Rafiul Hassan

Adaptive workflow scheduling for dynamic grid and cloud computing environment

Effective 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

Artificial neural networks in smart homes

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.

Fuzzy Logic-based Recognition of Gait Changes due to Trip-related Falls

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

A voting approach to identify a small number of highly predictive genes using multiple classifiers

BackgroundMicroarray 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.ResultsBy using a new type of multiple classifier voting approach, we have identified gene sets that can predict breast cancer prognosis accurately, for a range of classification algorithms. Unlike a wrapper approach, our method is not specialised towards a single classification technique. Experimental analysis demonstrates higher prediction accuracies for our sets of genes compared to previous work in the area. Moreover, our sets of genes are generally more compact than those previously proposed. Taking a biological viewpoint, from the literature, most of the genes in our sets are known to be strongly related to cancer.ConclusionWe show that it is possible to obtain superior classification accuracy with our approach and obtain a compact gene set that is also biologically relevant and has good coverage of different biological processes.

Network topology measures for identifying disease-gene association in breast cancer

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.

A new approach to enhance the performance of decision tree for classifying gene expression data

BackgroundGene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor performance specially when classifying gene expression dataset.ResultsBy using a new decision tree algorithm where, each node of the tree consists of more than one gene, we enhance the classification performance of traditional decision tree classifiers. Our method selects suitable genes that are combined using a linear function to form a derived composite feature. To determine the structure of the tree we use the area under the Receiver Operating Characteristics curve (AUC). Experimental analysis demonstrates higher classification accuracy using the new decision tree compared to the other existing decision trees in literature.ConclusionWe experimentally compare the effect of our scheme against other well known decision tree techniques. Experiments show that our algorithm can substantially boost the classification performance of the decision tree.

HMM-Fuzzy Model for Recognition of Gait Changes due to Trip-related Falls

This paper reports the use of HMM-based fuzzy rules generation for identifying the differences in gait between people with tendencies to fall and healthy people. This work is built on the work reported earlier by the authors where fuzzy rules were successfully applied in gait pattern recognition. This paper reports the hybridization of HMM with fuzzy logic for improving the recognition accuracy. Gait features were extracted from minimum foot clearance (MFC) data that was collected during continuous walking on a treadmill from 20 elderly subjects, 10 healthy and 10 with reported balance problem and history of falls. The input feature space was divided into a number of groups based on HMM generated log-likelihood values, and consequently each group was applied to construct a new fuzzy rule. Gradient descent method was used to optimize the parameters of the generated rules. These were then applied to recognize differences in the gait in subjects with trip-related falls history. The models performance was evaluated using a cross- validation protocol applied on the training and testing data. The HMM-Fuzzy model outperformed the Fuzzy-based gait recognition as reflected both in the receiver operating characteristics (ROC) results as well as absolute percentage accuracy

Periodic Associated Sensor Patterns Mining from Wireless Sensor Networks

Mining interesting knowledge from the massive amount of data gathered in wireless sensor networks is a challenging task. Works reported in literature all-confidence measure based associated sensor patterns can captures association-like co-occurrences and the strong temporal correlations implied by such co-occurrences in the sensor data. However, when the user given all-confidence threshold is low, a huge amount of patterns are generated and mining these patterns may not be space and time efficient. Temporal periodicity of pattern appearance can be regarded as an important criterion for measuring the interestingness of associated patterns in WSNs. Associated sensor patterns that occur after regular intervals is called periodic associated sensor patterns. Even though mining periodic associated sensor patterns from sensor data stream is extremely important in many real-time applications, no such algorithm has been proposed yet. In this paper, we propose a compact tree structure called Periodic Associated Sensor Pattern-tree (PASP-tree) and an efficient mining approach for finding periodic associated sensor patterns (PASPs) from WSNs. Extensive performance analyses show that our technique is time and memory efficient in finding periodic associated sensor patterns.

A machine learning approach for prediction of pregnancy outcome following IVF treatment

Infertility affects one out of seven couples around the world. Therefore, the best possible management of the in vitro fertilization (IVF) treatment and patient advice is crucial for both patients and medical practitioners. The ultimate concern of the patients is the success of an IVF procedure, which depends on a number of influencing attributes. Without any automated tool, it is hard for the practitioners to assess any influencing trend of the attributes and factors that might lead to a successful IVF pregnancy. This paper proposes a hill climbing feature (attribute) selection algorithm coupled with automated classification using machine learning techniques with the aim to analyze and predict IVF pregnancy in greater accuracy. Using 25 attributes, we assessed the prediction ability of IVF pregnancy success for five different machine learning models, namely multilayer perceptron (MLP), support vector machines (SVM), C4.5, classification and regression trees (CART) and random forest (RF). The prediction ability was measured in terms of widely used performance metrics, namely accuracy rate, F-measure and AUC. Feature selection algorithm reduced the number of most influential attributes to nineteen for MLP, sixteen for RF, seventeen for SVM, twelve for C4.5 and eight for CART. Overall, the most influential attributes identified are: ‘age’, ‘indication’ of fertility factor, ‘Antral Follicle Counts (AFC)’, ‘NbreM2’, ‘method of sperm collection’, ‘Chamotte’, ‘Fertilization rate in vitro’, ‘Follicles on day 14’ and ‘Embryo transfer day.’ The machine learning models trained with the selected set of features significantly improved the prediction accuracy of IVF pregnancy success to a level considerably higher than those reported in the current literature.

Breast Density Classification for Cancer Detection Using DCT-PCA Feature Extraction and Classifier Ensemble.

It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN.