Evaluation of fortran vector compilers and preprocessors

Many scientific codes can achieve significant performance improvement when executed on a computer equipped with a vector processor. Vector constructs in source code should be recognized by a vectorizing compiler or preprocessor. This paper discusses, from a general point of view, how a vectorizing compiler/preprocessor can be evaluated. The areas discussed include data dependence analysis, IF loop analysis, nested loops, loop interchanging, loop collapsing, indirect addressing, use of temporary storage, and order of arithmetic. The ideas presented are based on vectorization of over a million lines of production codes and an extensive test suite developed to evaluate preprocessors under varying degrees of code complexity. Areas for future research are also discussed.     

Machine Learning with Dimensionality Reduction for DDoS Attack Detection

With the advancement of internet, there is also a rise in cybercrimes and digital attacks. DDoS (Distributed Denial of Service) attack is the most dominant weapon to breach the vulnerabilities of internet and pose a significant threat in the digital environment. These cyber-attacks are generated deliberately and consciously by the hacker to overwhelm the target with heavy traffic that genuine users are unable to use the target resources. As a result, targeted services are inaccessible by the legitimate user. To prevent these attacks, researchers are making use of advanced Machine Learning classifiers which can accurately detect the DDoS attacks. However, the challenge in using these techniques is the limitations on capacity for the volume of data and the required processing time. In this research work, we propose the framework of reducing the dimensions of the data by selecting the most important features which contribute to the predictive accuracy. We show that the ‘lite’ model trained on reduced dataset not only saves the computational power, but also improves the predictive performance. We show that dimensionality reduction can improve both effectiveness (recall) and efficiency (precision) of the model as compared to the model trained on ‘full’ dataset.     

An adaptive doctor-recommender system

ABSTRACT

Recommender systems use machine-learning techniques to make predictions about resources. The medical field is one where much research is currently being conducted on recommender system utility. In the last few years, the amount of information available online that relates to healthcare has increased tremendously. Patients nowadays are more aware and look for answers to healthcare problems online. This has resulted in a dire need of an effective reliable online system to recommend the physician that is best suited to a particular patient in a limited time. In this article, a hybrid doctor-recommender system is proposed, by combining different recommendation approaches: content base, collaborative and demographic filtering to effectively tackle the issue of doctor recommendation. The proposed system addresses the issue of personalization through analysing patient's interest towards selecting a doctor. It uses a novel adoptive algorithm to construct a doctor's ranking function. Moreover, this ranking function is used to translate patients’ criteria for selecting a doctor into a numerical base rating, which will eventually be used in the recommendation of doctors. The system has been evaluated thoroughly, and result show that recommendations are reasonable and can fulfil patient's demand for reliable doctor's selection effectively.     

Toward scalable cloud data center simulation using high-level architecture

Existing simulators are designed to simulate a few thousand nodes due to the tight integration of modules. Thus, with limited simulator scalability, researchers/developers are unable to simulate protocols and algorithms in detail, although cloud simulators provide geographically distributed data centers environment but lack the support for execution on distributed systems. In this paper, we propose a distributed simulation framework referred to as CloudSimScale. The framework is designed on top of highly adapted CloudSim with communication among different modules managed using IEEE Std 1516 (high-level architecture). The underlying modules can now run on the same or different physical systems and still manage to discover and communicate with one another. Thus, the proposed framework provides scalability across distributed systems and interoperability across modules and simulators.     

Correction to: Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification

Neural Computing and Applications - The article [Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification], written by [Muhammad Nabeel Asim,...     

Interference‐resilient spectrum sharing with collision detection and data recovery for cognitive radio networks

Data recovery under collision of primary/secondary users in cognitive radio networks is largely an unaddressed problem. Existing approaches require either (a) retransmissions, (b) interference‐free alignment of transmission parameters, or (c) prior knowledge for data recovery. In contrast to the existing approaches, the paper proposes estimation of channel state information based on received collided frames and recommends techniques for spectrum sharing, collision detection, and data recovery. Specifically, based on estimated channel vector, interference‐resilient spectrum‐sharing protocol and techniques are proposed for (a) identification of primary user (PU) activity before (or during) secondary user's (SU's) transmission, (b) MIMO channel estimation under collision, (c) optimal pilot permutation, and (d) interference cancellation (with/without receiver diversity). Results are reported for different baseband modulation techniques under frequency selective/flat fading channel scenarios that demonstrate the efficacy of a proposed work.     

Solution of nonlinear boundary layer equation for flat plate via optimal homotopy asymptotic method

In this article, we use the optimal homotopy asymptotic method (OHAM) to compute the solution of two‐dimensional incompressible laminar boundary layer flow over a flat plate (Blasius problem). The obtained results for the stream function and velocity profile were comparable in terms of accuracy with that obtained by Esmaeilpour and Ganji (2007) who studied the same problem using the homotopy perturbation method and results obtained by using a numerical method (RK4). The good agreement obtained shows the effectiveness of OHAM. © 2013 Wiley Periodicals, Inc. Heat Trans Asian Res, 43(3): 197–203, 2014; Published online 19 June 2013 in Wiley Online Library ( wileyonlinelibrary.com/journal/htj ). DOI 10.1002/htj.21070     

Letter to the editor

Fifty-one surficial sediment samples were collected from 21 stations situated in the nearshore area of the central Red Sea. Sediments were analysed for the major elements Ca, Mg, Al and Fe and the trace elements Mn, Cu, Zn, Ni, Cr, Co, Cd and Pb. The major grain-size classes gravel, sand and mud as well as organic carbon and carbonates were determined. Some samples were subjected to X-ray mineralogy. Sediments were generally coarse-grained but the fine fraction increased in the sheltered areas. The major mineralogical constituents of the sediments were carbonate minerals (calcite and aragonite) and detrital silicates (quartz and feldspars). Organic carbon was low (0.38%) in the northern part of the area but greatly increased in areas receiving direct sewage discharge (1-9.5%). Concentrations of trace elements were highly variable and appeared to be related to the grain size and the mineralogical composition. Generally, trace element concentrations were positively correlated with the mud and Al and Fe contents. In the polluted sites these regular associations were perturbed and trace elements were associated with organic matter. Element/Al ratios were used to group the sampling sites according to their degree of enrichment. The resulting order was different from that based on the absolute concentrations. The use of a combination of the element/Al ratio and the absolute concentration resulted in the distinction of highly and moderately contaminated and uncontaminated sites.     

Optimal homotopy asymptotic method for heat transfer in hollow sphere with robin boundary conditions

In this article, we have investigated heat transfer from a hollow sphere using a powerful and relatively new semi‐analytic technique known as the optimal homotopy asymptotic method (OHAM). Robin boundary conditions are applied on both the inner and outer surfaces. The effects of Biot numbers, uniform heat generation, temperature‐ dependent thermal conductivity, and temperature parameters on the dimensionless temperature and heat transfer are investigated. The results of OHAM are compared with a numerical method and are found to be in good agreement. It is shown that the dimensionless temperature increases with an increase in Biot number at the inner surface and temperature and heat generation parameters, whereas it decreases with an increase in the Biot number at the outer surface and the dimensionless thermal conductivity and radial distance parameters. © 2013 Wiley Periodicals, Inc. Heat Trans Asian Res 43(2): 124‐133, 2014; Published online 20 June 2013 in Wiley Online Library ( wileyonlinelibrary.com/journal/htj ). DOI 10.1002/htj.21067