Academic leadership bio-inspired classification model using negative selection algorithm

Negative selection algorithm has been successfully used in several purposes such as in fault detection, data integrity protection, virus detection and etc.due to the unique ability in self-recognition by classifying self or non-self’s detectors. Managing employee’s competency is considered as the top challenge for human resource professional especially in the process to determine the right person for the right job that is based on their competency.As an alternative approach, this article attempts to propose academic leadership bio-inspired classification model using negative selection algorithm to handle this issue.This study consists of three phases; data preparation, model development and model analysis. In the experimental phase, academic leadership competency data were collected from a selected higher learning institution as training data-set based on 10-fold cross validation. Several experiments were carried out by using different set of training and testing data-sets to evaluate the accuracy of the proposed model.As a result, the accuracy of the proposed model is considered excellent for academic leadership classification.For future work, in order to enhance the proposed bio-inspired classification model, a comparative study should be conducted using other established artificial immune system classification algorithms i.e. clonal selection and artificial immune network

A Potential Heuristic-based Block Matching Algorithms for Motion Estimation in Video Compression

Motion estimation (ME) is one of the element keys in video compression that takes up to 60% in processing time. Block matching algorithm (BMA) is a technique that is used to reduce the computational complexity of ME algorithm due to its efficiency and good performance. Strategy of searching is one of the factors in developing motion estimation algorithm that has the potential to provide good performance. This study aims to implement several selected BMAs for achieving the least number of computations and to give better Peak Signal to Noise Ratio (PSNR) values using different video sequences. The proposed algorithms are modified based on the search strategy adapted from the standard algorithms approach. The results have proved that both modification algorithms (MDS and MARPS) have the potential in reducing the number of computations and achieved good PSNR values in all motion types as compared to DS and ARPS respectively. This work could be improved by using metaheuristic algorithms approach such as particle swarm optimization (PSO), artificial bee colony (ABC), tabu search (TS) and etc to provide the better result of PSNR values without increasing the number of computation

Lexicon-based and immune system based learning methods in Twitter sentiment analysis

Nowadays, there are increasingly numbers of studies on seeking ways to mine Twitter for sentiment analysis. Machine learning approach such as immune system based learning methods is an alternative way for sentiment classification.This method is centered on prominent immunological theory as computation mechanisms that emulate processes in biological immune system in achieving higher probability for pattern recognition. The aim of this article attempts to study the potential of this method in text classification for sentiment analysis.This study consists of three phases; data preparation; classification model development using three selected Immune System based algorithms i.e. Negative Selection algorithm (NSA), Clonal Selection algorithm (CSA) and Immune Network algorithm (INA); and model analysis. As a result, NSA algorithm proposed slightly high accuracy in experimental phase and that would be considered as the potential classifiers for Twitter sentiment analysis. In future work, the accuracy of proposed model can be strengthened by comparative study with other heuristic based searching algorithms such as genetic algorithm, ant colony optimization, swam algorithms and etc

Mobile-Based Word Matching Detection using Intelligent Predictive Algorithm

Word matching is a string searching technique for information retrieval in Natural Language Processing (NLP). There are several algorithms have been used for string search and matching such as Knuth Morris Pratt, Boyer Moore, Horspool, Intelligent Predictive and many other. However, there some issues need to be considered in measuring the performance of the algorithms such as the efficiency for searching small alphabets, time taken in processing the pattern of the text and extra space to support a huge table or state machines. Intelligent Predictive (IP) algorithm capable to solve several word matching issues discovered in other string searching algorithms especially with abilities to skip the pre-processing of the pattern, uses simple rules during matching process and does not involved complex computations. Due to those reasons, IP algorithm is used in this study due to the ability of this algorithm to produce a good result in string searching process.  This article aims to apply IP algorithm together with Optical Character Recognition (OCR) tool for mobile-based word matching detection. There are four phases in this study consists of data preparation, mobile based system design, algorithm implementation and result analysis. The efficiency of the proposed algorithm was evaluated based on the execution time of searching process among the selected algorithms. The result shows that the IP algorithm for string searching process is more efficient in execution time compared to well-known algorithm i.e. Boyer Moore algorithm. In future work, the performance of string searching process can be enhanced by using other suitable optimization searching techniques such as Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization and many others

Mobile-Based Word Matching Detection using Intelligent Predictive Algorithm

Word matching is a string searching technique for information retrieval in Natural Language Processing (NLP). There are several algorithms have been used for string search and matching such as Knuth Morris Pratt, Boyer Moore, Horspool, Intelligent Predictive and many other. However, there some issues need to be considered in measuring the performance of the algorithms such as the efficiency for searching small alphabets, time taken in processing the pattern of the text and extra space to support a huge table or state machines. Intelligent Predictive (IP) algorithm capable to solve several word matching issues discovered in other string searching algorithms especially with abilities to skip the pre-processing of the pattern, uses simple rules during matching process and does not involved complex computations. Due to those reasons, IP algorithm is used in this study due to the ability of this algorithm to produce a good result in string searching process.  This article aims to apply IP algorithm together with Optical Character Recognition (OCR) tool for mobile-based word matching detection. There are four phases in this study consists of data preparation, mobile based system design, algorithm implementation and result analysis. The efficiency of the proposed algorithm was evaluated based on the execution time of searching process among the selected algorithms. The result shows that the IP algorithm for string searching process is more efficient in execution time compared to well-known algorithm i.e. Boyer Moore algorithm. In future work, the performance of string searching process can be enhanced by using other suitable optimization searching techniques such as Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization and many others