Search results for: Multinomial dirichlet classification model

Authors: Mohammed Ibrahim M, Anupama R.

Abstract:

Robustness is one of the primary performance criteria for an Intelligent Video Surveillance (IVS) system. One of the key factors in enhancing the robustness of dynamic video analysis is,providing accurate and reliable means for shadow detection. If left undetected, shadow pixels may result in incorrect object tracking and classification, as it tends to distort localization and measurement information. Most of the algorithms proposed in literature are computationally expensive; some to the extent of equalling computational requirement of motion detection. In this paper, the homogeneity property of shadows is explored in a novel way for shadow detection. An adaptive division image (which highlights homogeneity property of shadows) analysis followed by a relatively simpler projection histogram analysis for penumbra suppression is the key novelty in our approach.

Keywords: homogeneity, penumbra, projection histogram, shadow correction

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Authors: Naushad Mamode Khan, Cheika Jahangeer, Maleika Heenaye-Mamode Khan

Abstract:

Breastfeeding is an important concept in the maternal life of a woman. In this paper, we focus on exclusive breastfeeding. Exclusive breastfeeding is the feeding of a baby on no other milk apart from breast milk. This type of breastfeeding is very important during the first six months because it supports optimal growth and development during infancy and reduces the risk of obliterating diseases and problems. Moreover, in Mauritius, exclusive breastfeeding has decreased the incidence and/or severity of diarrhea, lower respiratory infection and urinary tract infection. In this paper, we give an overview of exclusive breastfeeding in Mauritius and the factors influencing it. We further analyze the local practices of exclusive breastfeeding using the Generalized Poisson regression model and the negative-binomial model since the data are over-dispersed.

Keywords: Exclusive breast feeding, regression model, generalized poisson, negative binomial.

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Authors: Davide Fuscà, Kanan Rahimli, Roberto Leuzzi

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Prevent abuse and illegal activities in a given area of the sea is a very difficult and expensive task. Artificial intelligence offers the possibility to implement new methods to identify the vessel class type from the kinematic features of the vessel itself. The task strictly depends on the quality of the data. This paper explores the application of a deep Long Short-Term Memory model by using AIS flow only with a relatively low quality. The proposed model reaches high accuracy on detecting nine vessel classes representing the most common vessel types in the Ionian-Adriatic Sea. The model has been applied during the Adriatic-Ionian trial period of the international EU ANDROMEDA H2020 project to identify vessels performing behaviours far from the expected one, depending on the declared type.

Keywords: maritime surveillance, artificial intelligence, behaviour analysis, LSTM

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Authors: Xiaoshu Lu, Esa-Pekka Takala, Risto Toivonen

Abstract:

This paper systematically investigates the timedependent health outcomes for office staff during computer work using the developed mathematical model. The model describes timedependent health outcomes in multiple body regions associated with computer usage. The association is explicitly presented with a doseresponse relationship which is parametrized by body region parameters. Using the developed model we perform extensive investigations of the health outcomes statically and dynamically. We compare the risk body regions and provide various severity rankings of the discomfort rate changes with respect to computer-related workload dynamically for the study population. Application of the developed model reveals a wide range of findings. Such broad spectrum of investigations in a single report literature is lacking. Based upon the model analysis, it is discovered that the highest average severity level of the discomfort exists in neck, shoulder, eyes, shoulder joint/upper arm, upper back, low back and head etc. The biggest weekly changes of discomfort rates are in eyes, neck, head, shoulder, shoulder joint/upper arm and upper back etc. The fastest discomfort rate is found in neck, followed by shoulder, eyes, head, shoulder joint/upper arm and upper back etc. Most of our findings are consistent with the literature, which demonstrates that the developed model and results are applicable and valuable and can be utilized to assess correlation between the amount of computer-related workload and health risk.

Keywords: Computer-related workload, health outcomes, dynamic association, dose-response relationship, systematic analysis.

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Authors: Frantisek Bozek, Alena Bumbova, Eduard Bakos

Abstract:

Paper is dealing with vulnerability concerning elements of hydrological structures and elements of technological equipments which are acceptable for groundwater resources. The vulnerability assessment stems from the application of the register of hazards and a potential threat to individual water source elements within each type of hazard. The proposed procedure is pattern for assessing the risks of disturbance, damage, or destruction of water source by the identified natural or technological hazards and consequently for classification of these risks in relation to emergency water supply. Using of this procedure was verified on selected groundwater resource in particular region, which seems to be as potentially useful for crisis planning system.

Keywords: Hazard, Hydrogeological Structure, Elements, Index, Sensitivity, Water Source, Vulnerability

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Authors: Sokjoon Lee, Howon Kim, Kyoil Chung

Abstract:

RFID system, in which we give identification number to each item and detect it with radio frequency, supports more variable service than barcode system can do. For example, a refrigerator with RFID reader and internet connection will automatically notify expiration of food validity to us. But, in spite of its convenience, RFID system has some security threats, because anybody can get ID information of item easily. One of most critical threats is privacy invasion. Existing privacy protection schemes or systems have been proposed, and these schemes or systems defend normal users from attempts that any attacker tries to get information using RFID tag value. But, these systems still have weakness that attacker can get information using analogous value instead of original tag value. In this paper, we mention this type of attack more precisely and suggest 'Tag Broker Model', which can defend it. Tag broker in this model translates original tag value to random value, and user can only get random value. Attacker can not use analogous tag value, because he/she is not able to know original one from it.

Keywords: Broker, EPC, Privacy, RFID.

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Authors: Irfan Rafique, Philip Lew, Maissom Qanber Abbasi, Zhang Li

Abstract:

The world wide web coupled with the ever-increasing sophistication of online technologies and software applications puts greater emphasis on the need of even more sophisticated and consistent quality requirements modeling than traditional software applications. Web sites and Web applications (WebApps) are becoming more information driven and content-oriented raising the concern about their information quality (InQ). The consistent and consolidated modeling of InQ requirements for WebApps at different stages of the life cycle still poses a challenge. This paper proposes an approach to specify InQ requirements for WebApps by reusing and extending the ISO 25012:2008(E) data quality model. We also discuss learnability aspect of information quality for the WebApps. The proposed ISO 25012 based InQ framework is a step towards a standardized approach to evaluate WebApps InQ.

Keywords: Data Quality Model, Information learnability, Information Quality, Web applications.

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Authors: M. Y. Ismail, M. Inam, A. F. M. Zain, N. Misran

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Progressive phase distribution is an important consideration in reflectarray antenna design which is required to form a planar wave in front of the reflectarray aperture. This paper presents a detailed mathematical model in order to determine the required reflection phase values from individual element of a reflectarray designed in Ku-band frequency range. The proposed technique of obtaining reflection phase can be applied for any geometrical design of elements and is independent of number of array elements. Moreover the model also deals with the solution of reflectarray antenna design with both centre and off-set feed configurations. The theoretical modeling has also been implemented for reflectarrays constructed on 0.508mm thickness of different dielectric substrates. The results show an increase in the slope of the phase curve from 4.61°/mm to 22.35°/mm by varying the material properties.

Keywords: Mathematical modeling, Progressive phase distribution, Reflectarray antenna, Reflection phase.

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Authors: M. Palizvan, M. T. Abadi, M. H. Sadr

Abstract:

Due to variations in damage mechanisms in the microscale, the behavior of fiber-reinforced composites is nonlinear and difficult to model. To make use of computational advantages, homogenization method is applied to the micro-scale model in order to minimize the cost at the expense of detail of local microscale phenomena. In this paper, the effective stiffness is calculated using the homogenization of nonlinear behavior of a composite representative volume element (RVE) containing fiber-matrix debonding. The damage modes for the RVE are considered by using cohesive elements and contacts for the cohesive behavior of the interface between fiber and matrix. To predict more realistic responses of composite materials, different random distributions of fibers are proposed besides square and hexagonal arrays. It was shown that in some cases, there is quite different damage behavior in different fiber distributions. A comprehensive comparison has been made between different graphs.

Keywords: Homogenization, cohesive zone model, fiber-matrix debonding, RVE.

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Authors: Djalal Eddine Khodja, Boukhemis Chetate

Abstract:

In this paper, an automatic system of diagnosis was developed to detect and locate in real time the defects of the wound rotor asynchronous machine associated to electronic converter. For this purpose, we have treated the signals of the measured parameters (current and speed) to use them firstly, as indicating variables of the machine defects under study and, secondly, as inputs to the Artificial Neuron Network (ANN) for their classification in order to detect the defect type in progress. Once a defect is detected, the interpretation system of information will give the type of the defect and its place of appearance.

Keywords: Artificial Neuron Networks (ANN), Effective Value (RMS), Experimental results, Failure detection Indicating values, Motor-converter unit.

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Authors: Reza Ebrahimpour, Samaneh Hamedi

Abstract:

Classifier fusion may generate more accurate classification than each of the basic classifiers. Fusion is often based on fixed combination rules like the product, average etc. This paper presents decision templates as classifier fusion method for the recognition of the handwritten English and Farsi numerals (1-9). The process involves extracting a feature vector on well-known image databases. The extracted feature vector is fed to multiple classifier fusion. A set of experiments were conducted to compare decision templates (DTs) with some combination rules. Results from decision templates conclude 97.99% and 97.28% for Farsi and English handwritten digits.

Keywords: Decision templates, multi-layer perceptron, characteristics Loci, principle component analysis (PCA).

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Authors: Afaq Ahmad

Abstract:

Using state space technique and GF(2) theory, a simulation model for external exclusive NOR type LFSR structures is developed. Through this tool a systematic procedure is devised for computing pseudo-random binary sequences from such structures.

Keywords: LFSR, external exclusive NOR type, recursivebinary sequence, initial state - next state, state transition matrix.

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Authors: K. Sandjak, B. Tiliouine

Abstract:

This article presents the main results of three-dimensional (3-D) numerical investigation of asphalt pavement structures behaviour using a coupled Finite Element-Mapped Infinite Element (FE-MIE) model. The validation and numerical performance of this model are assessed by confronting critical pavement responses with Burmister’s solution and FEM simulation results for multi-layered elastic structures. The coupled model is then efficiently utilised to perform 3-D simulations of a typical asphalt pavement structure in order to investigate the impact of two tire configurations (conventional dual and new generation wide-base tires) on critical pavement response parameters. The numerical results obtained show the effectiveness and the accuracy of the coupled (FE-MIE) model. In addition, the simulation results indicate that, compared with conventional dual tire assembly, single wide base tire caused slightly greater fatigue asphalt cracking and subgrade rutting potentials and can thus be utilised in view of its potential to provide numerous mechanical, economic, and environmental benefits.

Keywords: Infinite elements, 3-D numerical investigation, asphalt pavements, dual and wide base tires.

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Authors: Tarek Aboueldah, Hanan Farag

Abstract:

Parallel Job Shop Scheduling Problem (JSSP) is a multi-objective and multi constrains NP-optimization problem. Traditional Artificial Intelligence techniques have been widely used; however, they could be trapped into the local minimum without reaching the optimum solution. Thus, we propose a hybrid Artificial Intelligence (AI) model with Discrete Breeding Swarm (DBS) added to traditional AI to avoid this trapping. This model is applied in the cost minimization of the Car Sequencing and Operator Allocation (CSOA) problem. The practical experiment shows that our model outperforms other techniques in cost minimization.

Keywords: Parallel Job Shop Scheduling Problem, Artificial Intelligence, Discrete Breeding Swarm, Car Sequencing and Operator Allocation, cost minimization.

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Authors: Nisar Ahmed Memon, Anwar Majid Mirza, S.A.M. Gilani

Abstract:

Segmentation is an important step in medical image analysis and classification for radiological evaluation or computer aided diagnosis. This paper presents the problem of inaccurate lung segmentation as observed in algorithms presented by researchers working in the area of medical image analysis. The different lung segmentation techniques have been tested using the dataset of 19 patients consisting of a total of 917 images. We obtained datasets of 11 patients from Ackron University, USA and of 8 patients from AGA Khan Medical University, Pakistan. After testing the algorithms against datasets, the deficiencies of each algorithm have been highlighted.

Keywords: Computer Aided Diagnosis (CAD), MathematicalMorphology, Medical Image Analysis, Region Growing, Segmentation, Thresholding,

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Authors: Sacide Güzin Mazman, Yasemin Koçak Usluel

Abstract:

Possible advantages of technology in educational context required the defining boundaries of formal and informal learning. Increasing opportunity to ubiquitous learning by technological support has revealed a question of how to discover the potential of individuals in the spontaneous environments such as social networks. This seems to be related with the question of what purposes in social networks have been being used? Social networks provide various advantages in educational context as collaboration, knowledge sharing, common interests, active participation and reflective thinking. As a consequence of these, the purpose of this study is composed of proposing a new model that could determine factors which effect adoption of social network applications for usage in educational context. While developing a model proposal, the existing adoption and diffusion models have been reviewed and they are thought to be suitable on handling an original perspective instead of using completely other diffusion or acceptance models because of different natures of education from other organizations. In the proposed model; social factors, perceived ease of use, perceived usefulness and innovativeness are determined four direct constructs that effect adoption process. Facilitating conditions, image, subjective norms and community identity are incorporated to model as antecedents of these direct four constructs.

Keywords: Adoption of innovation, educational context, social networks.

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Authors: S. N. Deepa, G. Sugumaran

Abstract:

This paper proposes a new algebraic scheme to design a PID controller for higher order linear time invariant continuous systems. Modified PSO (MPSO) based model order formulation techniques have applied to obtain the effective formulated second order system. A controller is tuned to meet the desired performance specification by using pole-zero cancellation method. Proposed PID controller is attached with both higher order system and formulated second order system. The closed loop response is observed for stabilization process and compared with general PSO based formulated second order system. The proposed method is illustrated through numerical example from literature.

Keywords: Higher order systems, model order formulation, modified particle swarm optimization, PID controller, pole-zero cancellation.

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Authors: Anel Hasić, Naser Prljača

Abstract:

In the domain of autonomous or piloted flights, the accurate control of quadrotor trajectories is of paramount significance for large numbers of tasks. These adaptable aerial platforms find applications that span from high-precision aerial photography and surveillance to demanding search and rescue missions. Among the fundamental challenges confronting quadrotor operation is the demand for accurate following of desired flight paths. To address this control challenge, among others, two celebrated well-established control strategies have emerged as noteworthy contenders: Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) control. In this work, we focus on the extensive examination of MPC and PID control techniques by using comprehensive simulation studies in MATLAB/Simulink. Intensive simulation results demonstrate the performance of the studied control algorithms.

Keywords: MATLAB, MPC, Model Predictive Control, PID, Proportional-Integral-Derivative, quadcopter, Simulink.

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Authors: Ikmal Hisyam Mohamad Paris, Lilly Suriani Affendey, Norwati Mustapha

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In this paper we compare the accuracy of data mining methods to classifying students in order to predicting student-s class grade. These predictions are more useful for identifying weak students and assisting management to take remedial measures at early stages to produce excellent graduate that will graduate at least with second class upper. Firstly we examine single classifiers accuracy on our data set and choose the best one and then ensembles it with a weak classifier to produce simple voting method. We present results show that combining different classifiers outperformed other single classifiers for predicting student performance.

Keywords: Classification, Data Mining, Prediction, Combination of Multiple Classifiers.

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Authors: Kyoung-Jae Kim, Hyunchul Ahn

Abstract:

This study proposes novel hybrid social network analysis and collaborative filtering approach to enhance the performance of recommender systems. The proposed model selects subgroups of users in Internet community through social network analysis (SNA), and then performs clustering analysis using the information about subgroups. Finally, it makes recommendations using cluster-indexing CF based on the clustering results. This study tries to use the cores in subgroups as an initial seed for a conventional clustering algorithm. This model chooses five cores which have the highest value of degree centrality from SNA, and then performs clustering analysis by using the cores as initial centroids (cluster centers). Then, the model amplifies the impact of friends in social network in the process of cluster-indexing CF.

Keywords: Social network analysis, Recommender systems, Collaborative filtering, Customer relationship management

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Authors: Ewa. M. Sztendur, Neil T. Diamond

Abstract:

Random Forests are a powerful classification technique, consisting of a collection of decision trees. One useful feature of Random Forests is the ability to determine the importance of each variable in predicting the outcome. This is done by permuting each variable and computing the change in prediction accuracy before and after the permutation. This variable importance calculation is similar to a one-factor-at a time experiment and therefore is inefficient. In this paper, we use a regular fractional factorial design to determine which variables to permute. Based on the results of the trials in the experiment, we calculate the individual importance of the variables, with improved precision over the standard method. The method is illustrated with a study of student attrition at Monash University.

Keywords: Random Forests, Variable Importance, Fractional Factorial Designs, Student Attrition.

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Authors: Joanna M. Dulińska, Maria Fabijańska

Abstract:

In the paper the results of calculations of the dynamic response of a multi-storey reinforced concrete building to a strong mining shock originated from the main region of mining activity in Poland (i.e. the Legnica-Glogow Copper District) are presented. The representative time histories of accelerations registered in three directions were used as ground motion data in calculations of the dynamic response of the structure. Two variants of a numerical model were applied: the model including only structural elements of the building and the model including both structural and non-structural elements (i.e. partition walls and ventilation ducts made of brick). It turned out that non-structural elements of multi-storey RC buildings have a small impact of about 10 % on natural frequencies of these structures. It was also proved that the dynamic response of building to mining shock obtained in case of inclusion of all non-structural elements in the numerical model is about 20 % smaller than in case of consideration of structural elements only. The principal stresses obtained in calculations of dynamic response of multi-storey building to strong mining shock are situated on the level of about 30% of values obtained from static analysis (dead load).

Keywords: Dynamic characteristics of buildings, mining shocks, dynamic response of buildings, non-structural elements

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Authors: Gopu Sreenivasulu, Bimlesh Kumar, Achanta Ramakrishna Rao

Abstract:

To define or predict incipient motion in an alluvial channel, most of the investigators use a standard or modified form of Shields- diagram. Shields- diagram does give a process to determine the incipient motion parameters but an iterative one. To design properly (without iteration), one should have another equation for resistance. Absence of a universal resistance equation also magnifies the difficulties in defining the model. Neural network technique, which is particularly useful in modeling a complex processes, is presented as a tool complimentary to modeling incipient motion. Present work develops a neural network model employing the RBF network to predict the average velocity u and water depth y based on the experimental data on incipient condition. Based on the model, design curves have been presented for the field application.

Keywords: Incipient motion, Prediction error, Radial-Basisfunction, Sediment transport, Shields' diagram.

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Authors: Igor Astrov, Andrus Pedai

Abstract:

This paper focuses on a critical component of the situational awareness (SA), the neural control of depth flight of an autonomous underwater vehicle (AUV). Constant depth flight is a challenging but important task for AUVs to achieve high level of autonomy under adverse conditions. With the SA strategy, we proposed a multirate neural control of an AUV trajectory for a nontrivial mid-small size AUV “r2D4" stochastic model. This control system has been demonstrated and evaluated by simulation of diving maneuvers using software package Simulink. From the simulation results it can be seen that the chosen AUV model is stable in the presence of noises, and also can be concluded that the proposed research technique will be useful for fast SA of similar AUV systems in real-time search-and-rescue operations.

Keywords: Autonomous underwater vehicles, multirate systems, neurocontrollers, situational awareness.

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Authors: Sofia Stoica

Abstract:

In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices over the past five years of 10 major tech companies: Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We implemented and tested three models – a linear regressor model, a k-nearest neighbor model (KNN), and a sequential neural network – and two algorithms – Multiplicative Weight Update and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: Finance, machine learning, opening price, stock market.

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Authors: M. Safa, S. Samarasinghe

Abstract:

An artificial neural network (ANN) approach was used to model the energy consumption of wheat production. This study was conducted over 35,300 hectares of irrigated and dry land wheat fields in Canterbury in the 2007-2008 harvest year.1 In this study several direct and indirect factors have been used to create an artificial neural networks model to predict energy use in wheat production. The final model can predict energy consumption by using farm condition (size of wheat area and number paddocks), farmers- social properties (education), and energy inputs (N and P use, fungicide consumption, seed consumption, and irrigation frequency), it can also predict energy use in Canterbury wheat farms with error margin of ±7% (± 1600 MJ/ha).

Keywords: Artificial neural network, Canterbury, energy consumption, modelling, New Zealand, wheat.

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Authors: Vijay Kumar Kukreja, Ravneet Kaur

Abstract:

In this paper, one dimensional advection diffusion model is analyzed using finite difference method based on Crank-Nicolson scheme. A practical problem of filter cake washing of chemical engineering is analyzed. The model is converted into dimensionless form. For the grid Ω × ω = [0, 1] × [0, T], the Crank-Nicolson spatial derivative scheme is used in space domain and forward difference scheme is used in time domain. The scheme is found to be unconditionally convergent, stable, first order accurate in time and second order accurate in space domain. For a test problem, numerical results are compared with the analytical ones for different values of parameter.

Keywords: Consistency, Crank-Nicolson scheme, Gerschgorin circle, Lax-Richtmyer theorem, Peclet number, stability.

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Authors: Sergey Sorokin, Irina Sorokina, Alexander Yazenin

Abstract:

We present a solution to the Maxmin u/E parameters estimation problem of possibility distributions in m-dimensional case. Our method is based on geometrical approach, where minimal area enclosing ellipsoid is constructed around the sample. Also we demonstrate that one can improve results of well-known algorithms in fuzzy model identification task using Maxmin u/E parameters estimation.

Keywords: Possibility distribution, parameters estimation, Maxmin u/E estimator, fuzzy model identification.

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Authors: Manpreet Singh, Parvinder Singh, Suman

Abstract:

The data is available in abundance in any business organization. It includes the records for finance, maintenance, inventory, progress reports etc. As the time progresses, the data keep on accumulating and the challenge is to extract the information from this data bank. Knowledge discovery from these large and complex databases is the key problem of this era. Data mining and machine learning techniques are needed which can scale to the size of the problems and can be customized to the application of business. For the development of accurate and required information for particular problem, business analyst needs to develop multidimensional models which give the reliable information so that they can take right decision for particular problem. If the multidimensional model does not possess the advance features, the accuracy cannot be expected. The present work involves the development of a Multidimensional data model incorporating advance features. The criterion of computation is based on the data precision and to include slowly change time dimension. The final results are displayed in graphical form.

Keywords: Multidimensional, data precision.

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Authors: Noura Boudiaf, Allaoua Chaoui

Abstract:

To analyze the behavior of Petri nets, the accessibility graph and Model Checking are widely used. However, if the analyzed Petri net is unbounded then the accessibility graph becomes infinite and Model Checking can not be used even for small Petri nets. ECATNets [2] are a category of algebraic Petri nets. The main feature of ECATNets is their sound and complete semantics based on rewriting logic [8] and its language Maude [9]. ECATNets analysis may be done by using techniques of accessibility analysis and Model Checking defined in Maude. But, these two techniques supported by Maude do not work also with infinite-states systems. As a category of Petri nets, ECATNets can be unbounded and so infinite systems. In order to know if we can apply accessibility analysis and Model Checking of Maude to an ECATNet, we propose in this paper an algorithm allowing the detection if the ECATNet is bounded or not. Moreover, we propose a rewriting logic based tool implementing this algorithm. We show that the development of this tool using the Maude system is facilitated thanks to the reflectivity of the rewriting logic. Indeed, the self-interpretation of this logic allows us both the modelling of an ECATNet and acting on it.

Keywords: ECATNets, Rewriting Logic, Maude, Finite-stateSystems, Infinite-state Systems, Boundness Property Checking.

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