National University of Computer and Emerging Sciences

About: National University of Computer and Emerging Sciences is a education organization based out in Islamabad, Pakistan. It is known for research contribution in the topics: Computer science & The Internet. The organization has 1506 authors who have published 2438 publications receiving 26786 citations.

Thermophysical analysis for three-dimensional MHD stagnation-point flow of nano-material influenced by an exponential stretching surface

Abstract: In the present paper a theoretical investigation is performed to analyze heat and mass transport enhancement of water-based nanofluid for three dimensional (3D) MHD stagnation-point flow caused by an exponentially stretched surface. Water is considered as a base fluid. There are three (3) types of nanoparticles considered in this study namely, CuO (Copper oxide), Fe3O4 (Magnetite), and Al2O3 (Alumina) are considered along with water. In this problem we invoked the boundary layer phenomena and suitable similarity transformation, as a result our three dimensional non-linear equations of describing current problem are transmuted into nonlinear and non-homogeneous differential equations involving ordinary derivatives. We solved the final equations by applying homotopy analysis technique. Influential outcomes of aggressing parameters involved in this study, effecting profiles of temperature field and velocity are explained in detail. Graphical results of involved parameters appearing in considered nanofluid are presented separately. It is worth mentioning that Skin-friction along x and y-direction is maximum for Copper oxide-water nanofluid and minimum for Alumina-water nanofluid. Result for local Nusselt number is maximum for Copper oxide-water nanofluid and is minimum for magnetite-water nanofluid.

Optimal Resource Allocation in Energy-Efficient Internet-of-Things Networks With Imperfect CSI

Abstract: Internet of Things (IoT) is an emerging networking paradigm that enhances smart device communications through Internet-enabled systems. Due to massive IoT devices connectivity with economic and greenhouse emission effects, the energy-efficiency poses critical concerns. Under imperfect channel state information (CSI), this article investigates joint optimization of user selection, power allocation, and the number of activated base station (BS) antennas of multiple IoT devices considering the transmit power and different Quality-of-Service (QoS) requirements in combinatorial mode to maximize energy-efficiency. The optimization problem formulated is a nonconvex mixed-integer nonlinear programming, which is NP-hard with no practical solution. The primal optimization problem is transformed into a tractable convex optimization problem and separated into inner and outer loop subproblems. This article proposes a joint energy-efficient iterative algorithm, which utilizes a successive convex approximation technique and the Lagrangian dual decomposition method to achieve near-optimal solutions with guaranteed convergence. The simulation results are provided to evaluate the proposed algorithm and its significant performance gain over the baseline algorithms in terms of energy-efficiency maximization.

Guidelines to Select Machine Learning Scheme for Classification of Biomedical Datasets

Abstract: Biomedical datasets pose a unique challenge to machine learning and data mining algorithms for classification because of their high dimensionality, multiple classes, noisy data and missing values. This paper provides a comprehensive evaluation of a set of diverse machine learning schemes on a number of biomedical datasets. To this end, we follow a four step evaluation methodology: (1) pre-processing the datasets to remove any redundancy, (2) classification of the datasets using six different machine learning algorithms; Naive Bayes (probabilistic), multi-layer perceptron (neural network), SMO (support vector machine), IBk (instance based learner), J48 (decision tree) and RIPPER (rule-based induction), (3) bagging and boosting each algorithm, and (4) combining the best version of each of the base classifiers to make a team of classifiers with stacking and voting techniques. Using this methodology, we have performed experiments on 31 different biomedical datasets. To the best of our knowledge, this is the first study in which such a diverse set of machine learning algorithms are evaluated on so many biomedical datasets. The important outcome of our extensive study is a set of promising guidelines which will help researchers in choosing the best classification scheme for a particular nature of biomedical dataset.

Embedding neural networks for semantic association in content based image retrieval

Abstract: Content based image retrieval (CBIR) systems provide potential solution of retrieving semantically similar images from large image repositories against any query image. The research community are competing for more effective ways of content based image retrieval, so they can be used in serving time critical applications in scientific and industrial domains. In this paper a Neural Network based architecture for content based image retrieval is presented. To enhance the capabilities of proposed work, an efficient feature extraction method is presented which is based on the concept of in-depth texture analysis. For this wavelet packets and Eigen values of Gabor filters are used for image representation purposes. To ensure semantically correct image retrieval, a partial supervised learning scheme is introduced which is based on K-nearest neighbors of a query image, and ensures the retrieval of images in a robust way. To elaborate the effectiveness of the presented work, the proposed method is compared with several existing CBIR systems, and it is proved that the proposed method has performed better then all of the comparative systems.

Testing Weak Form of Efficient Market Hypothesis: Empirical Evidence from South-Asia

Abstract: 2 Abstract: With the advent of world crisis in developed western economies during first decade of 21 century st investors' focus shifted towards East. South Asian markets displayed outstanding performance in this era. One of the major attractions for investors is the market efficiency of underlying economy. This study has examined the weak form of efficient market hypothesis on the four major stock exchanges of South Asia including, India, Pakistan, Bangladesh and Sri Lanka. Historical index values on a monthly, weekly and daily basis for a period of 14 Years (1997-2011) were used for analysis. We applied four statistical tests including runs test, serial correlation, unit root and variance ratio test. Findings suggest that none of the four major stock markets of south-Asia follows Random-walk and hence all these markets are not the weak form of efficient market. To our knowledge this is the first ever study is being conducted which covers leading South Asian markets, hence an evidence on market efficiency of this region is being contributed in literature.