Fatimah Ahmad

Bio: Fatimah Ahmad is an academic researcher from National Defence University of Malaysia. The author has contributed to research in topics: Image retrieval & Content-based image retrieval. The author has an hindex of 9, co-authored 45 publications receiving 328 citations. Previous affiliations of Fatimah Ahmad include National University of Malaysia & National Defence University, Pakistan.

Experiments with a stemming algorithm for Malay words

Abstract: Stemming is used in information retrieval systems to reduce variant word forms to common roots in order to improve retrieval effectiveness. As in other languages, there is a need for an effective stemming algorithm for the indexing and retrieval of Malay documents. The Malay stemming algorithm developed by Othman is studied and new versions proposed to enhance its performance. The improvements relate to the order in which the dictionary is looked-up, the order in which the morphological rules are applied, and the number of rules. © 1996 John Wiley & Sons, Inc.

Rough neural expert systems

Abstract: The knowledge acquisition process is a crucial stage in the technology of expert systems. However, this process is not well defined. One of the promising structured sources of learning can be found in the recent work on neural network technology. A neural network can serve as a knowledge base of expert systems that does classification tasks. Another way of learning is by using the rough set as a new mathematical tool to deal with uncertain and imprecise data. Two methods based on rough set analysis were developed and merged with the integration of neural networks and expert systems, forming a new hybrid architecture of expert systems called a rough neural expert system. The first method works as a pre-processor for neural networks within the architecture, and it is called a pre-processing rough engine, while the second one was added to the architecture for building a new structure of inference engine called a rough neural inference engine. Consequently, a new architecture of knowledge base was designed. This new architecture was based on the connectionist of neural networks and the reduction of rough set analysis. The performance of the proposed system was evaluated by an application to the field of medical diagnosis using a real example of hepatitis diseases. The results indicate that the new methods have improved the inference procedures of the expert systems, and have showed that this new architecture has some properties over the conventional architectures of expert systems.

Rules Frequency Order Stemmer for Malay Language

Abstract: Summary The importance of stemmer is obvious with the advent of effective information retrieval systems. Unfortunately, Malay stemming problems are difficult to solve due to complexity of words morphology. The Rules Application Order (RAO) stemmer is examined for enhancing performance to minimize the percentage of stemming errors. This paper presents a stemming approach called Rules Frequency Order (RFO). RFO rearranges the stemming rules according to the frequency of their usage from the previous execution. It shows that the approach provides a higher percentage of stemming correctness as compared to RAO stemming approach.

Content-based image retrieval based on color-spatial features

Abstract: A novel technique for Content-Based Image Retrieval (CBIR) that employs both the color and spatial information of images is proposed. A maximum of three dominant color regions in an image together with its respective coordinates of the Minimum-Bounding Rectangle (MBR) are first extracted. Next, the Sub-Block technique is then used to determine the location of the dominant regions by comparing the coordinates of the region’s MBR with the four corners of the center of the location map. The cell number that is maximally covered by the region is supposedly to be assigned as the location index. However, the Sub-Block technique is not reliable because in most cases, the location index assigned is not the cell number that is maximally covered by the region and sometimes a region does not overlap with the cell number assigned at all. The effectiveness of this technique has been improved using the Improved Sub-Block technique by taking into consideration the total horizontal and vertical distances of a region at each location where it overlaps. The color-spatial technique is accessed on a Query-byExample CBIR system consisting of 900 images. From the experiments it is shown that retrieval effectiveness has been significantly improved by 85.86%.

Parallel solution of high speed low order FDTD on 2D free space wave propagation

Abstract: Finite Difference Time-Domain (FDTD) is one of the most widely used numerical method for solving electromagnetic problems. Solutions for these problems are computationally expensive in terms of processing time. Recently, we develop a method, High Speed Low Order FDTD (HSLO-FDTD) that is proven to solve one dimensional electro-magnetic problem with a reduction of 67% of processing time from the FDTD method. In this paper, we extend the method to solve two dimensional wave propagation problem. Since the problem is large, we develop the parallel version of HSLO-FDTD method on distributed memory multiprocessor machine using message-passing interface. We examine the parallelism efficiency of the algorithm by analyzing the execution time and speed-up.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

The Porter stemming algorithm: then and now

Abstract: Purpose – In 1980, Porter presented a simple algorithm for stemming English language words. This paper summarises the main features of the algorithm, and highlights its role not just in modern information retrieval research, but also in a range of related subject domains.Design/methodology/approach – Review of literature and research involving use of the Porter algorithm.Findings – The algorithm has been widely adopted and extended so that it has become the standard approach to word conflation for information retrieval in a wide range of languages.Orinality/value – The 1980 paper in Program by Porter describing his algorithm has been highly cited. This paper provides a context for the original paper as well as an overview of its subsequent use.

A Study of Stemming Effects on Information Retrieval in Bahasa Indonesia

Abstract: Stemming is a process which provides a mapping of different morphological variants of words into their base/common word (stem). This process is also known as conflation. Based on the assumption that terms which have a common stem will usually have similar meaning, the stemming process is widely used in Information Retrieval as a way to improve retrieval performance. In addition to its ability to improve the retrieval performance, the stemming process, which is done at indexing time, will also reduce the size of the index file. This thesis is about a study of stemming algorithms in Bahasa Indonesia, especially their effect on the information retrieval. We try to evaluate the existing stemmer for Bahasa Indonesia and compare it with a purely rule-based stemmer, which we created for this purpose. This rule-based stemmer is developed based on a study of morphological structure of Bahasa Indonesia words.