Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
 
Articles by Rayner Alfred
Total Records ( 4 ) for Rayner Alfred
  Raymond Alfred , Koh Pei Hue , Lee Shan Khee and Rayner Alfred
  Problem statement: The lowland forest of Sabah is the most important habitat for orangutans and pygmy elephants. This is shown in the WWF-Malaysia’s elephant tracking programme in which satellite-based Global Position System (GPS) collar devices are used to monitor their movement and the range of their habitats, as well as an aerial survey on orangutan’s nest is performed to determine the spatial distribution pattern. We observed the activities of both species and we found that these species stay inside lowland forests with on flat ground or with gentle slopes, below 500 m elevation, which is mostly covered by natural forest. The density of orangutan’s population was estimated to be higher in a certain location in natural lowland forests where the soils are more fertile. A suitable long term habitat for both species is located in the lowland dipterocarp forests. However, most of the pristine habitats in the lowland area have been converted into other land use activities such as a large scale plantation. This is due to the fact that most of the lowland forests are facing a continuous degradation process that will decrease its commercial value when it comes to generating revenue to the state government. As a result, the efforts to restore the forest are very vital. Approach: This study described the technical and biological aspects in the forest restoration planning, prioritizing, implementation and monitoring process, integrated with the data on habitat utilization by orangutan in lowland degraded dipterocarp forest. Key habitats for orangutans were identified, forest condition were mapped and field works are carried out using a plot sampling technique to identify the diversity and density of the forest (current and potential), in order to support the forest restoration planning. A proper database on forest restoration and tree maintenance planning had been developed to enable the monitoring process. Results: This study outlined some of the findings that include the main challenges that were faced in the forest restoration programme in North Ulu Segama (NUS), which could be used as lessons and guideline in the future. Conclusion: A long term monitoring programme is important in order to have a successful forest restoration programme as well as to have the opportunity to study the impact of this restoration on the behavior of orangutan as a result of their adaptation to the new forest structure.
  Aslina Baharum , Nurul Hidayah Mat Zain , Aryanto Taharudin , Rozita Hanapi , Azali Saudi and Rayner Alfred
  In this research paper, it will explore the issues regarding the user interface design for elderly mobile phone users and what the factors lead to the limitations of using the mobile applications. Besides, this study also discuss about a mental model, what is it means and how we are going to assess a mental model by using a few methodologies that act as an approach to solving the problem faced by elderly when using particular mobile applications. Moreover, the mental model will work as a vital element in this research because it will affect the interface design that we will develop in future progress. Hence, this research will be an age-related matter as it will only focus on what are the elderly expect in interface design so that hopefully it will increase the elderly’s usability and learnability when using a mobile applications.
  Rayner Alfred
  Problem statement: The importance of input representation has been recognized already in machine learning. Feature construction is one of the methods used to generate relevant features for learning data. This study addressed the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process. In other words, this paper discusses the application of genetic algorithm to optimize the feature construction process to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). Approach: The DARA algorithm was designed to summarize data stored in the non-target tables by clustering them into groups, where multiple records stored in non-target tables correspond to a single record stored in a target table. Here, feature construction methods are applied in order to improve the descriptive accuracy of the DARA algorithm. Since, the study addressed the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process, the involved task includes solving the problem of constructing a relevant set of features for the DARA algorithm by using a genetic-based algorithm. Results: It is shown in the experimental results that the quality of summarized data is directly influenced by the methods used to create patterns that represent records in the (n*p) TF-IDF weighted frequency matrix. The results of the evaluation of the genetic-based feature construction algorithm showed that the data summarization results can be improved by constructing features by using the Cluster Entropy (CE) genetic-based feature construction algorithm. Conclusion: This study showed that the data summarization results can be improved by constructing features by using the cluster entropy genetic-based feature construction algorithm.
  Rayner Alfred
  Problem statement: In solving a classification problem in relational data mining, traditional methods, for example, the C4.5 and its variants, usually require data transformations from datasets stored in multiple tables into a single table. Unfortunately, we may loss some information when we join tables with a high degree of one-to-many association. Therefore, data transformation becomes a tedious trial-and-error work and the classification result is often not very promising especially when the number of tables and the degree of one-to-many association are large. Approach: We proposed a genetic semi-supervised clustering technique as a means of aggregating data stored in multiple tables to facilitate the task of solving a classification problem in relational database. This algorithm is suitable for classification of datasets with a high degree of one-to-many associations. It can be used in two ways. One is user-controlled clustering, where the user may control the result of clustering by varying the compactness of the spherical cluster. The other is automatic clustering, where a non-overlap clustering strategy is applied. In this study, we use the latter method to dynamically cluster multiple instances, as a means of aggregating them and illustrate the effectiveness of this method using the semi-supervised genetic algorithm-based clustering technique. Results: It was shown in the experimental results that using the reciprocal of Davies-Bouldin Index for cluster dispersion and the reciprocal of Gini Index for cluster purity, as the fitness function in the Genetic Algorithm (GA), finds solutions with much greater accuracy. The results obtained in this study showed that automatic clustering (seeding), by optimizing the cluster dispersion or cluster purity alone using GA, provides one with good results compared to the traditional k-means clustering. However, the best result can be achieved by optimizing the combination values of both the cluster dispersion and the cluster purity, by putting more weight on the cluster purity measurement. Conclusion: This study showed that semi-supervised genetic algorithm-based clustering techniques can be applied to summarize relational data with more effectively and efficiently.
 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility