International Journal of Knowledge-Based and Intelligent Engineering Systems

The core competence, which is the new development of strategy government theory, is able to bring about differential competitive advantages. The innovation is the progressive soul of a nation, the inexhaustible motive force of a country’s boom. The discussion plays an important part in the sustainable development of corporations in theory and in practice in the way of integrating technological innovation into the core competence of enterprises. The textile industry, which is China’s traditional industry, plays a decisive role in the development of economy and society. Then, we investigate the multiple attribute decision making (MADM) problems for evaluating the core competitiveness of China’s cotton textile enterprises with intuitionistic fuzzy information. We utilize the intuitionistic fuzzy Bonferroni mean (IFBM) operator to aggregate the intuitionistic fuzzy information, then rank the alternatives and select the most desirable one(s) according to the score function and accuracy function. Finally an illustrative example has been given to show the developed approach.

The current decade has been experiencing a lot of research opportunities and challenges in the domain of biometric security. Simultaneously, Internet-of-Things (IoT) is also gaining seed functionality in early aspects of human lives. Cloud computing is the central thematic node in these two areas. In this work, we have proposed a novel biometric authentication scheme which is not based on conventional minutiae features, rather it is based on the frequency domain information of the fingerprint image. Input fingerprint is subjected to suitable quick pre-processing and then the discrete orthonormal Fourier transformation (DOST) features are extracted. Through suitable feature selection, chosen feature points are given to the classification stage where the recognition is accomplished using the standard AdaBoost-RF (AdaBoost Random Forest) algorithm. An overall accuracy of 98.5% has been obtained on a k -fold cross-validation ( k = 5 ) measure. The result obtained is compared with that of the four other state-of-the-art methodologies. On this, the proposed method outperforms the others in terms of accuracy and time of computation.

The requirement of cloud data sharing is becoming increasingly important to achieve flexible, scalable, reliable and fine grained access control. Currently decentralized access control model is widely used for data sharing in cloud environment. The existing multi authority decentralized data sharing model is discouraged for practical use due to its inefficiency in decryption and attribute revocation. Attribute revocation method is not supported by most of existing decentralized data sharing model. In this paper we propose a new data sharing model which supports decryption outsourcing as well as attribute revocation. In our proposed model most of the computational operations of decryption is transfered to cloud server which was earlier carried out by user. The use of linear secret sharing scheme strengthen the access control policy. The performance of our model outperforms LWABE in comparison with communication cost, secret key update ciphertext update and decryption cost. The cost incurred during encryption process of our model is almost same as LWABE.

The major drawbacks of the routing model energy consumption of cluster head (CH) are increased due to their additional functions. Most CH formations depend on a single criterion, which is that CH is generally elected on the basis of randomly or residual energy, sink distance or node density. If the head node is chosen on the basis of residual energy, but the higher energy node is too far away to sink, then the problem appears for election on CH. Similarly, the same problem will arise when distance is shorter to the sink and CH is created near the BS. So, a single criterion is not enough for the chosen head node. A multiple criterion is proposed to overcome this problem. In this paper multi-hopping communication is considered in the cases of intra-cluster and inter-cluster communication, where the CH formation is calculated using Analytical Hierarchy Process (AHP). The outcomes are recorded with 5% improvement when compared to conventional methods in terms of energy efficiency, network life span, control overhead, less cluster head deformation.

In the present scenario of educational technology inter-networking have provided a platform to access and share learning materials spread across various educational institutions. E-learning platforms provide an interface for accessing and sharing of heterogeneous educational resources (Learning Materials) to the various types of learners and content providers. These materials are created as the smallest digital imprints called as learning objects for the better usability. However, due to the variation in learner’s competence, providing a right content to the learner has become a cumbersome task. Consequently, a lot of personalization towards the creation and storage of the learning objects has become obligatory. Strategies involving learning style detections have provided a source of solution for the personalization. Yet, these methods are carried out with a limited number of learning styles and learning objects. And, most of these methods fail to upgrade the competency level of the learners as it provides less concentration to the learner’s capability. In order to personalize the system in consideration of the learner’s capability demands, there is a demand to understand the individual learner’s strength and weakness in the learning process. One of the features that decide the learner’s capability is the cognitive skill of the learners. It is desirable to maintain e-learning materials with respect to cognitive skill of the learners so the learning process becomes enjoyable. This paper focuses on the grouping of the e-materials with respect to the dominant cognitive content of learning objects. Hence, an organized storage mechanism is envisioned to aid the faster search and recovery of learning objects depending on the individual’s capabilities. The process is aimed to classify the materials that could be stored in a dedicated repository maintained in a distributed environment for a good usability.

In Natural Language Processing, word sense disambiguation (WSD) is an open challenge which improves the performance of the applications such as machine translation and information retrieval system. Many verbal languages will have many ambiguous words. The meaning of these ambiguous words differ per context. To choose the correct meaning of the word in the given context is known as WSD. In this article, the proposed work is to develop a WSD system using machine learning technique and knowledge-based approach for Telugu language. The knowledge resource used to develop the WSD system is Lexical Knowledge Base (LKB). The efficiency of WSD system is good when compared with other unsupervised approaches.