behavior and emergency response in a subway environment among a set of players in an immersive virtual reality environment. Our proposed multi-user VR-based training subway environment offers flexibility to run multiple scenarios and evacuation drills for disaster preparedness and response. We present three ways for controlling crowd behavior. First by defining rules for computer simulated agents, second by providing controls to the users to navigate in the VR environment as autonomous agents and the third by providing controls to the users with a keyboard/joystick along with an immersive VR head set in real time. Our contribution lies in our approach to combine these three approaches of behavior in order to simulate the crowd behavior in emergencies.

关键词： Solid modeling computational modeling Training Three-dimensional displays Computers Virtual environments Photonics

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dataMPI: Extending MPI to Hadoop-Like big data Computing

DataMPI: Extending MPI to Hadoop-Like Big Data Computing

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International symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiaoyi Lu Fan Liang Bing Wang Li Zha Zhiwei Xu Institute of Computing Technology Chinese Academy of Sciences

ISBN: (纸本)9781479938018

MPI has been widely used in High Performance Computing. In contrast, such efficient communication support is lacking in the field of big data Computing, where communication is realized by time consuming techniques such as HTTP/RPC. This paper takes a step in bridging these two fields by extending MPI to support Hadoop-like big data Computing jobs, where processing and communication of a large number of key-value pair instances are needed through distributed computation models such as MapReduce, Iteration, and Streaming. We abstract the characteristics of key-value communication patterns into a bipartite communication model, which reveals four distinctions from MPI: Dichotomic, Dynamic, data-centric, and Diversified features. Utilizing this model, we propose the specification of a minimalistic extension to MPI. An open source communication library, dataMPI, is developed to implement this specification. Performance experiments show that dataMPI has significant advantages in performance and flexibility, while maintaining high productivity, scalability, and fault tolerance of Hadoop.

关键词： big data computational modeling Libraries data models Bandwidth Analytical models Schedules

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