Rosni Abdullah

Normal Forms of Spiking Neural P Systems With Anti-Spikes

Spiking neural P systems with anti-spikes (ASN P systems, for short) are a variant of spiking neural P systems, which were inspired by inhibitory impulses/spikes or inhibitory synapses. In this work, we consider normal forms of ASN P systems. Specifically, we prove that ASN P systems with pure spiking rules of categories (a, a) and (a, a̅) without forgetting rules are universal as number generating devices. In an ASN P system with spiking rules of categories (a, a̅) and (a̅, a) without forgetting rules, the neurons change spikes to anti-spikes or change anti-spikes to spikes; such systems are proved to be universal. We also prove that ASN P systems with inhibitory synapses using pure spiking rules of category (a, a) and forgetting rules are universal. These results answer an open problem and improve a corresponding result from [IJCCC, IV(3), 2009, 273-282].

Automatic Topic Identification Using Ontology Hierarchy

This paper proposes a method of using ontology hierarchy in automatic topic identification. The fundamental idea behind this work is to exploit an ontology hierarchical structure in order to find a topic of a text. The keywords that are extracted from a given text will be mapped onto their corresponding concepts in the ontology. By optimizing the corresponding concepts, we will pick a single node among the concepts nodes that we believe is the topic of the target text. However, a limited vocabulary problem is encountered while mapping the keywords onto their corresponding concepts. This situation forces us to extend the ontology by enriching each of its concepts with new concepts using the external linguistics knowledge-base (WordNet). Our intuition of a high number keywords mapped onto the ontology concepts is that our topic identification technique can perform at its best.

Harmony Search Based Supervised Training of Artificial Neural Networks

This paper presents a novel technique for the supervised training of feed-forward artificial neural networks (ANN) using the Harmony Search (HS) algorithm. HS is a stochastic meta-heuristic that is inspired from the improvisation process of musicians. Unlike Backpropagation, HS is non-trajectory driven. By modifying an existing improved version of HS & adopting a suitable ANN data representation, we propose a training technique where two of HS probabilistic parameters are determined dynamically based on the best-to-worst (BtW) harmony ratio in the current harmony memory instead of the improvisation count. This would be more suitable for ANN training since parameters and termination would depend on the quality of the attained solution. We have empirically tested and verified our technique by training an ANN with a benchmarking problem. In terms of overall training time and recognition, our results have revealed that our method is superior to both the original improved HS and standard Backpropagation.

Protein Conformational Search Using Bees Algorithm

Proteins perform many biological functions in the human body. The structure of the protein determines its function. In order to predict the protein structure computationally, protein must be represented in a proper representation. To this end, an energy function is used to calculate its energy and a conformational search algorithm is used to search the conformational search space to find the lowest free energy conformation. In this paper, the Bees Algorithm, i.e. a Swarm Intelligence based algorithm inspired by the foraging behaviour of honey bees colony, is adapted to search the protein conformational search space. The algorithm was able to find the lowest free energy conformation of Met-enkephaline using ECEPP/2 force fields.

Protein Tertiary Structure Prediction Using Artificial Bee Colony Algorithm

Proteins are essential for the biological processes in the human body. They can only perform their functions when they fold into their tertiary structure. Protein structure can be determined experimentally and computationally. Experimental methods are time consuming and high-priced and it is not always feasible to identify the protein structure experimentally. In order to predict the protein structure using computational methods, the problem is formulated as an optimization problem and the goal is to find the lowest free energy conformation. In this paper, Artificial Bee Colony algorithm (ABC) is a swarm intelligence based optimization algorithm inspired by the behaviour of honey bee foraging. This algorithm is adapted to search the protein conformational search space to find the lowest free energy conformation. Interestingly, the algorithm was able to find the lowest free energy conformation for a test protein (i.e. Met enkephaline) using ECEPP/2 force fields.

A dynamic self-adaptive harmony search algorithm for continuous optimization problems

In solving global optimization problems for continuous functions, researchers often rely on metaheuristic algorithms to overcome the computational drawbacks of the existing numerical methods. A metaheuristic is an evolutionary algorithm that does not require the functions in the problem to satisfy specific conditions or mathematical properties. A recently proposed metaheuristic is the harmony search algorithm, which was inspired by the music improvisation process and has been applied successfully in the solution of various global optimization problems. However, the overall performance of this algorithm and its convergence properties are quite sensitive to the initial parameter settings. Several improvements of the harmony search algorithm have been proposed to incorporate self-adaptive features. In these modified versions of the algorithm, the parameters are automatically tuned during the optimization process to achieve superior results. This paper proposes a new dynamic and self-adaptive harmony search algorithm in which two of the optimization parameters, the pitch adjustment rate and the bandwidth, are auto-tuned. These two parameters have substantial influence on the quality of the final solution. The proposed algorithm utilizes two new quality measures to dynamically drive the optimization process: the current best-to-worst ratio of the harmony memory fitness function and the improvisation acceptance rate. The key difference between the proposed algorithm and most competing methods is that the values of the pitch adjustment rate and bandwidth are determined independently of the current improvisation count and therefore vary dynamically rather than monotonically. The results demonstrate the superiority of the proposed algorithm over various other recent methods based on several common benchmarking functions.

A hybrid harmony search algorithm for ab initio protein tertiary structure prediction

Predicting the tertiary structure of proteins from their linear sequence is a big challenge in biology. The existing computational methods are not powerful enough to search for the precise structure in a huge conformational space. This inadequate capability of the computational methods, however, is a major obstacle when trying to tackle this problem. The observations of some previous studies have revealed much interest in hybridizing a local search-based metahuristic algorithm within the population-based metahuristic algorithm. This study introduces a hybrid harmony search algorithm (HHSA) as a means to solve ab initio protein tertiary structure prediction problem. In HHSA, the iterated local search (ILS) is incorporated with the harmony search algorithm (HSA) to empower it so as to find the local optimal solution within the search space of the new harmony. Furthermore, the global-best concept of particle swarm optimization (PSO) is incorporated in memory consideration as a selection scheme to accelerate the convergence speed. The HHSA predicts the tertiary structure of a protein giving its sequence alone (i.e., from scratch). Our algorithm converges faster than the classical harmony search algorithm. We evaluate our algorithm using two protein sequences. The results show that our algorithm can find more precise solutions than other previous studies.

ACOPIN: An ACO Algorithm with TSP Approach for Clustering Proteins from Protein Interaction Network

In this paper, we proposed an ant colony optimization (ACO) algorithm together with traveling salesman problem (TSP) approach to investigate the clustering problem in protein interaction networks (PIN). We named this combination as ACOPIN. The purpose of this work is two-fold. First, to test the efficacy of ACO in clustering PIN and second, to propose the simple generalization of the ACO algorithm that might allow its application in clustering proteins in PIN. We split this paper to three mainsections. First, we describe the PIN and clustering proteins in PIN. Second, we discuss the steps involved in each phase of ACO algorithm. Finally, we present some results of the investigation with the clustering patterns.

Performance enhancement of smith-waterman algorithm using hybrid model: Comparing the MPI and hybrid programming paradigm on SMP clusters

Nowadays, database pattern searching is the most heavily used operation in computational biology. Indeed, sequence alignment algorithm plays an important role to find the homologous groups of sequences which may help to determine the function of new sequences. Meanwhile Smith-Waterman algorithm is one of the most prominent pattern matching algorithms. However, it cost the large quantity of time and resource power. By the aid of parallel hardware and software architecture it becomes more feasible to get the fast and accurate result in efficient time. In this paper, Smith-Waterman algorithm is parallelized base on various types of parallel programming, pure MPI, pure OpenMP and Hybrid MPI-OpenMP model. In addition, based on the experiments it will be proved that hybrid programming which employ the coarse grain and fine grain parallelization, is more efficient compare with pure MPI and pure OpenMP in cluster of SMP machines.

Fast Dynamic Programming Based Sequence Alignment Algorithm

Protein sequence alignment is basic operation mostly used in protein sequence analysis. The most optimal algorithm used in sequence alignment is based on the dynamic programming method. Smith-Waterman algorithm is the most commonly used dynamic programming based sequence alignment algorithm. However the algorithm uses quadratic time and space. Heuristic algorithm such as FASTA and BLAST were introduced to speed up the sequence alignment algorithm. FASTA is based on word search whereas BLAST is based on maximum segment pairs. In word search algorithm, lists of words from the query and database sequence are being compared to determine if two sequences have a region of sufficient similarity to merit further alignment using the Smith-Waterman Algorithm. All the different algorithms use the substitutions matrix based on the twenty alphabet amino acids. However research shows that reducing the number of amino acids to 10 does not affect the similarity measure. Our proposed algorithm uses the reduced amino acids alphabet to transform the protein sequences into a sequence of integer and uses n-gram to reduce the length of the sequence. Then the Smith-Waterman algorithm is used to get the similarity measure between two sequences. Result shows that the new proposed algorithm is as sensitive as the Smith-Waterman algorithm yet uses less space and performs better