Educating the educators: Incorporating bioinformatics into biological science education in Malaysia

Bioinformatics can be defined as a fusion of computational and biological sciences. The urgency to process and analyse the deluge of data created by proteomics and genomics studies has caused bioinformatics to gain prominence and importance. However, its multidisciplinary nature has created a unique demand for specialist trained in both biology and computing. In this review, we described the components that constitute the bioinformatics field and distinctive education criteria that are required to produce individuals with bioinformatics training. This paper will also provide an introduction and overview of bioinformatics in Malaysia. The existing bioinformatics scenario in Malaysia was surveyed to gauge its advancement and to plan for future bioinformatics education strategies. For comparison, we surveyed methods and strategies used in education by other countries so that lessons can be learnt to further improve the implementation of bioinformatics in Malaysia. It is believed that accurate and sufficient steerage from the academia and industry will enable Malaysia to produce quality bioinformaticians in the future

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM

An automated framework for software test oracle

Context: One of the important issues of software testing is to provide an automated test oracle. Test oracles are reliable sources of how the software under test must operate. In particular, they are used to evaluate the actual results that produced by the software. However, in order to generate an automated test oracle, oracle challenges need to be addressed. These challenges are output-domain generation, input domain to output domain mapping, and a comparator to decide on the accuracy of the actual outputs. Objective: This paper proposes an automated test oracle framework to address all of these challenges. Method: I/O Relationship Analysis is used to generate the output domain automatically and Multi-Networks Oracles based on artificial neural networks are introduced to handle the second challenge. The last challenge is addressed using an automated comparator that adjusts the oracle precision by defining the comparison tolerance. The proposed approach was evaluated using an industry strength case study, which was injected with some faults. The quality of the proposed oracle was measured by assessing its accuracy, precision, misclassification error and practicality. Mutation testing was considered to provide the evaluation framework by implementing two different versions of the case study: a Golden Version and a Mutated Version. Furthermore, a comparative study between the existing automated oracles and the proposed one is provided based on which challenges they can automate. Results: Results indicate that the proposed approach automated the oracle generation process 97% in this experiment. Accuracy of the proposed oracle was up to 98.26%, and the oracle detected up to 97.7% of the injected faults. Conclusion: Consequently, the results of the study highlight the practicality of the proposed oracle in addition to the automation it offers

Multi-agent reinforcement learning for route guidance system

Nowadays, multi-agent systems are used to create applications in a variety of areas, including economics, management, transportation, telecommunications, etc. Importantly, in many domains, the reinforcement learning agents try to learn a task by directly interacting with its environment. The main challenge in route guidance system is to direct vehicles to their destination in a dynamic traffic situation, with the aim of reducing travel times and ensuring efficient use of available road network capacity. This paper proposes a multi-agent reinforcement learning algorithm to find the best and shortest path between the origin and destination nodes. The shortest path such as the lowest cost is calculated using multi-agent reinforcement learning model and it will be suggested to the vehicle drivers in a route guidance system. The proposed algorithm has been evaluated based on Dijkstra's algorithm to find the optimal solution using Kuala Lumpur (KL) road network map. A number of route cases have been used to evaluate the proposed approach based on the road network problems. Finally, the experiment results demonstrate that the proposed approach is feasible and efficient

Nonlinear dynamic modelling of flexible beam structures using neural networks

This paper investigates the utilisution of back propagation neural networlu (NNs) for modelling flexible beam structures infixed-free mode; a simple repsentation of an aircrufr wing or robot arm. A comparative performance of the NN model and conventional recursive least square scheme, in characterising the system is carried out in the time and frequency domains. Simulated results demonstrate that using NN approach the system is modelled better than with the conventional linear modelling approach. The developed neuro-modelling approach will firther be utilized in the design and implementation of suitable controllers, for vibration slippression in such system

A combination of PSO and local search in university course timetabling problem

The university course timetabling problem is a combinatorial optimization problem concerning the scheduling of a number of subjects into a finite number of timeslots in order to satisfy a set of specified constraints. The timetable problem can be very hard to solve, especially when attempting to find a near-optimal solutions, with a large number of instances. This paper presents a combination of particle swarm optimization and local search to effectively search the solution space in solving university course timetabling problem. Three different types of dataset range from small to large are used in validating the algorithm. The experiment results show that the combination of particle swarm optimization and local search is capable to produce feasible timetable with less computational time, comparable to other established algorithms

The New Multipoint Relays Selection in OLSR using Particle Swarm Optimization

The standard optimized link state routing (OLSR) introduces an interesting concept, the multipoint relays (MPRs), to mitigate message overhead during the flooding process. This paper propose a new algorithm for MPRs selection to enhance the performance of OLSR using particle swarm optimization sigmoid increasing inertia weight (PSOSIIW). The sigmoid increasing inertia weight has significance improve the particle swarm optimization (PSO) in terms of simplicity and quick convergence towards optimum solution. The new fitness function of PSOSIIW, packet delay of each node and degree of willingness are introduced to support MPRs selection in OLSR. The throughput, packet loss and end-to-end delay of the proposed method are examined using network simulator 2 (ns2).  Overall results indicate that OLSR-PSOSIIW has shown good performance compared to the standard OLSR and OLSR-PSO, particularly for the throughput and end-to-end delay. Generally the proposed OLSR-PSOSIIW shows advantage of using PSO for optimizing routing paths in the MPRs selection algorithm

Comparative analysis on adaptive features for RFID middleware

Middleware is software that connects between hardware and application layer. Traditional middleware is limited in its ability to support adaptation while adaptive middleware enables modifying its behavior to conform to new situation. RFID applications grow widely and are used in many purposes such as supply chain management and ubiquitous computing enabled by pervasive, low cost sensing and identification. Implementing adaptive characteristic in RFID middleware will increase the capability of adaptation to specific environment such as different reader/tag, different application, and different platform. Adaptive middleware enables modifying the behavior of a distributed application after the application is developed in response to some changes in functional requirements or operating conditions. An extensive study has been carried out, and comparative analysis has been done on identifying the standard features that reflect the functionalities of RFID middleware and adaptive features that represent the non-functionalities of RFID middleware address to overcome the specific problems of application systems. This paper discusses the outcome of this study and adaptive middleware architecture for RFID applications is proposed that supports multi readers and multi applications

Overview of PSO for Optimizing Process Parameters of Machining

In the current trends of optimizing machining process parameters, various evolutionary or meta-heuristic techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Artificial Bee Colony algorithm (ABC) have been used. This paper gives an overview of PSO techniques to optimize machining process parameter of both traditional and modern machining from 2007 to 2011. Machining process parameters such as cutting speed, depth of cut and radial rake angle are mostly considered by researchers in order to minimize or maximize machining performances. From the review, the most machining process considered in PSO was multi-pass turning while the most considered machining performance was production costs

Bat echolocation-based algorithm for device discovery in D2D communication

Proximal device discovery is an essential initial phase in the installment of a device-to-device communication system in cellular networks. Therefore, an efficient device discovery scheme must be proposed with characteristics of minimum latency, discover maximum devices, and energy-efficient discovery in dense areas. In this paper, a bat echolocation-based algorithm derived from the bat algorithm is proposed and analyzed to fulfill the requirement of a proximal device discovery procedure for the cellular networks. The algorithm is applied to multiple hops and cluster devices when they are in a poor coverage zone. In this proposed algorithm, devices are not required to have prior knowledge of proximal devices, nor device synchronization is needed. It allows devices to start discovering instantly at any time and terminate the proximal device discovery session on completion of the discovery of the required proximal devices. Finally, device feedback is utilized to discover the hop devices in the clusters and analyze proximal discovery in a multi-hop setting. Along with this, a random device mobility pattern is defined based on human movement, and the device discovery algorithm is applied. The device discovery probability is calculated based on the contact duration and meeting time of the devices. We set up an upper bound less than 10 ms in long-term evolution of running time of the bat echolocation-based algorithm; this upper bound signifies the maximum degree of device discovery (more than 75% of the system) and the total number of devices. The outcomes thus imply that the proposed bat echolocation-based algorithm upper bound is better than 10 ms