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 (45 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.

  • 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.

  • 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.

  • 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   Order a copy of this article
    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, in 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 difficulty in writing 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 the 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 conducted a questionnaire survey about the usability of this system and compared it 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; personalisation; cubic Bezier curve; cursive English.

  • Improved low-cost recognition system for handwritten Bengali numerals   Order a copy of this article
    by Md Aktaruzzaman, Tewodros Mulugeta Dagnew, Massimo Walter Rivolta, Roberto Sassi 
    Abstract: Handwritten recognition has drawn massive attention during the last decade owing to its numerous potential applications. This paper is concerned with 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%, was obtained for the test database at resolution of 32 x 32. The use of PCA reduces the feature dimension from 142 to 68, resulting in a slight reduction 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 centre   Order a copy of this article
    by Demis Gomes, Guto Leoni, Glauco Gonçalves, Patricia Endo, Paulo Maciel, Djamel Sadok 
    Abstract: Cooling plays an important role in data centre availability by mitigating the overheating of information technology (IT) equipment. Although many existing works have evaluated the performance of the cooling subsystems in data centres, only a 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 analyse 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.

  • Design of capacitance sensor for two-phase flow monitoring based on finite element models   Order a copy of this article
    by Zhihong Huang, Hanxiang Wang, Xin Zhang 
    Abstract: Monitoring water-oil two-phase flow is of great industrial significance. In this work, an electrical capacitance sensor was designed and tested to monitor the two-phase flow. The geometry of the capacitance sensor is a key factor for sensor design, which greatly influences the performance of the sensor. In order to optimise the capacitance sensor for two-phase flow monitoring, it is necessary to analyse capacitance sensors with different geometries. In this paper, the capacitance sensors with concave plates, double ring electrodes, helical electrodes, parallel strips and perpendicular strips are proposed and briefly introduced. The finite element (FE) models for capacitance sensors were constructed in COMSOL multiphysics, and the preliminary experiments were also carried out. Both simulation and experimental results show that the capacitance of the sensor tends to increase with decreasing oil content. In addition, the capacitance sensors with different geometries were studied using FE models. The measurement sensitivity distributions obtained from FE models vary with different electrode geometries, based on which design parameters can then be selected to address different requirements.
    Keywords: flow monitoring; capacitance sensor; sensor design; finite element models.

  • A Hybrid Collaborative Filtering Recommendation Algorithm for Requirements Elicitation
    by Qusai Shambour, Mosleh Abualhaj, Mayy Al-Tahrawi 
    Abstract: Requirements elicitation is one of the most critical and difficult tasks in software development. The quality of elicited requirements is a significant aspect that affects the success of a software project. Requirements reuse has shown to be an effective and efficient elicitation technique that can enhance the quality of the requirements elicitation process and, as a result, lead to a project’s success. However, the information overload problem, which caused by the rapidly growing number of reusable software requirements in large requirements repositories, hinders the effectiveness of the requirements reuse process. Recommender systems proved to be a well-known solution to such problem. This paper focuses on the adoption of recommender systems to mitigate the problem of information overload that is inherent in the requirements elicitation process, specifically, by assisting requirement engineers in retrieving relevant reusable requirements from large-scale requirements repositories. This paper proposes an effective hybrid user-item based collaborative filtering recommendation algorithm by integrating enhanced versions of the user-based and item-based collaborative filtering approaches. The validation results on the RALIC and sparse datasets illustrate that the proposed algorithm outperforms and mitigates the drawbacks the benchmark collaborative filtering-based recommendation approaches in terms of recommendation accuracy, precision, recall and F1 measures, as well as significantly alleviating the data sparsity problem.
    Keywords: Requirements engineering, Requirements elicitation, Requirements reuse, Information Filtering, Collaborative filtering, Recommender systems.

  • Multidimensional data visualization method based on Convex-corrected Radviz
    by Jingjing Yin, Haibo Shi, Xiaofeng Zhou, Liang Jin, Shuai Li, Yichi Zhang 
    Abstract: Abstract: Radviz is one of the most commonly used multidimensional data visualization methods. Considering the projection points overlapping a lot by Radviz, this paper puts forward a new Radviz optimization method to correct the position of the projected data points. Firstly, the new method introduces the Prim algorithm to realize the optimal ordering of the dimension anchors on Radviz. Secondly, the convex hull mapping and the second Radviz mapping are used to correct the position of the projected data points. Finally, the data points are visualized. In addition, in order to verify the effectiveness of the algorithm, the Dunn index was used to do a quantitative evaluation of visualization. By comparing multiple sets of dataset experiments, the results show that the new method is beneficial to obtain the better visualization effect of multidimensional data in Radviz projection.
    Keywords: Radviz, Visualization, Multidimensional data, Convex-corrected

  • Adaptive Self Recurrent Wavelet Neural Network and Sliding Mode Controller/Observer for a Slider Crank Mechanism
    by Ahmad Taher Azar, Fernando E. Serrano, Josep M. Rossell, Sundarapandian Vaidyanathan, Quanmin Zhu Zhu 
    Abstract: In this paper, a novel control strategy based on an adaptive self recurrent wavelet neural network (SRWNN) and a sliding mode controller/observer for a slider crank mechanism is proposed. The aim is to reduce the tracking error of the linear displacement of this mechanism while following a specified trajectory. The controller design consists into two parts. The first one is a sliding mode control strategy and the second part is an SRWNN controller. This controller is trained offline first, and then the SRWNN weights are updated online by the adaptive control law. Apart from the hybrid control strategy proposed in this paper, a velocity observer is implemented to replace the use of velocity sensors. The outcomes obtained in the numerical experiment section prove that the smallest tracking error is obtained for the linear and angular displacements in comparison with other strategies found in literature due to the uncertainty and disturbance rejection properties of the sliding mode and the self recurrent wavelet neural network controllers. Besides, it is corroborated an accurate velocity estimation due to the precise theoretical design of the proposed observer in order to reduce the error between the measured and estimated state to zero. This paper begins with the derivation of the dynamic equations of the slider crank mechanism followed by the derivation of the proposed observer and control strategies. Finally, to demonstrate the effectiveness of the controller/observer strategy, a numerical example is supplied to analyze the variables of the system, tracking error and the estimated variables
    Keywords: Adaptive wavelet neural networks; Sliding mode control; Sliding mode observer; Slider crank mechanism

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

  • Arabian Horse Identification based on whale Optimized Multi Class Support Vector Machine
    by Ayat Taha, Ashraf Darwish, Aboul Ella Hassanien, Ahmed ElKholy 
    Abstract: The protection of the Arabian horse from extinction and conservation of the purity of this oldest breed needs an approach for identification. The classical identification approach causes harmful, duplication, theft, and liable to fraud for the Arabian horse so the biometric identification approach such muzzle print is considered the solution for the Arabian horse identification which is effective and animal welfare. In this study, biometric identification approach for Arabian horse identification is proposed based on the optimized Multi-Class Support Vector Machine (MCSVM). The identification approach performed on three phases, feature extraction, classification, and optimize the classification. The feature extraction phase use Histogram of Oriented Gradient (HOG) to extract features vectors from the muzzle print image of the Arabian horses and then stored in the database with its labels. The second phase is the classification phase which uses MCSVM for training and testing classification. Finally the optimized MCSVM phase, three different swarms: Particle Swarm Optimization (PSO), Gray Wolf Algorithm (GWA) and Whale Optimization (WO) are used to optimize MCSVM parameters to enhance the identification accuracy of the Arabian horse. The results obtained show that the polynomial kernel of MCSVM achieves height accuracy 93.2% compare to linear and Radial Basis Function (RBF) kernels and increased to 97.4% with WO algorithm which achieves the best accuracy compare to PSO and GWA.
    Keywords: Arabian Horse Identification, Histogram of Oriented gradient, Multi Class Support Vector Machine, Whale Optimization Algorithm

  • An Implementations Method for Arabic Keyword Tendency Using Decision Tree
    by El-sayed Atlam, Hassan Hashim 
    Abstract: The concept of keywords refers to the fact that the title of a document can generally be identified by looking at specific keywords or sentence in that document. Generally, Keyword recurrences change over certain periods of time. Traditional approaches estimated classes (increasing, relatively constant, and decreasing) that indicate keywords? attributes changes in a document over certain periods of time using a decision tree. Furthermore, all earlier approaches are based on keywords in English and French languages. Therefore, the extension of keywords to other language such as Arabic could strengthen further researches in this domain. This paper introduces a new method to extract, Arabic keywords from corpora based on their recurrences changes in a document over given periods of time using a decision tree. The new approach is applied on a new data set field (computer science) which makes it different to traditionally used methods. For training data, we extracted the attribute values of 450 nouns that were collected from 2,825 articles of Arabic Wikipedia dumps and Alhayah newspaper (2015-2017) that discussed computer science topics. For testing data, 480 proper nouns were extracted from 975 articles of Arabic Wikipedia dumps and Alhayah newspaper (2015-2018) and then classified using a decision tree approach. The comparison between the manually classified results and the evaluation of the decision tree results reveals that; F-measures of decreasing, relatively constant and increasing classes were 0.188 , 0.789 , and 0.877 respectively. This indicates that the effectiveness of this method is achieved.
    Keywords: Arabic Wikipedia dumps, Alhayah news, time-changing, keywords attribute, decision tree

  • A multifunctional BCI system for exigency assistance and environment control based on ML and IoT.
    by Mayank Singh 
    Abstract: Brain-Computer Interface (BCI) a modality to create an interface which sustains a direct and bidirectional communication between the brain and computers. The major disadvantage in implementing such systems is its bulky design and system cost. This paper implements a simple multifunction BCI system for the environment control and exigency assistance by just using single channel Electroencephalogram (EEG). In proposed model environment control is achieved through the Internet of Things (IoT) as a function of the cognitive state of the person while for exigency assistance served as a function of Event-Related Potential (ERP) generated during oddball paradigm. Hardware-based on Arduino microcontroller (AMC) designed for controlling the environment. Different Machine Learning (ML) algorithm used and observed for training the classifiers. Weighted k-Nearest Neighbour (Wk-NN) algorithm trained classifiers delivers the best result, with an accuracy of 98.3% to detect ERP and 95%accuracy for cognitive state detection. The simple, low-cost prototype system was implemented in practice for environment control and exigency assistance.
    Keywords: Brain Computer Interface, EEG, Event Related Potential, Machine Learning, IoT, IFTTT, Arduino Micro-controller, GUI

  • An Efficient Binary Whale Optimization Algorithm with Optimum Path Forest for Feature Selection
    by Ahmed Samy, Khalid M.Hosny, Abdel Nasser H.Zaied 
    Abstract: The process of feature selection is an essential process in image processing and computer vision applications. This process aimed to find the most representative features from the extracted features. Generally, the reduced selected features improve the efficiency of utilized classifier and increase the classification rate. Also, this process reduced required time for classification. In this paper, a new binary whale optimization algorithm for feature selection is proposed. This optimization algorithm based on behavior of the whales. The optimum-path forest (OPF) technique is used as an objective function. This function is much faster than the other classification techniques. The proposed algorithm binary whale optimization algorithm is evaluated using five well-known datasets of color images. The proposed algorithm is outperformed over the existing binary whale optimization algorithms. The performance of the proposed algorithm is compared with the well-known optimization algorithms such as Particle Swarm Optimization Algorithm (PSOA), Firefly Algorithm (FFA), Gravitational Search Algorithm (GSA), Binary Harmony Search (BHS), Binary Clonal Flower Pollination Algorithm (BCFA), Binary Cuckoo Search Algorithm (BCSA), Binary Bat Algorithm (BBA) where the obtained results clearly show the superiority of the proposed algorithm in terms of classification accuracy, number of selected features and execution times.
    Keywords: WOA; OPF; Feature Selection

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: Computer Technology Applications in the Immersed Tunnel Engineering

  • Experimental and modelling study on the response of mooring container ships in port under medium to long period waves   Order a copy of this article
    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 first in the analysis, and the influences of wave period and direction on ships are discussed. The comparison of calculated and experimental results 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 100 s because the mooring stiffness is relatively small compared with the ship's inertia. So when wave period extends, the ship's 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's 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 squares method   Order a copy of this article
    by Liangzhi Chen, Jie Qin, Yongchang Lu 
    Abstract: Earthquake is a typical natural stochastic process, and the ground movement caused by earthquake cannot be accurately predicted. The traditional method of generating an artificial seismic record is based on the empirical and least squares 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 synthesise an 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 synthesised artificial seismic record in this paper is close to the natural seismic record, which has a significant engineering reference value.
    Keywords: seismic record; artificial synthesis; adaptive regression; positive definite; response spectrum.

  • Vortex dislocation characteristics of a dual-step cylinder in low-Re flow   Order a copy of this article
    by Chunning Ji, Xiaoxiao Yang, Yuting Cui, Weilin Chen 
    Abstract: Flow around a dual step cylinder for D/d = 2 and L/D = 5 at a low Reynolds number of ReD = 200 is numerically investigated utilizing the immersed boundary method. The vortex dislocation caused by two steps is investigated. It was found that a pair of streamwise vortex the center vortex exists at the downstream of the large cylinder. Meanwhile, due to the barrier effect of the center vortex, a very weak L-cell at the downstream behind the centre of large cylinder is also observed. In a complete dislocation cycle, three positive and negative half-loop connections appear alternately, and this cycle is not only related to the phase realignment of the different vortex cells, but also to the connection mode realignment. The variations and spectra of three velocities (U, V and W) at different downstream locations are analysed in detail. It was found that the lateral velocity is always dominated by the frequency of vortex shedding in the whole downstream. While as the streamwise velocity travels downstream, vortex shedding frequency is gradually dissipated, only leaving lower frequencies of dislocation.
    Keywords: vortex dislocation; dual-step cylinder; wake; low-Re flow.

  • A large-scale field test on sand compaction piles including three-dimensional numerical analysis   Order a copy of this article
    by Hongtao He, Jianyu Li, Yougao Lin 
    Abstract: This paper presents a large-scale field test study performed on soft ground improved by a set of 216 sand compaction piles in a mesh of 12
    Keywords: field test; sand compaction piles; ground improvement; three-dimensional numerical analysis.

  • Numerical simulation on offshore artificial island cofferdam of Hong Kong-Zhuhai-Macao bridge   Order a copy of this article
    by Tingting Wang, Heng Liang, Zhihao Peng 
    Abstract: The deep burying steel cylinder structure has the characteristics of gravity structure and pile foundation structure, the mechanism and design method still being in its infancy. Based on the offshore artificial island of Hong Kong-Zhuhai-Macao Bridge, spatial 3D elastic-plastic numerical analysis of the island wall structure was established, and the stability and deformation characteristics of the deep-buried steel cylinder cofferdam was studied throughout the rapid island construction process. laws. Stability calculation of the deep-buried steel cylinder on deep soft foundation was carried out based on the embedded steel cylinder theoretical formula in OCDI, and the measured values in situ was summarized. The results show that the steel cylinder displacement using 3D numerical simulation method is consistent with the measured data, and much larger than that using 2D equivalent calculation method. The safety stability characteristics of 2D theoretical calculation is consistent with the 3D numerical simulation and measured data, indicating that it is suitable for analyzing the stability of deep-buried steel cylinders on soft foundations. The instability judgment of the buried steel cylinder and the selection criteria of the bottom bearing layer are also given.
    Keywords: Hong Kong-Zhuhai-Macao bridge; artificial island; deep-buried large cylinder; deep soft foundation; stability.

  • Prediction on hydroelastic responses of very large floating structure with the eigenfunction expansion-matching method   Order a copy of this article
    by Zhinan Wan, Gangjun Zhai, Yong Cheng 
    Abstract: This paper investigates the hydroelastic responses of a mat-like, rectangular very large floating structure with analytical method. The very large floating structure is considered as a two dimensional thick plate and the wave linearity theory are employed in this paper. The flow field is divided into three regions and the eigenfunction expansion-matching method is applied in solving velocity potential in each region, including diffraction potential and radiation potential. and then the the Rayleigh-Ritz method is used to solve the elastic equation of motion. The analytical method has the advantages of high speed, small memory and high precision, and verified by comparing with the published numerical and experimental results. The elastic deformation and wave exciting force of the very large floating structure are computed and the effects of water depth, wavelength, draft and stiffness on the elastic deformation of the very large floating structure are further discussed in this paper
    Keywords: very large floating structure; eigenfunction expansion-matching method; Rayleigh-Ritz method; hydroelastic response.

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