A study of the methodologies currently available for the maintenance of the knowledge-base in an expert system

This research studies currently available maintenance methodologies for expert system knowledge bases and taxonomically classifies them according to the functions they perform. The classification falls into two broad categories. These are: (1) Methodologies for building a more maintainable expert system knowledge base. This section covers techniques applicable to the development phases. Software engineering approaches as well as other approaches are discussed. (2) Methodologies for maintaining an existing knowledge base. This section is concerned with the continued maintenance of an existing knowledge base. It is divided into three subsections. The first subsection discusses tools and techniques which aid the understanding of a knowledge base. The second looks at tools which facilitate the actual modification of the knowledge base, while the last secttion examines tools used for the verification or validation of the knowledge base. Every main methodology or tool selected for this study is analysed according to the function it was designed to perform (or its objective); the concept or principles behind the tool or methodology: and its implementation details. This is followed by a general comment at the end of the analysis. Although expert systems as a rule contain significant amount of information related to the user interface, database interface, integration with conventional software for numerical calculations, integration with other knowledge bases through black boarding systems or network interactions, this research is confined to the maintenance of the knowledge base only and does not address the maintenance of these interfaces. Also not included in this thesis are Truth Maintenance Systems. While a Truth Maintenance System (TMS) automatically updates a knowledge base during execution time, these update operations are not considered \u27maintenance\u27 in the sense as used in this thesis. Maintenance in the context of this thesis refers to perfective, adaptive, and corrective maintenance (see introduction to chapter 4). TMS on the other hand refers to a collection of techniques for doing belief revision (Martin, 1990) . That is, a TMS maintains a set of beliefs or facts in the knowledge base to ensure that they remain consistent during execution time. From this perspective, TMS is not regarded as a knowledge base maintenance tool for the purpose of this study

Intra-industry comformity in dividend policy in Malaysia

The topic on determinants of dividend policy has remained as a hot topic even though much relevant research had conducted. Every company wishes to increase their company's value by having a suitable dividend policy. The dividend policy of a company may be affected by other industry players due to the similar business environment. Besides, dividend decision can convey message to public about its performance which may indirectly affect a company's share price. Intra-industry effect on dividend policy was identified outside Malaysia. Since dividend is a crucial decision to a company, the same research should be conducted in Malaysia. The main purpose of this paper is to test the intra-industry effect on dividend policy in Malaysian market. In order to explore the influence of intra-industry effect, the logistic regression was performed by using the variables that may affect the probability of a company to pay dividend. The findings revealed a significant positive relationship between probability of a company paying a dividend and number of companies within plantation industry that pay a dividend. However, there is an insignificant relationship between probability of a company paying a dividend and number of companies within construction industry that pay a dividend. Overall, the findings support the view of intra-industry conformity in dividend policy in Malaysia. Thus, the intra-industry effect should be considered as one of the determinants in dividend policy. The findings are useful not only for investors but the company as wel

Item Tracer

One of our daily issues for searching indoor lost item remain unresolved until today as there is no any systematic way of locating it. Unaccounted amount of time and energy has been wasted each day trying to retrieve it based on memory. Therefore, in this project, a prototype is proposed to locate indoor lost item utilizing received signal strength (RSS) for distance estimation. The prototype primary consists of a small size tag for attaching on any item and a reader for computing the estimated location of the tag. A positioning algorithm is developed to analyse the behaviour of received signal strength and calculate the probability of the target location. As the nature of indoor environment varies across each location, the prototype is tested at multiple indoor locations for refining the algorithm and verifying its robustness and consistency in estimating the target location. The results obtained showed that the percentage of error for direction probability is 32 % and accuracy of distance is at 0.9m

Linear Structure of the Oligosaccharide Chains in α_1-Protease Inhibitor Isolated from Human Plasma

Two glycopeptides present in equal amounts were isolated from a pronase digest of alpha1-protease inhibitor of human plasma by gel filtration on Sephadex G-50 and chromatography on DEAE-cellulose. The carbohydrate side chains in both glycopeptides are linked through asparaginyl residues. The glycopeptides were digested sequentially with specific glycosidases; and after each step, the released sugars as well as the composition of the residual peptides were determined. The linear structures of these glycopeptides deduced from these data are shown below. Based on the total carbohydrate content of the intact protein and with these structural data, it is postulated that 4 oligosaccharide units are attached to 1 molecule of the protein; 2 of these were represented as in Equation 1, the other 2 as in Equation 2

Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Rules

The Wang–Mendel (WM) method is one of the earliest methods to learn fuzzy If-Then rules from data. In this article, the WM method is used to generate fuzzy If-Then rules for a zero-order Takagi–Sugeno–Kang (TSK) fuzzy inference system (FIS) from a set of multi-attribute monotone data. Convex and normal trapezoid fuzzy sets are used as fuzzy membership functions. Besides that, a strong fuzzy partition strategy is used. Our empirical analysis shows that a set of multi-attribute monotone data may lead to non-monotone fuzzy If-Then rules. The same observation can be made, empirically, using adaptive neuro-fuzzy inference system (ANFIS), a well-known and popular FIS model with neural learning capability. This finding is important for the modeling of a monotone FIS model, because it shows that even with a “clean” data set pertaining to a monotone system, the generated fuzzy If-Then rules may need to be preprocessed, before being used for FIS modeling. In short, it is imperative to develop methods for preprocessing non-monotone fuzzy rules from data, e.g., monotone fuzzy rules relabeling, or removing non-monotone fuzzy rules, is important (and is potentially necessary) during the course of developing data-driven FIS models

Monotone Data Samples Do Not Always Produce Monotone Fuzzy If- Then Rules: Learning with Ad hoc and System Identification Methods

In this paper, ad hoc and system identification methods are used to generate fuzzy If-Then rules for a zeroorder Takagi-Sugeno-Kang (TSK) Fuzzy Inference System (FIS) using a set of multi-attribute monotone data. Convex and normal trapezoidal fuzzy sets, with a strong fuzzy partition strategy, is employed. Our analysis shows that even with multi-attribute monotone data, non-monotone fuzzy If- Then rules can be produced using an ad hoc method. The same observation can be made, empirically, using a system identification method, e.g., a derivative–based optimization method and the genetic algorithm. This finding is important for modeling a monotone FIS model, as the result shows that even with a “clean” data set pertaining to a monotone system, the generated fuzzy If-Then rules may need to be preprocessed, before being used for FIS modeling. As such, monotone fuzzy rule relabeling is useful. Besides that, a constrained non-linear programming method for FIS modelling is suggested, as a variant of the system identification method

Chronic lateral ankle pain secondary to peroneus brevis injury: a case report

The peroneus brevis and peroneus longus are both muscles in the lateral compartment of the leg responsible for dorsiflexion and eversion of ankle. Peroneal tendinopathy including tendinitis, rupture and dislocation has gained attention in the recent literature and is a recognised cause of lateral ankle pain. However, due to the lack of awareness of this condition, diagnosis is often missed and as a result treatment is often delayed, leading to the chronicity of the condition. This is a case report of a young lady presented with chronic left lateral ankle pain with preceding history of ankle inversion injury. Magnetic resonance imaging of her left ankle confirmed an isolated split tear of the peroneus brevis tendon. She underwent a successful peroneus tendon repair and superficial peroneal retinaculum reconstructive surgery with a good clinical outcome after 6 months of outpatient follow-up

Parametric Conditions for a Monotone TSK Fuzzy Inference System to be an n-Ary Aggregation Function

Despite the popularity and practical importance of the fuzzy inference system (FIS), the use of an FIS model as an n-ary aggregation function, which is characterized by both the monotonicity and boundary properties, is yet to be established. This is because research on ensuring that FIS models satisfy the monotonicity property, i.e., monotone FIS, is relatively new, not to mention the additional requirement of satisfying the boundary property. The aim of this article, therefore, is to establish the parametric conditions for the Takagi–Sugeno–Kang (TSK) FIS model to operate as an n-ary aggregation function (hereafter denoted as n-TSK-FIS) via the specifications of fuzzy membership functions and fuzzy rules. An absorption property with fuzzy rules interpretation is outlined, and the use of n-TSK-FIS as a uninorm is explained. Exploiting the established parametric conditions, a framework for which an n-TSK-FIS model can be constructed from data samples is formulated and analyzed, along with a number of remarks. Synthetic data sets and a benchmark example on education assessment are presented and discussed. To be best of the authors’ knowledge, this article serves as the first use of the TSK-FIS model as an n-ary aggregation function

Parametric Conditions for a Monotone TSK Fuzzy Inference System to be an n-Ary Aggregation Function

Despite the popularity and practical importance of the Fuzzy Inference System (FIS), the use of an FIS model as an n -ary aggregation function, which is characterized by both the monotonicity and boundary properties, is yet to be established. This is because research on ensuring that FIS models satisfy the monotonicity property, i.e., monotone FIS, is relatively new, not to mention the additional requirement of satisfying the boundary property. The aim of this paper, therefore, is to establish the parametric conditions for the Takagi-Sugeno-Kang (TSK) FIS model to operate as an n -ary aggregation function (hereafter denoted as n -TSK-FIS) via the specifications of fuzzy membership functions (FMFs) and fuzzy rules. An absorption property with fuzzy rules interpretation is outlined, and the use of n -TSK-FIS as a uni-norm is explained. Exploiting the established parametric conditions, a framework for which an n -TSK-FIS model can be constructed from data samples is formulated and analyzed, along with a number of remarks. Synthetic data sets and a benchmark example on education assessment are presented and discussed. To be best of the authors' knowledge, this study serves as the first use of the TSK-FIS model as an n -ary aggregation function

HISTOPATHOLOGICAL CHANGES OF KIDNEY OF BROILER CHICKEN EXPOSED TO CHRONIC HEAT STRESS

The aim of this research was to know histopathological changes of kidney of broiler chicken exposed to chronic heat stress. Twenty broilers were divided randomly into 2 groups, 10 broilers each are being exposed to chronic heat stress for 21 days. The first 21 days were the adaptation period in a chamber with temperature 24-28o C and humidity 40-55%. After 21 days, continue with exposure to heat stress in a chamber with temperature 36-40o C and humidity 50-65% in 8 hours per day. After exposed to chronic heat stress, kidney tissues were processed, and kidney tissue histopathological changes were evaluated by using the Klopfleisch modified scoring method. The data was analyzed by Mann – Whitney Test. The result of this research showed that chronic heat stress exposure causing the presence of degeneration of tubular epithelial cell, necrosis of tubular epithelial cell, necrosis of glomerular and interstitial infiltration. From the analysis data the overviewof multiparametric showed that when control group compared with the treatment group showed significantly difference (p<0.05)