Assoc Prof Nasimul Noman

Internet of things (IoT) has significantly altered the traditional lifestyle to a highly technologically advanced society. Some of the significant transformations that have been achieved through IoT are smart homes, smart transportation, smart city, and control of pollution. A considerable number of studies have been conducted and continues to be done to increase the use of technology through IoT. Furthermore, the research about IoT has not been done fully in improving the application of technology through IoT. Besides, IoT experiences several problems that need to be considered in order to get the full capability of IoT in changing society. This research paper addresses the key applications of IoT, the architecture of IoT, and the key issues affecting IoT. In addition, the paper highlights how big data analytics is essential in improving the effectiveness of IoT in various applications within society.

Noise is inevitable in microarray data. The real challenge lies in identifying the biomolecular interactions in spite of the significant noise level present in the expression proflies that current technology offers. In this paper, we study the usefulness of an evolutionary approach in reverse engineering the biomolecular connections in a gene circuit from observed system dynamics that is contaminated with noise. The method uses an Information Criteria based fitness evaluation for selecting models, represented in decoupled S-system formalism, instead of the conventional Mean Squared Error (MSE) based fitness evaluation. The suitability of the method is tested in experiments of reconstructing an artificial network from gene expression profiles with varying noise levels. The proposed fitness function has been found more suitable for identifying correct network topology and for estimating the accurate parameter values compared to the existing one. © 2006 IEEE.

This paper proposes an improved evolutionary method for constructing the underlying network structure and inferring effective kinetic parameters from the time series data of gene expression using decoupled S-system formalism. We employed Trigonometric Differential Evolution (TDE) as the optimization engine of our algorithm for capturing the dynamics in gene expression data. A more effective fitness function for attaining the sparse structure, which is the hallmark of biological networks, has been applied. Experiments on artificial genetic network show the power of the algorithm in constructing the network structure and predicting the regulatory parameters. The method is used to evaluate interactions between genes in the SOS signaling pathway in Escherichia coli using gene expression data.

Because of the number of different architectures, numerous settings of their hyper-parameters and disparity among their sizes, it is difficult to equitably compare various deep neural network (DNN) architectures for sentence classification. Evolutionary algorithms are emerging as a popular method for the automatic selection of architectures and hyperparameters for DNNs whose generalisation performance is heavily impacted by such settings. Most of the work in this area is done in the image domain, leaving text analysis, another prominent application domain of deep learning, largely absent. Besides, literature presents conflicting claims regarding the superiority of one DNN architecture over others in the context of sentence classification. To address this issue, we propose a genetic algorithm (GA) for optimising the architectural and hyperparameter settings in different DNN types for sentence classification. To enable the representation of the wide variety of architectures and hyperparameters utilised in DNNs, we employed a generalised and flexible encoding scheme in our GA. Our study involves optimising two convolutional and three recurrent architectures to ensure a fair and unbiased evaluation of their performance. Furthermore, we explore the effects of using F1 score versus accuracy as a performance metric during evolutionary optimisation of those architectures. Our results, using ten datasets, show that, in general, the architectures and hyperparameters evolved using the F1 score tended to outperform those evolved using accuracy and in the case of CNN and BiLSTM the results were significant in statistical measures. Of the five architectures considered, the GA-evolved gated recurrent unit (GRU) performed the strongest overall, achieving good generalisation performance while using relatively few trainable parameters, establishing GRU as the preferred architecture for the sentence classification task. The optimised architectures exhibited comparable performance with the state-of-the-art, given the large difference in trainable parameters.

Background: Like it happens in other tumours, glioma cells co-evolve in a microenvironment consisting of bona fide tumour cells as well as a range of parenchymal cells, which produces numerous signalling molecules. Recently, the results of an in silico experiment suggested that a combination therapy that would target multiple key cytokines at the same time may be more effective for suppressing the growth of a tumour. The in silico experiments also showed that the optimal combination therapy is very much dependent on a patient's molecular profile. Method: In this work, we employ evolutionary algorithms for designing optimal combination therapy tailored to the patient's tumour microenvironment. Experiments were performed using a state-of-the-art glioma microenvironment model, capable of imitating many characteristics of human glioma development, and many virtual patient profiles. Conclusions: Results show that the therapies designed by the presented memetic algorithm were very effective in impeding tumour growth and were tailored to the patient's personal tumour microenvironment.

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble's output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (a, ß) - k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer's disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.

In this paper, we introduce our approach for evolving reaction networks. It is an efficient derivative of the neuroevolution of augmenting topologies algorithm directed at the evolution of biochemical systems or molecular programs. Our method addresses the problem of meaningful crossovers between two chemical reaction networks of different topologies. It also builds on features such as speciation to speed up the search, to the point where it can deal with complete, realistic mathematical models of the biochemical processes. We demonstrate this framework by evolving credible biochemical answers to challenging autonomous molecular problems: in vitro batch oscillatory networks that match specific oscillation shapes. Our experimental results suggest that the search space is efficiently covered and that, by using crossover and preserving topological innovations, significant improvements in performance can be obtained for the automatic design of molecular programs.