Species motif extraction using LPBS

This paper presents the use of the ̳Linear-PSO with Binary Search‘ (LPBS) algorithm for discovering motifs, especially species specific motifs.In this study, fragments of mitochondrial cytochrome C oxidase subunit I (COI/COX1) and genome of COI were collected from the Genbank online database.For the first experiment, the genome of COI was used as a reference set and other DNA sequences were used as a comparison set.All the collected DNA sequences are from the same species.The results show that the LPBS algorithm is able to discover motifs. For the second experiment, all the discovered motifs were used as a reference set and the genome of COI from other species were used as a comparison set.The results show that the LPBS algorithm is able to identify correct motifs for species identification

Extreme programming and its positive affect on software engineering teams

This paper presents an early empirical study on Extreme Programming (XP) practices employing Positive Affect metric.The study was conducted on university students doing development projects to gain an insight understanding of the effect of using agile practices on software engineering (SE) teams.The finding indicates that XP practices do have positive affectivity on the SE teams. This is to be expected because of the existence of the practices such as simple design,pair programming, continuous testing, continuous integration and frequent review (release) that command feedback.This finding helps to provide early empirical evidences on the impact of XP methodology on the positive affectivity of the developers

The Impact of Agile Methodology on Software Team’s Work-Related Well-Being

Agile methodology is people-oriented.However, little evidence demonstrates the methodology effectiveness on humanistic aspects.Work-related well-being is measured to what extent the agile methodology can give impact on anxiety, contentment, depression, and enthusiasm level among software engineering (SE) teams.This paper aims to investigate empirically the effect of agile methodology on software development team’s work-related well-being.To achieve this goal, a comparison study was carried out in an academic setting. A quantitative approach using statistical analysis was used to investigate the effect. Results showed that agile does not significantly affect work-related well-being.Nonetheless, the team that is able to apply the agile practices as closely as possible experienced higher level of enthusiasm during software project.This study provides additional empirical data in software engineering research and practices specifically on human aspects.Further investigation needs to be carried out on the software projects with higher task complexity

Educational approach of refactoring in facilitating reverse engineering

Refactoring improves software codes and design.This activity often neglected by software developers because they need time to decide tactically where and when to refactor codes.Although the concepts theoretically instilled in the developer’s mind, this activity is not easy to apply and visualize.This situation became more problematic when deals with inexperienced developers. Therefore, there is a need to develop an educational approach to comprehend refactoring activity.This activity was applied through reverse engineering tasks.The software engineering (SE) teams were required to apply reverse engineering activity in order to check the consistency between codes and design.The teams were encouraged to apply Model-View-Controller (MVC) pattern architecture in order to facilitate the activities.Findings revealed that Extreme Programming (XP) teams managed to complete reverse engineering tasks earlier than Formal teams.This study found that the approach is important to increase understanding of refactoring activities in reverse engineering process.This approach will be furthered applied for others SE teams to gain more insight and perceptions towards improving SE course

eTiPs: A Rule-based Team Performance Prediction Model Prototype

Understanding human potentials in teams are crucial because having the right people in a team can impact team performance.However, to date, there is no consensus on the right composition of team members because team dynamism and its interrelated factors is complex to uncover. Therefore, this paper presents an implementation of a rule-based team performance prediction model prototype or known as eTiPs.This prototype was developed to predict team effectiveness based on four factors: prior academic achievement, personality types, personality diversity, and software development methodology.Three main components of the eTiPs consist of interface, rule-based inference engine, and database was developed to realise the prediction model. A tested and verified IF-THEN rules extracted from rough set technique were used as inference engine of the eTiPs prototype, thus increasing validity and reliability of eTiPs to determine team effectiveness. To assess the usefulness and ease of use of the eTiPs, a usability evaluation was carried out by 12 experts from academic and industrial domain.Results show that the eTiPs able to provide a useful tool for decision makers as early preventive mechanism to predict team effectiveness.Future works will incorporate the eTiPs with intelligent elements to improve decision making process

DNA motif identification using LPBS

In recent years, several deoxyribonucleic acid (DNA)-based approaches have been developed for species identification including DNA sequencing. The search for motif or patterns in DNA sequences is important in many fields especially in biology. In this paper, a new particle swarm optimization (PSO) approach for discovering species-specific motifs was proposed. The new method named as Linear-PSO with Binary Search (LPBS) is developed to discover motifs of specific species through DNA sequences. This enhanced method integrates Linear-PSO and binary search technique to minimize the execution time and to increase the correctness in identifying the motif.In this study, two fragments samples of ‘mitochondrial cytochrome C oxidase subunit I’ (COI or COX1) were collected from the Genbank online database. DNA sequences for the first sample are fragments of COI for one species and the second samples are a complete COI from a different species. The genome of COI was used as a reference set and other DNA sequences were used as a comparison set. The results show that the LPBS algorithm is able to discover motifs of a species when using DNA sequences from the same fragment of COI

Product assembly sequence optimization based on genetic algorithm

Genetic algorithm (GA) is a search technique used in computing to find approximate solution to optimization and search problem based on the theory of natural selection. This study investigates the application of GA in optimizing product assembly sequences. The objective is to minimize the time taken for the parts to be assembled into a unit product. A single objective GA is used to obtain the optimal assembly sequence, exhibiting the minimum time taken. The assembly experiment is done using a case study product and results were compared with manual assembly sequences using the ‘Design for Assembly’(DFA) method. The results indicate that GA can be used to obtain a near optimal solution for minimizing the process time in sequence assembly. This shows that GA can be applied as a tool for assembly sequence planning that can be implemented at the design process to obtain faster result than the traditional methods

A modified algorithm for species specific motif discovery

Motif discovery can be used to categorize unknown DNA sequences into their corresponding families. For this study, PSO was modified for discovering motif.The modified Linear-PSO is chosen even though it is a slower because linear search is not a choice but a necessary criteria for identifying motif of pig (Sus Scrofa).Pig motif identification is a critical for halal authentication.The modified Linear-PSO algorithm used linear number for population initializing and next position updating.For each cycle, only a particle called ‘target motif’ was selected and compared with other DNA sequences for fitness calculation. Motif discovered can be used as a standard motif for species identification. Experimental results show that the modified algorithm is able to identify motifs as expected. This study showed that a slower algorithm is still needed and has value based on how critical the problem is

Rule based reasoning and case based reasoning techniques for juvenile delinquency legal reasoning model

The ability to extract and analyse correct precedent cases and court orders is critical because recommending correct court orders is of utmost importance in ensuring that every case is trialed accordingly.Currently, precedent cases and court orders are searched and extracted manually thus, causing backlog in juvenile delinquency trials.This paper discusses the development of a Juvenile Delinquency Legal Reasoning (JDLRes) Model that has the ability to imitate human reasoning in assisting probation officer to recommend court orders for juvenile delinquency cases.Rule-based reasoning (RBR) and case-based reasoning (CBR) are the techniques used to add consistency with flexibility when recommending court orders for new cases.The simulation model was developed to validate JDLRes Model.The comparison results between the model and human expert reveal the existence of generality aspect in the legal domain.Future work requires the study of precedent cases and court orders in different states in Malaysia

Intelligent segmentation of fruit images using an integrated thresholding and adaptive K-means method (TSNKM)

Recent years, vision-based fruit grading system is gaining importance in fruit classification process.In developing the fruit grading system, image segmentation is required for analyzing the fruit objects automatically.Image segmentation is a process that divides a digital image into separate regions with the aim to obtain only the interest objects and remove the background. Currently, there are several segmentation techniques which have been used in object identification such as thresholding and clustering techniques.However, the conventional techniques have difficulties in segmenting fruit images which captured under natural illumination due to the existence of non-uniform illumination on the object surface.The presence of different illuminations influences the appearance of the interest objects and thus misleads the object analysis.Therefore, this research has produced an innovative segmentation algorithm for fruit images which is able to increase the segmentation accuracy.The developed algorithm is an integration of modified thresholding and adaptive K-means method.The integration of both methods is required to increase the segmentation accuracy for fruits images with different surface colour.The results showed that the innovative method is able to segment the fruits images with high accuracy value