Terrestrial Vegetation Management Expert System (ES) Prototype for Environmental Impact Assessment (EIA)

The tropical rainforest of Malaysia holds one of the richest flora in the world. The favourable climate has produced flora of amazing richness and variety. Terrestrial vegetation of tropical rainforests is an important feature of the environment. Plants play a major role in the environment. Conservation of a particular environment depends fundamentally on the maintenance of existing plants and their communities and hierarchies. Therefore, the application of a computer technology in the form of an expert system (ES) will be able to help in the analysis and management of the EIA information. The ES is named VEGEVIC. A rule-based system is developed to guide the user in producing an EIA report. Knowledge for this system is elicited from the domain expert through interviews, existing literature and EIA reports between 1995 and 1997. Application of the system will lead to greater consistency in producing a standard approach when preparing an EIA report

Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic

OBJECTIVE: This study proposes a new approach that considers uncertainty in predicting and quantifying the presence and severity of diabetic peripheral neuropathy. METHODS: A rule-based fuzzy expert system was designed by four experts in diabetic neuropathy. The model variables were used to classify neuropathy in diabetic patients, defining it as mild, moderate, or severe. System performance was evaluated by means of the Kappa agreement measure, comparing the results of the model with those generated by the experts in an assessment of 50 patients. Accuracy was evaluated by an ROC curve analysis obtained based on 50 other cases; the results of those clinical assessments were considered to be the gold standard. RESULTS: According to the Kappa analysis, the model was in moderate agreement with expert opinions. The ROC analysis (evaluation of accuracy) determined an area under the curve equal to 0.91, demonstrating very good consistency in classifying patients with diabetic neuropathy. CONCLUSION: The model efficiently classified diabetic patients with different degrees of neuropathy severity. In addition, the model provides a way to quantify diabetic neuropathy severity and allows a more accurate patient condition assessment

Review of Rule Quality Measurement: Metrics and Rule Evaluation Models

A rule-based system is a system based on the set of rules used to make inference knowledge. The system gathers knowledge into the representation of knowledge in the form of a rule. However, the knowledge in the form of the rule is inductive, meaning that the algorithm can construct the rule by studying a limited number of cases and then the induced rule of a limited number of cases and then generalize it to the general reality from time to time. This, of course, has the degree of inaccuracy in expressing reality into knowledge, or an experienced expert builds it but it is not absolute that the knowledge it possesses is 100% accurate or always consistently true from one time-space location to another time-space location. Therefore, the need for a formula that can measure the quality of the resulting rule and assess the consistency of the rule. In this study, we did a review of the ideas of people trying to measure knowledge built inductively by either the algorithm or the experts. These measurements are based on several parameters defined by them according to the underlying assumptions. This review seeks to partially present how ideas to measure the rule as knowledge representation from a varied viewpoint and how people construct evaluation models to assess the resulting regulations either from the experts or human experts as well as those resulting from the induction rule algorithm much developed

ACMiner: Extraction and Analysis of Authorization Checks in Android's Middleware

Billions of users rely on the security of the Android platform to protect phones, tablets, and many different types of consumer electronics. While Android's permission model is well studied, the enforcement of the protection policy has received relatively little attention. Much of this enforcement is spread across system services, taking the form of hard-coded checks within their implementations. In this paper, we propose Authorization Check Miner (ACMiner), a framework for evaluating the correctness of Android's access control enforcement through consistency analysis of authorization checks. ACMiner combines program and text analysis techniques to generate a rich set of authorization checks, mines the corresponding protection policy for each service entry point, and uses association rule mining at a service granularity to identify inconsistencies that may correspond to vulnerabilities. We used ACMiner to study the AOSP version of Android 7.1.1 to identify 28 vulnerabilities relating to missing authorization checks. In doing so, we demonstrate ACMiner's ability to help domain experts process thousands of authorization checks scattered across millions of lines of code