Forthcoming articles

International Journal of Knowledge Engineering and Soft Data Paradigms

International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP)

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International Journal of Knowledge Engineering and Soft Data Paradigms (9 papers in press)

Regular Issues

  • Hybrid Key Management Scheme for Heterogeneous Wireless Sensor Networks   Order a copy of this article
    by R. Sharmila, V. Vijayalakshmi 
    Abstract: Wireless Sensor Network (WSN) is a large scale network with thousands of tiny sensor nodes deployed in the field which is widely used in real time applications that includes Internet of Things (IOT), Smart Card, Smart Grid, Smart Phone and Smart City. The greatest issue in wireless sensor network is secure key establishment and communication. The key management plays vital role to provide efficient key establishment in resources constraint device. Existing key management techniques have many limitations such as prior deployment knowledge, transmission range, insecure communication and node capture by the adversary. In this paper, hybrid key management scheme is proposed for heterogeneous WSN. Initially the keys are generated and pre-distributed into sensor nodes using hyper elliptic curve equation. After the deployment, the node tries to form a cluster and then establishes a symmetric key using Orthogonal Latin Square method, where the cluster heads and base station use public key encryption method based on Hyper Elliptic Curve Cryptography (HECC). This symmetric key encryption enhances the security with less communication and computational overhead between adjacent nodes in the cluster to improve the connectivity, network resilience and reduces the storage overhead. The simulation results show that the proposed scheme is better in terms of robustness, connectivity and lesser computational overhead with reduced key size.
    Keywords: wireless sensor network; clustering; key management scheme; orthogonal Latin square; hyper elliptic curve cryptography.
    DOI: 10.1504/IJKESDP.2019.10020639
  • Estimation of the threshold parameter of a wear-out failure period in the case of a three-parameter Weibull distribution   Order a copy of this article
    by Takatoshi Sugiyama, Toru Ogura, Takakazu Sugiyama 
    Abstract: We aimed to estimate an interest for the threshold parameter for the wear-out failure period of a three-parameter Weibull distribution. Many researchers have studied various types of parameter estimation. It is possible to estimate the scale and shape of the distribution to a high degree of accuracy. However, estimating the value of the threshold parameter is sometimes difficult. In this paper we propose the unbiased estimator of the threshold parameter, minimum variance of unbiased estimator ``$T_{BEST}$''. In Section 2 we verify the validity of the $T_{BEST}$ by comparing existing threshold parameter estimator, based on the simulation studies of mean and mean squared error (MSE). Statistics $T_{BEST}$ tells us a lot of things, in the process of finding statistics. In the process of finding statistics, we know that sample other than the smallest sample have the information for the threshold parameter as the same as the smallest sample. In Section3, by simulation studies we compare $T_{BEST}$ and other estimator with known goodness. We show a numerical example to measure of fatigue lives in hours of 10 bearings from McCool (1974).
    Keywords: Estimation; threshold parameter; Weibull distribution; Minimum-variance unbiased estimator.

  • An Optimal Dimension Reduction based Feature Selection and Classification Strategy for Geospatial Imagery   Order a copy of this article
    Abstract: Driven by the explosive growth on the available data nowadays and advancement of technologies, the strong need arises for utilizing and maintaining the available data. However, while building an expert prediction system, the inconsistency present in the information system, incompleteness of available knowledgebase, continuous natured attribute values and noise present in the system (especially in case of spatial image data handling), are prime factors which may degrade the process of classification with available traditional methods. Our proposed construction adopts an efficient strategy for classification. Here we explore the problem of classifying remote sensing satellite images. Image data pre-processing and its categorization refers to the labeling of individual pixel object instances into one of a number of predefined categories. Although this is usually not a much intractable task for humans, it has proved to be an extremely difficult problem for machines. We performed experimental analysis for classification using NWPU-RESISC45 dataset. Experiment result shows the improvement in classification by adopting our proposed strategy over other significant state of the art.
    Keywords: Knowledge discovery; Machine learning; Classification; High-resolution satellite imagery.

  • Image Data Hiding Scheme Based On Spline Interpolation and OPAP   Order a copy of this article
    by Amine Benhfid, El Bachir AMEUR 
    Abstract: Recently, the security of information has become a matter of the utmost importance. Steganography is one of the techniques that enter into this field, it is the art of dissimulating data into digital files. In this paper we propose an image steganography method based on the Spline Interpolation. Firstly, digital images are often transmitted over the Internet which would arouse little suspicion. Secondly, the high correlation between pixels provides rich space for data embedding; in addition, Optimal Pixel Adjustment procedure (OPAP) is employed to minimize the error between the input image and the output image in order to ameliorate the fidelity of the proposed steganography method. We propose image steganography methods in spatial domain by using a nearest neighbor, bilinear and bicubic spline interpolation technique, which can embed a large amount of secret data into images with imperceptible modification. The interpolation error, which is measured by distance between the maximum value and the predicted value, is used to embed the secret data. The experimental results show that the proposed schemes provide a larger payload and a good image quality.
    Keywords: Steganography; Data hiding; Spline Interpolation; Optimal Pixel Adjustment Procedure.

  • Role-Based Access Control in BagTrac Application   Order a copy of this article
    by Yassir Rouchdi, Kenza Oufaska, Achraf Haibi, Khalid El Yassini, Mohammed Boulmalf 
    Abstract: the purpose of this study is to enhance RFID application benefits as a luggage tracking system, first, by defining RFID architecture, components, functioning and middleware roles. Secondly, by discussing the implementation of Role-Based Access Control as a tool regulating access to RFID data, therefore making authentication methods more robust and flexible. To eventually presenting our BAG TRAC application, allowing easier manipulation and real-time visualization of the luggage transportation process.
    Keywords: RFID; Middleware; BagTrac; RBAC.

Special Issue on: BDCA'18 Special Issue Intelligence and Data Management

  • Visual Content Summarization for Instructional Videos Using Adaboost and SIFT
    by Zaynab El khattabi, Youness Tabii, Abdelhamid Benkaddour 
    Abstract: Research contributions in video retrieval field are rising to propose solutions for automatic understanding and retrieval of video content. The aim is to make the user able to retrieve specific video sequences in a large database, based on semantic information. In this paper, we process a special case of videos, instructional videos, where text presents very rich semantic information for understanding video content. Indeed, lecture videos are the source of information used in learning systems by educators and students for archiving and sharing knowledge. However, users usually have difficulties to access accurate parts in instructional videos. In our paper, we propose a method to summarize the visual content in instructional videos. For that, first, we segment the video into shots based on SIFT. Then, key frames which are rich in text and figures are extracted from each shot based on entropy measurement. These keyframes are classified using AdaBoost to eliminate non-text frames. The text content in the lecture video summary can be detected and recognized to identify keywords for indexing and classification.
    Keywords: Video summarization; Instructional videos; Adaboost; SIFT; Entropy.

  • Learning Combined Features for Automatic Facial Expression Recognition   Order a copy of this article
    by Nabila ZRIRA, Mehdi Abouzahir, El Houssine Bouyakhf, Ibtissam Benmiloud, Majid Mohammed Himmi 
    Abstract: Facial expressions are one of the most natural and powerful means for the human being in his social communications, whether to share his internal emotional states or to display his moods or intentions, which, in fact, may be true or simply played in a theatrical way. Given the numerous and variety of applications that can be easily planned, building a system able to automatically recognizing facial expressions from images has been an intense field of study in recent years. In this paper, we propose a new framework for automatic facial expression recognition based on combined features and deep learning method. Before the feature extraction, we use Haar feature based cascade classifier in order to detect then crop the face in the images. Next, we extract Pyramid of Histogram of Gradients (PHOG) as shape descriptors and Local Binary Patterns (LBP) as appearance features to form hybrid feature vectors. Finally, we use those vectors for training deep learning algorithm called Deep Belief Network (DBN). The experimental results on publicly available datasets show promising accuracy in recognizing all expression classes, even for experiments which are evaluated on more than seven basic expressions.
    Keywords: Facial expressions; Haar features; PHOG; LBP; DBN.

  • An efficient Similarity search using a combination between descriptors: a case of study in human face recognition   Order a copy of this article
    by Nawfal EL MALIKI, Hassan Silkan, Mounir EL Maghri 
    Abstract: Face recognition is one of the important fields of search in computer vision. Its principle consists to look for images that represent the similar faces to a given face image the image request. This process is done by extracting a set of features of the request image then making comparison between features generated by the request one and the others extracted from whole face image database. Recently, numerous face representation and classification methods have been proposed in the literature. Nevertheless, many problems related to indexing, combination of adequate descriptors and time computing has not been resolved yet. In this paper, we deal with problems related to features combination and that by conceiving a preformat content-based image retrieval that is mainly oriented to handle face authentication challenges. Its convivial interface allows to user the selection of appropriate weighting coefficient values associate to each feature based on human judgment in order to enhance the retrieval performance. We have tested our proposed method on ORL database by using a set of known features. The obtained results show the performance of our proposed method.
    Keywords: face recognition ; principal component analysis ; local Binary Patterns ; features; combination; CBIR ; HOG; Fourier ; Distance ; combination.

  • Fuzzy detection Orange Tree Leaves Diseases Using a Co-occurrence Matrix Based K nearest neighbors classifiers   Order a copy of this article
    by Fatimazahra Jakjoud, Anas Hatim, Abella Bouaaddi 
    Abstract: The improvement of production efficiency in the farming environment is nowadays of a great interest in the agriculture industry. Several techniques and technologies are used to overcome the main problems facing the developments on this field. In this paper the farms image processing based automatic monitoring is treated. We propose an image processing approach for lemon tree leaves diseases. Our approach is a combination of a fuzzy decision maker and KNN classifiers based on Haralick parameters extracted from the co-occurrence matrix. The testing results are very satisfactory and the efficiency of our system could reach aver than 90% for a limited database.
    Keywords: Leaf disease detection;Orange tres leaves; Co-occurrence Matrix; K-nearest neighbors;Fuzzy logic.