A knowledge-based learning system is developed to demonstrate that intelligently selecting a subset of examples based on domain knowledge for rule induction can be more productive than using all provided examples. Kno...
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Cloud computing has emerged as one of the most influential paradigms in the IT industry. In this, new computing technology requires users to entrust their valuable data to cloud providers, there have been increasing s...
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Cloud computing has emerged as one of the most influential paradigms in the IT industry. In this, new computing technology requires users to entrust their valuable data to cloud providers, there have been increasing security and privacy concerns on outsourced data. Several schemes employing attribute-based encryption (ABE) have been proposed for access control of outsourced data in cloud computing. The most[1][2] of them suffer from inflexibility in implementing complex access control policies. In this paper, allowing cloud service providers (CSPs), which are not in the same trusted domains as enterprise users, to take care of confidential data, may raise potential security and privacy issues. To keep the sensitive user data confidential against untrusted CSPs, a Natural way is to apply cryptographic approaches, by disclosing Decryption keys only to authorized users. But also provide full delegation, and scalability, so as to best serve the needs of accessing data anytime and anywhere, delegating within enterprises, and achieving a dynamic set of users. [3][4]HASBE employs multiple value assignments for access expiration time to deal with user revocation more efficiently than existing schemes. It can be providefine-grained access control and full delegation. Finally, we propose a scalable revocation scheme by delegating to the CSP most of the computing tasks in revocation, to achieve a dynamic set of users efficiently.
Rocchio's relevance feedback model is a classic query expansion method and it has been shown to be effective in boosting information retrieval performance. The selection of expansion terms in this method, however,...
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A number of multi-objective genetic algorithms (MOGAs) have been developed to obtain Pareto optimal solutions for multi-objective optimization problems. However, as these methods involve probabilistic algorithms, ther...
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A number of multi-objective genetic algorithms (MOGAs) have been developed to obtain Pareto optimal solutions for multi-objective optimization problems. However, as these methods involve probabilistic algorithms, there is no guarantee that the global search will be conducted in the design variable space. In such cases, there are unsearched areas in the design variable space, and the obtained Pareto solutions may not be truly optimal. In this paper, we propose an optimization method called NSDIRECT-GA to conduct a global search over the design variable space as much as possible, which improves the reliability of the obtained Pareto solutions. The effectiveness of NSDIRECT-GA was examined through numerical experiments. NSDIRECT-GA can obtain not only Pareto solutions, but also grasp the landscape of the search space, which results in higher reliability of the obtained solutions compared to MOGAs.
In this paper we give a brief history on conceptual modeling in computerscience and we discuss state-of-the-art approaches. It is claimed that a number of problems remain to be solved. "Schema-first" is no ...
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A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exp...
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A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki's neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm.
Extreme learning machine (ELM) is a single hidden layer feed forward neural network (SLFN). It expanded to semi-supervised ELM (SSELM) to deal with unlabeled data problem. In such a problem, labeled data is either rar...
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Extreme learning machine (ELM) is a single hidden layer feed forward neural network (SLFN). It expanded to semi-supervised ELM (SSELM) to deal with unlabeled data problem. In such a problem, labeled data is either rare or not cheap. Although SSELM has a good generalization performance, it might be influenced by heterogeneous data from different sources. It is discernible that unbalanced data issue inflicts obstacles in real-world applications including medical diagnostics and credit card fraud detection. To deal with this issue in this paper, we introduce a multi-kernel semi-supervised ELM (MKSSELM). It is more flexible to deal with discrete data from various sources. It matches diverse information from disparate sources and it shows distinction among the data. Instead of using one kernel, we optimize both ELM structural parameters and kernel combination weights. The optimization process accomplished by commanding an L1-norm as a regulation term. Meanwhile, a non-negative constraint on the kernel combination weights is used. The validity of MKSSELM algorithm is confirmed through classification results on real-world benchmark datasets. The proposed algorithm achieved better or comparable results with respect to previous approaches.
This study proposes to examine the effects of Stefan blowing and thermal radiation on the Marangoni convective velocity of a trihybrid nanofluid across a disk by using LTNE (local thermal non-equilibrium) effect and h...
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This study proposes to examine the effects of Stefan blowing and thermal radiation on the Marangoni convective velocity of a trihybrid nanofluid across a disk by using LTNE (local thermal non-equilibrium) effect and heat generation. Thermal and mass transfer properties are studied utilizing the Cattaneo-Christov mass and heat flux model. Nanoparticles of silicon carbide ( S i C ) , cobalt oxide ( C o 3 O 4 ) , and diamond ( N D ) dissolved in water ( H 2 O ) make up the trihybrid nanofluid flow model. In this work, the trihybrid nanofluid's rate of heat transfer based on diamond − S i C − C o 3 O 4 / water is investigated by c contrasting the improved model with the Hamilton-Crosser model in its classical form. Thermal transmission efficiency in nuclear reactors, energy storage devices, and microelectronic cooling is improved by the investigation of Cattaneo-Christov flux in trihybrid nanofluids with LTNE effects. The design of effective cooling mechanisms in the automotive and aerospace industries is also aided by the optimization of thermal conductivity predictions through the use of classical and improved Hamilton-Crosser models. The system of PDEs is converted into a non-linear ODE system by applying the required transformations. The Bvp4c method is applied to solve this problem mathematically. When the values of the thermal and solutal relaxation parameters increase, the thermal and solutal distributions decrease.
Collective knowledge is understood as the common knowledge state of a collective consisting of autonomous units. The knowledge states referred from these autonomous units to some degree reflect the real knowledge stat...
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The Traditional Chinese Medicine's ancient literature recorded the massive medical theories and abundant medical experiences. To better understand and utilize, the knowledge from the literature, the Acupuncture an...
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