Rule-based Reasoning Approach Research Articles

A Decentralized Fuzzy Rule-Based Approach for Computing Topological Relations between Spatial Dynamic Continuous Phenomena with Vague Boundaries Using Sensor Data.

Sensor networks (SN) are increasingly used for the observation and monitoring of spatiotemporal phenomena and their dynamics such as pollution, noise and forest fires. In multisensory systems, a sensor node may be equipped with different sensing units to observe and detect several spatiotemporal phenomena at the same time. Simultaneous detection of different phenomena can be used to infer their spatial interactions over space and time. For this purpose, decentralized spatial computing approaches have shown their potential for effective reasoning on spatial phenomena within a sensor network. However, in most cases, spatial extents of continuous dynamic phenomena are uncertain, and their relations and interactions cannot be inferred by the existing approaches at the sensor node level. To address this limitation, in this paper, we propose and develop a decentralized fuzzy rule-based spatial reasoning approach to depict the spatial relations that hold between two evolving spatial phenomena with fuzzy boundaries. The proposed method benefits from a more adapted fuzzy-crisp representation of dynamic phenomena observed by SN where each vague phenomenon is composed of five distinguished zones including the kernel, conjecture and exterior zone and their boundaries. For each detected phenomenon, a sensor node will report one of these zones based on its location. Aggregation of the information reported from the sensor nodes allows reasoning on spatial relations between the observed phenomena and their evolution. Such spatial information provides users with more valuable near real-time information on the state of different phenomena that can be used for informed decision-making.

Web-based expert system to determine digital forensics tool using rule-based reasoning approach

Digital forensics is a method to trace the digital evidence using knowledge of science. There are several stages in the method of digital forensics. Each stage has their own way to use the method collaborate with the tool of digital forensics. Nowadays, there are tools that we can use in digital forensics. Therefore, not all the tool coming with the help document on how to use the tool. This situation makes the investigator have to check the feature of the tool one by one in order to suit which one is the best tool to use in some stage. To overcome this problem, we made a system to determine the right tool in digital forensics using rule-based reasoning approach. The result of this paper is web-based system to determine the right tool in digital forensics. The system shows that only 40% in suitability to help the investigator to determine the right tool. This cause by lacking of the rule consists in the reasoning approach.

Rule-Based Actionable Intelligence for Disaster Situation Management

Managing natural disasters is a social responsibility as they might cause a gloomy impact on human life. Efficient and timely alert systems for public and actionable recommendations for decision makers may well decrease the number of casualties. Web semantics strengthen the description of web resources for exploiting them better and making them more meaningful for both human and machine. In this work, the authors propose a semantic rule-based approach for disaster situation management (DSM) to reach the next level of decision-making power and its architecture for providing actionable intelligence in the domain of the earthquake. The system itself is based on a data pre-processing layer, a computation layer, and the middle layer relies on an extensive rule base of experts' advice stored over time and a disaster ontology along with its inherent semantics. The rule-based reasoning approach uses this knowledge base in combination with the expert rule base, written in SWRL rules, to infer recommendations for the response to an earthquake.

Web-Based Expert System To Detecte Chili Desease Using Rule Base Reasoning Approach

Along with the progress of global Era, the development of science is also growing rapidly especially for the progression of technology, information and communication. Artificial intelligence is part of computer science specifically aimed for designing intelligent automation of behavior in system of computer intelligence. Expert system as part of the application of artificial intelligence is for combining knowledge, facts and search technique to solve the problem that normally requires expertise from an expert. One of directions from the system of expert in agriculture is the system expert for detecting diseases of chili plant. In this study, the expert system was designed to detect disease of chili plants on web-based using the rule-based reasoning approach method, because the method can decide a disease of chili plant with a set of facts - symptoms or symptoms that exist in a sequential manner and this approach can also provide an explanation of the steps in achieving the solution for the disease. Web-Based Expert System To Detecte Chili Desease Using Rule Base Reasoning Approach is expected to solve the problems in agriculture quickly, precisely and accurately especially for the chili’s farmer.

Transferability, accuracy, and uncertainty assessment of different knowledge-based approaches for soil types mapping

Soil legacy data are important sources of soil information, especially when dealing with limited resources. In countries with high geographical diversity and few financial resources, such as Brazil, they represent an economical alternative to obtaining soil spatial information in higher resolution. By retrieving the soil scientist's knowledge, it can be used as guidance for knowledge-based digital soil mapping approaches. In this sense, this work aimed to evaluate Rule-Based Reasoning and Case-Based Reasoning knowledge-based approaches to predict soil types up to the third categorical level (U.S Soil Taxonomy) in a non-sampled area, by retrieving and then extrapolating the information of a detailed soil legacy map, from a reference area. The study was carried out in Minas Gerais state, Southeastern Brazil. The methodology includes three main steps: i) knowledge acquisition; ii) soil inference; and iii) accuracy and uncertainty assessment. For the validation, 23 independent samples were chosen by means of the Regional Random method, and the accuracy was assessed by Kappa index, Overall Accuracy, Users', and Producers' Accuracy. The uncertainty was evaluated through entropy and exaggeration. A total of 24 inference models were obtained with the Case-Based Reasoning approach, in which the best model had an overall accuracy of 61% and a Kappa index of 0.52. The Rule-based reasoning approach performed better, with an overall accuracy of 82% and 0.75 for Kappa index. These approaches generated a higher accuracy soil map for an unmapped area that was 15 times larger than the reference area and at lower cost.

Sistem Pakar Pemupukan Kelapa Sawit Menggunakan Metode Forward Chaining

<em>Oil palm smallholders require knowledge of fertilization because the fertilization is an influential factor in the productivity of fresh fruit bunches. On the other hand, the oil palm smallholders generally don’t have fertilizer expertise and they face a difficulty in determining good fertilization (good in type, dosage, time and manner) based on various literatures. Therefore, a simple application that can provide recommendations for palm oil fertilization is developed. The recommendations are based on expert knowledge that have been written in various literatures. Like an expert system, the application is designed by compiling knowledge base, constructing an inference engine and creating a prototype. The knowledge bases, that are facts and rules, are prepared using rule-based reasoning approach, the inference engine is compiled using forward chaining methods and the prototype is made using a visual basic application in a spreadsheet application. The application design uses facts of plant varieties, phases or age of plants, soil types, nutrient deficiency in soil and plants, rainfall, fertilizer content, and nutrient properties (synergies or antagonist) on fertilizers. The Applications made capable of providing fertilization recommendations by conducting forward chaining based on data-given facts and rules that have been made on the knowledge base.</em>

Development of a weighted probabilistic risk assessment method for offshore engineering systems using fuzzy rule-based Bayesian reasoning approach

This study presents a new safety methodology that is capable of transforming qualitative expert judgement into probabilistic risk outcomes for offshore engineering systems. In the framework, fuzzy set theory is applied to describe variables. Fuzzy data of each input is expressed in terms of the belief degree format representing the extent to which the fuzzy data belongs to the associated fuzzy set. Such data is subsequently combined to derive appropriate consequents using a fuzzy rule approach considering the weights of each input. The information generated in the antecedent and consequent of each rule are then synthesized to reach the fuzzy conclusions using the Bayesian reasoning approach. Such fuzzy conclusions are defuzzified and consequently transformed into the probabilistic nature. The framework is validated using two axioms and demonstrated by a risk study of the propulsion malfunction of an offshore Floating Production, Storage and Offloading (FPSO) during tandem offloading operations. The results are consistent with the axioms since the outcomes are sensitive to the minor alterations of input data and weights. It is concluded that the new approach produces reasonable results considering input weights and the logicality between inputs and outputs without losing too much useful information in the inference process.

A Rule-Based Spatial Reasoning Approach for OpenStreetMap Data Quality Enrichment; Case Study of Routing and Navigation

Finding relevant geospatial information is increasingly critical because of the growing volume of geospatial data available within the emerging “Big Data” era. Users are expecting that the availability of massive datasets will create more opportunities to uncover hidden information and answer more complex queries. This is especially the case with routing and navigation services where the ability to retrieve points of interest and landmarks make the routing service personalized, precise, and relevant. In this paper, we propose a new geospatial information approach that enables the retrieval of implicit information, i.e., geospatial entities that do not exist explicitly in the available source. We present an information broker that uses a rule-based spatial reasoning algorithm to detect topological relations. The information broker is embedded into a framework where annotations and mappings between OpenStreetMap data attributes and external resources, such as taxonomies, support the enrichment of queries to improve the ability of the system to retrieve information. Our method is tested with two case studies that leads to enriching the completeness of OpenStreetMap data with footway crossing points-of-interests as well as building entrances for routing and navigation purposes. It is concluded that the proposed approach can uncover implicit entities and contribute to extract required information from the existing datasets.

Towards emergence phenomenon in business process management

Abstract A standard solution regarding business process management automation in enterprises is the use of workflow management systems working by the Rule-Based Reasoning approach. In such systems, the process model which is designed entirely before the implementation has to meet all needs deriving from business activity of the organization. In practice, it means that great limitations arise in process control abilities, especially in the dynamic business environment. Therefore, new kinds of workflow systems may help which typically work in more agile way e.g. following the Case-Based Reasoning approach. The paper shows another possible solution – the use of emergence theory which indicates among other conditions required to fulfill stimulation of the system (for example the business environment) to run grass-roots processes that lead to arising of new more sophisticated organizing forms. The paper also points the using opportunity of such techniques as the processing of complex events to fulfill key conditions pointed by the emergence theory.

Use of fuzzy rule-based evidential reasoning approach in the navigational risk assessment of inland waterway transportation systems

A novel approach incorporating a fuzzy rule base technique and an Evidential Reasoning (ER) algorithm is applied to conduct the navigational risk assessment of an Inland Waterway Transportation System (IWTS). A hierarchical structure for modeling IWTS hazards (hazard identification model) is first constructed taking into account both qualitative and quantitative criteria. The quantitative criteria are converted to qualitative ones by applying a fuzzy rule-based quantitative data transformation technique, which enables the use of ER to synthesize the risk estimates from the bottom to the top along the hierarchy. Intelligent Decision System (IDS) Software is used for facilitating risk synthesis and estimation. The proposed method is tested in a case study to compare the navigational safety levels of three different regions in the Yangtze River.

Selecting Tanker Steaming Speeds under Uncertainty: A Rule-Based Bayesian Reasoning Approach

In the tanker industry, there are a lot of uncertain conditions that tanker companies have to deal with. For example, the global financial crisis and economic recession, the increase of bunker fuel prices and global climate change. Such conditions have forced tanker companies to change tankers speed from full speed to slow speed, extra slow speed and super slow speed. Due to such conditions, the objective of this paper is to present a methodology for determining vessel speeds of tankers that minimize the cost of the vessels under such conditions. The four levels of vessel speed in the tanker industry will be investigated and will incorporate a number of uncertain conditions. This will be done by developing a scientific model using a rule-based Bayesian reasoning method. The proposed model has produced 96 rules that can be used as guidance in the decision making process. Such results help tanker companies to determine the appropriate vessel speed to be used in a dynamic operational environmental.

SignsWorld Facial Expression Recognition System (FERS)

Live facial expression recognition is an effective and essential research area in human computer interaction (HCI), and the automatic sign language recognition (ASLR) fields. This paper presents a fully automatic facial expression and direction of sight recognition system, that we called SignsWorld Facial Expression Recognition System (FERS). The SignsWorld FERS is divided into three main components: Face detection that is robust to occlusion, key facial features points extraction and facial expression with direction of sight recognition. We present a powerful multi-detector technique to localize the key facial feature points so that contours of the facial components such as the eyes, nostrils, chin, and mouth are sampled. Based on the extracted 66 facial features points, 20 geometric formulas (GFs), 15 ratios (Rs) are calculated, and the classifier based on rule-based reasoning approach are then formed for both of the gaze direction and the facial expression (Normal, Smiling, Sadness or Surprising). Sign...

Study on Case-Based Reasoning Decision-Making Model

Beginning with studying the characteristics of the war from a complex system, the limitations of rule-based reasoning approach was discussed, case-based reasoning to the integrated reasoning mechanism was supplemented. In accordance with epistemological, researched on case-based reasoning mechanism of integration and components of decision model, studied feature vector based case representation and matrix representation of non-isomorphic case set, presented a case retrieval method based on feature.

Semantic Ontology Mapping for Interoperability of Learning Resource Systems using a rule-based reasoning approach

There is an increasing demand for sharing learning resources between existing learning resource systems to support reusability, exchangeability, and adaptability. The learning resources need to be annotated with ontologies into learning objects that use different metadata standards. These ontologies have introduced the problems of semantic and structural heterogeneity. This research proposes a Semantic Ontology Mapping for Interoperability of Learning Resource Systems. To enable semantic ontology mapping, this research proposes conflict detection and resolution techniques for both semantic and structural conflicts. The Semantic Bridge Ontology has been proposed as a core component for generating mapping rules to reconcile terms defined in local ontologies into terms defined in the target common ontology. This work defines the reasoning rules to classify related learning objects to enhance the powerful deductive reasoning capabilities of the system. As a consequence, ontology-based learning object metadata are generated and used by the semantic query engine to facilitate user queries of learning objects across heterogeneous learning resource systems.

Applying formalized rules for treatment procedures to data delivered by personal medical devices

The paper presents a novel approach to online application of formalized rules for medical treatment procedures when processing data from personal medical devices. The rules are formalized by using a rule-based reasoning approach and are applied in order to enhance patient safety and support physicians in their daily work. The presented approach relies on dividing data processing into two stages: (1) the event processing stage and (2) the knowledge application stage. At the event processing stage raw data produced by personal medical devices is transformed into an aggregated/correlated form, as required by the rules for treatment procedures. At the knowledge application stage formalized rules are applied to transformed data, resulting in execution of various support actions. This paper describes how rules for treatment of patients suffering from cardiovascular diseases can be expressed in terms of an event processing statement set and a rule engine knowledge base. The technical feasibility of the proposed approach is supported by a detailed description of the TeleCARE remote healthcare framework – an implementation of the proposed approach along with evaluation performed using a large number of simulated personal medical devices.

Fuzzy rule-based reasoning approach for event detection and annotation of broadcast soccer video

This paper presents an approach for event detection and annotation of broadcast soccer video. It benefits from the fact that occurrence of some audiovisual features demonstrates remarkable patterns for detection of semantic events. However, the goal of this paper is to propose a flexible system that can be able to be used with minimum reliance on predefined sequences of features and domain knowledge derivative structures. To achieve this goal, we design a fuzzy rule-based reasoning system as a classifier which adopts statistical information from a set of audiovisual features as its crisp input values and produces semantic concepts corresponding to the occurred events. A set of tuples is created by discretization and fuzzification of continuous feature vectors derived from the training data. We extract the hidden knowledge among the tuples and correlation between the features and related events by constructing a decision tree (DT). A set of fuzzy rules is generated by traversing each path from root toward leaf nodes of constructed DT. These rules are inserted in fuzzy rule base of designed fuzzy system and employed by fuzzy inference engine to perform decision-making process and predict the occurred events in input video. Experimental results conducted on a large set of broadcast soccer videos demonstrate the effectiveness of the proposed approach.

A context-aware lighting control system for smart meeting rooms

To keep the brightness suitable, manual lighting control usually distracts the attendees’ attentions in a meeting. Moreover, manual control needs several rounds of try to find a feasible solution, which delays the progress of meeting. And it's hard to find the suitable lightness for the most comfortable visual effect manually. A context-aware lighting control system is developed in this paper to overcome the shortcomings of the manual control. With the technology of context-awareness and Internet of Things (IoT), people and devices can be connected by seamless services. In this paper, the proposed system can sense the changes of contexts and adapt the light status appropriately. For that aim, an ontology-based context model and a rule-based reasoning approach are designed to detect and infer the context changes. Meanwhile, we also propose an allocation model to offer the optimal adjustment scheme of lighting when needed. This paper integrates the context-awareness and IoT techniques into Illuminating Engineering field, so as to extend its ability to provide automatic support to the changing tasks of human in the lit space.

Construction of Typical Shell Ring Cutting Database System

According to the process characteristics of shell ring, the cutting parameters have been optimized by adopting appropriate boundary conditions at different machining stages and the optimal cutting parameters have been achieved. The objective function and constraints boundary have been stored into the database, which form basic data structures of database. Cutting parameters have been obtained for new type tools using rule-based reasoning approach. According to the characteristics of large number of changing tool for shell ring, tool life prediction is designed, which is very favorable for large shell ring processing. In addition, on-site processing recorded video will be added to the database by multi-media technology, which achieves the diversity of data structure.

Fuzzy Rule-Based Bayesian Reasoning Approach for Prioritization of Failures in FMEA

This paper presents a novel, efficient fuzzy rule-based Bayesian reasoning (FuRBaR) approach for prioritizing failures in failure mode and effects analysis (FMEA). The technique is specifically intended to deal with some of the drawbacks concerning the use of conventional fuzzy logic (i.e. rule-based) methods in FMEA. In the proposed approach, subjective belief degrees are assigned to the consequent part of the rules to model the incompleteness encountered in establishing the knowledge base. A Bayesian reasoning mechanism is then used to aggregate all relevant rules for assessing and prioritizing potential failure modes. A series of case studies of collision risk between a floating, production, storage, and off loading (FPSO) system and a shuttle tanker caused by technical failure during tandem off loading operation is used to illustrate the application of the proposed model. The reliability of the new approach is tested by using a benchmarking technique (with a well-established fuzzy rule-based evidential reasoning method), and a sensitivity analysis of failure priority values.

Self-tuning of fuzzy belief rule bases for engineering system safety analysis

A framework for modelling the safety of an engineering system using a fuzzy rule-based evidential reasoning (FURBER) approach has been recently proposed, where a fuzzy rule-base designed on the basis of a belief structure (called a belief rule base) forms a basis in the inference mechanism of FURBER. However, it is difficult to accurately determine the parameters of a fuzzy belief rule base (FBRB) entirely subjectively, in particular for complex systems. As such, there is a need to develop a supporting mechanism that can be used to train in a locally optimal way a FBRB initially built using expert knowledge. In this paper, the methods for self-tuning a FBRB for engineering system safety analysis are investigated on the basis of a previous study. The method consists of a number of single and multiple objective nonlinear optimization models. The above framework is applied to model the system safety of a marine engineering system and the case study is used to demonstrate how the methods can be implemented.