Brain cancer classification is an important step that depends on the physician's knowledge and experience. An automated tumor classification system is very essential to support radiologists and physicians to identify brain tumors. However, the accuracy of current systems needs to be improved for suitable treatments. In this paper, we propose a hybrid feature extraction method with a regularized extreme learning machine (RELM) for developing an accurate brain tumor classification approach. The approach starts by preprocessing the brain images by using a min-max normalization rule to enhance the contrast of brain edges and regions. Then, the brain tumor features are extracted based on a hybrid method of feature extraction. Finally, a RELM is used for classifying the type of brain tumor. To evaluate and compare the proposed approach, a set of experiments is conducted on a new public dataset of brain images. The experimental results proved that the approach is more effective compared with the existing state-of-the-art approaches, and the performance in terms of classification accuracy improved from 91.51% to 94.233% for the experiment of the random holdout technique.INDEX TERMS Brain tumor classification, hybrid feature extraction, NGIST features, PCA, regularized extreme learning machine.

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SUMMARYEffective scheduling is a key concern for the execution of performance‐driven grid applications such as workflows. In this paper, we first define the workflow scheduling problem and describe the existing heuristic‐based and metaheuristic‐based workflow scheduling strategies in grids. Then, we propose a dynamic critical‐path‐based adaptive workflow scheduling algorithm for grids, which determines efficient mapping of workflow tasks to grid resources dynamically by calculating the critical path in the workflow task graph at every step. Using simulation, we compared the performance of the proposed approach with the existing approaches, discussed in this paper for different types and sizes of workflows. The results demonstrate that the heuristic‐based scheduling techniques can adapt to the dynamic nature of resource and avoid performance degradation in dynamically changing grid environments. Finally, we outline a hybrid heuristic combining the features of the proposed adaptive scheduling technique with metaheuristics for optimizing execution cost and time as well as meeting the users requirements to efficiently manage the dynamism and heterogeneity of the hybrid cloud environment. Copyright © 2013 John Wiley & Sons, Ltd.

Many wonderful technological developments in recent years have opened up the possibility of using smart or intelligent homes for a number of important applications. Typical applications range from overall lifestyle improvement to helping people with special needs such as the elderly and the disabled to improve their independence, safety and security at home. Research in the area has looked into ways of making the home environment automatic and automated devices have been designed to help the disabled people. Also, possibilities of automated health monitoring systems and usage of automatic controlled devices to replace caregiver and housekeeper have received significant attention. Most of the models require acquisition of useful information from the environment, identification of the significant features and finally usage of some sort of machine learning techniques for decision making and planning for the next action to be undertaken. This chapter specifically focuses on neural networks applications in building a smart home environment.

BackgroundMassive biological datasets are generated in different locations all over the world. Analysis of these datasets is required in order to extract knowledge that might be helpful for biologists, physicians and pharmacists. Recently, analysis of biological networks has received a lot of attention, as an understanding of the network can reveal information about life at the cellular level. Biological networks can be generated that examine the interaction between proteins or the relationship amongst different genes at the expression level. Identifying information from biological networks is recognized as a significant challenge, due to the inherent complexity of the structures. Computational techniques are used to analyze such complex networks with varying success.ResultsIn this paper, we construct a new method for predicting phenotype-gene association in breast cancer using biological network analysis. Several network topological measures have been computed and fed as features into two classification models to investigate phenotype-gene association in breast cancer. More importantly, to overcome the problem of the skewed datasets, a synthetic minority oversampling technique (SMOTE) is adapted in order to transform an imbalanced dataset to a balanced one. We have applied our method on the gene co-expression network (GCN), protein–protein interaction network (PPI), and the integrated functional interaction network (FI), which combined the PPIs and gene co-expression, amongst others. We assess the quality of our proposed method using a slightly modified cross-validation.ConclusionsOur method can identify phenotype-gene association in breast cancer. Moreover, use of the integrated functional interaction network (FI) has the potential to reveal more information and hidden patterns than the other networks. The software and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/NetTop.zip.

The main aim of this paper is to explore application of fuzzy rules for automated recognition of gait changes due to falling behaviour. Minimum foot clearance (MFC) during continuous walking on a treadmill was recorded on 10 healthy elderly and 10 elderly with reported balance problem and tripping falls. MFC histogram characteristic features were used as inputs to the set of fuzzy rules; the features were extracted based on estimating the clusters in the data. Each of the clusters found corresponded to a new fuzzy rule, which were then applied to associate the input space to an output region. Gradient descent method was used to optimise the rule parameters. Both cross-validation and Jack-knife (leave-one-out) techniques were utilized for training the models and subsequently, testing the performance of the optimized fuzzy model. Receiver operating characteristics (ROC) plots, as well as accuracy rates were used to evaluate the performance of the developed model. Test results indicated up to a maximum of 95% accuracy in discriminating the healthy and balance-impaired gait patterns. These results suggest good potentials for fuzzy logic to use as gait diagnostics.

Background: Microarray gene expression profiling has provided extensive datasets that can describe characteristics of cancer patients. An important challenge for this type of data is the discovery of gene sets which can be used as the basis of developing a clinical predictor for cancer. It is desirable that such gene sets be compact, give accurate predictions across many classifiers, be biologically relevant and have good biological process coverage.

The widening gap between CPU and memory speed has made caches an integral feature of modern highperformance processors. The high degree of configurability of cache memory can require extensive design space exploration and is generally performed using execution-driven or trace-driven simulation. Execution-driven simulators can be highly accurate but require a detailed development flow and may impose performance costs. Trace-driven simulators are an efficient alternative but maintaining large traces can present storage and portability problems. We propose a distribution-driven trace generation methodology as an alternative to traditional executionand trace-driven simulation. An adaptation of the Least Recently Used Stack Model is used to concisely capture the key locality features in a trace and a twostate Markov chain model is used for trace generation. Simulation and analysis of a variety of embedded application traces demonstrate the cacheability characteristics of the synthetic traces are generally very well preserved and similar to their real trace, and we also highlight the potential performance improvement over ISA emulation.