Forthcoming articles

International Journal of Computer Applications in Technology

International Journal of Computer Applications in Technology (IJCAT)

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International Journal of Computer Applications in Technology (40 papers in press)

Regular Issues

  • Non-linear modified equation modeling in dynamical systems (Case Study research on Long Jump patterns)
    by Farzad Sharifat 

  • Improving Arabic Text Categorization using FA Words with K-Nearest Neighbor and Centroid-Based classification algorithms
    by El-Sayed Atlam, M.E. Abd El-Monsef, O. El-Barbary 
    Keywords: .

  • 3D Scanning Machine and Additive Manufacturing: Concurrent Product and Process Development
    by Ismet P. Ilyas 
    Keywords: .

  • Simulation and visualisation approach for accidents in chemical plants   Order a copy of this article
    by Feng Ting-Fan, Tan Jing, Liu Jin, Deng Wensheng 
    Abstract: A new general approach to lay the foundation for building a more effective and real-time evacuation system for accidents in chemical plants is presented. In this work, we build the mathematical models and realise automatic grid generating based on the physical models stored in advance with several algorithms in jMonkeyEngine environment. Meanwhile, the results of the simulation data through finite difference method (FDM) are visualised coupling with the physical models. Taking fire as an example, including fire with single and multiple ignition sources, shows the feasibility of the presented approach. Furthermore, a coarse alarm and evacuation system from fire have been developed with a multiple SceneNode and roam system, which also includes the making and importing of the physical models. However, to improve the accuracy of the mathematical models, adaptability and refinement of the grids and universality of the evacuation system is the direction of efforts.
    Keywords: simulation; chemical accidents; alarm and evacuation system; jMonkeyEngine.

  • Detecting occluded faces in unconstrained crowd digital pictures   Order a copy of this article
    by Chandana Withana, S. Janahiram, Abeer Alsadoon, A.M.S. Rahma 
    Abstract: Face detection and recognition mechanisms, a concept known as face detection, are widely used in various multimedia and security devices. There are significant numbers of studies into face recognition, particularly for image processing and computer vision. However, there remain significant challenges in existing systems owing to limitations behind algorithms. Viola Jones and Cascade Classifier are considered the best algorithms from existing systems. They can detect faces in an unconstrained crowd scene with half and full face detection methods. However, limitations of these systems are affecting accuracy and processing time. This project proposes a solution called Viola Jones and Cascade (VJaC), based on the study of current systems, features and limitations. This system considered three main factors: processing time, accuracy and training. These factors are tested on different sample images, and compared with current systems.
    Keywords: face detection; unconstrained crowd digital pictures; face recognition.

  • Adaptive neural-fuzzy and backstepping controller for port-Hamiltonian systems   Order a copy of this article
    by Ahmad Taher Azar, Fernando E. Serrano, Marco A. Flores, Sundarapandian Vaidyanathan, Quanmin Zhu 
    Abstract: In this article, a novel control strategy is shown for the stabilisation of dynamic systems in the form of port-Hamiltonian systems. This hybrid approach composed by a neural-fuzzy and backstepping controller is implemented to stabilise the port-Hamiltonian system by dividing it into two blocks in order to separate the variables and yielding an efficient control strategy. Many kinds of dynamical system, such as power, electrical, mechanical and fluid systems, can be represented in port-Hamiltonian form. Thus, it is important to develop new control strategies to stabilise port-Hamiltonian systems considering that this is not a simple task, especially to increase the robustness, to deal with the uncertainties and to improve the system performance. The proposed control strategy consists in an hybrid approach formed by a neural-fuzzy and backstepping controller. A four-layer neural-fuzzy controller is implemented to stabilise the port-Hamiltonian system, where fuzzification, fuzzy rules inference system and defuzzification layers are considered. The neural-fuzzy controller consists of two steps: an offline training implementing a gradient descent algorithm and an online training by a Lyapunov stability approach. The backstepping controller is designed by a recursive method considering the port-Hamiltonian system properties and implementing a Lyapunov stability approach. Along with the proposed control strategy, a neural-fuzzy observer is implemented to estimate the port-Hamiltonian system states considering the properties of the system representation. Finally, a cart-pendulum example is shown to verify the effectiveness of the proposed observer and controller along with a comparative analysis
    Keywords: neural-fuzzy system; backstepping control; observer design; port-Hamiltonian system.

  • DANP-based method for determining the adoption of hospital information system   Order a copy of this article
    by Khuram Shahzad, Zeng Jianqiu, Asma Zubedi, Xin Wen, Lei Wang, Muhammad Hashim 
    Abstract: Hospital information system (HIS) is an integrated electronic system that provides comprehensive information regarding every aspect of the hospital and patients whenever it is required. In Pakistan, the diffusion of HIS is in the early stages and the rate of adoption is very slow. The primary purpose of this study is to identify the essential factors that are significantly driving or hindering the decision to adopt HIS. For better understanding, this study proposed the initial theoretical model that integrates Technology Organization Environment (TOE) framework, Human Organization Technology (HOT) fit model and institutional theory. Hence, the initial model consists of four main dimensions and 13 variables, which are the most frequently used in prior literature and are essential for the investigation of HIS adoption. The data were collected from healthcare experts who have full knowledge of HIS. Accordingly, the recently developed DANP (Decision Making Trial and Evaluation Laboratory (DEMATEL) based Analytic Network Processes (ANP)) method is employed for assessing interdependency and give weights to dimensions and criteria. According to the experts' knowledge and experience, the results indicate that perceived technical competence, compatibility, top management support, and vendor support are found to be the most essential variables for the successful adoption of HIS concerning people, technology organisation and environment, respectively. Hence, the finding of this study has contributed theoretically, and the practical implementation of this integrated model will give deep insight to healthcare providers for the successful implementation of HIS.
    Keywords: hospital information system; public hospitals; TOE framework; HOT-fit model; institutional theory; DANP method; public hospitals; Pakistan.

  • Hand-drawn electronic component recognition using deep learning algorithm   Order a copy of this article
    by Haiyan Wang, Tianhong Pan, Mian Khuram Ahsan 
    Abstract: Hand-drawn circuit recognition plays an increasingly important role in circuit design work and electrical knowledge teaching. Hand-drawn electronic component recognition is an indispensable part of hand-drawn circuit recognition. Accurate electronic component recognition ensures accurate circuit recognition. In this paper, a hand-drawn electronic component recognition method using a convolutional neural network (CNN) and a softmax classifier is proposed. The CNN is composed of a convolutional layer, an activation layer and an average-pooling layer and is designed to extract features of a hand-drawn electronic component image. The kernel function for the CNN is obtained by a sparse autoencoder method. A softmax classifier is trained for classification based on the features extracted by the CNN. The recognition method can identify rotating electronic components because of the added rotated image and achieve 95% recognition accuracy.
    Keywords: electronic component recognition; convolutional neural network; sparse autoencoder.

  • FCM: a component-based platform with explicit support of crosscutting and dynamic features   Order a copy of this article
    by Abdelhakim Hannousse 
    Abstract: Dealing with crosscutting and dynamic features in component software is a longstanding problem, primarily owing to the nature of used components: components may be available only as black box software units and their implementations may be protected against alteration. Aspect-orientation provides a valuable means to deal with crosscutting features in different paradigms; however, existing endeavours to use aspects in component software have several limitations, such as the lack of suitable design of aspects and the absence of proper aspect runtime weaving mechanisms. In this paper, we contribute by proposing a new aspect component model to solve such problems. In the proposed model, components and aspects are first-class entities that remain separated from design to implementation; and aspects can be added and removed at runtime. We also developed a tool support for the model in Java. We demonstrate the viability of the model through the implementation of a running example.
    Keywords: component-based software engineering; aspect-oriented programming; crosscutting and dynamic features.

  • Correlation-based search for time series data   Order a copy of this article
    by Ibrahim A. Ibrahim, Abdullah Albarrak 
    Abstract: Exploration of time series data based on correlation is a key ingredient of various analysis tasks. However, such exploration entails massive CPU and I/O costs owing to the quadratic nature of the exploration space. Searching for a time sub-interval in which all time series pairs are correlated within certain values is one aspect of time series exploration and has various applications in many domains. Consequently, in this paper, we formulate the targeted correlation matrix search problem where the goal is to find an optimal sub-interval with a correlation matrix that maximises the closeness and similarity to targeted pairwise correlation values. We show the computational hardness of this problem, and propose the RELATE scheme to address the associated challenges by using the incremental property of correlation. Further, we propose two-level pruning techniques for the RELATE scheme to minimise the associated computational and I/O costs. These techniques enable RELATE to avoid exhaustively traversing the search space by pruning unqualified candidate queries, and avoid computing pairwise correlation of every time series pair wherever possible.We demonstrate by experiments the performance gains of RELATE against state-of-the-art algorithm with real and synthetic datasets.
    Keywords: correlation; time series; search.

  • Comparison among different tools for tolerance analysis of rigid assemblies   Order a copy of this article
    by Wilma Polini, Andrea Corrado 
    Abstract: Tolerance analysis is an important task to design and to manufacture high precision mechanical assemblies; it has received considerable attention in the literature. Actually, there are some different tools used or proposed in the literature to make the tolerance analysis of an assembly, but none of them is completely and univocally accepted. A comparison between a Computer Aided Tolerancing (CAT) tool for geometry assurance and some methods proposed in the literature is discussed in this work. Therefore, the aim of this work is to solve, by a CAT software, five case studies that were already solved by different methods in the literature. The potentialities and the limits in using a CAT technique for geometry assurance are highlighted.
    Keywords: geometry assurance; tolerance analysis; computer aided tolerancing.

  • Cloud-based electricity consumption analysis using neural network   Order a copy of this article
    by Nand Kumar, Vilas Gaidhane, Ravi Kant Mittal 
    Abstract: In recent years, optimisation of resource usage is very much required to analyse and understand energy consumption patterns. This analysis has previously been carried out using algorithms, which needs many assumptions, and meeting all the assumptions in practice is a very difficult task. However, there are other methods available to analyse and understand energy consumption. In this paper, an efficient approach for energy consumption pattern analysis is proposed. It is based on the Levenberg-Marquardt algorithm-based neural network (LMNN) and clustering technique. The energy consumption data is collected from the educational institute building using a smart system. The various experimentations are carried out on the collected real time database. The experimental results illustrate that the proposed approach is effective and computationally efficient for consumption pattern classification. The performance of the presented approach is found superior to existing clustering approaches.
    Keywords: educational institute building; Levenberg-Marquardt algorithm; neural network; classification; confusion matrix; ROC curve.

  • A novel ANN-based four-dimensional two-disk hyperchaotic dynamical system, bifurcation analysis, circuit realisation and FPGA-based TRNG implementation   Order a copy of this article
    by Sundarapandian Vaidyanathan, Ihsan Pehlivan, Leutcho Gervais Dolvis, Kengne Jacques, Murat Alcin, Murat Tuna, Ismail Koyuncu 
    Abstract: This paper describes the modelling, bifurcation analysis, circuit realisation and FPGA implementation of a novel ANN-based four-dimensional two-disk dynamical system exhibiting hyperchaos and hidden attractor. This paper provides a detailed analysis of the multistability, coexisting attractors and bifurcation properties of the novel system. The system does not possess any rest point pinpointing the presence of hidden hyperchaotic attractor. We realise the dynamic equations of the two-disk hyperchaotic dynamical system with a real circuit. Next, we build, design and implement the ANN-based two-disk hyperchaotic dynamical system on FPGA. Finally, using the FPGA-based implementation, we design and implement a novel high speed True Random Number Generator (TRNG).
    Keywords: circuit design; hyperchaos; hyperchaotic systems; artificial neural network; FPGA implementation; TRNG.

  • Prediction modelling of exhaust characteristics of a marine engine for SCR urea dosing calibration   Order a copy of this article
    by Zhuo Zhang, Mingwei Shi, Zibin Yin, Defeng Wu, Leyang Dai 
    Abstract: The International Maritime Organization (IMO) issued Annex VI of the MARPOL Convention to control the serious exhaust pollution of marine diesel, specifying the NOx emission limitation requirements. In this paper, the exhaust characteristics of a marine diesel engine were tested, and the exhaust characteristic model was established by a BP neural network, which has been verified via learning ability and generalisation ability. The relative errors of the exhaust flow, NOx concentration and exhaust temperature prediction are within 6%, which can be used to predict the exhaust performance of a marine diesel engine in steady state. The calibration for urea dosing of an SCR system was based on an ammonia-nitrogen ratio of 1:1, whose data are predicted by the exhaust characteristic model.
    Keywords: marine engine; exhaust characteristics; BP neural network; SCR system.

  • A comparative study of meta-heuristic optimisation techniques for prioritisation of risks in agile software development   Order a copy of this article
    by B. Prakash, Viswanathan Viswanathan 
    Abstract: Risks are in general termed as threats or uncertainties that influence the project performance and its outcomes to the greater extent. To ensure software quality and project success, every organisation should enforce a proper mechanism to efficiently manage the risks irrespective of the development model they follow. Risk prioritisation is a most critical step in risk management process that helps the organisation to resolve the risks in a shorter time. In this paper, a comparative study about different meta-heuristic optimisation techniques for prioritising the risks in agile environments is presented. The five most effective meta-heuristic optimisation algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Grey Wolf Optimization (GWO) and Analytical Hierarchy Process (AHP), are considered and the results are evaluated based on four key criteria, namely error rate, accuracy, reliability, and running time. The result proves that GWO outperforms the other four meta-heuristic optimisation techniques for the prioritisation of risks in an agile environment.
    Keywords: risk management; risk prioritisation; agile software development; meta-heuristic optimisation; project management.

  • Reasoned bargaining protocol in construction contracts using a novel Bayesian game   Order a copy of this article
    by Vu Hong Son Pham 
    Abstract: The objective of this paper is to provide new insights on some dimensions of the bargaining process asymmetries and uncertainties in particular- by using a Novel Expert Bayesian Game (NEBG). We develop decision support system for determining the price in construction supply contract. Our results confirm that uncertainty affects negotiators behavior and modify the likelihood of a self-enforcing agreement to emerge. The validation analysis revealed that a novel approach to BN construction by combining domain knowledge from experts possessing incomplete observation data substantially improved the estimation ability of negotiators. Exhibiting a high success rate and profit as well as low negotiation time, the proposed model is superior to those reported in previous research.
    Keywords: decision support system; expert system; Bayesian game.

  • Manifold multi-view learning for cartoon alignment   Order a copy of this article
    by Wei Li, Huosheng Hu, Chao Tang, Yuping Song 
    Abstract: Cartoon alignment is a key to retrieve cartoon characters and synthesise new cartoon clips. To successfully achieve the tasks, it is necessary to extract visual features that comprehensively denote cartoon characters and to align the feature points accurately between cartoon characters. In this paper, Speed Up Robust Feature (SURF) and Shape Context (SC) are introduced to characterise the cartoon character from multi-view. The two features are complementary to each other, and each feature set is thought as a single view. To increase accuracy rate of cartoon character alignment, traditional methods, such as semi-supervised alignment and Procrustes alignment, require predetermining the correspondence. However, this is a tedious task. To overcome the flaw, we propose a manifold multi-view learning (MML) to align cartoon characters. MML learns a projection that maps data instance (from cartoon characters with different dimensionality) to a lower dimensional space, which simultaneously matches the local geometry and preserves the neighbourhood relationship within each cartoon character. The matching relationship can be obtained from local geometry structure. Experimental results show the good performance, such as a matching accuracy rate of more than 90% and processing time of average 100 milliseconds that is only 30% of the traditional algorithm in the certain dataset. Hence, MML can also be potentially applied in mobile devices.
    Keywords: cartoon alignment; manifold; multi-view; speed-up robust feature; shape context.

  • Relevant harmonics selection based on mutual information for electrical appliances identification   Order a copy of this article
    by Abdenour Hacine-Gharbi, Philippe Ravier, Mohamed Nait-Meziane 
    Abstract: Recently, research on electrical appliances identification for non-intrusive load monitoring has become attractive, particularly for smart grid applications. Many appliance identification systems use harmonics of current signals as features. However, the choice of the order and number of relevant harmonics for this task has never been demonstrated. Here, we propose to tackle this issue using relevant feature selection algorithms. Indeed, investigating harmonics in the whole spectral band leads to high dimensional feature vectors for high sampling frequency. This makes the selection intractable if the identification accuracy is used as a relevance criterion. Hence, we propose to analyse the relevance and redundancy of harmonics for appliance identification using feature selection algorithms based on the mutual information criterion. Six heuristic strategies were implemented and their selection results compared. For the choice of a minimal subset of relevant harmonics, we propose a stopping criterion in the selection procedure. In order to validate the selected subset of harmonics, a hidden Markov model based classier was used and evaluated on PLAID dataset. Results highlight odd order harmonics relevance. Furthermore, the feature subset {1, 2, 3, 4, 5, 7, 9} was selected by 3 strategies as strongly relevant since this minimal subset is sufficient for essentially explaining appliances signatures. Mainly, this study shows that the harmonic order 2 is a strongly relevant feature among the first ones, which was never demonstrated in the state-of-the-art studies.
    Keywords: electrical appliances identification; harmonics relevance; mutual information; filters feature selection; feature extraction; hidden Markov models.

  • Towards optimal thread pool configuration for run-time systems of integration platforms   Order a copy of this article
    by Daniela L. Freire, Rafael Z. Frantz, Fabricia Roos-Frantz 
    Abstract: Companies seek technological alternatives to increase competitiveness. One example is the integration platforms, which develop integration processes in order to connect functionalities and data from applications that compose software ecosystems. Threads are computational resources of the platforms, responsible for integration processes execution. Thus, the configuration of threads has a direct influence on the performance of platforms. However, this is a challenge faced by software engineers, who do this configuration empirically. Our scientific and technical literature review did not identify a systematic approach to find the ideal configuration, which depends on factors such as workload, hardware and integration process. Thus, it is appropriate to seek alternatives for a configuration that provides a positive impact on the performance of the run-time system, increases productivity, and reduces costs. Inspired by the particle swarm optimisation meta-heuristic, this article proposes an algorithm that finds the ideal configuration for local thread pool, minimising the total average processing time to improve the execution of integration platforms. The algorithm was implemented and tested using a real-life integration process, and its performance measures show the feasibility and efficiency of our proposal, supported by a rigorous statistical analysis of results.
    Keywords: enterprise application integration; optimisation; particle swarm optimisation; meta-heuristics; multi-thread; makespan; workflow; integration patterns.

  • Ensuring the correctness of adaptive business processes: a systematic literature review   Order a copy of this article
    by Fairouz Fakhfakh, Hatem Hadj Kacem, Ahmed Hadj Kacem 
    Abstract: Adaptability in process management systems is gaining an increasing attentionrnto satisfy the variable enterprise requirements. This concept has been recognized by the process community for a long time and various approaches in this area have been developed so far. In this context, one of the most difficult challenge is to ensure that change operations are applied correctly and do not cause any inconsistencies. This paper presents a survey that examines the existing studies ensuring the correctness of process changes.rnOur survey follows the guidelines of systematic literature reviews (SLR). It provides a comparison of the existing approaches based on some criteria such as verified properties, modeling languages and verification tools. Finally, we highlight some recommendations and possible future researches which need further investigations. So, throughout this present paper, we provide information for researchers and developers to understand the contributions and challenges of the current studies to pave the way for improving theirrnsolution.
    Keywords: Process; adaptability; correctness; systematic literature review; challenges.

  • Developing a biotech scheme using fuzzy logic model to predict occurrence of diseases using a person's functional state   Order a copy of this article
    by Riad Taha Al-kasasbeh, Nikolay Korenevskiy, Altyn Amanzholovna Aikeyeva, Sofia Nikolaevna Rodionova, Ashraf Shaqadan, Maksim Ilyash 
    Abstract: The work deals with the issues of the synthesis of combined fuzzy decision rules for classification and evaluation of the level of functional states on two blocks of heterogeneous characteristics: the subjective test questionnaires and indicators describing the human attention.
    Keywords: level of functional reserve; psycho emotional pressure; intellectual exhaustion; physical exhaustion; heterogeneous fuzzy models.

  • Improved XGBoost model Based on Genetic Algorithm   Order a copy of this article
    by Feng Zhao, Jinxiang Chen, Yanguang Sun, Yilan Yin 
    Abstract: An optimised XGBoost model based on genetic algorithm to search for optimal parameter combinations be proposed in this paper. It was proved that the improved algorithm has better classification effect through the liver disease data set Liver Disorders Data Set in the UCI Machine Learning Repository. In recent years, there have been many excellent intelligent algorithms in the field of machine learning and XGBoost is one of them. However, when using the XGBoost algorithm, it usually involves the adjustment of various parameters in the XGBoost model, and the selection of different parameter combinations has a greater impact on the classification performance of the model. In this paper, after encoding the XGBoost model parameters optimized by genetic algorithm, the global approximate optimal solution is obtained through operations such as selection, crossover and mutation, which greatly improves the performance of the model.
    Keywords: XGBoost; parameter optimisation; genetic algorithm.

  • Mining expertise of developers from software repositories   Order a copy of this article
    by Maen Hammad, Haneen Hijazi, Mustafa Hammad, Ahmed Otoom 
    Abstract: This paper presents a technique to mine the developers contributions to explore their expertise in open source projects. The proposed technique is based on analyzing archived code changes, committed by developers, to identify their expertise. The technique analyses the keywords that appear in the textual content of commits. It is a lightweight technique since the text in commits is analyzed without making any syntactic code differencing. Each developer is linked with a list of keywords, with their frequencies, that appeared in his commits. Based on these keywords, three types of expertise are defined; unique, common and frequent. Unique expertise reflects topics exclusively handled by developers. Common expertise reflects shared topics while frequent expertise reflects frequent topics handled by each developer. The identified expertise can help in identifying topics or issues that are handled by specific or group of developers. A tool is developed to automatically mine and analyze committed code changes to support expertise identification. A case study is presented on three open source projects to investigate three research questions and to show how the proposed techniques can be applied. The main observations and conclusions of the study showed that frequent terms provide useful and variant information about developers expertise.
    Keywords: software maintenance and evolution; mining software repositories; expertise mining.

  • Reliability-Aware Fixed Priority energy management with Shared resources in Real -time System   Order a copy of this article
    by Yiwen Zhang, Huizhen Zhang, Weixian Jiang 
    Abstract: In this paper, we focus on preemptive rate monotonic scheduling and comprehensively consider energy consumption and system reliability about scheduling periodic tasks with shared resources. Firstly, a static scheme for fixed priority periodic tasks with shared resources is proposed, which executes with a uniform speed and ignores the system reliability. Secondly, the problem of energy consumption and system reliability for scheduling fixed priority periodic tasks with shared resources is proved to be NP-hard and a maximum execution time first (MF) algorithm is proposed. Finally, a dynamic scheme for fixed priority periodic tasks with shared resources (DA) is proposed. The DA algorithm exploits the dynamic slack time to save energy while preserving the system reliability. The experimental results show that the DA algorithm saves about 19.28% energy compared with the MF algorithm.
    Keywords: fixed priority; energy; scheduling; reliability; shared resources.

  • Waiting time influence on customer repeated behaviour in chat service systems with staffing policy   Order a copy of this article
    by Ying Liu, Miao Yu, Tao Zou 
    Abstract: In this paper, a behavioral queuing model with feedback is developed for chat service system, in which managers determine capacity sizing to maximize profit. We explicitly consider customer repeated and queueing behavior, because this will have a positive impact on arrival rates. For this case, customer satisfaction as perceived from the perspective of waiting time changes according to the capacity sizing. We define the optimization of the staffing of chat service system as the number of agents to maximize profits, including the income from customers and the costs from agents. Using a numerical analysis, we compare it with the traditional model without considering repeated customer behavior. In addition, we also point out how managers can optimize staffing through various customer behavior mechanisms.
    Keywords: behavioural queuing; staffing; chat service system; customer satisfaction.

  • Experimental and Modeling Study on the Response of Mooring Container Ships in Port under Medium to Long Period Waves
    by Zhu Feng, Geng Ying, Miao Hui 
    Abstract: For understanding the influence of medium to long period waves on the motion and mooring force response of ships and knowing the sensitivity of wave period and wave direction to the dynamic response of ships, the hydrodynamic analysis of three typical moored container ships under wave interaction is carried out based on potential flow theory. The response amplitude operators of ships are calculated firstly in the analyze, and the influence of wave period and direction on ships are discussed. The calculated and experimental results comparison shows that the numerical calculation is more suitable for simulation of long-period wave, as the dynamic response of ship is larger, but more accurate. For the larger ship, the peak response period of the mooring system usually reaches 100s because the mooring stiffness is relative small to the ship's inertia. So with wave period extends, the ship motion response increases almost linearly. The calculation results also show that the symmetrical mooring arrangement and the similar mooring stiffness can effectively reduce the inhomogeneity response of mooring forces. In addition, the ship motion and mooring force response can reach a minimum value at a special angle between ship and wave.
    Keywords: Mooring System; Medium to Long Period Wave; Motion Response; Mooring Force; Potential Flow; Numerical simulation

  • Research on Artificial Synthetic Seismic Record Based on Adaptive Positive Definite Least Square Method
    by Liangzhi Chen, Jie Qin, Yongchang Lu 
    Abstract: Earthquake is a typical natural stochastic process, and the ground movement caused by earthquake can not be accurately predicted. Traditional method of generating artificial seismic record is based on empirical and least square method. This method is suitable for general geology, but has poor applicability and low efficiency for unconventional cases. An adaptive regression method is proposed in this paper to synthesize artificial seismic record, and the effect of seismic duration on response spectrum is considered. A numerical model is further established to verify the effectiveness of the artificial seismic record. The results show that the structure dynamic response under the synthesized artificial seismic record in this paper is close to the natural seismic record, which have significant engineering reference value.
    Keywords: Seismic Record; Artificial Synthesize; Adaptive Regression; Positive Definite; Response Spectrum

  • Data acquisition and stream processing with Matlab using system objects   Order a copy of this article
    by Agostino Giorgio, Alessio Melibeo 
    Abstract: In this paper we will describe how to perform Data Acquisition and Stream Processing using MATLAB
    Keywords: audio applications; development tools; signal processing; computer applications; software engineering.

  • Application of novel big data processing techniques in process industries   Order a copy of this article
    by Pavel Maksimov, Tuomas Koiranen 
    Abstract: Modern process engineering industry offers great opportunities for harvesting tremendous amounts of data, both structured and unstructured. However, traditional data analysis tools can be rather inefficient when dealing with significant volumes of information. Still, this issue is even further aggravated by frequently occurring inconsistencies and discrepancies within analyzed datasets. Novel methods and advanced algorithms are being developed to address this challenge, yet, as regards analytics of actual industry related data, application of these instruments has been comparatively limited so far. In light of this, within the limits of this work, applicability of novel analytical instruments in the context of process engineering industry is studied for both structured and unstructured data processing. In the former case, the data describing matte smelting process of copper production is analyzed focusing on identification of interdependencies between key process parameters and products quality indicators, while in the latter case a dataset consisting of relevant scientific articles is investigated with a view to extracting key concepts and determining relations among them. In the scope of copper production process analysis, crucial operating parameters affecting composition of main and secondary products are determined and major relations between concentrations of key constituents are revealed and explained. In the frame of unstructured data exploration, a cognitive search platform is configured and trained in accordance with specific traits and peculiarities of the analyzed information with further filtering of contained documents and identification of the most relevant insights.
    Keywords: data processing; predictive analysis; cognitive search; data extraction; process engineering; metal industries.

  • A Novel Approach of Cursive Signature Generation for Personal Identity
    by Jungpil Shin, Md Abdur Rahim, Md Rashedul Islam, Keun Soo Yun 
    Abstract: Signatures are used in many situations of daily life. In many Non-English speaking countries like Japan, general people are not familiar with English and they do not have English signatures in most cases. English signatures are written by combining the letters of the English alphabet in cursive. Many people find difficulties to write English in cursive, and most of them want to learn how to write a good signature. To meet this demand, this study proposes a technique for generating an English signature for people who cannot write in cursive and hope to have an English signature. In the proposed model, we use a cubic Bezier curve for the cursive connection of input characters for a signature generation and an affine transformation is used for the modification of the character of generated signature. Modifications were made in the slant, scale, space between the characters and line emphasis. In addition to these, we added some decorative functions, such as putting a circle around the signature. The system also provided an animation to teach users how to write cursive English and the order of strokes used in a signature. After that, we conduct a questionnaire survey about the usability of this system and compare with the state of the art system. As a result, we were able to provide system generated signatures that satisfied the users.
    Keywords: signature generation; human computer interaction; personalization; cubic Bezier curve; cursive English

  • Improved Low Cost Recognition System for Handwritten Bengali Numerals
    by Md Aktaruzzaman, Tewodros Mulugeta Dagnew, Massimo Walter Rivolta, Roberto Sassi 
    Abstract: Handwritten recognition has drawn massive attention since the last decade due to it's numerous potential applications. This paper is concerned about the low cost features extraction for the development of an improved Bengali handwritten numeral recognition system. Each numeral was first transformed and resampled to a binary image of fixed size. A set of new features based on shape analysis was derived from the resampled image, and then a multilayer neural network was trained using the extracted features. The recognition accuracy of the developed system was tested on both training and test sets of a publicly available Bengali handwritten numerals database at three different resolutions. Besides accuracy, the reliability of the system was also estimated using Cohen's kappa. The highest accuracy, 99.12% with reliability about 99% were obtained for the test database at resolution of 32x32. The use of PCA reduces the feature dimension from 142 to 68 resulting in a slight reduce in accuracy to 98.80%. The proposed system provided very high accuracy with the lowest number of simple features.
    Keywords: Feature Extraction; Bengali; Handwritten Numerals Recognition; Artificial Neural Network; Machine Learning; OCR

  • A new four-dimensional two-scroll hyperchaos dynamical system with no rest point, bifurcation analysis, multi-stability, circuit simulation and FPGA design
    by Sundarapandian Vaidyanathan, Esteban Tlelo-Cuautle, Aceng Sambas, Leutcho Gervais Dolvis, Omar Guillén-Fernández 
    Abstract: This paper deals with the design of a new four-dimensional two-scrollrnhyperchaos dynamical system with three quadratic nonlinear terms. The new two-scroll dynamical system does not possess any rest point suggesting the existence of hidden attractor. This paper provides a detailed analysis such as multistability, symmetry,coexisting hyperchaos attractors and bifurcation properties of the new hyperchaos dynamical system with two-scroll hidden attractor. We realise the dynamic equations of the two-scroll hyperchaos system with an electronic MultiSim circuit. Next, we build design and implementation of the two-scroll hyperchaos system on FPGA.
    Keywords: Hyperchaos; hyperchaotic systems; bifurcations; FPGA; circuit design.

  • Temperature variation impact on estimating costs and most critical components in a cloud data center
    by Demis Gomes, Guto Leoni, Glauco Gonçalves, Patricia Endo, Paulo Maciel, Djamel Sadok 
    Abstract: Cooling plays an important role in data center availability by mitigating the overheating of Information Technology (IT) equipment. While many existing works evaluated the performance of the cooling subsystems in data centers, only few studies have considered the important relationship between cooling and IT subsystems. This work provides efficient models (using Stochastic Petri Nets (SPNs)) to represent a cooling subsystem and analyze the impact of its failures in terms of service downtime and financial cost. The study identifies the components that are the most critical with respect to service availability through the use of sensitivity analysis. Results show that the adoption of a redundant architecture reduces the annual costs related to downtime by about 70\%. The chiller is observed as the main component that affects service availability and operation costs.
    Keywords: Availability; downtime cost; operational cost; sensitivity analysis; cooling subsystem

Special Issue on: Computational Intelligence and Applications

  • Intelligent game-based learning: an effective learning model approach   Order a copy of this article
    by Tanzila Saba 
    Abstract: Game-Based Learning (GBL) broadly refers to the use of video games applications to support teaching and learning processes. This research focuses on the concept of GBL in the context of stimulating interest in the field of computer science education specifically. In contrast to theoretical learning, GBL is a practical learning approach that is meant to teach and be enjoyed at the same time. Additionally, a GBL model with visual features has been proposed and tested. Promising feedback has received from learners through the post conducted surveys. The research findings exhibit that GBL is an effective methodology in transferring knowledge, enhancing learning, and making the learning a more enjoyable process in computer science studies than just the theoretical approach.
    Keywords: binary games; game-based learning; logical games; theoretical learning.

Special Issue on: Theoretical Advances and Applications of Computational Intelligence

  • Time sensitive clustering evolving textual data streams
    by Mohamed Ammar, Minyar Sassi Hidri, Adel Hidri 
    Abstract: Clustering a stream of text documents is an emerging subject of interest since it is widely used in analyzing the content in social media and e-journals. The aim is to find a certain structure for unlabeled data based on a similarity criterion. However, few works have focused in this field and falls in this perspective, that's why a new document clustering approach adapted to a stream of text data and test it on news articles datasets is proposed. A distributed representation of words is used, and a bottom-up approach is proceeded to represent documents as vectors on a unit hyper-sphere. The proposed approach gains its roots from the SPherical K-Means (SPKM) algorithm and its underlying mixture of von-Mises Fisher (vMF) distributions. The proposed approach yields comparable results to baseline batch algorithm for stable data streams and superior results for rapidly evolving data streams.
    Keywords: Natural Language Processing; Document clustering; Competitive learning; Data streams; Machine learning.

  • Intelligent system for feature selection based on rough set and chaotic binary grey wolf optimization
    by Ahmad Taher Azar, Ahmed M. Anter, Khaled M. Fouad 
    Abstract: Feature selection (FS) has non-trivial role in supervised learning; like classification, for many causes. FS aims at facilitating the model processes and reducing the computation time. In feature selection, trivial features are eliminated from the data to produce transparently and comprehensibly a model. Furthermore, feature selection process can decrease a noise data, wherefore; feature selection enhances the accuracy measure of classification process. This paper proposes a robust hybrid dynamic model for feature selection; called RS-CBGWO-FS. RS-CBGWO-FS is a combination of both rough set (RS), chaos theory and binary grey wolf optimization (BGWO). GWO parameters are estimated and tuned by using ten various chaotic maps. Five complex medical datasets are used in the evaluation experiments. The selected datasets have various uncertainty attributes and missing values. The overall result indicates that RS-CBGWO-FS with the singer and piecewise chaos maps provide the better effectiveness, minimal error, higher convergence speed and lower computation time.
    Keywords: Grey wolf optimization (GWO); Meta-heuristics, Rough Set Theory, Chaos Theory, Feature reduction and selection, and Data Classification

  • Intelligent approach for large-scale data mining
    by Khaled M. Fouad, Doaa Elbably 
    Abstract: Large-scale data mining has become a very difficult issue using traditional methods because of the data complexity is very high. Therefore, many techniques rely on correction; such as statistics, data mining and data science, can be used to analyze large-scale datasets. The researchers continue to improve these techniques and develop new techniques, particularly in response to the requirements for large-scale data analysis.rnIn the proposed approach, an integration of three methods; optimized principal component analysis (OPCA), optimized enhanced extreme learning machine (OEELM) and stratified sampling; called OPCA-EELM2SS, is presented to provide intelligent and enhanced large-scale data mining. By using OPCA, a proper number of principal components (PCs) is achieved by using particle swarm optimization PSO, which is necessary to transform the high dimensional spaces into low dimensional data. OPCA provides a good representation of large-scale datasets by using the stratified sample (SS); sample with a perfect distribution of categories, to select the optimal components with the minimum computation time. By using OEELM, the optimal number of hidden nodes (HNs) in ELM is exploited to build a single hidden layer feedforward neural network (SLFN) to obtain the optimized enhanced ELM.rnThe proposed approach is experimented by using nineteen benchmark datasets. The experimental results demonstrate the effectiveness of the proposed approach by performing different experiments for classical PCA and independent component analysis (ICA), which are integrated with the enhanced ELM using different evaluation criteria. For more reliability, the proposed approach is compared with many previous methods, which used in the domain of selecting the optimal number of hidden nodes of ELM and in the domain of dimensionality reduction by feature selection techniques.
    Keywords: Principal Component Analysis, Extreme Learning Machine, Particle Swarm Optimization, large-scale data mining

Special Issue on: Advances in Computer Graphics and Imaging

  • Research on the Design of Visual Interface in Information Visualization
    by Guangtao Ma, Tao Liu, Yang Zhou, Jun Li 

Special Issue on: Machine Vision and Computational Intelligence in Recent Industrial Practice

  • Robust Skin Segmentation using Color Space Switching
    by Ankit Chaudhary, Ankur Gupta 

Special Issue on: Xxxx

  • A Query Driven Method of Mapping from Global Ontology to Local Ontology in Ontology-based Data Integration
    by Haifei Zhang