Forthcoming articles

International Journal of Intelligent Engineering Informatics

International Journal of Intelligent Engineering Informatics (IJIEI)

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International Journal of Intelligent Engineering Informatics (4 papers in press)

Regular Issues

  • Human Face Gender Identification using Thepades Sorted N-ary Block Truncation Coding and Machine Learning Classifiers   Order a copy of this article
    by Sudeep Thepade, Deepa Abin, R.I.K. DAS, TANUJA SARODE 
    Abstract: Human face gender identification is increasingly gaining popularity because of exponential popularity and interest in ubiquitous and pervasive computing. The computing embedded in the environment is pervasive computing, where the environment can be made to feel the person is present there and according to the person whether male or female the environment may induce certain decisions with help of ubiquitous computing devices to make environment suited to the person. The challenge of detecting a face is male or female is very trivial due to similarity of features of faces. The paper presents use of Thepades Sorted N-ary Block Truncation Coding (TSBTC N-ary) for extraction of features for the faces and further deploys various machine learning classifiers to identify the face as male or female. Here TSnBTC is explored for six different combinations (from two-ary to seven-ary). Fourteen machine learning classifiers are explored with each of these feature extraction generation methods for face gender identification giving 96 variations of the proposed method, all these are tested using Faces94 dataset. Classification accuracy is used as performance measure. Overall Random Forest has given best performance and TSBTC-7ary outperformed other feature extraction variations.
    Keywords: Gender Identification; Machine learning Classifier; Thepade’s Sorted BTC N-ary; TSBTC N-ary.

  • A Hybrid Framework for Ranking Reviewers Based on Interval Type-2 Fuzzy AHP and VIKOR   Order a copy of this article
    by Hossein Abbasimehr, Esmaeil Nourani, Mostafa Shabani 
    Abstract: Online reviews are crucial resources both for users and business enterprises. However, the quality of online reviews varies greatly. To address the problem of low-quality reviews, we focus on the problem of reviewer credibility and propose a new framework. The proposed framework incorporates three main parts including identification of source credibility factors, preprocessing, and ranking via interval type-2 fuzzy analytical hierarchy process (IT2FAHP) and VIKOR method. A major distinction of the proposed framework is utilizing multiple factors obtained from different sources, which leads to considerably improved reviewers ranking. This is in contrast to the other approaches which consider limited factors for credibility computing. Furthermore, we propose a new method for defuzzification of interval type-2 fuzzy sets that yields more reasonable results when compared with the existing methods. The last but not least is that it can be employed by companies to filter customers reviews based on their credibility rank. The framework is evaluated using real data crawled from Epinions. The results indicated that the proposed framework effectively ranks the reviewers based on their credibility.
    Keywords: Online Reviews; Reviewer credibility; Interval type-2 fuzzy set; AHP; VIKOR.

  • Estimation of centroid, ensembles, anomaly and association for the uniqueness of human footprint features   Order a copy of this article
    by Kapil Kumar Nagwanshi, Sipi Dubey 
    Abstract: Present study proposes an approach for the estimation of the distinctiveness of human footprints under the machine learning environment. In this system, a sum of 880 raw footprints have been segmented to get the twenty-one features for ensemble learning. All the features have been analyzed for computation of minimum, mean gray value, median, maximum, standard deviation, kurtosis, and skewness for footprint dataset. The G-means clustering offers centroid information of footprint features. A set of ten ensembles has analyzed for surrogate footprint attributes. Ten anomaly models were created for anomaly scores among these features. The association of features gives the uniqueness of the human footprints for personal identification through fuzzy rules for every set of ensembles. As a consequence, centroid, ensembles, anomaly, and affiliation proved the individuality of human footprints.
    Keywords: Anomaly Score; Association; Centroid; Ensemble; Footprint; G-means Cluster; Machine Learning; Recognition; Rule-base; Segmentation.

  • Detecting Intrusive Transactions in Databases using Partially-ordered Sequential Rule Mining and Fractional-distance based Anomaly Detection   Order a copy of this article
    by Indu Singh, Mononito Goswami, Rishabh Mathur, Minkush Manuja 
    Abstract: Databases are popular means to store, retrieve and query massive quantities of transactional data critical to the regular functioning of organizations.The reliance of organizations on this data, coupled with the proliferation of the internet, has made databases prone to data breaches by disgruntled employees and hackers. Illegitimate access to databases may compromise their integrity and confidentiality, resulting in legal and financial ramifications for organizations. To this end, we propose a Database Intrusion Detection System (DIDS) called Fractional-distance based Anomaly Detection with Partially-ordered Dependency Analysis (FADPDA) to identify malicious transactions issued to databases. To weed out such transactions, our DIDS combines data dependency analysis using security-sensitive Partially-ordered Sequential Rules (POSRs) with fractional-distance based anomaly detection. Data dependency rules capture sequential patterns in database access while the anomaly detection module builds profiles of regular transactions based on their syntactic features. Unlike most prior work, FADPDA can seamlessly run on both RBAC administered and non-RBAC databases. Detailed experiments on two databases a TPC-C benchmark and a synthetic database, revealed that POSRs effectively and efficiently represent data dependencies. Furthermore, combining data dependency analysis and anomaly detection reduces our systems reliance on hyper-parameters such as support and confidence thresholds, and enhances its intrusion detection capabilities. Through our experiments, we also show that our approach FADPDA outperforms major existing DIDS in terms of precision and recall values.
    Keywords: Database Intrusion Detection; Anomaly Detection; Attribute Sensitivity; Sequence Reactivity; Partially-Ordered Sequential Rule Mining; Fractional Distance Metrics.