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

Intelligent decision support system for employee's performance prediction / Hamidah Jantan … [et al.]

The hidden and valuable knowledge can be discovered through data mining process. In data mining, classification is one of the major tasks to impart knowledge from huge amount of data. Knowledge discovered form data mining classification process can be embedded with Decision Support System (DSS) development which is known as Intelligent DSS (IDSS). IDSS uses Artificial Intelligent techniques to complement the work of human professionals. Nowadays, data mining techniques are widely used in various fields, but it has not attracted much attention people in Human Resource(HR) field. HR system is known as integrated and interrelated approaches to managing human resources and most of their activities involve a lot of unstructured processes such as staffing, training, motivation and maintenance. In addition, human decisions are subject to limitation where sometimes people forget the crucial details of a problem. Fair and consistent in evaluations are very important for HR professionals in any organizations. IDSS application using data mining can be used for evaluation, it will make many routine decisions in assessment easier and can be reallocated to less experienced evaluators. This system will encourages the use of explicit criteria for evaluating the employee performance, increases the assessment consistency, hence perceived fairness and provide help for junior evaluator to evaluate their staff consistently. This research focus on the execution of data mining approach for employee development regarding on their future performance. By using this approach, the performance patterns can be discovered from the existing database and it will be used for future performance prediction especially for their career development. In the experimental phase, we have used selected classification techniques to propose the appropriate technique for the dataset. An experiment is carried out to demonstrate the feasibility of the suggested classification techniques using employee's performance data. Thus, the experiment results, we suggest the potential classification techniques and the possible prediction model for employee's performance forecasting. Finally, the constructed model embedded in a system prototype for employee's performance prediction

An artificial immune system model as talent performance predictor / Siti ‘Aisyah Sa’dan, Hamidah Jantan and Mohd Hanapi Abdul Latif

In any organization, the management has to struggle effectively in terms of cost, quality, service or innovation. The success of these tasks depends on having enough right people with the right skills, employed in the appropriate locations at the appropriate point of time. Recently, among the challenges of Human Resource professionals has been managing an organization talent which involves a lot of managerial or human decisions, which sometimes are very uncertain and difficult. Prediction in data mining is among the popular machine learning techniques as a part of intelligent techniques, for example, Bayesian methods, neural network, support vector machine, association rule mining, k-nearest-neighbor, rough sets and fuzzy logic. Soft computing techniques, such as bio-inspired algorithms, can be used for information processing by employing methods which are capable to deal with imprecision and uncertainty. However, limited studies were found in bio-inspired algorithms especially immune based algorithm in talent prediction. Immune based algorithm is part of bio-inspired algorithms elicits theories which can act as an inspiration for computer-based solutions. Most of the researchers used conventional techniques to compare the process by looking at the exact similarity; where the comparison process relies on distance value calculated. It is hoped that this will increase the accuracy of the result. The objective of this study is to propose a prediction model based on bio-inspired algorithm for talent knowledge discovery through some experiments. To achieve this objective, the research was divided into three phases, which consist of talent data identification and data preparation phase, algorithm development for prototype phase and testing and evaluation phase to identify the most suitable prediction model for talent prediction. From this research, some of the potential applications that can use this prediction model are employee recruitment planning in industry sectors and higher learning student enrollment

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

Handheld devices 3D video streaming compression algorithm / Mohamad Taib Miskon, Hamidah Jantan and Wan Ahmad Khusairi Wan Chek

The field of three-dimensional display (3D) has become one of the fascinating research areas as the race of offering an improved viewing experience among broadcasters has increased in recent years. In the entertainment perspective, 3D viewing experience helps to significantly enhance the quality of television programs [1]. Obviously, among the main benefits of 3D features on televisions or other services include the ability to provide greater sense of depth [2, 3], enhance the perfection of sharpness [4], sense of presence [3] and naturalness [4], All these characteristics are also important especially in telemedical system in which real-time emergency video transmission and recording are sometimes required [5], A 3D viewing is achievable through various methods. Authors in [6] summarize three major approaches with the simplest are two-view systems which at any instant reproduce just two views, one for the left eye and one for the right eye. Next, a more advanced approach is the horizontal-parallax displays which produce multiple horizontal parallax views of scene, also known as a parallax panaromagram. Besides, the most complete display type consists of those that utilize full-parallax features which offer variations in the images seen by the viewer with both horizontal and vertical head movements. Just like any digital video, 3D video sequences must be compressed in order to make it suitable for consumer domain applications [7], However, regular compression methods implemented in modern video coding model such as H.264, MPEG-4 and MPEG-2 are not proficient in constructing sufficient compression while preserving the 3D clues. Luckily, an enormous amount of redundancies can be found in an integral video sequence in terms of motion and disparity. In recent years, a Three Dimensional Discrete Cosine Transform (3D-DCT) coding algorithm has been developed for compression of still 3D integral images [8-10], The main benefit of using transform coding is that integral 3D images are inherently divided into small non-overlappin

Assessment method for potential educational technology competency standard based on TPCK in Malaysian Higher Education Institutions / Yau’Mee Hayati Hj Mohamed Yusof, Hamidah Jantan and Nur Muslimah Kamilah Abdullah

Technology in education is purposely designed to help both educators and students in knowledge transfer and knowledge gain simultaneously. In many aspects, technology in education is supposed to prove that education can be delivered effectively and efficiently. However, there are cases in which technology in education can be frustrating and annoying for both parties. Government and university management have invested a lot of money to ensure that educators and students can really benefit from the technology. In spite of huge investment on educational technology tools (hardware and software) over the past decades in various education initiatives, the potential of technology usage at university level has not reached the desired level among educators and students. What is the missing link for the realisation of the expected return-of-investment? Recent researches (C Akarawang, 2015; Bibi, 2017; Hersh, 2014) indicate that the problem is due to the gap between technical ICT skills and the knowledge of good pedagogical practice among educators. The outcome of this study proposes an Educational Technology standard to be applied in university setting using TPCK (Technological Pedagogical Content Knowledge) as the basic framework. However, this paper will only discuss a part of our standard development highlighting the assessment method that was used during the implementation of ETC standard in our institutions. Overall, the descriptive result using pre and post means scores as assessment method towards proposed standard shows that the educators’ acceptance score in our institutions are mostly good. However the element within the standard least accepted are TCK (Technology Content Knowledge) and TPK (Technology Pedagogical Knowledge). The assessment and finding in this study nevertheless are suggested to be used as a guidance for ETC Standard implementation in university setting in order to stress the importance of considering technological possibilities in light of developmentally appropriate practices and specific learning goals in ICT/ET training provided for educators in HEI in Malaysia