Journals / IASC / Vol.26, No.2

  • ARTICLE

    Hybrid Soft Computing Technique Based Trust Evaluation Protocol for Wireless Sensor Networks

    Supreet Kaur*, Vijay Kumar Joshi
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 217-226, 2020, DOI:10.31209/2018.100000064
    Abstract Wireless sensor networks (WSNs) are susceptible to safety threats due to cumulative dependence upon transmission, computing, and control mechanisms. Therefore, securing the end-to-end communication becomes a major area of research in WSNs. A majority of existing protocols are based upon signature and recommended-based trust evaluation techniques only. However, these techniques are vulnerable to wormhole attacks that happen due to lesser synchronization between the sensor nodes. Therefore, to handle this problem, a novel hybrid crossover-based ant colony optimization-based routing protocol is proposed. An integrated modified signature and recommendationbased trust evaluation protocol for WSNs is presented. Extensive experiments reveal that the proposed… More >

  • ARTICLE

    Self-Organizing Gaussian Mixture Map Based on Adaptive Recursive Bayesian Estimation

    He Ni1,*, Yongqiao Wang1, Buyun Xu2
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 227-236, 2020, DOI:10.31209/2019.100000068
    Abstract The paper presents a probabilistic clustering approach based on self-organizing learning algorithm and recursive Bayesian estimation. The model is built upon the principle that the market data space is multimodal and can be described by a mixture of Gaussian distributions. The model parameters are approximated by a stochastic recursive Bayesian learning: searches for the maximum a posterior solution at each step, stochastically updates model parameters using a “dualneighbourhood” function with adaptive simulated annealing, and applies profile likelihood confidence interval to avoid prolonged learning. The proposed model is based on a number of pioneer works, such as Mixture Gaussian Autoregressive Model,… More >

  • ARTICLE

    A Fuzzy Ontological Infrastructure for Semantic Interoperability in Distributed Electronic Health Record

    Ebtsam Adel1, Shaker El-sappagh2, Mohammed Elmogy3, Sherif Barakat1, Kyung-Sup Kwak4,*
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 237-251, 2020, DOI:10.31209/2019.100000151
    Abstract Information technology is a beneficial tool for the healthcare industry. Health informatics is concerned with using ICT within the healthcare system. Different electronic health record (EHR) systems independently store large amounts of medical data in various structures and formats. Achieving semantic interoperability in EHR environments will improve the healthcare industry. In our previous studies, we proposed a framework that identifies the different heterogeneous medical data sources. In this paper, we move towards implementing the first module of that framework. We expect our framework to be a step towards improving performance and reducing both human mediation and data losses. More >

  • ARTICLE

    An Improved Crow Search Based Intuitionistic Fuzzy Clustering Algorithm for Healthcare Applications

    Parvathavarthini S1,*, Karthikeyani Visalakshi N2, Shanthi S3, Madhan Mohan J4
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 253-260, 2020, DOI:10.31209/2019.100000155
    Abstract Intuitionistic fuzzy clustering allows the uncertainties in data to be represented more precisely. Medical data usually possess a high degree of uncertainty and serve as the right candidate to be represented as Intuitionistic fuzzy sets. However, the selection of initial centroids plays a crucial role in determining the resulting cluster structure. Crow search algorithm is hybridized with Intuitionistic fuzzy C-means to attain better results than the existing hybrid algorithms. Still, the performance of the algorithm needs improvement with respect to the objective function and cluster indices especially with internal indices. In order to address these issues, the crow search algorithm… More >

  • ARTICLE

    Effective and Efficient Ranking and Re-Ranking Feature Selector for Healthcare Analytics

    S.Ilangovan1,*, A. Vincent Antony Kumar2
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 261-268, 2020, DOI:10.31209/2019.100000154
    Abstract In this work, a Novel Feature selection framework called SU embedded PSO Feature Selector has been proposed (SU-PSO) towards the selection of optimal feature subset for the improvement of detection performance of classifiers. The feature space ranking is done through the Symmetrical Uncertainty method. Further, memetic operators of PSO include features and remove features are used to choose relevant features and the best of best features are selected using PSO. The proposed feature selector efficiently removes not only irrelevant but also redundant features. Performance metric such as classification accuracy, subset of features selected and running time are used for comparison. More >

  • ARTICLE

    Personalized Nutrition Recommendation for Diabetic Patients Using Optimization Techniques

    Bhavithra Janakiraman1,*, Saradha Arumugam2
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 269-280, 2020, DOI:10.31209/2019.100000150
    Abstract Personalization in recommendation system has been emerging as the most predominant area in service computing. Collaborative filtering and content based approaches are two major techniques applied for recommendation. However, to improve the accuracy and enhance user satisfaction, optimization techniques such as Ant Colony and Particle Swarm Optimization were analyzed in this paper. For theoretical analysis, this paper investigates web page recommender system. For experimentation, Diabetic patient’s health records were investigated and recommendation algorithms are applied to suggest appropriate nutrition for improving their health. Experiment result shows that Particle Swarm Optimization outperforms other traditional methods with improved performance and accuracy. More >

  • ARTICLE

    Color Image Segmentation Using Soft Rough Fuzzy-C-Means and Local Binary Pattern

    R.V.V. Krishna1,*, S. Srinivas Kumar2
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 281-290, 2020, DOI:10.31209/2019.100000121
    Abstract In this paper, a color image segmentation algorithm is proposed by extracting both texture and color features and applying them to the one -against-all multi class support vector machine (MSVM) classifier for segmentation. Local Binary Pattern is used for extracting the textural features and L*a*b color model is used for obtaining the color features. The MSVM is trained using the samples obtained from a novel soft rough fuzzy c-means (SRFCM) clustering. The fuzzy set based membership functions capably handle the problem of overlapping clusters. The lower and upper approximation concepts of rough sets deal well with uncertainty, vagueness, and incompleteness… More >

  • ARTICLE

    Intelligent Speech Communication Using Double Humanoid Robots

    Li-Hong Juang1,*, Yi-Hua Zhao2
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 291-301, 2020, DOI:10.31209/2020.100000164
    Abstract Speech recognition is one of the most convenient forms of human beings engaging in the exchanging of information. In this research, we want to make robots understand human language and communicate with each other through the human language, and to realize man–machine interactive and humanoid– robot interactive. Therefore, this research mainly studies NAO robots’ speech recognition and humanoid communication between double -humanoid robots. This paper introduces the future direction and application prospect of speech recognition as well as its basic method and knowledge of speech recognition fields. This research also proposes the application of the most advanced method—establishment of the… More >

  • ARTICLE

    Global Levy Flight of Cuckoo Search with Particle Swarm Optimization for Effective Cluster Head Selection in Wireless Sensor Network

    Vijayalakshmi. K1,*, Anandan. P2
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 303-311, 2020, DOI:10.31209/2020.100000165
    Abstract The advent of sensors that are light in weight, small-sized, low power and are enabled by wireless network has led to growth of Wireless Sensor Networks (WSNs) in multiple areas of applications. The key problems faced in WSNs are decreased network lifetime and time delay in transmission of data. Several key issues in the WSN design can be addressed using the Multi-Objective Optimization (MOO) Algorithms. The selection of the Cluster Head is a NP Hard optimization problem in nature. The CH selection is also challenging as the sensor nodes are organized in clusters. Through partitioning of network, the consumption of… More >

  • ARTICLE

    Robust Visual Tracking Models Designs Through Kernelized Correlation Filters

    Detian Huang1, Peiting Gu2, Hsuan-Ming Feng3,*, Yanming Lin1, Lixin Zheng1
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 313-322, 2020, DOI:10.31209/2019.100000105
    Abstract To tackle the problem of illumination sensitive, scale variation, and occlusion in the Kernelized Correlation Filters (KCF) tracker, an improved robust tracking algorithm based on KCF is proposed. Firstly, the color attribute was introduced to represent the target, and the dimension of target features was reduced adaptively to obtain low-dimensional and illumination-insensitive target features with the locally linear embedding approach. Secondly, an effective appearance model updating strategy is designed, and then the appearance model can be adaptively updated according to the Peak-to-Sidelobe Ratio value. Finally, the low-dimensional color features and the HOG features are utilized to determine the target state… More >

  • ARTICLE

    Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks

    Omer Berat Sezer*, Ahmet Murat Ozbayoglu
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 323-334, 2020, DOI:10.31209/2018.100000065
    Abstract Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our… More >

  • ARTICLE

    Identification and Segmentation of Impurities Accumulated in a Cold-Trap Device by Using Radiographic Images

    Thamotharan B1,*, Venkatraman B2, Chandrasekaran S3
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 335-340, 2020, DOI:10.31209/2019.100000156
    Abstract Accumulation of impurities within cold trap device results in degradation of efficient performance in a nuclear reactor systems. The impurities have to be identified and the device has to be replaced periodically based on the accumulation level. Though there are a few techniques available to identify these impurities from the cold trap device, there are certain limitations in these techniques. In order to overcome these constraints, a new harmless and easy approach for identifying and separating the impurities using the radiographic images of cold traps is proposed in this paper. It includes a new segmentation algorithm to segregate the deposited… More >

  • ARTICLE

    LSTM Neural Network for Beat Classification in ECG Identity Recognition

    Xin Liu1,*, Yujuan Si1,2, Di Wang1
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 341-351, 2020, DOI:10.31209/2019.100000104
    Abstract As a biological signal existing in the human living body, the electrocardiogram (ECG) contains abundantly personal information and fulfils the basic characteristics of identity recognition. It has been widely used in the field of individual identification research in recent years. The common process of identity recognition includes three steps: ECG signals preprocessing, feature extraction and processing, beat classification recognition. However, the existing ECG classification models are sensitive to limitations of database type and extracted features dimension, which makes classification accuracy difficult to improve and cannot meet the needs of practical applications. To tackle the problem, this paper proposes to build… More >

  • ARTICLE

    Dynamic Horizontal and Vertical Scaling for Multi-tier Web Applications

    Abid Nisar1, Waheed Iqbal1,*, Fawaz Bokhari1, Faisal Bukhari1, Khaled Almustafa2
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 353-365, 2020, DOI:10.31209/2019.100000159
    Abstract The adaptive resource provisioning of cloud-hosted applications is enabled to provide a better quality of services to the users of applications. Most of the cloud-hosted applications follow the multi-tier architecture model. However, it is challenging to adaptively provision the resources of multi-tier applications. In this paper, we propose an auto-scaling method to dynamically scale resources for multi-tier web applications. The proposed method exploits the horizontal scaling at the web server tier and vertical scaling at the database tier dynamically to maintain response time guarantees. We evaluated our proposed method on Amazon Web Services using a real web application. The extensive… More >

  • ARTICLE

    A Study of Single Image Haze Removal Using a Novel White-Patch RetinexBased Improved Dark Channel Prior Algorithm

    Yao-Liang Chung1,*, Hung-Yuan Chung2, Yu-Shan Chen2
    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 367-383, 2020, DOI:10.31209/2020.100000206
    Abstract In this study, we introduce an algorithm which is based on a series of wellknown algorithms and mainly uses an improved dark channel prior algorithm and the White-Patch Retinex algorithm (both are heterogeneous algorithms) in order to effectively remove the haze from a single image. When used in conjunction with a heterogeneous architecture, the value of the algorithm becomes even greater. With an effective design and a novel procedure, the proposed algorithm can not only restore a clear image, but also solve the halo effect, color distortion, and long operating time issues resulting from the dark channel prior. Rich experimental… More >

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